Inappropriate medications are those for which the risk of an adverse outcome with use of the medication outweighs the potential benefit. The goal of this project was to design and develop modules that would educate physicians to help guide prescribing habits towards therapies that are more appropriate and pose a lower risk for the elderly population.

A three year, multi-phase prospective demonstration project targeting all 303 Parma AUSL general practitioners (GPs) prescribing for approximately 80,000 outpatient elderly was established and introduced in 2007. The quality improvement initiative hinged upon increasing GPs’ awareness of prescribing for the elderly and included three key elements:

• The initial dissemination of a list of medications deemed potentially inappropriate (PIMs) in older patients, along with a list of alternative drugs to PIMs
• Annual reviews of PIMs prevalence data
• A series of educational sessions on PIMs via academic detailing and case studies reviews

To evaluate if the educational intervention has had an effect in improving prescribing behaviors among physicians, we analyzed the prescribing behavior of physicians in the Parma AUSL per-post intervention, from 2006-2009 in order, and found that the intervention led to significant reductions in PIMs exposures and likely translated to significant population health benefit among their older patients.

Jefferson is collaborating with the Emilia-Romagna region to profile primary care physicians with a focus on Primary Care Units (PCUs). The aim of the project is to profile physicians by comparing practice patterns among PCUs within each of the 11 AUSL of the region across various dimensions of care with the goal of assisting the PCUs in assessing and improving the quality of care provided to their patients. Currently, the 2011 regional health care data is being used to perform a descriptive analysis of the selected dimensions of care for the PCUs for each AUSL, including demographic, morbidity and healthcare utilization of PCU patients. At the end of this process, each PCU receives a brief summary of the results, which are discussed and used to establish initiatives to continually improve the quality of care of the PCU patients. Repeating the analyses will allow each AUSL to compare and contrast results in order to monitor and evaluate the PCUs’ progress.

This project builds on the models that we have developed for predicting patients at high risk of hospitalization. In collaboration with the Parma Local Health Authority, the Center is working on a project to explore approaches to using these models in the newly formed Medical Homes. We developed individual patient level profiles of patients predicted to be at high risk of hospitalization. These profiles of high risk patients have been provided to their primary care physicians who may then be able to more proactively help these patients manage their risk. Summary level reports of high risk patients have also been prepared for each primary care physician and Medical Home to help them identify areas where disease/case management programs may help these patients manage their chronic disease.

Thomas Jefferson University and the Parma Local Health Authority have extended their collaboration to include profiling hospitals located in Parma or where residents of Parma receive their care. The goal of the project is to provide information useful to the hospitals, health districts and the Local Health Authority of Parma in their ongoing efforts to improve the quality and efficiency of care provided to their populations and to support the integration of hospital and territorial care. Analyses performed at the hospital and primary care unit level include:

• Hospital utilization
• Distribution of hospital admissions to Parma and non-Parma hospitals
• Potentially inappropriate admissions
• Hospital readmissions (30-day and 90-day) and comparisons to regional rates
• Inpatient quality indicators
• Follow-up prescribing patterns after hospital discharge
• Potentially inappropriate prescribing for the elderly
• Analyses of referral patterns from primary care units to hospitals using the Disease Staging classification