Medical metrics: The digital health revolution is on, but patients need more than operational efficiencies

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HAVING SPENT decades in search of the next wonder drug, American medicine has started to think about improving the way care is actually delivered, and has begun to dig deeply into the processes of health care. The science of operational improvement is on the rise. There's been an explosion of interest in measurement, metrics, and analytics, as researchers try to figure out how best to improve the quality of care.

The pursuit of quality is powerfully enabled by the emerging "digital health" sector, which develops the tools and technologies that enable improved health data collection and sophisticated analysis, and permits us to contemplate the transition of medicine from an episodic, symptom-driven practice to a more holistic vision focused on presymptomatic care and a more continuous assessment of health.

The rapid evolution of digital health has been driven by an impassioned cadre of entrepreneurs hoping to bring the dazzle of tech start-ups to the challenges of contemporary health care. The recently reported acquisition of Humedica, the Boston-based clinical informatics start-up, by insurance giant UnitedHealth highlights the value accorded to innovation in this space, and offers a useful lens into the way many digital health start-ups are thinking about problems.

Traditionally, medical information - physician notes, diagnostic images, lab tests, and prescription orders - has been dispersed in manila folders scattered in offices and file rooms across a sprawling care network. As providers (led by large hospital systems) embrace electronic medical records, these disparate data are increasingly stored electronically; however, extracting the stored data points and organizing them in a useful and actionable way remains a significant challenge - which is where analytics companies such as Humedica come in.

Humedica is hired by large hospitals to extract information from their electronic medical records system and perform fairly basic analyses that assess the quality of patient care and suggest areas of improvement, pointing out instances, for example, where patients inadvertently have been prescribed medications known to interact, or where a provider forgot to order a key diagnostic test or veered significantly from accepted best practice. By identifying and correcting these problems, hospital systems hope to ensure each patient receives the best care possible while avoiding unnecessary, potentially harmful treatments.

Digital health entrepreneurs hope that through the right combination of improved measurement (often using "smart" sensors), sophisticated analytics, and user engagement, they can help find ways to make the best use of today's treatments, and ensure each patient consistently receives the best and most cost-effective care available. Entrepreneurs also hope to progressively raise the bar, applying the empirical intelligence of operational improvement to further elevate the quality of health care delivery.

As lofty as these aspirations are, we worry this view of quality - ambitious as it is - isn't quite ambitious enough.

Consider how today's community of digital health entrepreneurs would likely respond were polio still unpreventable and widespread: Start-ups would focus on refining the design of the iron lung, increasing the efficiency of scheduling, improving the transparency of costs, and analyzing the operational performance of each unit while tracking its location by GPS.

Without question, these tweaks would significantly improve the patient experience, and might reduce the cost of care. However, none of these approaches would have either the clinical or the economic effect of a radical new therapy - in this case, the polio vaccine.

Even as we strive to incrementally elevate health care quality through gradual operational improvement, our audacious goal must include figuring out how to use digital health's technology, data, and computational tools to increase our fundamental understanding of disease and generate profound new treatments.

Sound familiar? It should: Medical scientists held out similar hopes for genetics, only to discover that, as Nobel laureates Brown and Goldstein famously observed, "a gene sequence is not a drug," and getting from one to the other was far more difficult than many experts had anticipated - though the occasional success story reminds us of the promise.

Similarly, turning digital data into profound clinical impact will not be easy. However, combining genetic and digital data - integrating our knowledge of the building blocks of life with a dynamic picture of how the pieces are behaving - may prove particularly powerful, offering unprecedented insight into health and disease, and enabling us to develop radically improved therapies.

By then, our operationally improved health care system should be ready to efficiently deliver these treatments to patients, who have waited far too long to receive them.


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