Waimei Tai, MD
Today at the Population Health colloquium(see agenda here) held at Stanford University, speakers from around the world engaged on a serious question that many traditional clinicians haven’t really thought about: taking care of the health of a population.
Today at the Population Health colloquium(see agenda here) held at Stanford University, speakers from around the world engaged on a serious question that many traditional clinicians haven’t really thought about: taking care of the health of a population.
This event was sponsored by the Stanford Population Health Initiatives with the aim to draw collaborators from different disciplines to help design and implement learning health care systems for the future of whole populations of patients. The aim is to continually improve the quality and efficiency of care by rapidly translating evidence from scientific research and patient outcomes into clinical practice.
As an audience member I couldn’t help but reflect upon my own clinical work as a stroke neurologist. Sure, I see patients in my clinic and counsel them individually on how to best manage their cholesterol and hypertension. Yes, I see patients when they present with acute stroke symptoms. But ask me how my panel of 500 patients is doing with their hemoglobin A1c or weight loss program and I would freeze. I have no idea how my population of patients are doing, and in fact, even asking that question seems preposterous. I can’t imagine how I would go about querying my panels’ data. I imagine the electronic health record programmers are not particularly interested in writing scripts for that query unless this was an initiative led by quality and performance improvement.
Of course, we all know this is where the future of healthcare is going:
Meaningful Use 3 is not too far away and we as clinicians must think long and hard about how to best care for not only the patient sitting across from you in the exam room, but also the whole population of patients.
One way we’re expanding our work for stroke care is to focus on not only individually managing the patients who present with stroke symptoms in the hospital, but how to best care for a population of such patients who has already suffered from a stroke and need optimization of their secondary stroke prevention risk factors, as well as targeting patients with a bevy of different vascular comorbidities predispose them to an even higher risk of stroke. We’re working on proactively engaging patients to manage their own health. Learn more about our program at the Clinical Excellence Research Center.
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Dr. Tai isn’t alone in not knowing how to answer questions about her panel of patients, and many medical professionals using EHRs, even QI specialists, haven’t the first idea how to write the queries to arrive at answers about patient populations.
This is partly because EHRs have highly normalized data architectures, a design that maximizes flexibility (all those modules, configuration options, ways to represent organizational entities, etc.) at the expense of reporting capabilities. EHRs are glorified order entry and billing systems designed to record point-in-time observations and provide lists to drive near term actions – medications to administer or test to be performed in the next 24 hours. They aren’t designed for reporting across individual patients. Some reporting is possible, though the vast majority of organizations using EHRs are too small to have staff with the time and skills to produce reports. Even when such reports can be produced they only capture a snapshot of the data at a point in time. EHRs lack the data structures to store these snapshots and perform useful analysis, leading organizations to build data marts in Excel or buy yet another multi-million dollar EHR module or clinical data warehouse to do what they were told their EHR could do in the first place.
Yet there is a simple solution that’d give Dr. Tai the answers she seeks. A number of EHR-neutral Population Health Management (PHM) vendor solutions are available, and they address all the EHR shortcomings mentioned above and add capabilities to help manage patients, deliver better outcomes, and lower costs. PHM solutions extract data from EHRs and other clinical systems, group patients into panels and populations, and make the data accessible and analyzable along any number of dimensions: Population, panel, comorbidity, age, race, geography, medications, treatment paths, social history, family medical history, etc. PHM risk-stratifies patients and produces action lists for care teams, who then contact patients to schedule check-ups and ensure they’re adhering to medication schedules and treatment plans.
Population Health Management is a key technology helping organizations transition from fee-for-service to accountable care and shared risk. Health systems and providers need a PHM strategy in order to survive the coming healthcare system changes and deliver better patient outcomes and lower costs.
[ Full disclosure: I'm a data and analytics guy. I work for Forward Health Group, a population health management vendor and maker of the MU-Certified PopulationManager® platform. Twitter: @ForwardHealthGp ]