Can Personal Health Records (PHR) help consumers?
Preparing for a flood of personal medical Big Data
Patient generated information coming from various wearable medical devices or manually imported and collected via mobile applications such as Personal Health Records (PHR) is expected to change our approach to healthcare. Instead of vague responses to generic questions kind of: “how do you feel?” patients will provide doctors with abundance of measurable data like blood pressure, ECG, glucose level, cholesterol, diets, weight, sleep patterns – you name it. From the populational, statistically oriented health our modern health system tends to shift to personalized care based on individual data. This will make diagnostics more precise, treatments more efficient and acute conditions less probable. Patients will join forces with medical professionals to cope with preventable and chronic diseases.
This is the vision. But voices of concern are coming already from both medical professionals and consumers. The flow of personal data is going to generate a flood of Big Data people will be supplying daily, hourly, from homes, during vacations, company meetings, sleepless nights and so on. Already today 80% of patients would like to use their smartphones to interact with the doctors . Ask your local GP if she/he is ready to receive numerous calls from patients worried about their ECG, blood pressure, BMI or stress level.
The answer is PHR, the tool that is expected to help patients tracking their health conditions better while preparing them to be a reliable partner to physicians in navigation through the ocean of personal medical data. “Properly designed and implemented, PHRs can help patients manage their health information and become full partners in the quest for good health” .
But does the existing concept of PHR really help people to turn the abundance of data into meaningful information to understand their health?
From data storage to data analysis and decision support.
The majority of consumer oriented PHR applications are based on three fundamental conceptual pillars: data collection, data protection and data storage. Preparing to this blog I went through 200+ applications that allegedly “every hospital market should know” published by the MobiHealthNews as the result of their “exhaustive search of Apple’s AppStore and the Google Play store for apps that were developed by or on behalf of hospitals and healthcare systems in the US” . Despite the fact that those apps were presumably developed on behalf of hospitals and thus heavily designed to facilitate appointments or to find a physician or a facility location, about 30% of them were clearly consumer-oriented PHRs. Moreover, many applications are variations of basically the same app. The prevailing approach: patients can store their vital signs, lab results and medication in one single PHR, but often in different places with no meaningful links to each other. One is still getting a static picture that does not allow to understand how your health reacts in time if one of those values varies.
The typical example of disconnected applications are blood pressure trackers. The majority of them contain only 3 fields: “systolic”, ‘diastolic” and “pulls”. At best, one can put manual comments. You can see what is going on, but with no idea why. For example, if a person switches from angiotensin-converting enzyme (ACE) inhibitor Ramipril to Valsartan? What medication works better for the patient?
A solution would be a simple application backend analysis, e.g., for a period of three months to provide a valuable information on blood pressure and medication interaction, preparing a person for a thoughtful discussion with a physician on the impact of prescribed medications. It will also free the doctor’s time from using a “medication reminder” application if a given medication simply does not work.
Patients are of course grateful now at least to have some information about their health and ability to use some of the services which were not available before. But as soon as these first aspirations are saturated, patients will expect to move from mere data tracking to understanding how they can use data flows to improve their health.
Analyses of Correlations between the data, not just data itself are especially important for the people who suffer from multiply related morbidities like diabetes, hypertension or heart failure. For example, numerous statistical data indicate that a high glucose level causes vessels blockage thus increasing the risk of hypertension and heart failure. But after a series of insulin injection or metformin tablets, can your PHR clearly show that the reduction of a glucose level brings your blood pressure down? Not for general statistics, but for you personally? Just the connection of these two parameters, vital signs and medication, not mentioning the external factors, in particular, weather conditions that can influence blood pressure, may provide patients with a much better insight into their health conditions, thus stimulating PHR adoption.
Big vendors like Microsoft with Health Vault, Apple with promised Health Kit or Samsung with coming Simband are offering citizens’ platforms to store their personal data that will be collected from more and more medical devices. That will generate more and more data. Will the developers embed the Big Data analytics into the concept of their PHR applications turning them into meaningful decision support systems? Or will these applications remain secure storage and sharing platforms, with more and more data to enter and more confusion to create?
The response to this question is crucial to predict the level of patient engagement in mHealth, the boom or the slump in its future. The consumers’ expectations for mHealth are high, and the disillusionment can be deep and irrevocable.