The impact of emerging standards adoption on automated quality reporting.

Current quality measurement processes are labor-intensive, involving manual chart reviews and use of paper-based quality measures that vary in format and definitions from measure to measure. Automated quality reporting is considered by many to be an important tool that will help close the gaps in the quality of US health by increasing the timeliness, effectiveness, and use of quality assessment. In 2007, the US Department of Health and Human Services Office of the National Coordinator for Health Information Technology (ONC) funded three Nationwide Health Information Network (NHIN) health information exchanges (HIE) to demonstrate the feasibility of automated quality reporting by using existing or emerging standards to aggregate information from multiple providers, transmit patient-level quality data in standardized formats, perform an automated quality assessment, and generate a quality report document for electronic transmission. Long Beach Network for Health (LBNH), a NHIN Cooperative HIE, developed a web-based, real-time quality assessment service that calculates quality of care measure using clinical data aggregated through a HIE. LBNH used a set of draft standards to demonstrate automated quality reporting, but noted three important recommendations for future work. First, greater coordination is needed around initiatives that address the gaps in electronic quality measurement standards and processes, including strong Federal involvement and guidance. Second, a harmonized, evergreen quality use case is needed to provide stakeholders with a common understanding on the constantly evolving approaches towards automated quality measurement and reporting. Finally, there needs to be substantial investment in building on existing work and developing a comprehensive set of data and messaging standards to preserve semantic interoperability of quality measure data.

Assessing Data Adequacy for High Blood Pressure Clinical Decision Support: A Quantitative Analysis.

OBJECTIVE: This study examines guideline-based high blood pressure (HBP) and hypertension recommendations and evaluates the suitability and adequacy of the data and logic required for a Fast Healthcare Interoperable Resources (FHIR)-based, patient-facing clinical decision support (CDS) HBP application. HBP is a major predictor of adverse health events, including stroke, myocardial infarction, and kidney disease. Multiple guidelines recommend interventions to lower blood pressure, but implementation requires patient-centered approaches, including patient-facing CDS tools. METHODS: We defined concept sets needed to measure adherence to 71 recommendations drawn from eight HBP guidelines. We measured data quality for these concepts for two cohorts (HBP screening and HBP diagnosed) from electronic health record (EHR) data, including four use cases (screening, nonpharmacologic interventions, pharmacologic interventions, and adverse events) for CDS. RESULTS: We identified 102,443 people with diagnosed and 58,990 with undiagnosed HBP. We found that 21/35 (60%) of required concept sets were unused or inaccurate, with only 259 (25.3%) of 1,101 codes used. Use cases showed high inclusion (0.9-11.2%), low exclusion (0-0.1%), and missing patient-specific context (up to 65.6%), leading to data in 2/4 use cases being insufficient for accurate alerting. DISCUSSION: Data quality from the EHR required to implement recommendations for HBP is highly inconsistent, reflecting a fragmented health care system and incomplete implementation of standard terminologies and workflows. Although imperfect, data were deemed adequate for two test use cases. CONCLUSION: Current data quality allows for further development of patient-facing FHIR HBP tools, but extensive validation and testing is required to assure precision and avoid unintended consequences.