![]() Although many systems have switched to using EHRs, especially in light of MU requirements, little is known about how different data sources compare in the quality of their data on preventive care. EHRs, now commonly used, have the potential to supplement (or supplant) claims data as a more complete electronic source of information. Claims data, however, do not capture unbilled services or services provided to uninsured patients. As electronic systems matured, administrative health insurance claims were increasingly used to capture information on larger populations. ![]() 1.0 COBLYN MANUALIn research, manual chart review is often still considered the “gold standard,”8, 9, 10, 11, 12 though this method is time consuming and expensive, and thus can only be applied to small numbers of patients. It is also useful to assess preventive care offered to the patient, such as orders for screening tests, particularly in settings such as community health centers (CHCs), where patients routinely face barriers to follow-through on referrals.īefore electronic data sources were used, delivery of preventive services was measured by manually extracting information from patients’ medical charts. The validity of data used to track receipt of preventive services has important implications. It is important for electronic data sources to be compared to manually extracted medical chart data to assess validity and completeness. ![]() ![]() Data reports that extract information from electronic health records (EHRs) and health insurance claims are increasingly used to track such measures of preventive care quality.1, 2, 4, 5, 6, 7 Federal programs, notably the Centers for Medicare and Medicaid Services’ “Meaningful Use” (MU) of EHRs Incentive Program, accelerated these efforts. Preventive care delivery is an important quality outcome in value-based care models.1, 2, 3 With the 2015 passage of the Medicare Access and Children’s Health Insurance Program (CHIP) Reauthorization Act, increasing emphasis will be placed on preventive service delivery and population health outcomes. ![]()
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