JAMA: CMS Should Take More Steps to Improve Patient Safety, Including Leveraging EHRs

April 15, 2016
Federal policy makers, especially the Centers for Medicare & Medicaid Services (CMS), could be taking more steps towards meaningful progress within patient safety, according to an article published in the Journal of the American Medical Association (JAMA).

Federal policy makers, especially the Centers for Medicare & Medicaid Services (CMS), could be taking more steps towards meaningful progress within patient safety, according to an article published in the Journal of the American Medical Association (JAMA).

The authors of the article—one from the department of health policy and management, Harvard T. H. Chan School of Public Health, and the other from Johns Hopkins Medicine—attest that whether meaningful progress has occurred within patient safety or not is controversial. “The Obama Administration’s internal evaluations suggest modest improvement on a subset of patient safety measures. Despite some questions about these findings, mostly due to a lack of a valid approach to measurement and evaluation, there is no disagreement that policy makers should do more to improve safety measurement,” they said.

As such, the authors said that CMS needs to eliminate unnecessary, unreliable metrics from government programs and oversee the development of a standardized set of validated metrics. For example, the Patient Safety Indicator (PSI) 90 is a conglomeration of various adverse events of varying importance that rely on administrative data, which have low validity compared with clinical data and are marked by long delays in reaching clinicians, hampering their usefulness, they said. Instead of focusing on PSIs, CMS instead should focus on the most common and clinically meaningful causes of harm and should use clinical data, not billing data, and monitor and report the validity of the measures. Such an approach will enable hospitals to focus on improving patient safety rather than changing coding, as the current programs have encouraged, they said.

What’s more, the Centers for Disease Control and Prevention (CDC) could harness clinical data from electronic health records (EHRs) to develop algorithms for detecting the other leading causes of preventable harm, including adverse drug events, venous thromboemboli, and others, the authors stated. “Even in the 1990s, before the development of sophisticated health information technology systems, this approach was shown to be nearly as sensitive—while far less resource intensive—as manual chart reviews at detecting adverse drug events, such as allergic reactions or nephrotoxicity due to medications. Given the sensational advances in computing in the past two decades and that most U.S. hospitals now have electronic health records, there are sufficient data that could be exploited to detect a wide variety of adverse events. With the CDC’s expertise in engaging professional societies, measurement experts, and consumers, these types of measures can be pilot tested and improved over time.”

The authors suggested additional steps that the federal government could take, including tasking an agency with defining standards of what makes good measures and setting accuracy requirements before implementing measures in pay-for-performance and public reporting, offering the National Quality Forum (NQF) as an option. Further, Congress needs to find funding for systems engineering research, the authors stated. For instance, the checklist intervention that substantially reduced central line infections was initially supported by a $500 000 grant from the Agency for Healthcare Research and Quality (AHRQ). “Complications are expensive, and reducing their frequency can provide significant savings to payers,” they said.

The authors concluded, “Despite thousands of deaths each year related to unsafe care, policy actions have not matched the scale of the problem. However, tools are now available to make meaningful progress in safety, starting with systematic collection and dissemination of high-quality, clinically based data.”

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