Background: Quality measurement needs to be embedded within EHR systems and become much more, dynamic, accurate and detailed in order to provide the highest level of care possible to all patients. Over the last two years, we have developed quality measurement programs to use our EHR data to measure quality of care for coronary artery disease, heart failure, diabetes, hypertension, and preventive services, and this data is now used to provide physicians with individual and group-level quarterly quality reports. We are now poised to take the next step and create systems that improve our quality data and seamlessly link this data to practicelevel quality improvement programs and point of care interventions.
Specific Aims :
Aim 1 - Integrate simple, standard ways for clinicians to document patient reasons or medical reasons for why quality measures are not met and assess the use of these exception codes, the impact of exception reporting on measured levels of quality, and the impact of using these codes on physician satisfaction and self-reported efficiency;
Aim 2 - Use the exception codes (patient reasons and medical reasons) that clinicians enter to target three forms of quality improvement, including a) peer review of all medical reasons for not adhering to guidelines followed by academic detailing if a clinician enters an unjustified reason for not following guidelines, b) counseling for patients whose physician enters an exclusion code stating that the patient cannot afford a needed medication to determine ways of overcoming barriers;and c) educational outreach to all patients who refuse recommended interventions (e.g., colorectal cancer screening), including mailing of plain language health education materials or DVDs);
Aim 3 - provide clinicians with highly accurate information on patients? quality deficits immediately prior to their visit as part of routine work flow and assess whether this intervention increases provision of recommended therapies/tests, and documentation of exclusion codes. Methods: This study will begin at a large academic internal medicine practice and then be implemented in 3 community practices that use the same electronic health record (EHR), Epic(R). Exception codes will be introduced into the EHR for 17 national quality measures. Data will be extracted from the EHR every 2 months to assess changes in the primary outcome, the proportion of eligible patients who do not satisfy a measure and do not have an exclusion criteria documented. The statistical significance of changes will be assessed with hierarchical, longitudinal modeling. In addition, physicians will be surveyed multiple times to assess their attitudes towards the interventions described in the Aims, and the outcomes of the quality improvement activities will be monitored along with the costs of the intervention. Dissemination: This study will produce computerized tools and educational materials that will allow rapid dissemination to over 1000 sites that use the Epic(R) ambulatory product.
