Historically, claims and billing data have been used to define patient data. However, billing data are limited and imprecise when identifying the clinical information required to operate in value-based contracting and perform population-based statistical analysis.
Medical practice executives are working with clinicians to implement population-health management technology in their organizations to aggregate patient data and monitor access, quality, outcomes and cost through the use of these systems.
Population registries are vital to effectively evaluate and manage population health. Within a practice or organization, vast arrays of data are available from different sources that, when effectively consolidated and filtered, can be structured to provide meaningful information about a disease or other patient state.
Procedure codes and claims data are foundational, and when combined with laboratory reports, imaging results, medications, problem lists, pathology, functional status and clinical observations, these criteria begin to build a population profile. For effective management, patients must also be correctly attributed to the providers who are responsible for their care.
In adapting to patient care, systems also must be able to flag nonmedical indicators that affect health. Patients with language barriers, or cognitive and physical inabilities to participate in their care will require accommodations. An effective system must also have a way to identify compliance issues such as economic, religious and geographic barriers to care. Building in system flags will allow the care management process to adapt to manage these patients differently through financial assistance, increased surveillance, earlier intervention and individualized education.
Effective population-management systems will identify each population cohort and allow the organization to strategically stratify patients at high risk or who face barriers to their care. As more data are collected, providers can identify patients proactively and intervene to help patients avoid developing a disease, rather than having to treat it later.
To monitor the clinical effectiveness and cost efficiency of interventions, organizations must be able to measure the variability in patient care and adherence to protocols, as well as monitor outliers. Quality, cost and outcome metrics about specific patients, as well as patient populations will prepare organizations for fixed-fee contracts and improve their negotiating position when contracting with payers.
When systems become more interoperable and sophisticated, external data can be incorporated into patient and population profiles to expand care at the regional level. Enterprise-level data warehouses are essential for population-health management.
Future success depends on turning data into information that is actionable and can be put to use throughout the organization to benefit its assigned patient populations. Collecting and accessing data in a thoughtful way will help to keep patients as healthy as possible, minimize expensive health interventions and improve outcomes over time.