According to a study from researchers at New York-based Weill Cornell Medical College, the accuracy of clinical quality measures (CQMs) from electronic health records (EHRs) can vary widely. The researchers of the study say electronic reporting can both underestimate and overestimate quality.
For the study, which appears in the latest issue of the Annals of Internal Medicine, researchers looked at how EHRs could help providers report CQMs, as per Meaningful use of EHRs under the government’s Health Information Technology for Economic and Clinical Health (HITECH)Act. Providers who don’t document and report CQMs electronically by 2015 will face penalties, however, the researchers say the process isn’t quite right.
"This study reveals how challenging it is to measure quality in an electronic era. Many measures are accurate, but some need refinement," stated the study's senior author, Rainu Kaushal, M.D., director of the Center for Healthcare Informatics Policy, chief of the Division of Quality and Medical Informatics and the Frances and John L. Loeb Professor of Medical Informatics at Weill Cornell.
"Getting electronic quality measurement right is critically important to ensure that we are accurately measuring and incentivizing high performance by physicians so that we ultimately deliver the highest possible quality of care. Many efforts to do this are underway across the country," he added.
Dr. Kaushal and his colleagues looked atclinical data from the EHRs of one of the largest community health center networks in New York. They looked at how accurate the electronic reporting was for 12 quality measures, 11 of which are included in the federal government's set of measures for incentives. While nine measures were consistent, three (patients receiving prescriptions for asthma, receiving vaccinations to protect from bacterial pneumonia, and patients with diabetes had cholesterol under control) were not.
Lisa Kern, M.D., the study’s lead investigator and general internist and associate director for research at the Center for Healthcare Informatics at Weill Cornell, said this variation shows there is a need to “test and iteratively refine traditional quality measures so that they are suited to the documentation patterns in EHRs.”