The Medicare Electronic Health Record Incentive Program: provider performance on core and menu measures.

OBJECTIVE: To measure performance by eligible health care providers on CMS's meaningful use measures. DATA SOURCE: Medicare Electronic Health Record Incentive Program Eligible Professionals Public Use File (PUF), which contains data on meaningful use attestations by 237,267 eligible providers through May 31, 2013. STUDY DESIGN: Cross-sectional analysis of the 15 core and 10 menu measures pertaining to use of EHR functions reported in the PUF. PRINCIPAL FINDINGS: Providers in the dataset performed strongly on all core measures, with the most frequent response for each of the 15 measures being 90-100 percent compliance, even when the threshold for a particular measure was lower (e.g., 30 percent). PCPs had higher scores than specialists for computerized order entry, maintaining an active medication list, and documenting vital signs, while specialists had higher scores for maintaining a problem list, recording patient demographics and smoking status, and for providing patients with an after-visit summary. In fact, 90.2 percent of eligible providers claimed at least one exclusion, and half claimed two or more. CONCLUSIONS: Providers are successfully attesting to CMS's requirements, and often exceeding the thresholds required by CMS; however, some troubling patterns in exclusions are present. CMS should raise program requirements in future years.

Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems.

BACKGROUND: Clinical decision support (CDS) is a valuable tool for improving healthcare quality and lowering costs. However, there is no comprehensive taxonomy of types of CDS and there has been limited research on the availability of various CDS tools across current electronic health record (EHR) systems. OBJECTIVE: To develop and validate a taxonomy of front-end CDS tools and to assess support for these tools in major commercial and internally developed EHRs. STUDY DESIGN AND METHODS: We used a modified Delphi approach with a panel of 11 decision support experts to develop a taxonomy of 53 front-end CDS tools. Based on this taxonomy, a survey on CDS tools was sent to a purposive sample of commercial EHR vendors (n=9) and leading healthcare institutions with internally developed state-of-the-art EHRs (n=4). RESULTS: Responses were received from all healthcare institutions and 7 of 9 EHR vendors (response rate: 85%). All 53 types of CDS tools identified in the taxonomy were found in at least one surveyed EHR system, but only 8 functions were present in all EHRs. Medication dosing support and order facilitators were the most commonly available classes of decision support, while expert systems (eg, diagnostic decision support, ventilator management suggestions) were the least common. CONCLUSION: We developed and validated a comprehensive taxonomy of front-end CDS tools. A subsequent survey of commercial EHR vendors and leading healthcare institutions revealed a small core set of common CDS tools, but identified significant variability in the remainder of clinical decision support content.