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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 (http://oreilly.com/) 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.

Following the Spread of APIs in Health: BaseHealth’s Genomic Health Analysis

Posted on June 3, 2015 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 (http://oreilly.com/) 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.

Because health care has come late to the party, companies in that field have had plenty of time to see the advantages that Application Programming Interfaces (APIs) have brought to other areas of computing and commerce. BaseHealth Enterprise, which has been offering comprehensive health assessments based on a patient’s genetic information and other health factors for five years through a Software as a Service (SaaS) platform, is now joining the race to APIs. The particular pressures that led to the development of their APIs makes an interesting case study.

Although the concept of an API is somewhat technical and its details call for a bit of programming background, the concept driving API use is simple. We all use web sites and mobile apps to conduct business and interact, but an API allows two applications to talk to each other, serving as a pipe of information transfer. Thus, crucial tasks can be automated and run on a routine basis using an API. BaseHealth modestly suggests in their press release that their API “marks the first time in human history that genomic data is on-call for developers across the globe.”

Example of request for sleep apnea information

Example of request for sleep apnea information

I talked last week to BaseHealth’s CEO Prakash Menon and to Hossein Fakhrai-Rad, founder and Chief Scientific Officer. They offer five basic services, all based on evaluating the genomic and phenomic (observed) data from a patient. A developer can call for such information as:

The patient’s risk for a particular common complex disease, along with risk factors that make it more likely and recommended lifestyle changes The likely effectiveness of a particular drug for a condition, given the patient’s genetic makeup Likely patient responses to various nutrients

Genomic testing is done by companies such as Illumina. Different testing services make very different judgments about the significance of various genes, but there are now evaluation sites (which perform a kind of crowdsourcing to accumulate information validating these judgments) to offer more confidence in the tests. BaseHealth accepts this data along with information about family history, lifestyle, and the patient’s environment to make useful recommendations about handling diabetes, cancer, stroke, gout, sleep apnea, and many other common conditions.

Previously, many health plans and hospitals were interested in the BaseHealth SaaS platform, but did not want to adopt a new application and UI into their existing systems because of the cost of implementation and the time it would take to train healthcare professionals on a new system. The BaseHealth API allows developers at these organizations to use specific features of BaseHealth’s comprehensive health assessment without having to overhaul their existing systems.

Furthermore, large genetic sequencing results are time-consuming and expensive to transmit, and it was wasteful to store them twice (at the provider and at BaseHealth). Some countries also prohibit the transfer of genetic data outside the country’s border for privacy reasons.

BaseHealth’s APIs therefore allow a totally different interaction model. Data can be stored by health care providers and patients, then combined by an application (usually run at the provider’s site) and submitted as a JSON data structure to the API. Only the specific information required by the API needs to be transferred. It is conceivable that apps could be developed for patient use as well. However, because BaseHealth does not offer direct-to-consumer genetic testing, they have none of the problems that 23andMe suffered.

In a field where many vendors scrutinize and limit access to APIs, it’s important to note that BaseHealth’s API is available for all to use–there is no gateway to get through, only a short registration process in which BaseHealth collects a developer’s email address. One can submit 1,000 requests each month for free-making participation easy for small providers-and then pay a small fee for further requests.

APIs hold the promise to streamline health care just as they have reduced information friction in other industries. The BaseHealth experiment illustrates why an API is useful and how it can alter the business of health care.