Using HIT to develop highly personalized interventions from wellness to disease management

Background

Many traditional borders in health care are becoming less distinct. Employers are increasingly interested in disease prevention, with almost 6 in 10 large employers offering health coaching services to employees. The line between wellness promotion and disease management is being replaced by recognition that individuals are at different points of the health care continuum for various diseases or conditions. Specialized health promotion that factors in multiple aspects of an individual's health status – e.g., family history or presence of a related disease – can be more effective than generic information. Disease managers who integrate information about a patient's health behaviors or other disease conditions can provide comprehensive, effective information and support.

Objective

Develop a methodology for integrating data from self-reported Health Risk Assessments (HRAs) and socioeconomic indicators into care management software that uses medical claims (disease, procedure, and revenue claims), pharmaceutical claims, laboratory results, and health insurance enrollment files. Develop tools enabling users to identify members with unique combinations of health conditions, family histories, behaviors, and attitudes.

Methods

Integrate data from 7 major HRAs with medical data. Develop systems to solve apparent conflicts from various data sources. Test internal validity and stability of models.

Results

An innovative, comprehensive approach to assessing health risks and benefits using health informatics that can be used to improve service by wellness providers, disease managers, and case managers.

Discussion

Health information technology has historically been used to manage data about diseases or other medical conditions. We will present our method of stratifying a population based on these and new data sources, selecting in and filtering out members of population based on where they fall on the spectrum of specific disease conditions (e.g., Diabetes, CHF), health-related behaviors (e.g., Tobacco Use, Physical Activity), or wellness conditions (e.g., Sleep Quality).