China spins real life Frankensteinian gene-editing tale

Gene-editing is of enormous significance in the prevention and treatment of human disease. But caution is not a bad thing. Original Link

Why Healthcare Companies Should Migrate To AWS

More and more healthcare providers are looking to digitize their services in various ways to take advantage of the benefits of digital transformation. Not so long ago, healthcare companies, and indeed patients themselves, were hesitant to embrace the opportunities offered by the cloud due to concerns about security. Thanks to the fast-moving pace of technological advances though, that has changed quite drastically. Most organizations operating in the healthcare vertical can now see the advantages of cloud technology for improving the convenience and quality of service for both patients and doctors. This gradual shift of operations towards the online space has naturally occurred alongside a corresponding rise in the awareness of public cloud providers, in particular, one which delivers specific healthcare solutions, Amazon Web Services (AWS).

Why The Cloud?

Every industry now uses cloud technology in some form or another. The possibilities that cloud services open up are effectively endless. Migrating your own healthcare systems to the cloud will tap into much of the potential it has to offer your business—no matter the size of your company.

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Advantages And Disadvantages Of Cloud Computing In Healthcare

The last decade has seen a growing rush amongst various industries and organizations from across the spectrum to migrate their operations and data to the Cloud. This isn’t that surprising when you consider everything that the Cloud has to offer. It is more than just a way for businesses to store files remotely so that they can be accessed securely from anywhere in the world. Cloud technology allows developers to harness the raw power of hardware and software that might be based on the other side of the world.

With this increasing awareness of the possibilities opened up by the Cloud, there has been a corresponding rise in the range of applications and services that open these possibilities up to a wider audience—and more industry sectors. Whether they are aimed at individuals, businesses, other organizations, even governments, virtually every conceivable computer service now has a cloud-based equivalent. The field of healthcare is no exception.

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DevOps in Healthcare Industry: Benefits and Case Studies

The healthcare industry has goldmines of data at their disposal. However, healthcare companies have to comply with multiple regulations and ensure strict security while processing the data. This is why a DevOps approach to infrastructure management is very beneficial for healthcare.

The healthcare industry has grown and matured significantly over the decades. From shelves filled with files, it has moved on to utilizing the latest IT technology available, dedicated data centers and public or private cloud infrastructure. However, despite being a noble activity, healthcare is still a business and must be profitable. This said, when all the healthcare market players leverage the same type of hardware, a competitive edge must be gained elsewhere. Reputable experts predict 2018 to become the year of mass adoption of DevOps approach in enterprise healthcare industry, to respond to the ever-increasing requests for data-driven, interactive and responsive care.

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Tencent eyes to secure China’s drug safety through new partnership

Tencent Joins Hands With Mediway, Jiontown to Make Drug Monitoring Platform – Yicai Global

What happened: Tencent’s cloud unit will team up with medical information service Mediway and drug retailer Jiontown Pharmaceutical to make an online platform that could verify medicines’ supply chains. The three companies will develop an information-sharing platform with which patients can review details of their prescriptions.

Why it’s important: The news comes in just a few days after a fake vaccine scandal hit China. An estimated 250,000 substandard DPT vaccines from one of the country’s largest vaccine maker Changsheng Bio-tech have been administrated to children in Shandong Province. Tech giants are trying to solve problems in China’s healthcare and medical system by applying emerging technologies such as cloud computing, big data, AI and blockchain.

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Why a Chinese-backed hearing aid maker is testing US ears before China launch

Why a Chinese-backed hearing aid maker is testing US ears before China launch · TechNode

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Yidu Cloud is making sense of the medical records of 1.3 billion people

Yidu Cloud is making sense of the medical records of 1.3 billion people · TechNode

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TechCrunch Hangzhou talks about China’s path to AI for healthcare

TechCrunch Hangzhou talks about China’s path to AI for healthcare · TechNode

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iFlytek Healthcare, YITU Medicare, WuXi NextCODE, and Zaixin Biotech: How they’re creating the future of medicine at TechCrunch Hangzhou

iFlytek Healthcare, YITU Medicare, WuXi NextCODE, and Zaixin Biotech: How they’re creating the future of medicine at TechCrunch Hangzhou · TechNode

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Beijing issues digital health cards on WeChat

Beijing issues digital health cards on WeChat · TechNode

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Health insurtech startup The CareVoice brings voice-based virtual doctor to China

Health insurtech startup The Care voice brings voice-based virtual health assistant to China

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New Study Utilizes Big Data to Understand Mental Health

Last year, I wrote about a fascinating project involving researchers from Swansea University. They were looking to utilize big data to better understand the issues surrounding the impact mental health has on young people.

They analyzed data from 358,000 people aged between six and 18 years of age living in Wales between 2003 and 2013. The data was gleaned from GPs and NHS primary care services.

The data revealed that antidepressant use rose significantly, with depression symptoms doubling in that time. Interestingly, however, actual diagnoses of depression fell by roughly a quarter.

“These findings add to the growing debate over increasing prescribing of anti-depressants to children and young people. The main issue is whether they are being prescribed appropriately. However, it’s worth remembering that there has been historical undertreatment of mental disorders in young people. It’s important that each individual young person is listened to and gets the right kind of help for their problem,” the researchers say.

Mental Health Comes to Biobank

Such a big data approach to medicine is something that I’ve touched upon numerous times, with the UK Biobank a pioneer in the space. Until recently, however, the enormous repository of health data contained at the facility has lacked anything of note on the mental health of volunteers.

That has changed, however, with a study undertaken by researchers from King’s College London providing solid data on the mental health of 157,366 volunteers.

“Our study suggests that UK Biobank could be a powerful tool for mental health research, and since it is open to all health researchers for work in the public good, we hope to inspire both existing and new users of UK Biobank,” the researchers say. “Our mental health questionnaire demonstrates the substantial burden of mental disorders. Given the known impact of mental health on physical health, mental health data should interest researchers from every biomedical specialty looking at associations with health and disease.”

The team believes that the rich dataset will provide them a number of opportunities. For instance, they hope to understand whether depression is one illness or a range of related ones.

Mental health is slowly beginning to gain parity with physical health, and it’s pleasing to see that the field is also beginning to utilize data-based approaches to better understanding the multiple facets of good mental health.

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Using Big Data to Reduce Drug Overdoses

The use of big data to identify at-risk groups is something that is showing considerable growth, both as more data is made available and greater computational power is available to make sense of the data.

A team from the University of Colorado highlights how this approach can help hospitals understand which patients might progress to chronic opioid therapy after discharge.

The issue is serious, as over 63,000 people died in the United States from a drug overdose last year, with opioids involved in around 75% of those deaths. What’s more, national data suggests that there are over two million people in the US with an opioid use disorder.

“Doctors and patients are increasingly aware of the dangers associated with overprescribing of opioids,” the authors say. “We can assist physicians in making informed decisions about opioid prescribing by identifying patient characteristics which put them at risk progressing to chronic opioid use.”

Risk of Progression

The researchers aimed to build a prediction model to accurately identify the hospitalized patients who were at the highest risk of progressing to chronic opioid use following their discharge from the hospital.

The model was built using data from the electronic medical records at Denver Health Medical Center. Patients were classified as being on chronic opioid therapy (COT) due either to receiving a supply of oral opioids for 90 days or more or filling ten or more opioid prescriptions over a one year period.

The data contained in the medical record allowed the team to identify a number of variables that were strongly linked to a progression to COT. For instance, it might reveal a history of substance abuse or the receipt of a benzodiazepine.

The model was able to accurately predict chronic opioid therapy in 79% of patients and indeed was also able to predict no COT correctly in 78% of patients. The team believes that their work is the first of its kind to be developed for COT risk, and improves upon software such as the Opioid Risk Tool (ORT), which they claim has not been validated in a hospital setting.

“This prediction model could be incorporated into the electronic health record and would activate when a physician orders opioid medication. It would inform the physician of their patient’s risk for developing COT and may impact their prescribing practices,” the authors say.

With the data required to function already available in the system, there are no extra requirements placed upon the physician. As such, the team believes it would be fairly inexpensive to implement and particularly helpful support in the busy life of the doctor. Before that can happen, however, the team need to test the system more rigorously in other health care systems to determine that it works in a range of patient populations.

“Our goal is to manage pain in hospitalized patients, but also to better utilize effective non-opioid medications for pain control,” the researchers conclude. “Ultimately, we hope to reduce the morbidity and mortality associated with long-term opioid use.”

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Using Java EE 7 to Develop Medical Apps

One of the most important things to continue to do for Java EE/Jakarta EE is highlight successful adoption stories at a regular cadence. The community has been doing just that for a long time. A number of these stories are curated here. In this vein, Vladimir Herman graciously agreed to share his Java EE adoption story. Vladimir is a young developer in the Czech Republic working on medical applications. He has experience with both Spring and Java EE applications. Thanks very much Pavel Pscheidl for helping facilitate the sharing of this Java EE adoption story.

Can you kindly introduce yourself?

My name is Vladimir Herman and I am a Java developer in one of the largest Czech software companies. I started working with Java professionally in 2013, when I was still a student at the University of Hradec Kralove. I was employed as a junior Java developer in the internal IT department of an insurance company. That’s why I look at my career in two separate stages — during and after my studies. I have learned a lot from academic life as well as from practical work experience.

Can you describe the applications that use Java EE?

In my previous job, we developed web applications in Java that were based on the Spring framework. In my current job, we are extensively using Java EE 7. We develop medical applications for foreign and domestic customers, such as ministries, hospitals or polyclinics.

The applications are mainly used by trained employees of these institutions. That means they are demanding customers who precisely know the problems related to the application domain. This greatly increases demands on the services provided by our system.

Why did you choose Java EE?

The first contact with programming was completely different for me. At lower grades of my education I encountered procedural programming and languages such as assembler and C. I got familiar with the object-oriented paradigm through Java SE/Java EE at the university and I immediately thought it was the right choice for me.

One of the major benefits of Java EE is its comprehensive portfolio of technologies and APIs. Java EE is a mature technology, it has a diverse ecosystem as well as a very active community. It is also beneficial that you can extend the platform with various frameworks when needed.

The seamless integration of the individual parts of Java EE is invaluable. Integration is not always trivial and you have a very powerful tool in your hands once you grasp this concept.

We must not forget the benefits of the JVM itself. It is great to have a portable and platform independent runtime, considering the diversity of our customers.

How do the applications use Java EE?

During my relatively short professional career, I was able to experience the development of Spring framework as well as Java EE technology stack applications.

The applications on which I work mostly follow a layered MVC architecture. Java EE technologies are used in all layers of the application. The main technologies in use are JPA, CDI, EJB, JAX-WS and JSF, but of course there are more. The vast majority of our applications are designed to be monolithic robust units that are self-contained. Individual applications are able to communicate with their neighbors using SOAP when needed, but we also run applications that use JMS. The resulting system can be thought of as composed of multiple large modules, but they are certainly not microservices quite yet.

I started working on an application based on J2EE 1.4 about a year ago. It is a key application for a few important customers and it has been in maintenance mode for many years. Developers were hesitant to make larger changes that could endanger existing functionality or would not be backward compatible. A recent breakthrough occurred when the application was bought by another customer. The application now needs to be rejuvenated as a result. Some of the technologies have become obsolete enough to be replaced outright and others were upgraded to the current version. One of the major changes was switching to Java EE 7 and replacing JSP with Facelets/JSF. Another crucial step was the introduction of DI through CDI. All changes were made with an emphasis on easier maintenance in the future.

What was your general experience with Java EE? Would you choose it again?

I gain experience with Java EE every day. Java SE 9 was released not too long ago. Java EE 8 brings changes like asynchronous events for CDI, HTTP/2 support in Servlet 4 and so much more. I am just in the constant process of learning, reading documentation and testing my knowledge in the real world.

I think there is a bright future for Java EE and I would certainly use it again.

I have also noticed the arrival of Spring Boot. It adds another layer of abstraction for applications based on the Spring framework but it is still a step in a good direction in my opinion.

How can people contact you if they have questions?

The best way to contact me is by email at hermicz at gmail dot com.

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Faster, cheaper, better: How China and AI are helping pharmaceutical development

Faster, cheaper, better: How China and AI are helping pharmaceutical development · TechNode

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Faster, cheaper, better: How China and AI are helping pharmaceutical development

Faster, cheaper, better: How China and AI are helping pharmaceutical development · TechNode

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Faster, cheaper, better: How China and AI are helping pharmaceutical development

Faster, cheaper, better: How China and AI are helping pharmaceutical development · TechNode

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Dos and Don’ts of BI Implementation in Healthcare

In 2017, HIMSS Analytics found that while adoption was on the rise, at the time, only 62 percent of U.S. healthcare organizations were using some form of clinical and business intelligence solution. So, in spite of awareness of BI products and benefits, about half of U.S. healthcare organizations are still standing on the sidelines of BI use. What’s happening?

Tom Lawry, Director, Worldwide Health at Microsoft provides a useful perspective:

“Frankly, the technology is the easy part. The real challenge is getting people to think and act and work differently. Nowadays, many more [healthcare organizations] are moving from old data, old processes, to real-time, predictive analytics, driving toward self-service or research-on-demand.”

James Gaston, Senior Director of Maturity Models at HIMSS Analytics, comments on the modest but steady progress healthcare providers are making in adopting BI tools and methodologies.

“[American healthcare] providers are using what they have on hand and are beginning to find their way around their data. We’re still at the point where we’re trying to develop analytical skills and capabilities, and this hasn’t been broadly operationalized yet.”

Start With the Right People and Processes for Healthcare BI Success

When the time comes to implement business intelligence in your healthcare organization (or if you’ve already started), there are plenty of things you can do. Here’s our lineup of best practices gleaned from successful HBI implementations and industry experts.

Develop a First-Things-First Approach

Adopt a “crawl first, then walk, then run” philosophy. This means it pays to square away basic processes such as data quality assurance and building a BI analytics team before starting to use BI tools and methods.

Want to see healthcare dashboards in action? Explore below.

For example, developing and following reporting procedures might be an old hat for healthcare organizations. But many clinical reporting and quality assurance measures are new and require strict adherence to procedures to ensure data accuracy. Practice here can help you avoid compliance fines caused by rookie mistakes.

Think of all the practical things that support the analytics process. How to report data accurately, automating data reporting processes, and constantly cross-checking data in canned reports against your data warehouse are just a few. This is not exciting stuff. But when you master the practical tasks, there’s a much better chance that your analyses will be accurate.

Get Executive and Stakeholder Buy-In

Part of the reason adoption rates are low is a lack of confidence in BI tools and methods. Successful BI implementation projects require making your hospital or facility a data-driven organization, and to do so, the initiative must either come from the top — or, at the very least, enjoy the support of the organization’s top brass. It’s not enough to have the tools in place. For people to actually use them they need to understand the value and potential of analyzing and visualizing healthcare data, and this could require a cultural change that must be facilitated by upper management.

Select the Right People for Your BI Team

Yes: careful, methodical planning of an analytical project can position your organization for success. But you should never underestimate the importance of the human element: create a skilled and effective group of people who know their data and want to use it to improve decision-making processes and who enjoy digging into the data to find new insights.

Start with people who have empathy for patients and excellent communications and management skills. These are the folks who know that the real bottom line is serving people. And don’t forget your organization’s BI evangelists. Your organization is likely to have at least one. They’re the folks who are passionate about BI and how it can save time, money, and lives.

When you mix professionals with varied backgrounds, you are more likely to get your priorities straight-people, then processes, then technology.

Make Sure That You Analyze the Right Data

You have a mountain of data that’s growing every day. The siren song of big data might tempt you to “put it to good use” and analyze it as quickly as you can.

Don’t do it. Instead, ask yourself: Are you analyzing data that addresses a strategic healthcare problem? Or are you analyzing data that’s easy to obtain? It’s all about getting answers to questions that matter the most to patient care and efficient operations at your facility.

Steven Escaravage and Joachim Roski, Principals at Booz Allen, have discovered that:

“When organizations develop a ‘weighted data wish list’ and allocate their resources toward acquiring high-impact data sources as well as easy-to-acquire sources, they discover greater returns on their big data investment.”

Simplify the Business Intelligence Process

A no-brainer? Perhaps. But when it’s time to select a healthcare analytics solution, it makes sense to choose one that makes data easy to understand and use, and that will enable medical and administrative staff to perform their own data analysis without over-reliance on IT. There’s a strong connection between ease of use and successful adoption. So, think of your analytical dashboards as more than eye candy. They are the eyes of your medical data and operations.

3 Common HBI Implementation Gotchas to Avoid

What about the other recommendations, the please-don’t-do-this items that could destroy the chances of your HBI implementation success? Similarly to the recommendations described earlier, they also involve people, processes, and technology elements of your implementation.

1. Viewing BI Applications as the Latest in a Long Line of Reporting Tools

This can severely limit the capabilities and value that your initiative delivers. Make sure that everyone-including key executives and stakeholders-understand the core capabilities and value that end-to-end business analytics functions can deliver, such as the ability to combine data from multiple disparate sources for deeper analytical insight. That way, everyone knows how the tool works and more importantly, delivers value to your organization.

2. Jumping Into the Data Before Deciding on Your Use Cases

Given the availability of nifty BI platforms with self-service analytics, it’s easy to take very deep dives into data that provide information but very little value. Self-service tools are powerful, so define your objectives and medical or business questions you want to be answered before you start analyzing. If you don’t know what you’re asking, you won’t get answers to questions that matter.

3. Limiting your HBI Team to Techies

If you believe that healthcare analytics initiatives should be owned by technical specialists alone, think again. IT specialists, no matter how skilled or experienced they might be, can’t understand your operations from provider and patient points of view as well as your clinical staff.

That’s why it’s critical to have clinical and operational professionals on the HBI team. This added point of view will deliver big dividends when you move to a value-for-performance environment.

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How to Leverage Big Data in Healthcare

Healthcare professionals and providers are slowly coming around to the promise and potential of big data analytics. Big data has already left significant impacts in several industries, and its ability to improve operations and uncover deep insights make it a crucial ingredient for healthcare’s future success. Even so, many health and medical services providers have yet to integrate the technology; not for lack of desire, but simply because the implementation can seem daunting without a clear understanding of the impact it can have.

How does big data fit into healthcare? There are several fields within the industry that can and have already benefited from leveraging analytics. From insurers and risk managers to hospital administrators, here are some ways professionals in healthcare can effectively deploy big data analytics.

Turn the Focus Toward a More Patient-Centric Approach

No matter which subsector of the industry you work in, healthcare is about dealing with people. For many hospitals, clinics, and other care centers, patients are sometimes deprioritized due to models that prioritize quotas and numbers. The issue is that focusing on patients and quality of service requires substantial amounts of data from diverse sources, a factor that can quickly complicate matters.

By integrating big data in healthcare, companies and professionals can start finding some order in these data points and begin identifying actionable insights. These come from optimizing reporting, data management, automation, and collection. By using the right healthcare BI tools, healthcare companies and organizations can start focusing on how to improve existing services and client outcomes, all while focusing on rewarding quality and cost-effectiveness.

Improve Treatment and Health Outcomes

The shift towards a larger focus on patients comes with the caveat that in addition to a better experience, their health outcomes must also improve. The problem with treating patients or even perfecting a treatment is that data is not always organized, and in some cases must be compared with other sets to make sense fully. Currently, information isn’t always freely shared or simply seems to be unrelated.

Using big data analytics tools, hospitals and other healthcare centers can track patients that have repeated procedures or conditions and use predictive tools to create a more tailored and success-oriented plan for their treatment in the future. By exploring treatments that showed success in specific cases and connecting them to successful outcomes, healthcare professionals can leverage big data to create unique and effective treatment strategies.

Streamline Hospital Operations

Hospitals are massive, both literally and figuratively. Between multiple wings, branches, units, and hundreds of staff, data is produced at breakneck paces, and many times goes unused or unnoticed due to the sheer numbers. Hospital administration is a demanding job, and more so because it requires making decisions that impact nearly every facet of the facility without having all the information available.

Incorporating big data analytics can help refine administration in a variety of ways. The first is simply improving staff management by better tracking of hours, shifts, and other metrics that may translate into success levels. Similarly, you can greatly enhance billing efforts with predictive analytics thanks to improved tracking and analytical tools. Hospitals can monitor patients that have paid in the past, see which are more likely to be transferred to collections, and which simply can’t pay. This way, administrators can be more proactive in helping patients pay their bills, avoid late fees, and keep their costs low.

Implement More Precise Risk Analysis

For insurers, understanding their clients is a vital component of offering the most attractive and useful services. Finding the right combination of premium, coverage, and riders can be difficult for companies that have clients with long or complex medical histories. The problem is worse when there are mitigating circumstances that aren’t always accounted for in existing calculations or risk analysis models.

Employing big data analytics can help create more flexible and dynamic models that account for a larger variety of factors. By combining predictive tools with visualization dashboards and powerful analytics, risk assessors can create a more comprehensive picture of a client’s health and financial situation all while creating insurance plans that better suit their needs, are more likely to be paid, and finally used.

The healthcare industry is changing, and it is beginning to embrace technologies that will push it into the future. Big data analytics for smarter healthcare makes sense on several levels and can help professionals and companies make better decisions, improve their operations, and have healthier, happier clients. By adopting big data in key areas and exploring ways to consistently make data-driven decisions, healthcare professionals can do more than simply subsist and survive in the field; they can thrive. Using the right healthcare business intelligence can make the difference between satisfied patients and struggling hospitals.

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Healthcare Dashboards: Examples of Visualizing Key Metrics and KPIs

The world of healthcare analytics is vast and can encompass a wide variety of organizations and use cases — from hospitals to medical equipment manufacturers, and emergency rooms to intensive care units. And while some of the dashboard metrics tracked by healthcare organizations can be fairly similar to the ones monitored in other industries such as finance or marketing, the use of business intelligence in hospitals presents a unique set of potential insights that can help physicians save lives by providing more effective and resourceful care to patients.

This article will examine a number of ways in which visualizing healthcare data can help physicians and management gain a better understanding of goings-on within hospitals and suggest ways to visualize commonly tracked metrics. But first, let’s understand where this data is coming from.

Common Data Sources in Healthcare

  • Electronic Medical Records (EMR): These are essentially a digital version of the patient’s paper chart, used by clinicians to monitor the patient’s condition, treatments he or she is due for, etc. These are usually kept within the bounds of the facility in which the patient is being treated.
  • Electronic Health Records (EHR): A broader set of digital records pertaining to the patient’s overall health, including information regarding previous treatment administered by other healthcare providers, specialists, laboratory tests, and more. These would typically move with the patient and be shared by various providers.
  • Specific departmental data: Gathered by specific divisions or units within the healthcare organization.
  • Administrative data: Collected in Healthcare Management Systems (HMS) and looks at the hospital’s overall operations. Would typically be used by a hospital’s senior managerial staff and may include information regarding matters such as resource utilization and human resources.
  • Financial data: Often stored in proprietary financial management systems for larger organizations.

As you can see, many healthcare providers often find themselves working with many disparate data sources. However, there can often be unique benefits in connecting data stored in these various sources to find correlations between them. Consolidating the data can be done in an enterprise data warehouse, which is a project best undertaken by heavily staffed IT departments.

Examples of Data Visualization in Healthcare

Once you’ve gathered all the required data and undergone the prerequisite data modeling steps, you can start looking at effectively monitoring key hospital analytics metrics and thinking of insightful ways to visualize them in a healthcare dashboard. Here are a few healthcare analytics examples, with the disclaimer that these are by no means the only things a hospital would generally be looking at, nor necessarily the most crucial ones.

For the purposes of this article, we’ve used sample data. 

Cost of Admission by Department

This is a very simple visualization, but nevertheless, one that can help hospitals understand how their financial resources are being utilized. By using a bar chart, we immediately provide additional information that might have been more difficult to notice in tabular format — such as shifts in the relative costs between departments, as well as peaks that could indicate an issue that needs to be addressed or at least further investigated.

A different way of visualizing the same data would be a line chart:

This visualization gives us a clearer idea of trends and outliers, and some people might find it more intuitive to examine the data regarding to a specific department in this format — the significant information becomes more apparent immediately. However, this is largely dependant on what the viewer’s emphasis is on when examining the data.

Another common way to look at the same data would be via the following visualization, which gives the exact revenue figures and a very clear idea of each department’s costs on an annual basis:

As we’ve mentioned before, an effective dashboard reveals detail on demand. This means that after providing a high-level KPI overview, you might want to give the dashboard viewer the ability to drill into the data — in this case, the admission costs of the various units within the operating rooms. We chose a line chart, as it gives us an immediate indication of highs and lows in admission costs:

ER Admissions and Length of Stay

This visualization gives us a single-glance view of data from several different sources. In our sample dataset, we had to join data from admissions, divisions, and ER tables. Combining these datasets gives us a clearer idea of hospital resource utilization by examining the number of patients being admitted to the emergency room and the average time these patients spend at the hospital. This sets the way for further investigation into peaks, trends, and patterns.

Leading Diagnoses by Number of Patients, Cost and Stay

Here, we’ve kept the data in tabular form. However, by combining financial and administrative data with departmental records, we gain the ability to quickly get answers to specific questions that can shed further insight into the various treatments being administered and how these affect hospital finances and room availability. Applying filters will enable us to examine specific dimensions such as region, time, or facility.

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Hospital Donations

If your organization bases its budget on donations, like so many do, it’s important to track trends in order to understand how to plan for the year ahead. A donations dashboard can help you to find ways to increase engagement of donors and ensure financial stability. If donated amounts are different from what you expected them to be or change dramatically, you can analyze the retention level of donors and find ways to engage more.

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Best Tyme Founders Represent Black Innovation in Healthcare Tech

The pharmaceutical/medical supply industry is a mega-lucrative business—sales revenue increased from $534 billion to $775 billion between 2006 and 2015. And the industry is notorious for the “hard-sell;” just look at those somewhat grandiose “ask-your-doctor-about” TV commercials everyone loves to hate.

Did you know pharmaceutical and medical supplies companies also have sales reps that make field calls to medical professionals often giving away samples—in order to sell a product? Now, one couple who have both been pharmaceutical sales reps for about 15 years, have created an innovative technology solution for sales reps and physicians.

Jilea Hemmings is the founder of Best Tyme, a startup she launched with her husband Jamie.

Jilea Hemmings (Image: Best Tyme)

Best Tyme is an app that lets doctors and clinicians set up meets with sales reps and conversely gives reps a way to organize their sales calls.

“I’m sure anytime you’ve gone to a doctor’s office; you’ll see representatives bring samples to doctors. Sometimes [doctors] are seeing reps before patients. All that planning process takes time. After 15 years, I saw this could be done better,” says Hemmings.

(Image: Best Tyme)

Their biggest competitor she says is RXVantage. But Hemmings says that solution doesn’t offer features that are available in Best Tyme including GPS capabilities, a lunch ordering feature, and sending invites to events.

The couple raised $200,000 from investors Dr. Anisio and Alexandra—a medical administrator. Both are also co-founders.

Hemmings says they are “thrilled” to have launched the app to the market and that it will “revolutionize how doctors and medical sales representatives schedule appointments.” Additionally, Hemmings says the technology shows the potential of black innovators in the space.

The app is available now for iPad, Android, iOS, and the web.

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How Tencent’s medical ecosystem is shaping the future of China’s healthcare

How Tencent’s medical ecosystem is shaping the future of China’s healthcare · TechNode

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Tencent-backed We Doctor scales up to prepare for planned Hong Kong IPO

Tencent-backed We Doctor scales up to prepare for planned Hong Kong IPO · TechNode

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Amazon, Warren Buffet, and JPMorgan Chase Teaming Up to Provide Affordable Healthcare

Amazon, Berkshire Hathaway, and JPMorgan Chase are teaming up to take on healthcare. In a move that could shake-up the healthcare industry, the three massive companies announced Tuesday that they’ll be launching a new and independent healthcare company to improve employee satisfaction and to lower costs for US-based employees.

“The ballooning costs of healthcare act as a hungry tapeworm on the American economy. Our group does not come to this problem with answers but we also do not accept it as inevitable,” said Berkshire Hathaway Chairman and CEO Warren Buffett in a released statement. “Rather, we share the belief that putting our collective resources behind the country’s best talent can, in time, check the rise in health costs while concurrently enhancing patient satisfaction and outcomes.”

The three companies—which employ 1.1 million workers combined—will pursue launching an independent company that will be free from profit-making incentives and constraints. The group also hopes to draw on its combined capabilities and resources to take a fresh approach to these critical matters, the group said in a statement.

“The healthcare system is complex, and we enter into this challenge open-eyed about the degree of difficulty,” said Jeff Bezos, Amazon founder and CEO. “Hard as it might be, reducing healthcare’s burden on the economy while improving outcomes for employees and their families would be worth the effort. Success is going to require talented experts, a beginner’s mind, and a long-term orientation.”

The initial focus of the new company will be on technology solutions that will provide employees and their families with simplified, high-quality, and transparent healthcare at a reasonable cost, the group said in a statement.

“Our people want transparency, knowledge, and control when it comes to managing their healthcare,” Jamie Dimon, CEO of JPMorgan Chase said. “The three of our companies have extraordinary resources, and our goal is to create solutions that benefit our U.S. employees, their families and, potentially, all Americans,” he added.

The idea—still in its early stages—will be jointly spearheaded by investment officers at Berkshire Hathaway; Marvelle Sullivan Berchtold, a managing director of JPMorgan Chase; and Beth Galetti, a senior vice president at Amazon. The longer-term management team, headquarters and key operational details are yet to be determined.

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Mobile News Round-up: CES, IoMT, and Healthcare

Despite the fact that a certain internet meme that involves eating a chemical substance has dominated the news this week, there’s actually a lot of intelligent conversation going on out there about digital transformation. From AI to blockchain to digital health, these are the ideas we wish would go viral. Enjoy!

CES 2018: Virtual Reality And Augmented Reality Get Another Shot – Anshel Sag, Forbes, January 25, 2018.
While both AR and VR are likely a decade away from their full potential market size, the wealth of announcements at CES 2018 and the sheer presence that AR/VR companies had at the event say a lot about the industry’s current momentum.

Doctors Can Now Use Augmented Reality to Peek Under a Patient’s Skin – Abby Norman, Futurism, January 25, 2018.
A team of researchers at the University of Alberta has created a medical imaging system that projects scans onto a patient’s body, adjusting to their underlying anatomy even as they move around.

H-E-B pilots augmented reality technology – Kristen Mosbrucker, San Antonio Business Journal, January 24, 2018.
San Antonio-based H-E-B Grocery Co. recently concluded a pilot program in augmented reality with an East Coast smart glasses manufacturer. The project with Vuzix Corp. involved H-E-B employees wearing augmented reality glasses to do their jobs without a separate computer terminal for instructions or training materials.

Intelligent World: The State of the IoT – Forbes.
The Internet of Things is one of the most defining technologies of the Fourth Industrial Revolution. It can transform how organizations design, produce and sell their products and services, as well as how they become part of a larger ecosystem – both physical and digital. While the bottom-line benefits of IoT are clear – higher revenue, reduced costs and improved efficiencies – the path to successfully leveraging these technologies is not.

Digital Transformation of Energy Utilities: Shifting Gears? – Marius Buchmann, The Energy Collective, January 25, 2018.
Since 2014, utilities have been increasing their investment in digital infrastructure. This is the key finding of the IEA report “energy and digitalization” which was published in November 2017. This report provides a detailed overview on how digitalization changes energy consumption in transportation, the industry and the residential sector.

Apple will launch Health Records feature at 12 hospitals with iOS 11.3 – Jonah Comstock, Mobihealthnews, January 24, 2018.
After many months of rumors, Apple announced today that it is launching a personal health record (PHR) feature with iOS 11.3, the beta of which launched today to users in Apple’s iOS Developer Program.

Why The Internet Of Medical Things (IoMT) Will Start To Transform Healthcare In 2018 – Bernard Marr, Forbes, January 25, 2018.
While the Internet of Medical Things (IoMT) has started to impact healthcare, it’s predicted that the rate of adoption and transformation will accelerate in 2018 and beyond.

Developments And Adoption Of Blockchain In The U.S. Federal Government – Steve Delahunty, Forbes, January 25, 2018.
With the rise of Bitcoin, one of the underlying supportive technologies that make it possible has gained more awareness — blockchain. The U.S. federal government has interest in the application of blockchain for various purposes.

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Using DevOps to Cure Cancer

Cancer sucks. But with folks like Sarah, DevOps is helping make a difference in the race to a cure.

Sarah Elkins is not curing cancer herself, but she is employing DevOps practices to help those who are. Sarah supports the technology infrastructure for those who are trying to cure cancer at the National Institute of Health (NIH).

Sarah Elkins (@configures) configures technology solutions at the National Cancer Institute (NCI), where she has worked for 9 years. NCI is a federal agency – part of the National Institutes of Health. They support over 700 websites, from basic HTML to content management systems to complex bioinformatics systems which have multiple tiers and thousands of servers. They use multiple operating systems, containers, and physical and virtual machines (both on-premise and some AWS). Developers range from lone scientists to large teams.

Sarah and I have been following one another on Twitter for several years, but I recently caught the session she delivered in the “DevOps in Government” track at All Day DevOps. Sarah led a discussion about how the National Cancer Institute is automating its builds and deployments – showing it is possible even in a large bureaucracy.

Her talk covered the processes and technologies which enable software to move from source code repositories all the way to production servers at, including the use of GitHub, Jenkins, Nexus, and more technologies, with a variety of teams involved.

We Need to Talk

Sarah’s cure begins with engagement – development, infrastructure, and security all working together — just as teams of specialists work with patients to help them beat cancer.

To help speed development and approvals, a necessary evil that can grind development to a halt if not managed well, they agree that applications drawing on an existing technology catalog are approved as operational and security teams provide guidance to development on what is required for an Authority to Operate (ATO). In other words, they are trying to pre-approve as much as possible and communicate earlier in the process.

DevOps Practices Are Maturing

Source code is primarily kept on GitHub, including a public repository for non-proprietary source code, and they primarily use Maven or Apache Ant for build scripts. Infrastructure teams provide XML templates to provide consistency.

Most NCI software relies on build dependencies on open source software components. They use artifacts during builds and utilize Sonatype’s Nexus as their repository manager.

All of this supports the automation of builds and deployments at the NCI. For builds, most active projects are either on, or migrating to, Jenkins. Build artifacts may be .zip, .war/.ear, or Docker images.

For application deployments, they use development, quality assurance, stage, and production stages. Most teams use some form of automation, from simple (copy content, stop/start container) to more complex scripts. They allow developers to perform deployments for robust applications, and some manual orchestration is required, for instance for database timing or related applications.

As an organization, they are moving towards Continuous Integration, with varying progress among teams.

Building Security In

The have also embraced involving security early, often, and automatically. They have deployed self-serve/on-demand Nessus scans, which allow developers to see, on their own, how they are doing as soon as the application is stable. Security teams run AppScan and Twistlock for Docker image scanning during Jenkins builds, just before deploying, and frequently scanning the repository in between. For issues found, development and infrastructure teams work together to remediate security concerns.

At the end of her talk, Sarah offered up three takeaways:

  1. DevOps at NCI is a work in progress (isn’t it everywhere?)
  2. The wide-ranging needs at NCI require flexibility, communication, and teamwork
  3. There is no shortage of work

Since it is a work in progress, what opportunities are ahead at NCI?

  • More automation for individual applications with Jenkins
    • Developers can perform container restarts on lower tiers
    • Database updates (some projects are using Liquibase already)
    • Some applications are still manual/batch scripted
  • More Docker and improving Jenkins / Docker instances
  • More orchestration automation and Puppet/Ansible integration
  • Security improvements through
    • Scan dependency artifacts before building
    • More integration and automation

Sarah dives into more details in her talk, which you can watch here. If you missed any of the other 30-minute long presentations from All Day DevOps, they are easy to find and available free-of-charge here. DevOps in Government is one of the 5 tracks.

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Shanghai-based healthcare startup The CareVoice completes $2 million investment round

Shanghai-based healthcare startup The CareVoice completes $2 million investment round · TechNode

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China’s Sputnik moment: Q&A with Jeremy Goldkorn on the global impact of Chinese tech

China’s Sputnik moment: Q&A with Jeremy Goldkorn on the global impact of Chinese tech · TechNode

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Large-Scale Health Data Analytics With OHDSI

Data analytics is increasingly being brought to bear to treat human disease, but as more and more health data is stored in computer databases, one significant challenge is how to perform analyses across these disparate databases. In this post, I take a look at the Observational Health Data Sciences and Informatics (or OHDSI, pronounced “Odyssey”) program that was formed to address this challenge, and which today accounts for 1.26 billion patient records collectively stored across 64 databases in 17 countries.

Randomized Controlled Trials and Observational Studies

Consider a researcher who has a new theory they want to test: does aspirin cause bone loss that can lead to osteoporosis? Traditionally, the main instrument for answering questions of this kind has been the randomized controlled trial (RCT). An RCT is an experiment that places patients in the trial into one of two groups: a group that receives the treatment and a control group that receives a placebo. By looking at the statistical effect of the treatment compared to the placebo on the outcome, you can learn if the treatment has an effect.

RCTs are considered to be the gold standard. However, it is not always possible to run an RCT due to cost (they are expensive), not meeting recruitment targets (trials that are too small don’t provide reliable evidence), or for ethical reasons (withholding a treatment may be unethical under some circumstances).

An alternative to the RCT is the observational study, where the researcher does not have control over which patient receives which particular treatment. This is because the researcher is not running an experiment; they are observing what is already happening in the world. The data may come from many sources: electronic medical records, pharmacy records, monitoring devices, insurance and billing records, and so on. When aggregated across millions of patients, real-world data offers an opportunity to untangle relationships between treatment and outcome. The application of analytics to real-world data produces real-world evidence about whether a treatment has an effect on an outcome.

Since observational studies are not randomized, there is an extra burden to eliminate bias from the study. However, a report in the New England Journal of Medicine from 2000 “found little evidence that estimates of treatment effects in observational studies reported after 1984 are either consistently larger than or qualitatively different from those obtained in randomized, controlled trials.”

Data Silos

Observational studies bring an integration challenge too: how is it possible to bring disparate data sources from multiple organizations together so that clinical questions can be asked of them? Patient confidentiality and privacy laws mean that it is simply not possible to create a central database containing a copy of all the data needed for a study.

This brings us to OHDSI, an international network of collaborators working on the problem of how to apply large-scale analytics to patient healthcare data using open-source tools. The program’s central insight is to define a common data model for patient data so that participating organizations using the model can use the same tools and queries on their own databases.

A Real-World Study

Going back to our hypothetical researcher, the first step in conducting an observational study with OHDSI is to solicit interest on the OHDSI forums about the question they are interested in answering (“is there a link between aspirin and osteoporosis?”). If there is positive interest from institutions with healthcare databases, the researcher would create a protocol for the study, which includes information about the study, such as the objective, the institutes involved, date of publication, and so on.

Crucially, the protocol includes code that runs an analysis on data in the OHDSI Common Data Model format and creates a results file. The results are high-level summary statistics that contain no patient-level data. All the steps in the process are conducted in the open (in the StudyProtocols GitHub repository), so the protocol and code can be scrutinized, and worked on in a collaborative fashion.

Next, the participating institutions download the protocol and run the analysis on their databases, then return the results (which contain no patient-level data) to the researcher carrying out the study. Finally, the results are aggregated and written up in a paper for publication.

The Common Data Model and Hadoop

The OHDSI Common Data Model (CDM) is defined as a set of SQL tables. The CDM is platform-neutral, so it doesn’t dictate any particular database technology. Community members have written implementations of the model for different databases, including Oracle, PostgreSQL, and Microsoft SQL Server.

With Cloudera’s work with OHDSI, the CDM now works with Apache Hadoop. In particular, there is a set of Apache Hive table definitions to store CDM data in Apache Parquet format, or optionally in Apache Kudu, and which can be queried using Apache Impala.

The CDM defines 30 or so tables, divided into a few domains (see the diagram below). The two main domains are the standardized vocabularies and the standardized clinical data. The standardized vocabularies contain a large number of healthcare concepts, such as drugs and health conditions. As the name suggests the concepts in the standardized vocabularies are derived from industry standards and existing databases, such as SNOMED-CT and RxNorm. The CDM integrates the different vocabularies in one database schema and allows concepts to be mapped to one another, and for parent-child relationships between concepts to be expressed.

The vocabulary tables are curated by the OHDSI community and updated periodically from the upstream sources. This centralized, shared resource is one of the valuable things that OHDSI provides.OHDSI OMOP data model

To briefly illustrate, let’s find the entry for aspirin in the concept table using the Impala shell:

> SELECT concept_id, concept_name, vocabulary_id, concept_class_id
> FROM concept
> WHERE concept_name = 'Aspirin' and standard_concept = 'S'; +------------+--------------+---------------+------------------+
| concept_id | concept_name | vocabulary_id | concept_class_id |
| 1112807 | Aspirin | RxNorm | Ingredient |
Fetched 1 row(s) in 0.47s
> SELECT concept_id, concept_name, vocabulary_id, concept_class_id
> FROM concept
> WHERE concept_name = 'Aspirin' and standard_concept = 'S'; +------------+--------------+---------------+------------------+
| concept_id | concept_name | vocabulary_id | concept_class_id |
| 1112807 | Aspirin | RxNorm | Ingredient |
Fetched 1 row(s) in 0.47s

We’ve found the single, standardized concept for aspirin, with concept ID 1112807. Of course, there are many drugs on the market that contain aspirin as an ingredient; we can find them by using the concept_ancestor table that records parent-child relationships between concepts. This query prints the first three direct descendants:

> SELECT c.concept_id, c.concept_name
> FROM concept_ancestor ca, concept c > WHERE ca.ancestor_concept_id = 1112807 > AND ca.descendant_concept_id = c.concept_id > AND max_levels_of_separation = 1
> LIMIT 3; +------------+----------------------------------------------------------+
| concept_id | concept_name |
| 19124734 | Aspirin 363 MG |
| 40003486 | Acetaminophen / Aspirin / Caffeine / Codeine Oral Tablet |
| 40012610 | Aspirin / Phenylpropanolamine Oral Solution |
Fetched 3 row(s) in 2.31s

The second domain of interest is the standardized clinical data. Unlike the vocabulary tables, which are small and remain relatively static, the clinical data tables hold observational data pertaining to patients (all linked from the person table), so they grow as the number of patients in the system grows and as the number of observations grows. The clinical data tables need to scale, so they are ideal candidates for being stored in a Hadoop system.

Continuing the example, we can use the drug_exposure table to find the number of people in the system who have taken aspirin:

> FROM drug_exposure
> WHERE drug_concept_id IN (
> SELECT descendant_concept_id > FROM concept_ancestor > WHERE ancestor_concept_id = 1112807
> ); +---------------------------+
| count(distinct person_id) |
| 244 |
Fetched 1 row(s) in 0.58s

This is a small public test dataset of 1,000 people, so the result says that just under one quarter of the people in the dataset have taken aspirin.

This query should give you a hint of what’s involved when writing a protocol for an observational study. It’s not just a case of finding a single database record for a drug, but finding many candidate drug products, and using domain knowledge to devise criteria for which to include in the study. The same considerations apply to finding patients with the condition being studied.

Since the clinical data tables are regular Hive tables, they can be populated using any of the usual tools in the Hadoop ecosystem. For example, Odysseus Data Services have used Apache Spark to perform ETL into the CDM tables.


Having a CDM is in itself liberating for many organizations, since they can perform SQL queries to answer the questions they want, as the previous section showed. Those that want higher-level or easier-to-use tools can find them at the OHDSI GitHub repository — this is another reason to use the CDM — it opens up a swath of tooling that works on your data.

We have also done some work at Cloudera to get Impala working with some of the more popular OHDSI components and tools, including:

  • DatabaseConnector for connecting to databases using JDBC via R.
  • SqlRender for translating from a standard SQL dialect to a database-specific one.
  • Achilles to produce summary statistics for data in the CDM.
  • ATLAS for patient cohort generation.
  • FeatureExtraction for generating features for a cohort.

The screenshot below shows the process of creating a cohort in the ATLAS web interface.atlas

The web interface makes it easy to define cohorts without having to write SQL, and the cohort definitions can be exported (as JSON or SQL) for use in other tools, and as input for running observational studies.

Patient-Level Prediction With Machine Learning

The question we started out with was “does aspirin cause bone loss that can lead to osteoporosis?” — which can be described as population-level effect estimation. In this type of analysis, we are trying to understand if a single drug has an effect on a particular outcome.

Another type of analysis is creating a model for a particular outcome using a much larger range of features. So, for example, we might try to build a model that predicts osteoporosis given all drug interactions the patient has had (rather than just aspirin), all medical diagnoses, observations, and measurements, as well as general patient information, such as age and gender. Such a model could be used to predict if a particular patient is at risk of developing osteoporosis in the future.

This is a classical machine learning problem, and there is an OHDSI tool called PatientLevelPrediction that can run algorithms like logistic regression and random forests in R on CDM data.

No work has yet been done to use Spark ML as an approach to implement machine learning algorithms for patient-level prediction, but it would be a natural next step to scale to large numbers of patients and observations.

Future Work and Getting Involved

OHDSI has a number of working groups that bring together groups of people who are interested in a particular topic within OHDSI. The Hadoop Working Group, for example, is for people who are interesting in developing and deploying OHDSI tools on Hadoop.

Another exciting direction is the possibility of adding genomic data to the CDM, a move that would enable it as a platform for precision medicine. A new working group has recently been set up to discuss this.

There are many OHDSI projects, and it can be daunting for someone new to the project to get started. The Broadsea project can make things easier; it’s a set of Docker containers for running the main parts of the OHDSI stack for development purposes. Broadsea and the OHDSI forums are a good starting point for anyone who wants to learn more or get involved with the project. Taken together — OHDSI’s active community, a large set of federated healthcare data, Hadoop integration, a free and open-source environment of open science — OHDSI makes for a great place to innovate in large scale healthcare analytics.

Further Reading

Thanks to Shawn Dolley for reviewing this article.

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Built With React Native: Create Real-Time Communication Functionality for Mobile Medical Apps

The mobile/web app market consists of not only random games and for-fun applications. The niche-oriented real-time and live chat mobile applications development, especially the creation of products meant for the internal corporate use, have a special purpose of simplifying the branch workers’ job and creating new opportunities with its functionality.

In this article, we’ll talk about how to create a care coordination React Native-based mobile app (and a React Native real-time chat for audio and video calls), how we at have managed to build one and what features apart from Bluetooth integration with medical devices we’ve included there.

The Importance of Healthcare Mobile App Development

Here we would like to provide you with some statistics on what place healthcare and care coordination mobile apps take among other app categories.

For iOS:



For Android:



Healthcare mobile apps for iOS are in the top 10 of the most downloadable apps in the App Store. In Google Play, the abundance of, for the most part, entertaining apps leaves medical mobile apps slightly trailing behind. Care coordination and healthcare mobile apps could be an inalienable part of apps for doctors and for patients. These allow medical workers to conduct a real-time communication between each other and their patients with the opportunity to do it remotely.

Such apps are aimed to provide all the necessary information about patient’s health. Every change in patient’s health can be tracked and investigated. That’s the great advantage of healthcare mobile apps development and utilizing mobile devices in medicine. Special integrations are a must for healthcare real-time mobile app development. This, for instance, includes Bluetooth integration with clinic tools, like weight scales or other various devices aimed to provide patient’s key health record data.

A Mobile Medical App Built With React Native

Here’s another React Native development case JSSolutions team has had experience with. We’ve assisted with the creation of one care coordination mobile app that would enable chatting and audio messaging: video calls between both medical staff, and between a doctor and a patient. Apart from this, the client requested that we develop Bluetooth integration with the weight scales using React Native and WebRTC. The same is planned with other devices which track patient’s health data, like blood pressure measurement devices.

We’re expanding the React Native Showcase list:

Our client and us both agreed on React Native app development. Why? Below you’ll find the list of benefits we’ve got by planning this mobile medical app and to be built with React Native:

  • Effortless integration of the app with Bluetooth using React Native Bluetooth Low Energy library;

  • Rapid process of React Native real-time chat functionality development and the overall app in general;

  • UX-friendly, fresh, robust and plain native look and feel.

WebRTC and React Native Real-Time Chat App Creation

To enable real-time video/audio communication via chat, we’ve implemented WebRTC technology. Briefly, on its pros:

  • Ability to power-up an app with the secured peer-to-peer audio/video connection in a real-time mode.

  • WebRTC real-time communication technology is an open-source solution, which makes its implementation and contribution to this technology easier.

  • To initiate and run live audio-video chatting and messaging, file sharing/transferring, screen sharing, web camera, and microphone access, there’s no need to install internal or external plugins or any additional tools for your browser.

All of the Solutions Mentioned Are Open-Source. Is It Secure to Use Them?

Such a medical live chat application development requires the implementation of class AAA security measures. There’s no reason to worry, though, WebRTC technology automatically encrypts information by using Datagram Transport Layer Security (DTLS), based on Transport Layer Security (TLS) method to avoid any kind of data leakage. Also WebRTC allows to get the most of the security and establish a completely secured any sort of communication between peers the way no, let’s say, “3rd party” server could decode the transmitted data.

As the additional security measures you can also combine the technology with the personal/identity verification solutions, like OAuth.

Now the Meat (in the Shape of Code)

Among our other React Native app development cases, this real-time communication project turned to be special. Below we’ll try to explain why by means of code.

The first thing we’ve dealt with was the way to get local stream with the further opportunity to transmit the data (video/audio) to the remote interlocutor’s device.

To make it possible, we’ve created the following method using WebRTC technology:

 static getLocalStream(isFront, callback) { let videoSourceId; // on android, you don't have to specify sourceId manually, just use facingMode // uncomment it if you want to specify if (Platform.OS === 'ios') { MediaStreamTrack.getSources(sourceInfos => { console.log('sourceInfos: ', sourceInfos); for (let i = 0; i < sourceInfos.length; i += 1) { const sourceInfo = sourceInfos[i]; if (sourceInfo.kind === 'video' && sourceInfo.facing === (isFront ? 'front' : 'back')) { videoSourceId =; } } }); } getUserMedia({ audio: true, video: { mandatory: { minWidth: 640, minHeight: 360, minFrameRate: 30, }, facingMode: (isFront ? 'user' : 'environment'), optional: (videoSourceId ? [{ sourceId: videoSourceId }] : []), }, }, (stream) => { callback(stream); }, logError); }

Given the fact that the real-time communication functionality, like in-app messaging (video/audio) is one of the development requirements, here’s the method to enable secured and private peer-to-peer connection:

 createPC(socketId, isOffer) { const pc = new RTCPeerConnection(configuration); const dataChannel = pc.createDataChannel('text'); dataChannel.onmessage = event => { const msg = JSON.parse(; this.dispatch(newIncomingMessage(msg)); }; dataChannel.onopen = () => { this.dispatch(connectionEstablished()); }; pc.textDataChannel = dataChannel; this.pcPeers[socketId] = pc; pc.onicecandidate = event => { if (event.candidate) { this.socket.emit('exchange', { to: socketId, candidate: event.candidate }); } }; const createOffer = () => { pc.createOffer(desc => { pc.setLocalDescription(desc, () => { this.socket.emit('exchange', { to: socketId, sdp: pc.localDescription }); }, logError); }, logError); }; pc.onnegotiationneeded = () => { if (isOffer) { createOffer(); } }; pc.oniceconnectionstatechange = event => { if ( === 'completed') { setTimeout(() => { this.getStats(); }, 1000); } }; return pc; }

The method to send messages via text channel which is secured by WebRTC protocol:

 sendMessage(message) { const stringifiedMessage = JSON.stringify(message); for (const key in this.pcPeers) { const pc = this.pcPeers[key]; pc.textDataChannel.send(stringifiedMessage); } }

As mentioned above, the other task of high importance for this React Native healthcare mobile app development, was to integrate weight scales with the app via Bluetooth. We’ve created the integration. But in the process we’ve faced the need to convert different weight items. What to do in this case?

The algorithm below represents how to convert weight info (items) (Chinese units to kilograms), received from the weight scales:

 function getWeightFromScale(charValue, callback) { const charHexValue = base64ToHex(charValue); const weightBytes = charHexValue.split(' '); // scales stabilized if (weightBytes[0] === '22') { callback(); } const hexWeight = weightBytes[1] + weightBytes[2]; const data = hexWeight.match(/../g); const buf = new ArrayBuffer(4); const view = new DataView(buf); data.forEach((b, i) => { view.setUint8(i, parseInt(b, 16)); }); const num = view.getInt32(0, 1); // convert to kilograms from chineese units return num / 200; }

The Outcome

What is the result of combining React Native app development + WebRTC technology for the healthcare mobile product? Done talking, let’s take a look:

1) Start screen 2) Peers view. List of users who are online:



3) Chat window where you can also 4) search for Bluetooth devices (BT is on),
hold calls:



5) React Native Bluetooth integration module in action, detecting weight scales to which you can connect via Bluetooth.

6) Now that we’ve chosen the detected weight scales, we can take our weight measurements. We get an instant result. Data received can be shared between a doctor and patient who are online, or copied and sent in a separate message.


Summing Up

It’s too early to pat our backs for the good work, since the React Native real-time chat project for medicine is still in process and promises to bring its users even more pleasing surprises, about which you’ll know a bit later.

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In-Memory Technologies: Meeting Healthcare’s Fast Data Challenges (Part 2)

This is the second in a two-part series on the use of fast data in healthcare and how in-memory technologies such as Apache Ignite can meet the requirements and challenges of the healthcare industry. Part 1 focused on identifying some of the key challenges in Healthcare. In Part 2, we will discuss a healthcare case study and learn how Apache Ignite and GridGain solved a customer’s problem.


The customer case study we will discuss is from a company called e-Therapeutics. The company, founded in the United Kingdom in 2003, specializes in drug discovery and development. In particular, it is focused on finding treatments for diseases such as cancer and diseases that cause degeneration of the nervous system such as Parkinson’s and Alzheimer’s.

The Business Challenges

The first challenge for e-Therapeutics was in the area of network pharmacology. This is where a specific network of proteins associated with a particular disease is analyzed and identified. The next step is to identify multiple intervention points to disrupt the network of proteins. The goal is to discover drug molecules that would provide the best disruption of a protein network. Therefore, significant data may need to be stored and analyzed. In the previous article, we discussed Apache Ignite’s architecture and its ability to scale.

The second challenge was in the area of computational analysis of the disease cells. To save time and resources, multiple analyses need to be performed. These analyses involve a varying range of parameters. The analyses are also very compute-intensive. Therefore, parallelism offered a good solution. Apache Ignite’s In-Memory Compute Grid allows the execution of distributed computations in parallel to obtain high performance, low latency, and linear scalability. Ignite’s Compute Grid provides a rich set of APIs that allow users to distribute computations and data processing across multiple computers in a cluster. Collocating data and bringing the processing to the server where the data reside also provides benefits, such as reduced network traffic.

Figure 1. Compute Grid

Figure 1: Compute Grid

Figure 1 shows an example Apache Ignite Compute Grid with two servers. Task C is split into multiple jobs (C1, C2). These jobs are sent to the two servers, respectively. The results received (R1, R2) from the two servers are combined (R) and returned to the client.

The Benefits of using Apache Ignite

e-Therapeutics opted to use GridGain’s solution for its Network Pharmacology platform. GridGain’s technology is built upon Apache Ignite and provides resources, support, and enterprise capabilities.

The first benefit for e-Therapeutics was improved performance. Originally, the company started with a cluster consisting of 20 nodes on a 20-core server. This later grew to 100 nodes on five servers (Figure 2).

Figure 2. e-Therapeutics Platform

Figure 2: e-Therapeutics platform

By using parallelism, the company saw a speed increase of nearly two orders of magnitude when compared to the old non-parallelized version. Improved performance meant that analyses could be completed in hours and minutes, while also allowing new projects to be undertaken that were previously infeasible.

The second benefit for e-Therapeutics was the improved productivity of staff members. Disease biology specialists and researchers are not computational informatics specialists. So, a web-based interface connecting to microservices was developed to allow access to the new platform. Staff members could run analyses without having to work from a command line. Furthermore, there was no need to consult with a computational informatics specialist. Scientists could also now work on multiple projects and achieve far more in less time. For example, over a period of 18 months, e-Therapeutics was able to run ten concurrent discovery projects with a small team of scientists. As a result of the faster processing and improved productivity, drug discoveries could be moved into testing phases much faster.

The third benefit for e-Therapeutics was peace of mind. Apache Ignite is a top-level project at the Apache Software Foundation (ASF). ASF has a great reputation for providing stability and longevity. Many projects have been hosted by ASF over a long period of time, providing high-quality, community-driven software.


e-Therapeutics provides a specialized approach to network biology using a computer-based drug discovery platform built upon Apache Ignite. The original problems for the company were the time required for computational analyses to be performed and the inability of existing algorithms to be parallelized. The solutions were to develop a new platform based on Apache Ignite that used parallelism and provided nearly two-orders of magnitude performance improvement, enabling work to be completed in hours and minutes rather than weeks and days.

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Why the Promise of Medical Data Remains Unfulfilled

I’ve written numerous times over the past few years about the power of medical data and the numerous issues surrounding it, from the new ways of collecting and analyzing it to the importance of strong data governance.

Despite the tremendous potential for delivering better care, and also better medical research, the joined-up use of medical data remains largely overlooked. A recent paper examines some of the reasons why that is.

The study found considerable variance in the IT systems across the NHS, with a continued reliance upon paper records and limited data sharing between departments. With patient medical records remaining the primary source of data around the patient, this undermines efforts to use such records both for better care and better research.

The poor use of data wasn’t confined to the NHS, however, with the study also finding that both the pharma industry and universities were not using data to its full potential. For instance, the pharma industry has a long and murky history over the selective publishing of data around trials, and even academia has been accused of similar practices in recent years.

Various well-publicized cases have led to steps to improve matters. For instance, projects such as the AllTrials campaign strive to promote the proper reporting of clinical trials. Even then, however, it’s estimated that fewer than 50% of trials are reported within two years of completion.

Better Governance

The analysis also revealed there were significant problems with the way data is regulated and governed. Existing regulations exist to ensure patients, the public, and medical professionals are safeguarded, but this can often result in excessive caution being used around patient data. Consent procedures can also limit the impact of studies, especially in niche fields.

“Sometimes, relying on the need for individual consent can limit studies about groups that are difficult to reach, as well as problems such as substance misuse, and any issues seen as sensitive,” the authors say.

…all of which adds up to a significant problem. The authors say that the misuse (or non-use) of data is costing lives. Equally, none of the problems outlined above stand in isolation but rather as part of a wider picture of data governance in healthcare. While there are clearly good reasons why data governance is crucial, there are also many areas that could be improved upon.

“It can be argued that data non-use is a greater risk to well-being than data misuse. The non-use of data is a global problem and one that can be difficult to quantify. As individuals, we have a role to play in supporting the safe use of data and taking part where we are able,” the authors conclude.

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5 Mobile Health Trends to Expect in 2018

Innovation and the use of new technologies have changed industries tremendously. As the world becomes progressively interconnected, adoption of technology has become the defining factor for the modern healthcare environment. With digital transformation becoming a big part of the healthcare industry, individuals can live healthier lives. Many modern technologies have already solved challenges regarding patient monitoring, patient-to-doctor communication, data management, medication adherence, etc. All these have only been possible with the effective use mHealth technologies.

As an eventful 2017 finally draws to an end, here are a few mobile health trends and predictions in 2018. The last 12 months saw several trends shape the global mHealth market. However, 2018 is expected to take our expectations of mHealth to the next level. Currently, there are a handful of technology trends looming over the horizon and are expected to take the mHealth world by storm! So, here are five mHealth trends and predictions for 2018!

Digital Interventions

This trend can be categorized under digital health. It is the idea of monitoring and improving patient treatment by using digital interventions. The idea behind digital interventions is to empower individuals to fix chronic or acute problems and improve patient outcomes.

This is particularly important as it decreases the chances of patient readmission, which is a benefit for insurance companies. While the potential of digital interventions is high, the platform may take some time to be implemented. However, it is one of the most powerful platforms that is being developed to improve patient care.

As we have seen often in 2017, people tend to believe that all this technology will replace humans in patient care. Many healthcare providers are against going digital as they feel that it would decrease their chance of survival. On the contrary, these digital health initiatives ease the burden but not replace human interaction. Going digital will only help in streamlining processes and in improving patient engagement but can never replace human interaction altogether.

Documentation Woes

The biggest woe that healthcare providers have in the 21 st century is of documentation. As one healthcare provider puts it “it is overwhelming and dreary at the same time!” At least here in North America, healthcare providers are being forced to document everything related to a patient. Due to this, their margins have tapered.

A great way to address these woes is by gathering the information from the patient itself or maintaining an electronic health record. Counterproductive? Maybe not! Many leading hospital chains have native apps that already store patient data. They also feature medication adherence, remote monitoring, patient-doctor communication, etc. Using these features, healthcare providers or doctors can easily pull up patient data.

This may not completely solve the documentation challenge, but will allow for process streamlining. It will also make sure that doctors are giving their complete attention to their patients.

Big Data and Analytics in Healthcare

This is possibly the most exciting and interesting concept within healthcare. As the gamut of big data and analytics in healthcare is vast, we will discuss only the top three aspects.

1. Resource Management: Big data helps in addressing challenges associated with resource management. Hospitals are among the few entities that require personnel to be present at all times. But at the same time, it’s imprudent to have the entire staff work for long hours just in anticipation of an emergency patient. Big data solves this problem by predicting the number of patients are expected to need a medical assistance.

How this challenge is solved is by feeding the system with years of data so that is can analyze with accurate algorithms and predict the influx of patients at each hospital. Through this analysis, hospitals can reduce waiting times during unconventional hours, and provide exceptional quality of care for patients around the clock, without any compromise.

2. Electronic Health Records: With the penetration of mHealth apps, patients today have their own digital record of their health. This includes medical history, demographics, lab test results, allergies, etc. This data is available to both private and public healthcare providers and using an EHR, doctors can know if a patient has been following their suggestions or not.

EHRs can be used to manage problem lists, manage medication lists, manage patient history, capture external clinical documents, present care plans, guidelines, and protocols, manage guidelines, protocols, and patient-specific care plans, generate and record patient-specific instructions, place patient care orders, and order diagnostic tests.

3. Predictive Analytics: This is something that is unheard of in the healthcare vertical/domain. The potential applications reach beyond the scope of any healthcare provider’s business. Coupled with EHRs of over a million patients, algorithms can accurately predict what diagnosis will suit a new patient.

Many believe that predictive analysis will overtake a physician’s role. However, the entire system is set up only to assist a physician or doctor but not to replace him. The analysis can help doctors make data-informed decisions and improve patient treatment.


This has been an emerging technology for a few years, but never actually took off! 2018 will prove to be different for telemedicine. Probably the reason for telemedicine to not take off over the years could be the availability of a mobile device to BPL families. Now, with the ubiquitousness of a mobile device – read smartphone, telemedicine is expected to radicalize how the healthcare industry functions.

Doctors can attend to their patients using mobile devices. Patients can video call their doctors and have their sickness/ailment diagnosed. They can even get medical attention from their doctors without having to travel to the hospital or clinic.

Doctors too feel that telemedicine offers a better way of treating and managing chronic conditions than conventional visits. Telemedicine offers a refreshing taste to accessibility and freedom to patients. It saves time and money as well.

Blockchain in Healthcare

Data, in the 21 st century is constantly on the move! From flash drives to laptops to mobile devices to emails to the cloud, data is always on the move. How then would healthcare providers safeguard themselves from data theft?

Safeguarding patient data is crucial for healthcare providers across the world. Not just safeguarding patient data, they also need to be compliant. With a plethora of regulations that need to be adhered to, healthcare providers are scrambling to address this challenge.

Blockchain Is the Answer

Deploying blockchain in healthcare allows for anti-counterfeiting. Blockchain is highly secure and immutable – write once, read only. This is extremely crucial for EHRs, as they travel from the public and private sector. Having a singular security protocol will only ease the process, and assure patients that their data is not vulnerable.

Similar to relational databases, various application layers can be built over blockchain. This is one technology that will see increased application in 2018.


Succinctly, mHealth is expected to be the driver for healthcare in 2018 and the years to come. The trend has potential to become the most effective in delivering better healthcare services to people around the world. As healthcare providers look to enhance their ability to evaluate and diagnose patients, mHealth has already seen a good share of adopters. 2018 will only prove the concept of mHealth to be robust!

What are your thoughts about mHealth? Do you think the aforementioned trends will define how healthcare is perceived in 2018? Share your thoughts and suggestions by commenting below.

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In-Memory Technologies: Meeting Healthcare’s Fast Data Challenges (Part 1)

This is a two-part series on the use of fast data in healthcare and how in-memory technologies such as Apache Ignite can meet the requirements and challenges of the healthcare industry. In this first part, we will focus on identifying some of the key challenges in healthcare. In the next part, we will discuss a healthcare case study and see how Apache Ignite and GridGain solved a customer’s problem.


Healthcare is big business and involving a very wide range of applications, such as electronic health record management, drug discovery, and health insurance claims. Apache Ignite is perfectly suited to these applications. Figure 1 shows a high-level view of the Apache Ignite in-memory computing platform.

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Figure 1: Apache Ignite in-memory computing platform

We can see in Figure 1 that at its heart, Apache Ignite is a distributed memory-centric data storage platform. It supports ACID semantics and powerful processing APIs including SQL, compute, key/value, and transactions. Ignite’s memory-centric approach enables it to use memory for high throughput and low latency. However, it can also utilize local disks or SSDs to provide durability and fast recovery.

One of Ignite’s major strengths is that memory is treated as fully functional storage, not just as a caching layer. For example, Apache Ignite can function in pure in-memory mode, in which case it can be treated as an in-memory database (IMDB) and in-memory data grid (IMDG) at the same time. However, when persistence is turned on, Ignite begins to function as a memory-centric system where most of the processing happens in memory, but the data and indexes are persisted to disk. Compared to traditional disk-based relational systems or NoSQL systems, Ignite is strongly consistent, horizontally scalable, and supports both SQL and key-value processing APIs. In Figure 1, we can also see that Ignite can be integrated with third-party database systems and external storage. In addition to Healthcare, Apache Ignite caters for a wide range of use cases.

Fast Data in Healthcare

Healthcare applications may cover a wide range of uses. However, there are several areas that are of particular importance and have been highlighted in a recent report from Stanford Medicine.

Today, there is a greater focus on precision medicine and personalization. This will aid more accurate diagnostics for many serious medical conditions, such as tumors and cancers, disorders of nerves and the nervous system, and diseases and abnormalities of the heart. Increasingly, there are better tools available that allow more collaboration between different parts of the healthcare system. Furthermore, clinical research can enable speedier drug discovery and the treatment of complex or rare diseases. We will discuss a customer involved with drug discovery work in Part 2 of this blog series. Apache Ignite provides distributed storage that allows huge quantities of data to be stored to support these requirements for precision medicine and clinical research. Figure 2 shows Ignite’s distributed storage architecture. Ignite’s Data Grid can be viewed as a distributed partitioned hash map with every cluster node owning a portion of the overall data. As more cluster nodes are added, more data can be stored.

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Figure 2: Distributed storage

Since more patient-specific data is available today through the use of health wearables, home monitors, and smartphones, managing streams of data and complex event processing will be required. The Internet of Things (IoT) is also very applicable to healthcare applications. Personalized data will improve the patient experience. Apache Ignite supports adapters and connectors for various streaming technologies and Figure 3 shows an example software stack for IoT applications that use open-source software from the Apache Software Foundation. Ignite has powerful in-memory capabilities and APIs, as previously mentioned.

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Figure 3: Streaming and complex event processing

Finally, the use of predictive models allows healthcare providers to anticipate, diagnose, and treat diseases earlier. This can provide improved patient health and cost savings. The use of machine learning can help in the development of more detailed patient health risk profiles and also allow earlier intervention and treatment. Patients can receive more personalized treatments for serious health conditions. Apache Ignite ships with a machine learning grid, as shown in Figure 4, with a range of machine learning algorithms that support both local and distributed processing. Since many machine learning models may need to be created and tested, the ability to use distributed processing and the power of the cluster enables more models to be evaluated, providing faster insights and saving time and resources.

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Figure 4: Machine learning grid


Modern healthcare has many demanding requirements for the storage and querying of various types of data. Apache Ignite provides a range of capabilities that can meet these needs perfectly. In the next part of this blog series, we will see how a customer has been able to benefit from using Apache Ignite and GridGain for drug discovery.

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How Big Data Can Support Epilepsy Treatment

I’ve written numerous times about the power of medical data to improve healthcare in the past year or so. A good example of the potential comes via a recently published paper, which describes how a consortium led by Swansea University Medical School aggregated data to forge the largest database of people with epilepsy in the world.

The team wanted to see if specific features of clinical epilepsy aggregate within families, and indeed whether there are distinct family syndromes that could help to better inform genetic research.

The data was derived from over 300 families where at least three members suffered from unprovoked seizures. The analysis revealed that the families would often have a range of epilepsies that were the same by diagnosis. What’s more, some forms that were previously thought of as rare types of epilepsy were actually more common than had been appreciated.

“Epilepsy has a significant effect on people’s lives and this project has increased our knowledge on how some kinds of epilepsy run in families. We are looking forward to further results from the study in the future which may help us develop new epilepsy treatment plans,” the authors say.

Smarter Treatment

Having access to this kind of data could also inform machine learning based efforts. For instance, a recent project from UCL utilized machine learning to better spot epilepsy in children.

The research — which was a collaborative project between Young Epilepsy, UCL Great Ormond Street Institute of Child Health, and the University of Cambridge — focused on focal cortical dysplasia, which is a major cause of epilepsy in children. It describes the way the brain fails to form normally, and because the abnormalities tend to be small, they tend to be very difficult to pick up on MRI scans.

What would make such work even cooler is if they could predict the onset of a fit before it occurs, raising the potential of then intervening to prevent the seizure. That’s exactly what a team from Rice University set out to do. The team developed an algorithm to predict when seizures might occur. After several iterations and a large dose of testing, it was able to predict seizures at least two minutes before their onset.

A team now hope to bring such predictive capabilities to market via a smartphone app that can predict seizures in advance. The project, which was documented in a recent paper, aims to combine information about seizure activity, medication, and other lifestyle factors with things such as environmental data and brain recordings. The app will use this data to predict the likelihood of a seizure occurring that day.

The app aims to provide users with probability ratings across five distinct risk levels (of 20% increments), but the app can become more honed as it tunes itself to your individual behaviors and seizure patterns. The developers hope it will allow users to modify their lifestyle according to the risk of an event occurring.

The app was trained using the world’s longest continuous database of brain recordings. The data was generated through a previous three-year long trial that looked at developing an implantable seizure warning device. That trial showed the potential to predict seizures, but the results were not accurate all the time.

These are just a few examples of how data can be used to improve the lives of people suffering from epilepsy. It’s a journey that we’ve only just embarked upon, and it will be fascinating to see just where it ends up.

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Tencent affiliates IPO spree to continue into next year

Tencent-backed online healthcare service WeDoctor Group is planning a Hong Kong IPO in the coming new-year at a market valuation of $5 billion to $6 billion, SCMP is reporting. To prepare for the listing, the firm is now seeking a $500 million funding before mid-February in 2018.

Started as Guahao, an appointment-scheduling site for patients, in 2010, WeDoctor gradually scaled up to a platform that includes various medical-related services from online diagnosis and medical tips to rating hospitals and doctors. The firm rebranded itself to WeDoctor in 2015 after receiving $394 million Series C led by a consortium that includes Hillhouse Capital and Goldman Sachs with the participation of Fosun, Tencent, and China Development Bank Capital.

Tencent, as an early-stage investor of the WeDoctor, led a $100 million round in the startup in 2014. Since then, the firm’s service has been integrated into Tencent’s mobile apps like WeChat and Mobile QQ.

WeDoctor is the latest addition to Tencent’s recent initiative to get its affiliate companies listed. This year, the Shenzhen-based internet giant has seen three of its most valuable assets go public. Sogou, the search engine arm of Tencent, made its debut on New York Stock exchange last month. Its online reading unit China Literature has raised US$1.1bln after pricing its Hong Kong IPO at the top of its range early in November. China’s first online-only insurance company Zhong An, in which Tencent holds a stake, raised $1.5 billion in a Hong Kong IPO.

It seems that Tencent’s IPO wave is not going to stop in the coming new year. In addition to WeDoctor, Tencent is also planning an IPO for its music-streaming unit Tencent Music. The Wall Street Journal reported that the music group is in talks with Spotify on swapping stakes of up to 10% in each other’s businesses ahead of their expected public listings next year.

Aside from its affiliates, the tech tycoon itself is performing exceedingly well in the stock market recently, joining the half-a-trillion-dollar club in this past November.

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ETL Pipeline to Analyze Healthcare Data With Spark SQL, JSON, and MapR-DB

This post is based on a recent workshop I helped develop and deliver at a large health services and innovation company’s analytics conference. This company is doing a lot of interesting analytics and machine learning on top of the MapR Converged Data Platform, including an internal “Data Science University.” In this post, we will:

  • Extract Medicare Open payments data from a CSV file and load into an Apache Spark Dataset.
  • Analyze the data with Spark SQL.
  • Transform the data into JSON format and save to the MapR-DB document database.
  • Query and Load the JSON data from MapR-DB back into Spark.

CSV Spark SQL and MapR-DB

A large health payment dataset, JSON, Apache Spark, and MapR-DB are an interesting combination for a health analytics workshop because:

  • JSON is an open-standard and efficient format that uses human-readable text to represent, transmit, and interpret data objects consisting of attribute-value pairs. Because JSON is easy for computer languages to manipulate, JSON has supplanted XML for web and mobile applications.
  • Newer standards for exchanging healthcare information such as FHIR are easier to implement because they use a modern web-based suite of API technology, including REST and JSON.
  • Apache Spark SQL, DataFrames, and datasets make it easy to load, process, transform, and analyze JSON data.
  • MapR-DB, a high-performance NoSQL database, supports JSON documents as a native data store. MapR-DB makes it easy to store, query, and build applications with JSON documents.

Apache Spark and MapR-DB

Apache Spark and MapR-DB

One of the challenges that comes up when you are processing lots of data is where you want to store it. With MapR-DB (HBase API or JSON API), a table is automatically partitioned into tablets across a cluster by key range, providing for scalable and fast reads and writes by row key.

Fast Reads and Writes by Key

The MapR-DB OJAI Connector for Apache Spark makes it easier to build real-time or batch pipelines between your JSON data and MapR-DB and leverage Spark within the pipeline. Included is a set of APIs that that enable MapR users to write applications that consume MapR-DB JSON tables and use them in Spark.

Spark MapR-DB connector

The Spark MapR-DB Connector leverages the Spark DataSource API. The connector architecture has a connection object in every Spark Executor, allowing for distributed parallel writes, reads, or scans with MapR-DB tablets.

Connection in every Spark Executor

Example Use Case Dataset

Since 2013, Open Payments has been a federal program that collects information about the payments drug and device companies make to physicians and teaching hospitals for things like travel, research, gifts, speaking fees, and meals.

The Facts About Open Payments Data

Below is an example of one line from an Open Payments CSV file:

"NEW","Covered Recipient Physician",,,,"132655","GREGG","D","ALZATE",,"8745 AERO DRIVE","STE 200","SAN DIEGO","CA","92123","United States",,,"Medical Doctor","Allopathic & Osteopathic Physicians|Radiology|Diagnostic Radiology","CA",,,,,"DFINE, Inc","100000000326","DFINE, Inc","CA","United States",90.87,"02/12/2016","1","In-kind items and services","Food and Beverage",,,,"No","No Third Party Payment",,,,,"No","346039438","No","Yes","Covered","Device","Radiology","StabiliT",,"Covered","Device","Radiology","STAR Tumor Ablation System",,,,,,,,,,,,,,,,,"2016","06/30/2017"

There are a lot of fields in this file that we will not use; we will select the following fields:

CSV Fields

And transform them into the following JSON object:

{ "_id":"317150_08/26/2016_346122858", "physician_id":"317150", "date_payment":"08/26/2016", "record_id":"346122858", "payer":"Mission Pharmacal Company", "amount":9.23, "Physician_Specialty":"Obstetrics & Gynecology", "Nature_of_payment":"Food and Beverage"

Apache Spark SQL, Datasets, and DataFrames

A Spark dataset is a distributed collection of data. Dataset is a newer interface, which provides the benefits of the older RDD interface (strong typing, ability to use powerful lambda functions) combined with the benefits of Spark SQL’s optimized execution engine. Datasets also provide faster performance than RDDs with more efficient object serialization and deserialization.


A DataFrame is a dataset organized into named columns Dataset[Row]. (In Spark 2.0, the DataFrame APIs merged with Datasets APIs.)

Unified Apache Spark 2.0 API

Read the Data From a CSV File Into a Dataframe

In the following code:

  1. The SparkSession read method loads a CSV file and returns the result as a DataFrame.
  2. A user-defined method is used to convert the amount column from a string to a double.
  3. A local temporary view is created in order to easily use SQL.

Read the data from CSV file into a Dataframe

One row of the DataFrame is shown below:

One row from DataFrame

Transform Into a Dataset of Payment Objects

Next, we want to select only the fields that we are interested in and transform them into a Dataset of payment objects. First, we define the payment object schema with a Scala case class:

Define the Payment Schema

Next, we use Spark SQL to select the fields we want from the DataFrame and convert this to a Dataset[Payment] by providing the Payment class. Then, we replace the Payment view.

Create a Dataset of Payment classes

One row of the Dataset[Payment] is shown below:

One row of the Dataset\[Payment\]

Explore and Query the Open Payment Data With Spark Dataset

Datasets provide a domain-specific language for structured data manipulation in Scala, Java, and Python; below are some examples. The Dataset show() action displays the top 20 rows in a tabular form.

Domain-specific language

Dataset’s printSchema() prints the schema to the console in a tree format:

printSchema() prints to console in tree format

Here are some example queries using the Scala Dataset API on the payments Dataset.

What is the Nature of Payments with reimbursement amounts greater than $1,000 ordered by count?

What are the Nature of Payments with payments &gt; $1000 with count

What are the top five Nature of Payments by count?

What are the Top 5 Nature of Payments by count

You can register a Dataset as a temporary table using a given name and then run Spark SQL. With the Zeppelin Notebook, you can display query results in table or chart formats. Here are some example Spark SQL queries on the payments dataset.

What are the top ten Nature of Payments by count?

What are the top 10 nature of payments by count?

What are the top ten Nature of Payments by total amount?

What are the top 10 nature of payments by total amount?

What are the top five physician specialties by total amount?

What are the top 5 physician specialties by total amount?

Here is the same query with the result displayed in a pie chart:

What are the Top 5 Physicians by total amount? (Chart)

Saving JSON Documents in a MapR-DB JSON Table

In order to save the JSON objects to MapR-DB, the first thing we need to do is define the _id field, which is the row key and primary index for MapR-DB. In the function below, we create an object with the id equal to a combination of the physician ID, the date, and the record ID. This way the payments will be grouped by physician and date. Next, we use a map operation with the createPaymentwId function to convert the Dataset[Payment] to a Dataset[PaymentwId], then we convert this to an RDD of JSON documents. (Note that with MapR-DB v6, the Spark connector will support Datasets.)

Transform Dataset into RDD of JSON documents

One row of the RDD of JSON documents is shown below:

One row of the RDD of JSON documents

In the code below, we save the RDD of JSON objects into a MapR-DB JSON table:

Save JSON RDD to MapR-DB

Note that in this example, the table was already created. To create a table using the shell, execute the following at the Linux command line:

mapr dbshell

After starting the shell, run the create command. See mapr dbshell.

Loading Data From a MapR-DB JSON Table

The code below loads the documents from the /user/user01/testable table into an RDD and prints out two rows:

Load the Payments from MapR-DB

Projection Pushdown and Predicate Pushdown for the Load API

The “load” API of the connector also supports select and where clauses. These can be used for projection pushdown of subsets of fields and/or can filter out documents by using a condition.
Here is an example of how to use the where clause to restrict the rows:

Load the Payments for a physician from MapR-DB

nature_of_payment and payer fields

Similarly, if one wants to project only the nature_of_payment and payer fields, and to use the where clause to restrict the rows by amount, the following code will generate the required output:

Load the Payment where the amount is greater than 100 from MapR-DB



In this blog post, you’ve learned how to ETL Open Payments CSV file data to JSON, explore with SQL, and store in a document database using Spark Datasets and MapR-DB.

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How to Make Telehealth Sustainable in Remote Areas

I wrote recently about the value telehealth can play in providing health coverage to those living in rural and remote areas. The paper highlights how retinal tele-screening significantly increased the evaluation rates in both rural and underserved areas, whilst also increasing access to care for minority and other high-risk groups.

Research from the University of Texas at Arlington highlights how micro-entrepreneurship can help to make the provision of telemedicine in such areas affordable and sustainable.

The authors advocate creating public-private partnerships to establish telemedicine centers. They suggest that each facility could cost between $9,000 and $12,000, which whilst being modest by western standards, is substantial for areas such as rural India.

The analysis focused specifically on non-profit telemedicine provider OTTET Telemedicine, who is based in Odisha, India. It aims to promote the use of telemedicine and expand the availability of healthcare into rural areas.

Odisha is a prime location for this, as it covers around 60,000 square miles and has a population of over 42 million. What’s more, 83% of this population lives in rural areas. As such, the availability of healthcare in the state is very bad.

The authors believe that a major barrier to successful implementation of telehealth is a lack of any real local ownership. This resulted in projects often failing whenever grants from funding agencies expire.

“One key to OTTET’s success in implementing telemedicine projects in rural areas is getting someone in the community to invest in a telemedicine project’s success,” the authors say. “OTTET also tackles the lack of technical manpower in rural areas by training unemployed rural youths on telemedicine technology, another factor essential in the project’s success.”

The answer, they believe, is a more effective public-private partnership.

“We discovered that OTTET Telemedicine offers a viable approach to incremental and sustainable implementation of Information and Communications Technology-based development projects as long as there is local ownership and the public-private partnership model at work,” they say.

They hope that their work will help to support the utilization of telemedicine in developing countries, and especially in rural areas that are historically underserved by health systems.

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The Importance of Data Governance for the Future of Healthcare

I’ve covered the growing importance of genomic data in healthcare extensively in the past year. One of the best examples is the partnership between the UK Biobank and European Genome-Phenome Archive (EGA), which is itself a joint resource developed by EMBL-EBI and the Centre for Genomic Regulation (CRG).

The partnership will see the data from all 500,000 participants in UK Biobank distributed via the EGA resource. Biobank participants provided blood, urine and saliva samples for future analysis — including genetic — and gave detailed information about themselves. They also agreed to allow UK Biobank to integrate information from their electronic health records.

Or you’ve got the 100,000 Israeli Genome Project, which aims to underpin the introduction of personalized medicine into Israel. The project is partnering with data analytics startup Genoox to help analyze the genetic data captured and provide actionable insights to hospitals, clinical labs, and researchers throughout Israel.

It’s the first genomic database in Israel but is part of a growing number of customers for Genoox, who recently secured $6 million in extra funding to support their expansion into the United States.

“Deep data analytics has the capacity to impact so many fields, and medicine is certainly included, but there seems to be a gap in innovation between the scientific developments surrounding genetics and the speed at which advancements are being made to make genetic sequencing accessible to treating clinicians,” the company said. “With Genoox, our mission is to close this gap by making genetic sequencing more accessible for the masses.”

Robust Data Governance

As healthcare becomes more and more data-intensive, it will be crucial that the governance of our health data is robust and transparent. It’s a state that we are not at yet.

A recent paper published in PLOS Biology by a pair of health law researchers from the University of Alberta argues that the whole industry lacks basic legal and ethical principles at the moment around consent, with this only likely to intensify as more genomic data is generated.

With projects such as the UK Biobank, researchers can embark upon projects with hundreds of thousands of participants. Issues around the ownership of those samples and the consent given by participants around their use persist, however. The authors contend that we need real policy movement in the area to cover these concerns, especially as the industry is getting increasingly involved.

“The international research community has built a massive and diverse research infrastructure on a foundation that has the potential to collapse, in bits or altogether. This issue would benefit from more explicit recognition of the vast disconnect between the current practices and the realities of the law, research ethics and public perceptions,” they say.

It’s a topic that was touched upon heavily in a recent paper from Professor Dame Sally Davies into the current state of genomic service provision in NHS England.

The report examines the potential for genomics to significantly improve the health of the nation. It provides clear evidence of its potential in areas such as screening, disease diagnoses, and personalized prevention services.

The paper goes on to highlight some serious shortfalls in areas such as infrastructure, public engagement, the organization of research, and the provision of services before providing clear recommendations on how each of these gaps can be addressed and access to genomic services widened.

It’s a feeling shared across the industry, with a recent study by security company VMware highlighting the challenges around strong data governance.

“As the NHS becomes increasingly data-driven, the threat to the security of that data is only going to increase. Already, we are seeing instances of patient data being compromised as a result of cyber criminals, to the point that almost two thirds (66%) of 2,000 UK adults, recently polled for a survey commissioned by VMware, admitted concern about the NHS’ ability to protect their personal data from a successful cyber attack. The future of healthcare relies on access to patient data through many apps, on any electronic device with data held in different locations, so if the industry cannot demonstrate that it is putting in place every measure to protect it then it could risk losing the confidence of the public and NHS practitioners. With 34% of those surveyed saying they would be happier sharing their data if they knew how it would be used, transparency is key.” Tim Hearn, Director, UK Government and Public Services, VMware, told me recently.

It’s clear that this is an area undergoing some pretty rapid changes, and as such, will be one that demands attention in the coming years.

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Big Data and Genomics

I’ve written a lot recently about the rise in genomic data and the applications being developed on top of this — for instance, a recent project featuring IBM and the New York Genome Center (NYGC), The Rockefeller University, and other NYGC member institutions.

The work compared a number of techniques that are commonly used to analyze genomic data from tumor cells and healthy cells. It utilized Watson for Genomics technology to help interpret the genome data. The project revealed that Watson was able to provide actionable insights in just ten minutes, which compares to approximately 160 hours of human analysis.

Of course, the more data you have the better, and I’ve covered efforts by genomics startup Genos to facilitate that before. They promise to sequence your entire exome for $499, with a fascinating caveat. The company has announced that they will contribute to the cost of your sequencing on the proviso that you donate your data to scientific research. The hope is that opening up data to research in this way will begin to close the apparent gap that is opening between the amount of genetic data being sequenced and our understanding of it.

Further evidence of the growing importance of genomic data comes via a partnership between the the UK Biobank and European Genome-Phenome Archive (EGA), which is itself a joint resource developed by EMBL-EBI and the Centre for Genomic Regulation (CRG).

The partnership will see the data from all 500,000 participants in UK Biobank distributed via the EGA resource. Biobank participants provided blood, urine and saliva samples for future analysis — including genetic — and gave detailed information about themselves. They also agreed to allow UK Biobank to integrate information from their electronic health records.

This provides a vast quantity of data for healthcare research, with any work that results from the use of this data then made available in the public domain for other researchers to build upon.


Of course, collecting the data is only one part of the equation, with the analysis being `equally important. Last year saw a new search engine released by the University of California San Diego that aims to make it easier for us to search our genomics data records.

The search engine, called GeNemo, has been documented in a recently published paper and aims to make it easier to search for functional genomic data.

Functional genomics data is valuable, as it helps record the range of activities of each piece of the genome. The new search engine hopes to help researchers uncover the various functional aspects of certain parts of the genome that we believe are responsible for diseases.

The search engine allows users to query a range of databases, including the entire ENCODE dataset. The search algorithm utilizes pattern matching to offer richer results than traditional text-based searches.

Swiss startup Sophia Genetics are arguably the market leaders in this space. They claim to have the largest clinical genomics community in the world, with an AI-powered platform to help make sense of the genetic data collected.

The company, which recently raised $30 million in a funding round led by Balderton Capital, have deployed their platform in 334 hospitals across 53 countries. To date, they’ve managed to analyze over 125,000 patients from around the world.

Privacy Concerns

One of the appealing aspects of the Sophia approach is that they only process the anonymized data collected by the hospitals themselves. With something as valuable as our genomic data, however, privacy remains a key concern.

A recent paper published in PLOS Biologyby a pair of health law researchers from the University of Alberta argues that the whole industry lacks basic legal and ethical principles at the moment around consent, with this only likely to intensify as more genomic data is generated.

With projects such as the UK Biobank, researchers can embark upon projects with hundreds of thousands of participants. Issues around the ownership of those samples and the consent given by participants around their use persist, however. The authors contend that we need real policy movement in the area to cover these concerns, especially as the industry is getting increasingly involved.

“The international research community has built a massive and diverse research infrastructure on a foundation that has the potential to collapse, in bits or altogether. This issue would benefit from more explicit recognition of the vast disconnect between the current practices and the realities of the law, research ethics and public perceptions,” they say.

It’s a topic that was touched upon heavily in a recent paper from Professor Dame Sally Davies into the current state of genomic service provision in NHS England.

The report examines the potential for genomics to significantly improve the health of the nation. It provides clear evidence of its potential in areas such as screening, disease diagnoses, and personalized prevention services.

The paper goes on to highlight some serious shortfalls in areas such as infrastructure, public engagement, the organization of research, and the provision of services, before providing clear recommendations on how each of these gaps can be addressed and access to genomic services widened.

It’s clear that this is an area undergoing some pretty rapid changes, and as such will be one that demands attention in the coming years.

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What Are Some Database Use Cases?

To gather insights on the state of databases today, and their future, we spoke to 27 executives at 23 companies who are involved in the creation and maintenance of databases.

We asked these executives, “What are real world problems you, or your clients, are solving with databases?” Here’s what they told us:

Financial Services

  • Capital One migrated to the cloud reducing spend and masking PCI.
  • Deliver changes to the database in DevOps which is frequently terabytes of customer data. Make sure the data is protected and secure. For highly regulated environments, like financial services and healthcare, have good data management, structure, and best practices to meet regulations.
  • Asset management software solution for a car company that leases management lifecycle of assets deployed on prem migrating to the cloud. They’re using us to move and will use on prem. A European brokerage company must deal with regulatory risk management (RIFIR) for European regulatory requirements and trades analysis. SQL server needs to scale out as demand and data require.
  • Provide operational data stores for telcos, finance, gaming, and ad tech. High throughput with strict latency guidelines. Important for fraud detection in any industry because you have to be able to approve or deny the transaction in milliseconds. We also do micro-personalization for websites drawing a custom page based on what the consumer has done recently. Personalization solutions in retail, aviation (frequent flyers), and online gaming for CX and player engagement.
  • Most of our customers are in data-intensive industries like financial and insurance, healthcare, services, software and technology, education, energy and utilities, retail, so there are a lot of cutting-edge use cases going on there. Anything with IoT is interesting, such as the all the real-time data processing of RFIDs, sensors, RSS feeds, smart meters, and smart grids going on in the energy industry.
  • We are solving real-time risk control in:
    • Financial verticals: by looking at how transactions/users are connected to known blacklisted entities through what types of connections.
    • E-commerce personalized recommendation: by connecting users, products, categories, purchasing patterns.
    • Supply-chain logistics optimization: by connecting products, manufacturers, shipments, parts, and customers all in a single graph.


  • Maxwell Health is a healthcare provider with an app on AWS EC2 S3 storage using MongoDB as their next generation database. We provide enterprise backup and recovery which helps Maxwell meet HIPPA requirements. Prior to this Maxwell was using manual scripting which was inefficient and did not scale. Backup windows were not able to keep up and the S3 storage costs were very high with all of the replicated data. We provided ongoing backup with single-click repair-free recovery and no time needed to restore along with semantic deduplication. This reduced backup storage cost by 90% and generated a 300% ROI in one year.
  • Interoperability with EMRs and ERPs. Sensor devices on the edge. Information from the sensors helps to see trends and update predictive models for predictive maintenance. Integrate with manufacturing, healthcare to provide the ultimate patient experience using real-time data. Stanley Healthcare has a line of business with medical equipment for hospitals to tie with EMRs and locations (equipment management location). Put sensors in the patient badge in ER tie to their medical record and monitor vital signs. Track room availability. Optimize patient flow. See what best practices are to optimize patient flow.
  • We work with a lot of different verticals. We support IoT platforms with Cassandra. We help accelerate clinical trials with real-time sensors.
  • We have customers using our product for a document search engine and another using it as a commercial IoT platform. HIPAA compliant demo to ingest EDI data to form a healthcare clearing house in a box for plan termination and patient data. There are opportunities with every business domain and every data domain.
  • Healthcare, financial services, telecommunications, and technology. Healthcare is able to take an outcome-based approach aligning doctors, patients, hospitals, and insurers to help prevent fraud, identify the right treatments, and optimize outcomes for an improved patient experience at a reduced cost. Medical device companies are changing how they approach business from machine provider to service provider with maintenance and management providing faster and cheaper service to their customer. Push updates to the machine to save on service calls and maintenance. MRI images can be shipped to the cloud and processed in five minutes. Obtain the images from all MRIs and use ML for better results analysis.
  • Spans spectrum — different vertical use cases. IMHE is using analytics of health data to correlate demos with infections to obesity. The solution helped them get their data into the White House by compressing a large data set so it would fit on a laptop.


  • Context — graphs bring a richness of understanding. eBay’s shop bot engages in a digital conversation. There’s a knowledge graph behind AI. Telia, a Swedish cable company, is able to map home features with people and devices in households to support new functionality. Marriott Hotels is doing real-time pricing across all properties micro-optimizing pricing.
  • The real-world problem I was always trying to solve was availability. As digital services become mission critical — and mainstream — a new architecture is needed to eliminate downtime. At Amazon, we had to build Dynamo to be multisite so it could survive a datacenter outage without causing downtime to, where downtime was directly correlated to losses of millions of dollars. Likewise, Cassandra was also built to be multi-site to survive East or West Coast datacenter outages at Facebook. Building resilient services is a higher priority than performance in today’s always-on, connected world. I’m more likely to abandon a site or stop using a service if it incurs frequent outages than if it’s a fraction of a second slower.
  • An owner of multiple malls is beginning to see the value of real-time data and insights so they can push notifications to customers while they’re in the mall. Online gaming and retail is analyzing behavior using AI/ML to observe betting and shopping patterns to stop bettors getting in over their heads and recommending specific products to customers. You must have speed, scale, and security without compromise. If apps are going to survive, they must have high performance, scale, and security which all demand greater data management. Tiered data management with most data in memory and the rest in a persistent state which can be requested as needed.
  • An online retailer is receiving guidance on the best price in the marketplace based on real-time analysis of 300 million items with the competition across multiple geographies. Logistics and planning for airlines, package delivery, and trucking. Financial services companies are doing risk analytics.


  • U.S. Postal Service tracking the entire mail distribution system. PG&E integrating seven different lines of business into a single database for a single view of the enterprise and the customer.
  • Ability to do background upgrades before you shut down the current system. How to manage data growth. Plan for warehousing so old data doesn’t cause performance problems. Figure out how to handle more data so it does not cause performance degradation. Determine the need to build a data warehouse.
  • The customer wants to get apps out quickly and uses IBM Urban Code to do so but this neglects the database. Using our solution, DBAs don’t have to spend their weekends and nights monitoring. Helped NBC Universal go from five-week to two-week sprints after they were able to automate database release.
  • We store and analyze human experiential and learning data. We use distributed databases to offer immediate insight to customers, leveraging rich logical queries and real-time data push to browsers.
  • Security compliance considerations. What do I need to audit and prevent? Get into standard regulations like PCI, HIPAA, SOX, GDPR, NIST, DISA-STIG. Tools for auditing and security policy.
  • MongoDB different between relational schema and schema-less. Support databases across client stores with different shapes of data. NoSQL databases allow you to work with dynamic data sets. Representation of data in NoSQL is more like what’s coming in and out of the API. Fundamental shift with document first data in the business intelligence space.
  • We see a wide range of adoption in every industry. Used for accounting, enterprise resource planning, supply chain management, human capital management. Industry specific for multiple verticals that can process, scale, be available, and be secure.
  • We’re working with clients all over the world and in every industry to help them overcome challenges and meet business goals through our database solutions and strategies.

What are some interesting use cases you are seeing with databases today?

Here’s who we talked to:

  • Emma McGrattan, S.V.P. of Engineering, Actian
  • Zack Kendra, Principal Software Engineer, Blue Medora
  • Subra Ramesh, VP of Products and Engineering, Dataguise
  • Robert Reeves, Co-founder and CTO and Ben Gellar, VP of Marketing, Datical
  • Peter Smails, VP of Marketing and Business Development and Shalabh Goyal, Director of Product, Datos IO
  • Anders Wallgren, CTO and Avantika Mathur, Project Manager, Electric Cloud
  • Lucas Vogel, Founder, Endpoint Systems
  • Yu Xu, CEO, TigerGraph
  • Avinash Lakshman, CEO, Hedvig
  • Matthias Funke, Director, Offering Manager, Hybrid Data Management, IBM
  • Vicky Harp, Senior Product Manager, IDERA
  • Ben Bromhead, CTO, Instaclustr
  • Julie Lockner, Global Product Marketing, Data Platforms, InterSystems
  • Amit Vij, CEO and Co-founder, Kinetica
  • Anoop Dawar, V.P. Product Marketing and Management, MapR
  • Shane Johnson, Senior Director of Product Marketing, MariaDB
  • Derek Smith, CEO and Sean Cavanaugh, Director of Sales, Naveego
  • Philip Rathle, V.P. Products, Neo4j
  • Ariff Kassam, V.P. Products, NuoDB
  • William Hardie, V.P. Oracle Database Product Management, Oracle
  • Kate Duggan, Marketing Manager, Redgate Software Ltd.
  • Syed Rasheed, Director Solutions Marketing Middleware Technologies, Red Hat
  • John Hugg, Founding Engineer, VoltDB
  • Milt Reder, V.P. of Engineering, Yet Analytics

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Singapore enterprise healthcare startup MyDoc raises $5.2m

Photo credit: yuliang11 / 123RF.

MyDoc, a Singapore-based healthtech startup, raised US$5.2 million in a series A funding round.

Since its inception in 2014, the startup has focused on solving employee healthcare management for large enterprises. Its new suite of products, MyDoc@work, is set to launch towards the end of this year, the firm tells Tech in Asia.

It will let employees book video consultation with doctors, order online prescriptions, and access insurance information. Some of the features, like online medical certificate application the startup launched not long ago, already existed before, but their use will be simplified as part of the new product suite.

The goal is to integrate all aspects of healthcare by connecting patients, healthcare professionals, pharmacies, insurers, and employers, on one platform, which saves time and provides all sides with valuable data to learn from.

“We’re solving the pain point some corporates have with increasing healthcare costs,” says Dr. Snehal Patel, CEO and Co-Founder of MyDoc.

Current clients include insurers such as AIA and AXA, Singapore’s Health Promotion Board (HPB), Guardian Pharmacy, AcuMed Medical Group and Farrer Park Hospital.

MyDoc has focused on its Singaporean home market in the past, with some trials in Sri Lanka and Hong Kong. The latter especially showed promise, says Patel, and he expects MyDoc to be more active there in the future. But other regional markets like Indonesia are also in the cards.

“Corporate healthcare and insurance markets tend to be quite uniform,” Patel says. Its existing relationships with globally operating enterprises and insurers should help MyDoc when entering new territories.

The round, which was led by global IT services company UST, makes one of the top 10 largest of healthtech investments in Southeast Asia, according to Tech in Asia data.

Singapore is the region’s leader in healthtech. All ten firms which have raised US$5 million or more are registered there. US$25 million, the largest round to date raised by a regional healthtech firm, went to CXA earlier this year. It also focuses on the enterprise healthcare space.

Early stage venture capital firm Wavemaker Partners also participated in MyDoc’s series A investment. Previous investors include Singapore government-related fund Spring Seeds, and August Capital Partners.

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Forward: The Doctor’s Office of the Future [VIDEO]

Image: Nick Deshpande/Forward

Have you ever dreaded going to the doctor, knowing that you have to sit in a cold, sterile room, only to be poked and probed with equipment that looks like it was set aside as props from a scene in the latest blockbuster horror flick? Well, times are changing. Welcome to Forward, the doctor’s office of the future.

No more nurses poking around for your veins. When you enter the facility, which almost feels like stepping into a modern day Apple store, you log-in with your information, and all of your data appears. Then, you digitally go through your playlist for the day and get started.

Forward (Image: Nick Deshpande/ Forward)

First, you head over to the body scanner, which takes all of your vitals using a quick and easy method; all you have to do is insert two of your fingers into the reader. I must say, instead of feeling as anxious as I normally do when I embark upon a trip to the doctor, I found myself fascinated by all the technology and how everything worked.

Once you finish there, you make your way into the physician’s room. Remember those ugly shrugs that you had to change into? No more! Forward offers cute garments that look more like gym clothes. I had to wonder if I had actually missed the doctor’s office and wandered into an Equinox instead, by mistake. It certainly felt that way. Then, you meet with your doctor, and together, you go over all of your health related goals.

Traditionally, people only go to the doctor after something tragic has happened, but Forward offers a new and different approach to medicine.

“Everything in the healthcare system is set up to be reactive instead of proactive. So, we kind of wait for something terrible to happen, and then we try to fix it,” says Forward co-founder, Ilya Abyzov.

However, Forward is meant to be preventative, forward-thinking medical office—hence the name.

I don’t know about you all, but my health is the most important thing to me, trumping everything else. For $149 a month, you get access to all of this, including 24/7 access to your physician via the app. According to Forward’s website, it also provides travel vaccine and has an on-site pharmacy that dispenses common generic medications to patients at no cost.

Take my money now! Well… not so fast. Forward is currently only located in the San Francisco Bay Area, but they are in the process of expanding to new locations, so hang tight! A new doctor’s office experience will be coming to a city near you soon.

“We want to bring better healthcare to everyone. So, that means being in all the major cities—New York City, Los Angeles, and so forth. Ultimately, it’s about bringing better healthcare to as many people as possible,” says Michael Plater,  who is on Forward’s growth team.

Watch My Tour of Forward’s Facilities Below:

(Video: Nick Deshpande)

Sequoia BlodgettSequoia Blodgett is the Technology Editor for Black Enterprise, Silicon Valley. She is also the founder of 7AM, a lifestyle, media platform, focused on personal development, guided by informed  pop culture.

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