Abstract
Background: As the patient role evolves from that of a traditional, passive participant in the care process to an engaged, active consumer of health care goods and services, new venues and/or tools for provider-patient communication will be required. The Electronic Health Record (EHR) can be used to facilitate such communication. We consider one model for how measures of individual risk derived from EHR data can be used in different ways to improve and maintain health.
Objective: To explore how use of absolute risk (AR) or relative risk (RR) of cardiovascular disease can be used to select patients for intervention, communicate risks for informed choice and shared decision making, and then guide clinical decision support for targeted intervention and continuous care.
Methods: We embedded the Framingham risk score in our EHR to calculate a patient’s individual risk for cardiovascular disease. Absolute risk for 10-year mortality from cardiovascular disease (CVD) was calculated based on actual patient data, with decision support automatically ordering any missing values such as labs. A patient’s relative risk (compared to a hypothetical, optimally managed age- and sex- matched control) was used to select patients for intervention. Patients then selected therapeutic preferences from among multiple evidence-based and rank-ordered options for their modifiable risk factors, via an interactive touch-screen kiosk in the waiting room. These selections were summarized and used to guide clinical decision support. The specific treatment options presented to each patient were selected as a function of the maximal relative risk reduction that could be achieved by compliance with those treatments.
Application: Risk scores were used for four purposes in this study: patient stratification, selecting only those patients for intervention who are at high risk, and have available options to reduce that risk; informed choice, allowing patients to model risk scenarios by selecting different combinations of interventions; optimizing decision making, presenting a final list of patient-selected interventions that summarize relative risk reduction across all modifiable risk factors for a patient; and outcomes tracking, following risk over time to guide care.
Conclusions: Behavior change is most likely to occur when patients understand the severity of their modifiable risks in the context of available treatment options.
