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Randomized Clinical Trial Validates BaseHealth’s Predictive Analytics

Posted on March 11, 2016 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site ( and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

One of the pressing concerns in health care is the validity of medical and health apps. Because health is a 24-hour-a-day, 365-day-a-year concern, people can theoretically overcome many of their health problems by employing apps that track, measure, report, and encourage them in good behavior. But which ones work? Doctors are understandably reluctant to recommend apps–and insurers to cover them–without validation.

So I’ve been looking at the scattered app developers who have managed to find the time and money for randomized clinical studies. One recent article covered two studies showing the value of a platform that provided the basis for Twine Health. Today I’ll look at BaseHealth, whose service and API I covered last year.

BaseHealth’s risk assessment platform is used by doctors and health coaches to create customized patient health plans. According to CEO Prakash Menon, “Five to seven people out of 1,000, for instance, will develop Type II diabetes each year. Our service allows a provider to focus on those five to seven.” The study that forms the basis for my article describes BaseHealth’s service as “based on an individual’s comprehensive information, including lifestyle, personal information, and family history; genetic information (genotyping or full genome sequencing data), if provided, is included for cumulative assessment.” (p. 1) BaseHealth has trouble integrating EHR data, because transport protocols have been standardized but semantics (what field is used to record each bit of information) have not.

BaseHealth analytics are based on clinical studies whose validity seems secure: they check, for instance, whether the studies are reproducible, whether their sample sizes are adequate, whether the proper statistical techniques were used, etc. To determine each patient’s risk, BaseHealth takes into account factors that the patient can’t control (such as family history) as well as factors that he can. These are all familiar: cholesterol, BMI, smoking, physical activity, etc.

Let’s turn to the study that I read for this article. The basic question the study tries to answer is, “How well does BaseHealth predict that a particular patient might develop a particular health condition?” This is not really feasible for a study, however, because the risk factors leading to diabetes or lung cancer can take decades to develop. So instead, the study’s authors took a shortcut: they asked interviewers to take family histories and other data that the authors called “life information” without telling the interviewers what conditions the patients had. Then they ran the BaseHealth analytics and compared results to the patients actual, current conditions based on their medical histories. They examined the success of risk assignment for three conditions: coronary artery disease (CAD), Type 2 diabetes (T2), and hypertension (HTN).

The patients chosen for the study had high degrees of illness: “43% of the patients had an established diagnosis of CAD, 22% with a diagnosis of T2D and 70% with a diagnosis of HTN.” BaseHealth identified even more patients as being at risk: 74.6% for CAD, 66.7% for T2D, and 77% for HTN. It makes sense that the BaseHealth predictions were greater than actual incidence of the diseases, because BaseHealth is warning of potential future disease as well.

BaseHealth assigned each patient to a percentile chance of getting the disease. For instance, some patients were considered 50-75% likely to develop CAD.

The study used 99 patients, 12 of whom had to be dropped from the study. Although a larger sample would be better, results were still impressive.

The study found a “robust correlation” between BaseHealth’s predictions and the patients’ medical histories. The higher the risk, the more BaseHealth was likely to match the actual medical history. Most important, BaseHealth had no false negatives. If it said a patient’s risk of developing a disease was less than 5%, the patient didn’t have the disease. This is important because you don’t want a filter to leave out any at-risk patients.

I have a number of questions about the article: how patients break down by age, race, and other demographics, for instance. There was also an intervention phase in the study: some patients took successful measures to reduce their risk factors. But the relationship of this intervention to BaseHealth, however, was not explored in the study.

Although not as good as a longitudinal study with a large patient base, the BaseHealth study should be useful to doctors and insurers. It shows that clinical research of apps is feasible. Menon says that a second study is underway with a larger group of subjects, looking at risk of stroke, breast cancer, colorectal cancer, and gout, in addition to the three diseases from the first study. A comparison of the two studies will be interesting.

Where Will We See Analytics in Ambulatory Medicine?

Posted on April 9, 2015 I Written By

John Lynn is the Founder of the blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of and John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

As I prepare to head to the mecca of healthcare IT conferences, I’m getting inundated with pitches. Much like last year, analytics is still a really popular topic. It seems like every healthcare IT vendor has some analytics offering. Many abuse the term analytics (which is fine by me) and that term has come to mean analyzing your health data in order to provide value.

As I think about analytics, I wonder how much of it will apply to the small physician practice. I should do this survey, but I bet if I asked 5 physicians working in small group or solo practices about their analytics strategy they’d all give me blank stares. Small physician practices don’t have an analytics strategy. They’re not looking for new ways to leverage analytics to improve their practice. That’s not how a small practice thinks.

So, does analytics have a place in small ambulatory medicine?

The short answer is that it absolutely does. However, I think that it will be delivered in two forms: packaged or purchased by the borg.

In the packaged approach, analytics will be part of a small practice’s EHR system. Much like a doctor doesn’t have a mobile EHR strategy (it just comes with the EHR), they won’t have an analytics strategy either. They’ll just take the analytics solutions that come with the EHR.

In some ways, the reporting capabilities in an EHR have been doing this forever. However, very few organizations have been able to use these reports effectively. The next trend in EHR analytics will be to push the data to the user when and where they need it as opposed to having to pull a report. Plus, the EHR analytics will start trying to provide some insights into the reports as opposed to just displaying raw data.

One key for ambulatory EHR vendors is that they won’t likely be able to build all the EHR analytics functions that a doctor will want and need. This is why it’s so important that EHR vendors embrace the open API approach to working with outside companies. Many of these third party software companies will provide EHR analytics on top of the EHR software.

In the purchased by the borg scenario, the small practice will get purchased by the borg (You know…the major hospital system in the area). This is happening all over the place. In fact, many small practices cite the reason for selling out to the local hospital is that they don’t think they’ll be able to keep up with the technology requirements. One of those major requirements will be around analytics. We’ll see how far it goes, but I think many small practices are scared they won’t be able to keep up.

Ok, there’s one other scenario as well. The local hospital system or possibly even a local ACO will purchase a package of analytics software (ie. purchased by the bog) and then you’ll tap into them in order to get the benefits of a healthcare analytics solution. We see this already starting to happen. I’ve heard mixed results from around the country. No doctor really likes this situation since it ties them so deeply with the local hospital, but they usually can’t think of a better option.

That’s my take on how analytics will make its way to ambulatory practices. Of course, most large hospital systems also own a large number of ambulatory practices as well. So, some of the analytics will trickle down to ambulatory in those systems as well. I just wonder how much value ambulatory doctors will get from the hospital analytics vendors that are chosen. I can already hear the ambulatory doctors complaining about the analytics reports that don’t work for them because they’re so hospital focused.

Where are you seeing analytics in the ambulatory setting?