ing the knowledge possessed by populations through longitudinal studies of large, distributed, consenting populations, will be the focus of work in population health over the next decade. Based on his experience developing Indivo—a patient-centered health record that places patients in control of their own health information—coupled with federal incentive initiatives, he predicts a shift in the health information economy from institutional to individual or patient control. This shift will likely change population health research in a way already being seen through forums such as PatientsLikeMe. Finally, Dr. Mandl suggests that a critical outstanding research question is how to achieve sustained engagement of patients in research.
Sophia Chang from the California HealthCare Foundation states that a digital infrastructure provides important opportunities for informing and improving the care of patients with chronic disease. She discusses the potential to actively engage patients in management of their conditions, but notes that, currently, the locus of control lies solely with the healthcare providers and not the patient. Additionally, Dr. Chang points to the lack of common nomenclature, data formats, and protocols for incorporating patient-generated information as barriers to aggregating and translating health data into useful decision support. Pointing to Kaiser Permanente and the Veterans Health Administration as examples of institutions that have successfully used electronic health records (EHRs) for population health management, she notes that smaller institutions or individual physicians might have less opportunity for exposure, and therefore be less aware of their value. In order to maximize the value of EHRs, she asserts, research paradigms must shift to real-time knowledge development and feedback. Finally, Dr. Chang highlights several steps to move toward the goals of recentering the system around the patient, such as providing useful support for chronic disease management, aligning EHR data elements with patient priorities, and developing better paradigms for learning from patient data.
M. Christopher Gibbons of the Johns Hopkins Urban Health Institute discusses opportunities for using a digitally supported learning health system to better comprehend and combat health disparities. Noting that understanding and treating health disparities requires integration of knowledge spanning many sources and disciplines, he points to several demographic trends that make this challenge ever more pressing—rising prevalence of chronic disease, an aging population, and the growing racial and ethnic diversity of the U.S. population. Dr. Gibbons introduces the terms “populomics” and “populovigilance” to describe the integrative, systems-oriented, and informatics-intensive approaches to understanding and monitoring the complex causes and manifestations of diseases and disparities. He suggests that as more and more data from diverse sources are collected and available for analysis, it will be important to adopt these new perspectives in order to enable advances in treatment, public health, and healthcare disparities.