Population Health Management: Data > Information

January 6, 2016

Population Health management

Know the difference, and why the solution is in data

Community mental health providers are the closest to at-risk behavioral health care populations, and are best equipped to improve population health management. However, the right management decisions must be made to effect positive change. Agencies have to have visibility into the needs of populations across multiple programs, providers, levels of care, and even specialties in order for integrated care initiatives to work.

The challenge so far has been the visibility and rapid extraction of healthcare data in order to inform decisions. The majority of EHRs as they currently exist do not support seamless “data goes in, data comes out” functionality. This results in very long delays between the acquisition of data, aggregating it at the population level, and the implementation of care in response. The key to understanding and addressing these challenges is to realize the distinctions between information and data.

Information is based on the old healthcare models in which each paper file is reviewed with human effort. Think of it in terms of a clinical workflow. The patient checks in, usually by filling out a paper form. The record is kept in a filing cabinet for later use. Legacy EHR systems acquire and store information the exact same way. They are essentially electronic versions of paper-based workflows, and, like paper systems, offer no solution to the population health management challenges discussed above.

Information records are only relevant in the particular task to which they pertain. There is no easy way to derive population insight from “data” gathered like this. Information is not readily extractable, reportable, or serviceable to the formation of enterprise strategy.

Data on the other hand, is. Data can be thought of as a set of values of qualitative or quantitative variables. Pieces of data are individual pieces of information that can be measured, collected, reported, analyzed, and even visualized through graphs or images. As a general concept, data can be reconstituted for another purpose in ways that information simply cannot.

Through data, several critical tasks for effective population health management become possible:

  • Make informed forecasts for preventive measures
  • Review the success of evidence-based practices
  • Track investments to ensure appropriate utilization of Medicaid funds
  • Monitor internal benchmarks and productivity

Getting away from information-based systems and into data-oriented systems is a very important first step in population health management. Unlike legacy software systems, modern EHR platforms offer extraordinary advantages with their highly accessible data models and a modern user experience that promotes data capture.

In a platform, each data entity is actually described by the attributes and values that are connected to it, creating an environment in which each element is meaningful across several forms of application. A data-first approach ensures a structured, discrete presentation of data, ensuring it is accessible and actionable at all times—all without subjecting the user organization to the time-consuming effort of deriving data from flat files or convoluted information. The high-level visibility of population health data and decision support tools give agency management the guidance they need to drive the best care initiatives forward.

Population health management is not as simple as a bolt-on feature to an EHR software system. It is a careful process that requires coordinated care, advanced analytics, and timely decisions. Modern challenges cannot be met by legacy solutions – they require modern solutions. An EHR platform with a highly accessible and actionable data model is key.

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Last Updated: March 29, 2017