In response to a healthcare crisis of epidemic proportions, thousands of software developers have been innovating new personal healthcare applications and technologies that leverage advances in medical and computing technology. Despite the endless streams of personal data that these tools process -- weight, activity, diet, heart rate, etc. -- they are relatively data poor. Left out of these applications is a comprehensive set of users' clinical electronic medical records, genomic data, comparative data with relevant subpopulations, and data on environmental influences important to health and quality of life.
There are numerous barriers to incorporating such data in applications, the dominant factors being the tremendous volume and heterogeneity of such data, much of it streaming in real-time and spread across disparate stakeholder platforms. A related problem is drawing inferences from these data. With the advances in databases and machine learning proposed, we envision a new era of health and healthcare where patients, providers and consumers are empowered by data access and applicability that we characterize as personalized population health. In particular, we anticipate a new category of healthcare applications that infer one's health status - and help execute interventions - in the perspective of one's entire life history and context.
This project is conducting fundamental and applied research in support of a platform, called DELPHI, that enables integrated access and analysis of all data relevant to health, and consequently promotes more rapid development of empowering, data-driven health apps and tools by a broad community of health-related software developers. The platform supports an integrated "whole health information model" of the individual that provides developers a single point of access that both (a) hides distribution and data heterogeneities, and (b) facilitates drawing inferences from these "noisy" data. The platform enables novel forms of analyses based on contextual and statistical metadata. Scalability is achieved through theoretically proven and newly proposed database and machine learning techniques. Our research is driven by three disparate case studies and field trials: a clinician-facing type-1 diabetes intervention, a patient and consumer-facing hypertension application, and a regional population health asthma and respiratory disease scenario.
Intellectual Merit
DELPHI is yielding fundamental advances in databases and machine learning that enable a wide community of programmers - from full-time professional to relative novices - to program on top of a "live", streaming population-scale medical dataset. Additionally, these techniques are being evaluated in at least three realistic field trials, yielding new insights on both the nature of computing on medical "big data" and the techniques we have proposed to make it tractable.
Broader Impact
This will be demonstrated through a personal well-being and population health applications ecosystem, with three immediate beneficiaries: 1) The San Diego Beacon Community, a model for health information exchanges currently under development nationally. 2) Governmental and non-profit agencies who serve as an example of public/private partnerships to promote community-wide health. 3) Private industry, in this case Qualcomm Life's/2net platform where we demonstrate how to utilize existing services in novel ways to handle health data. Finally, this project will serve as a training ground in personalized population health for graduate students, post docs and medical residents.
