Story in today’s Boston Globe that highlights the experiences of a cancer patient, David Debronkart, who exported his medical records from Beth Israel Deaconess Medical Center (BIDMC) to his Google Health account. His experiences, which he discusses at length (beware, it’s a long post), exposes a very real problem we have today in the healthcare sector, that of inaccurate data – the old “Garbage In, Garbage Out” problem, though in this case it is the inverse, Garbage Out, Garbage In.
There are many potential sources of healthcare data (claims, clinical, PBM, self-reported, biometric, etc.) that a consumer might use to populate their PHR. Today, however, the most prevalent source (we estimate 80%) is claims data as it is digital, and both employers and payer sponsored PHRs can use it readily in their offerings to employees/members. And as in the case BIDMC, it is what they push to Google Health at a customer’s request. Problem is, claims data is based on limited code sets (ICD-9) that physicians use for reimbursement. This can lead to inaccurate entries/codes as physicians struggle to fit a diagnosis to a given code or possibly prescribe a drug for off-label use, or even “game” the reimbursement system. Either way, end result is an inaccurate picture of the consumer’s health.
Now some may argue that this is exactly the reason we need PHRs that are tethered to an EMR. EMR data does not necessarily rely on claims codes and could provide a better, more accurate view of the consumer’s health. Problem here, as we have outlined before, is that tethered PHRs do not aggregate consumer health data from multiple care providers for a full longitudinal record, only the data from those associated with the entity using the EMR to which the PHR is tethered. These tethered PHRs also do not support portability (MyNYP.org breaks from this tradition, thankfully) which in untenable.
Big Question is: Will Inaccurate Data Stall PHR Adoption?
Consumers are just beginning to wake-up and realize that maybe taking more control of their health records is a good thing. We are still very much at the early adopter stage with only some 3.5% of consumers using an Internet-based PHR today, but we are beginning to see a measurable uptick in adoption, best represented by Kaiser-Permanente who now has nearly 50% of members actively using the KP PHR.
Challenge in the PHR market from day one though is: How do we provide compelling value within the context of a PHR for the average consumer to encourage use? A primary component of that value proposition has been: Let’s help the consumer by pre-populating the PHR with healthcare data and automating the process for future updates. The consumer can then use this data as the basis to better understand their health, their risk profile, assist in engaging the healthcare industry and even help them manage a chronic disease or more importantly, prevent them from contracting such a disease.
But this value proposition may quickly unravel if upon entering their brand, spanking new PHR a consumer finds the kind of inaccuracies that Dave found in his experience with Google Health. How many consumers will go to the trouble of trying to correct the PHR by either annotating each data field that is wrong (and even if they do that, would another physician looking at the PHR trust it?) or contacting the source of the erroneous data to have it corrected, which we imagine would be a Herculean task. Our guess is few would be willing to take on this task and simply walk away from the PHR.
At least two things need to happen to maintain PHR adoption growth.
1) In the near term, we need to begin setting appropriate expectations for the consumers i.e., yes, we have done the best job to provide you an accurate portrait of your health via your records, but there may indeed be inaccuracies that you will need to identify and correct.
Along with setting expectations, we need to provide tools for the consumer that allow them to make necessary corrections (annotations) to their records and a process whereby a trusted source (e.g., physician or other care provider) verifies such changes are correct. Will Crawford of IndivoHealth believes that a good starting point is to simply point out to the consumer what types of claim data sets (codes) are known to be problematic that may require their attention. A great idea to move along this path.
2) Longer term the adoption of ICD-10, which will assist in insuring that claims data is more reflective of actual diagnosis and treatment, combined with wide-spread adoption and meaningful use of EMRs via the HITECH Act have the potential to significantly increase data accuracy and liquidity. This, in turn, will facilitate the consumer’s ability to access and aggregate accurate health data and eliminate the need for those actions listed in Number 1.
Looking even farther down the road though at PHR adoption…
While data may indeed be the foundation of any PHR, it is hardly compelling, engaging and unlikely to drive adoption in any significant fashion. For far too long, most PHR providers have focused on the data issue and not what a consumer will find of value. This is simply a legacy of most PHRs in the market today that were built by physicians. Such simple solutions we give the moniker of PHR0.25. Adding associated content brings us to PHR0.5. Combining some journaling and disease management brings us to PHR1.0. You get the idea.
The slow evolution and growth in the PHR market is partly a function of low market demand (consumers rarely engage in their health at that level) and poor solutions that offer only modest value. For adoption to continue to accelerate, PHR vendors need to begin thinking beyond the PHR construct that is prevalent today. A new model is required that divorces itself from the old model striking new ground and offering a compelling new value proposition. With Google Health (if it gets its act together) and MS HealthVault now doing the heavy data lifting work, developers have a unique opportuntiy to refocus their resources and create new and compelling solutions that truly engage the consumer and provide direct, measurable value.