Archive for the 'Health 2.0' Category

Flatlining: Can Health 2.0 be Resuscitated by Access to Prescription Drug Histories?

Where is the firehose of data we were promised?

Access to personal health data is the foundation requirement for any movement toward patient engagement. Don’t get me wrong, access to the data itself will not solve any problem.  In fact, in the early stages, access to vast amounts of personal health data will probably create a lot more problems than it solves.  However, with widespread access to personal health data, enterprising entrepreneurs will have the keys to provide users with highly customized and personalized  health-related experiences, or such is the much hyped promise of Health 2.0.  And when I say access, I am talking about simple and seamless access to my data.  I recently tried to link my Google Health account to Walgreens Pharmacy.  It took 8 minutes to complete a profile at Walgreens.com, and I still did not get my data.  It took another few days for the data to drop into GHealth.  I am pretty sure I clicked a release allowing Google to send a fax to Walgreens for permission.  That would be pretty funny if it wasn’t so sad.  Call me lazy, but I think I deserve an OAuth for my data, where, as a user, I simply need to provide permission and credentials.  Then let the app do the rest.

Given the current state of access to health data, most ‘successful’ Health 2.0 companies are limited, and will continue to be limited, to very early adopters.  Health 2.0 will not succeed on any meaningful scale as long as apps require users to input their own health data to receive a service in return.  Most users are not that motivated (some might even say lazy like me), most do not have access to their information and many do not feel comfortable enough with the data to use it.  The watershed for Health 2.0 will come when big data starts to flow effortlessly into consumers hands in huge chunks.

The Data Watershed: E-Prescribing and Medication History

Fortunately, I think this data watershed may be coming.  Today, there are only a few sources of big data in healthcare.  Data is captured in medical charts (or EMRs for the select few), it is captured in medical claims databases and it is captured as prescription drug histories.

Due to the rise in e-prescribing, the first of these firehoses of data to reach patients will probably be prescription drug histories.  E-prescribing is pumping digital data into the system in large and growing quantities.  And in March 2010, Google Health announced a partnership with Surescripts, the largest e-prescribing network in the US, to make it possible for millions of users to access to their medication histories.

The Importance of Medication History

So, what’s the a big deal? Medication history is only one small part of the complete data package, right?  Wrong. Medication history is the backbone of a patient’s medical history, and, for better or for worse, pharmaceutical use is the foundation of our current medical system.  In the vast majority of cases, when a patient gets sick, they go to the doctor, get diagnosed, and get a prescription.  So a patient’s medication history gives a detailed, longitudinal medical history for the patient.  One just needs to know how to translate it.

Below are a few examples showing simplified medication histories for three hypothetical patients.  Let’s see what we can learn about these patients from these simple data points.

At first glance, patient #1 has a pretty random assortment of medications, including some pain medications, a prescription stool softener (Doc-Q-Lace), a 4 times a day antibiotic and hemorrhoid medication.  But any woman who has given birth probably recognizes this assortment of medications as a pretty typical post-childbirth discharge list.  All of these meds prescribed on 3/20/08 tells us that this is a woman of childbearing age who gave birth on 3/18/08 +/- 2 or 3 days.  We also know from the Diclo antibiotic that she breastfed her child for at least 2 months, and had mastitis, a common breast feeding infection.  I bet that Diapers.com would like this information.  Or possibly Mattel, knowing that this woman now has a two year old child?  How about the entrepreneur who is developing the next BabyCenter.com app for new moms?

Patient #2 has a pretty straight-forward and easily identifiable medical history.  The point of this example is to show the depth of personal information that can be derived from five simple data points.  We know that this patient is HIV positive and began treatment at the end of 2006.  We also know that they are probably not doing well, as they have recently failed on their first-line antiretroviral therapy and have progressed to second-line treatment with a protease inhibitor (Reyataz).  We can also make some assumptions, based on demographics, and assume there is about a 75% chance this patient is male and make assumptions on age.  I would have to assume that this patient would want to strictly control who has access to these five data points.

Patient #3 is a little more complicated, but is a pretty typical patient with cardiovascular disease.  We know this patient has high cholesterol (Lipitor), high blood pressure (Norvasc) and type 2 diabetes (Metformin).  We know from the pretty rapid intensification in oral anti-diabetic therapy (Metformin 500 to Metformin 1000 to Janumet, which is a combination of Metformin and Januvia) that this patient’s diabetes is less well controlled than the average type 2 diabetic.  We also know their high cholesterol is unconrolled on a standard dose of Lipitor, due to the increase to Lipitor 80.  The addition of Coreg (a beta-blocker), Plavix (an anti-platelet drug) and Altace (an ACE inhibitor), all on August 15, 2009, leads us to believe that this patient had a major cardiac event, most likely a heart attack requiring percutaneous intervention (PCI) and a stent to open a clogged coronary artery, as this list is a pretty common myocardial infarction discharge list.  What else can we infer from this data?  We can be pretty sure this patient has good health insurance, and if they are on Medicare, they are probably in a Medicare Advantage plan, due to the number of branded medications and lack of generic substitution (i.e. generic simvastatin vs. branded Lipitor).

The Good and Bad of Medication History

I believe widespread access to medication histories will infuse new life into Health 2.0 and the entrpreneurs working in this field.  Any of these entrepreneurs not actively thinking about how to use medication histories in their apps are missing the boat. With easy access to medication history, users can give permission for App X to access their medication history (hopefully with one click), and receive highly personalized services in return.  Keas is beginning to do this with taylored Care Plans, but I am waiting for the apps that look at my medication history and tell me other drugs I can take (to save money), whether my physician is following guidelines, news I should read, and FDA alerts I should know about.  I am waiting for the app that connects me to similar patients based on our medication histories, the app that looks at my history and asks simple, taylored questions to help me further populate a more robust PHR, and the app that pays me to allow pharma companies to see my de-identified medication history and ask me questions about my data (pharma loves medication history information, just ask IMS Health, which recently sold for $5.2 billion).

But, of course, with this much personal data coming into the mix so quickly, there are significant privacy risks coming too.  The same algorithms used to translate a medication history into a health profile for customized applications can also be used by others to learn deeply personal facts about individuals.  So, with all the good that will come from access to medication histories, the first major privacy breaches and big scare stories are probably coming as well.  Get ready, the Health 2.0 ride is getting more interesting.

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Patient-Reported Data: FDA Cares, So Should You

The FDA has long recognized patient-reported data in clinical trials.  Many widely used drugs have been approved primarily based on patient-reported improvements.  Migraine drugs are typically approved using patient diaries measuring levels of pain relief, phonophobia, photophobia and nausea; the primary endpoint of depression trials is the change from baseline in a standard depression rating scale; Pfizer’s Lyrica (pregabalin) showed efficacy in neuropathic pain based on a difference in average self-reported pain scores between patients treated with Lyrica and placebo; and the list goes on.  Whether FDA’s acceptance of these patient-reported endpoints is due to FDA’s belief in the value of patient-reported outcomes (PROs), or due to the lack of a ‘harder’ endpoint for these trials is debatable.  However, below is a selected list of drugs primarily approved based on PROs, and their peak WW sales.  One can see that relying on patient-reported clinical data has not hindered the success of these products.

FDA Guidance on Patient-Reported Outcome Data

In December 2009, FDA issued a guidance document instructing industry on how to use PROs in drug development and clinical trials.  Although FDA has historically accepted patient-reported outcomes for approval when a more objective endpoint is lacking, they have been very reluctant to include secondary PRO endpoints (such as improvements in quality of life) in approved product labeling.  Some in industry have viewed this new guidance as sign of a shift within FDA toward the importance of patient experience and quality of life measures.  If true, this could have a broad impact on the way drugs are designed, developed and marketed.

To date, PRO data has been underutilized in clinical practice.  However, if FDA begins to recognize PRO measures such as quality of life, pharma companies will begin to generate data on their drugs’ impact on these measures. Physicians will have additional data available when deciding which drug might be best for their patients and will begin to rely more heavily on PRO data.

Importance of PRO Measures

There are many reasons to value PRO measurements.  Some treatment effects are only known to the patient, and the patient provides a unique perspective on treatment effectiveness that goes beyond traditional clinical measurements.  European pricing authorities have long valued the impact of quality of life on drug evaluation, using the quality-adjusted life year (QALY) measurement of disease burden, which includes both the quality and the quantity of life lived.  But even for those who do not believe a focus on the patient is reason enough, there is evidence of the clinical importance of PRO measures.  A study of 293 heart failure patients showed that patient-reported functional status was a prognostic predictor of hospitalizations, quality of life and death; and a meta-analysis of oncology research from 1982-2008 showed a correlation between patient-reported quality of life and survival in cancer patients.

Opportunity for mHealth

All of this, of course, creates a tremendous opportunity for companies working to collect patient-reported data, and I have previously written about the value of longitudinal personal health data streams, or healthstreams, that include both physician/system-generated data (EMR), combined with more frequently added patient-generated data.  Traditionally, patient-reported data has been captured disproportionaley via written diaries.  However, since the most common measures for a PRO instrument are based on the Likert scale and Visual Analog Scale (VAS), it seems clear that any PRO data commonly captured via written patient diaries can be more easily and efficiently captured by electronic means, and work is being done to validate electronic capture methods.

In the future, we will see this type of PRO data regularly captured by patients via mobile devices and mobile apps, and this data will become a common tool for physicians in clinical practice.  Some companies, such as Ringful Health and ReliefInsite (recently acquired by PatientsLikeMe), are already moving in this direction with mobile versions of asthma diaries and pain journals.

Patient-reported outcomes data is valuable and gaining importance in the practice of medicine.  When you see your first pharmaceutical ad featuring claims of improvements to quality of life, you will know the era of patient-reported outcomes has arrived.

Completing the Picture: The Value of User-Generated Health Data Streams

The healthcare industry is undergoing a dramatic and necessary transformation. Empowered patients are taking action and participating in their own healthcare, marking a significant change from the long-standing paternalistic relationship between physician and patient.  Underlying this transformation is a new found access to health information via the internet and personal health data via consumer health devices and applications.

Much has been written about this long coming transformation, which has spawned the Health 2.0 movement, a successful conference series of the same name started by Mathew Holt and Indu Subaiya, the peer reviewed Journal of Participatory Medicine and a series of grassroots unconferences championed by Mark Scrimshire.

To date, many have focused on the challenge of digitizing the vast amounts of health data in the current health system and on the one-way transfer of existing data from the system to the patient via EMRs and PHRsGoogle and Microsoft have launched data warehouses for personal health information, and a few large institutions have embraced this one-way flow of information. The Mayo Clinic recently partnered with Microsoft HealthVault to provide patients access to their medical records, and Kaiser Permanente has been very successful with their “My Health Manager” offering.

Providing access to health information and health data is a logical first step.  The foundation of the modern health industry is health data.   Each time a prescription is dispensed (3.8 billion times in 2007), a medical claim is submitted, or a lab is ordered, a piece of personal health data is generated.  Evidence-based treatment guidelines often focus on specific health data values, such as BP, LDL or HbA1c, for treatment recommendations.  And a modern medical record is simply an aggregation of health data generated at the point of care, including patient reported symptoms, diagnoses, treatments, test results, physician commentary and observations.  However, medical records are punctuated data streams with large gaps in data from time periods in between physician visits.  It is the doctor’s difficult job to fill in these gaps with imperfect information recounted by the patient to complete the picture and treat the patient effectively.

Focusing only on the EMR, or the one-way transmission of personal health data from the system to the patient, misses half of the health data equation.  With increasing frequency, patients are beginning to generate and report their own personal health data.  Sites such as CureTogether and PatientsLikeMe are collecting patient reported data, analyzing the data and generating valuable services for their members.  And recently, we have seen a rapid rise in ‘health trackers’ to record health and wellness information (MedHelp, TheCarrot, DailyMile, hLog, Polka, Ringful).  Data is also being collected and uploaded in the home by personal health devices such as BP monitors with SD cards, WiFi weight scales, USB glucose meters and activity monitors, some now sold at Best Buy.  To date, these patient generated data streams have existed in silos, both disconnected from traditional data streams generated within the current health system and from data generated on other sites.

In the future, each individual’s traditional health data stream (EMR) and user-generated data streams will merge into one personal health data stream, or healthstream, and this healthstream will be owned solely by the patient.  Diagnoses, treatment decisions and lab results will be overlaid on top of more frequently added data from the patient including symptoms, adherence to medication, daily weight, diet, mood, activity levels, sleep patterns, etc.  This complete longitudinal data stream will be valuable to both the physician and patient and become a part of everyday medical practice.  Physicians will prescribe trackers, apps and home health devices, along with their standard Rx, and will receive a report at the patient’s next visit.  Rather than relying on recollections, physicians will have more complete pictures of their patients, and patients will begin to feel like partners in their own care.

This longitudinal healthstream will become a valuable asset for the patient, and will increase in value with each added data point.  Patients will have the right to grant permission to use all or part of their healthstream for medical care, for research and discovery, or even for profit.  I will explore the topics of permission and use of the healthstream in future posts.

For another view of the future of the healthstream, see Dr. Vijay Goel’s post “HealthStreaming: What data would you need in your stream to make your health decisions?”


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