Bringing High Performing Systems to Small Practices - 2011
Summary: To date, there is limited evidence on the ability of small community health care providers to improve quality of care through the use of electronic health records (EHRs), and limited data on the impact of financial incentives for quality improvement on small providers. Investments in health information technology (IT) are being made to improve quality of care and while there is evidence of improved quality in integrated delivery systems, such as the Kaiser Permanente system, there is less evidence of the effectiveness of health IT on patient outcomes in nonintegrated health systems.
This study provides information on the effects that supportive EHR implementation, clinical decision support (CDS) systems, and pay-for-quality improvements have on small community providers' cardiovascular health outcomes. The New York City Primary Care Information Project (PCIP) is comparing the implementation of EHRs at 60 small ambulatory primary care practices that are early adopters of EHRs and part of an integrated delivery system throughout New York City to 60 similar practices in the area that are late adopters of EHRs. The project targets EHR implementation throughout New York City and focuses on some of the poorest neighborhoods.
The study will evaluate the impact of an EHR implemented with the support from technical assistance and added tools, including integrated registry systems and CDS, on improvements in quality of care as compared to practices that do not have an EHR or the aforementioned support. The primary goal is to determine whether practices that have supportive EHR implementation provide higher-quality care and experience a rapid rate of improvement of their quality measures. A secondary goal is to determine what characteristics, if any, indicate that supported EHR practices are atypical or have any consistently different characteristics as compared to other small independent practices. At a more nuanced level, the research will assess the attributable impact of various interventions on changes in four cardiovascular health outcomes at small practices that provide adult primary care. This will provide specific information on the value of various types of support on the rate of improvement on cardiovascular quality measures.
- Determine whether practices that participated in the PCIP program experienced a more rapid rate of improvement on their quality measures than practices that did not participate. (Ongoing)
- Determine if PCIP-participating practices are atypical in comparison to other small independent practices in New York City. (Ongoing)
- Assess the attributable impact of each intervention: adoption of EHR, CDS, and pilot pay-for-quality program. (Upcoming)
2011 Activities: The project team began to analyze the data for examining the effect of each successive stage of health IT implementation on higher-quality performance. This included analysis of a baseline survey describing providers' experiences with quality measurement, reporting, and incentives, as well as a survey of general provider characteristics. The project team continued analysis of baseline survey data that provides information on the characteristics of the practices that are early versus late adopters. EHR adoption among small clinics in New York has moved rapidly since the writing of the grant application, and there are fewer practices that have not begun EHR implementation. As a result, the project shifted the definition of the control practices from non-EHR adopters to a subset of practices that are late adopters of the EHR. For the later adopters, 58 practices representing a total of 134 providers were recruited. Similar to the early adopters, the majority of practices that were recruited as the late adoption group are solo or two-provider practices. The project defines 'early adopters' as those that adopted an EHR prior to January 2009. 'Late adopters' are those that adopted between January 2009 and March 2010.
At 6 months into the implementation of their EHR, each practice was asked to complete a followup survey to provide contextual information on the components of the EHR they were using, and their thoughts on Meaningful Use and other topics. Through the review of clinical outcomes data, the project team is beginning to measure the impact of each successive stage of IT integration. Clinical data are being gathered through chart review and where applicable, electronically. The team developed a form, database, and instruction set to collect the clinical data elements from paper charts that can be used to calculate the same quality measures as those calculated through the EHR. The other metric under review is the relationship between IT implementation and medical-home certification from the National Committee for Quality Assurance.
As last self-reported in the AHRQ Research Reporting System, project progress is mostly on track and the project budget funds are somewhat underspent due to delays in contracting.
Preliminary Impact and Findings: Preliminary analysis was conducted with the early cohort of practices to understand trends in quality measurement before and after EHR adoption, as well as 6-months after use of EHR. Within a cohort of 36 practices, 3,120 patient records were manually reviewed in two time periods prior to EHR adoption, a few months after EHR adoption, and 6-months after EHR adoption. Trends were calculated for the following quality-of-care measures: antiplatelet therapy; blood pressure control; cholesterol screening and control; hemoglobin A1c screening and control; smoking status recorded; smoking cessation intervention; and body mass index. Performance generally remained flat for most of the measures while using paper-based health records. For seven of the nine measures, the observed performance declined slightly after EHR adoption and then rebounded to pre-EHR levels or increased to higher rates after 6 months. The research team hypothesizes that the rebound may be a result of office staff and providers becoming more accustomed to the EHR systems.
Provider surveys have identified that while practices may have electronic tools, they may not realize that they need assistance to learn to use them. One specific tool that practices have struggled to use is referral tracking. The project team has published a manuscript in the Journal of the American Medical Informatics Association on the reliability of EHR-derived quality data: "Validity Of EHR Derived Quality Measurement For Performance Monitoring."
Target Population: Adults, Inner City*, Medicaid, Medically Underserved, Safety Net
Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.
Business Goal: Knowledge Creation
* This target population is one of AHRQ's priority populations.