November 15 2016 | 0 Comments | 273 reads Average Rating: 3
Getting to Know Six Non-Healthcare Data Types
Population health management programs typically rely on clinical and claims information to determine how patients fare under various treatment approaches. But that information only tells part of the story.
To get the complete picture, these programs should combine traditional information from payers, providers or life sciences organizations with various behavioral data from non-healthcare sources. Here are six non-healthcare data types that can help:
Socio-economic data such as a person’s income, education attained, or employment status, could be determinants of well-being.
Demographic information including data on population size, age, race and ethnicity could have implication related to the type of healthcare services needed.
Geographic data can shed light on the resources available to certain patients. For example, if a patient lives in a food desert that would have significant implications when treating obesity.
Psychographic data, which zeros in on a patient’s attitudes, lifestyle, interests, personality and values can provide insight into how likely patients or populations are to comply with various treatment interventions.
Attitudinal data provides insight into the importance a patient places on their health or health maintenance.
Performance data can help organizations access how well various programs or interventions are working – and then make any necessary adjustments.
Harvesting and analyzing behavioral data can offer a better understanding of why clinical outcomes have occurred and enable more accurate predictive evaluations because it accounts for the impact various stimuli and outside forces have on patient decisions and reactions. The integration of behavioral data also can offer ways to test new interventions, as well as build upon current ones that have proven effective and eliminate those that haven’t produced the desired results.
While population health programs should continue to use clinical and claims data first to identify results and outliers for their patients and groups, layering in one or multiple types of behavior data will help to better understand variances and predict how patients may respond to future interventions. For example, once an initiative determines and understands a problem one of their patient groups is facing, they could use socio-economic data to better predict if a new intervention would be too costly to be successful. Or the program could turn to psychographic or geographic data to see how well these patients would adhere to an intervention that requires traveling to take tests or report data.
Combining clinical and behavioral data has an unlimited potential to help population health programs make better decisions and drive better outcomes, while also improving operational efficiencies and patient satisfaction across the care continuum.
Can you think of any patient care situations where it would be beneficial to tap into some non-traditional data sources?