IMPLEMENTING A PRESCRIPTION-BASED DECISION SUPPORT INTERVENTION DISSEMINATION MODEL IN COMMUNITY-BASED PRIMARY CARE

Purpose:   Implementing patient decision support interventions (DESIs) in primary care practice is challenging. Prescription-based models that enable patients to review DESIs at home may be feasible, but little is known about such dissemination models, especially in community-based primary care practices.

Method:   This study explored the feasibility of a prescription-based DESI dissemination model in 4 small community-based primary practices in Los Angeles in two phases. During the first phase academic detailing was used to assist practices in finding the most efficient ways of identifying eligible patients and prescribing DESIs. During the second phase, academic detailing continued and practices also received a $15 financial incentive for each DESI prescribed. Practices’ quantitative DESI prescribing rates were monitored descriptively. Qualitative field notes and in-depth interviews documented barriers and facilitators to prescribing DESIs. Patients received a brief survey to evaluate the DESIs. During the first phase, response rates provide a conservative indicator of how many patients reviewed the programs prescribed. During the second phase of the project, telephone surveys were conducted with a sub-sample of patients, enabling more precise determination of DESI review rates.

Result:    Most practices (3/4) required significant academic detailing before beginning to prescribe DESIs to patients. During the 9 months of the initial project phase, practices prescribed an average of 6 programs per month (Range 3-9). During the 6 months of the second phase, when practices received a financial incentive for providing DESIs to patients, prescribing increased to an average of 11 programs per month (Range 4-21). Financial incentives had limited impact on physicians and appeared most effective when applied to clinical staff. Short-term workforce changes (e.g., staff illness) impeded practices’ ability to prescribe programs.  Patient response rates to the DESI survey were 24.4% during the initial phase and 37.3% during the second phase, suggesting that many patients did not review the programs they were prescribed. Among patients completing the telephone survey during the second phase, 64.4% reported not having reviewed the DESI.

Conclusion:   Prescription-based models are feasible in small primary care practices, however practices’ vulnerability to short-term staffing changes present challenges to ongoing sustainability. Financial incentives show some effectiveness in boosting prescribing rates when applied to clinical support staff. Further research to develop effective strategies for increasing patient viewing of programs is needed.