Executive Summary
Healthcare organizations expend significant resources ensuring that they provide safe, efficient, and equitable care to their consumers. The measurement used to assess and compare the quality of that care is taken from the documentation in medical records and reported to the public through a handful of quality ratings organizations, each with their own analytics frameworks for quality assessment. These consistently published lists have thrust healthcare decision-making into the public consciousness, and consumers of care have become increasingly knowledgeable regarding variability in the quality and cost of care. As a result, consumers are utilizing these reports to make healthcare decisions causing healthcare leaders to become increasingly concerned regarding the accuracy of the data that measures the quality of care being provided.
In a perfect world, the quality of care provided during a patient encounter would never be questioned. But too often, the medical record documentation—that becomes the platform for measurement of the metrics that contribute to these published quality ratings and rankings—lacks specificity and clarity. This can negatively impact healthcare organizations by causing appearances of poor performance in these publications. Budgetary constraints, logistical hurdles, human error, and most importantly, documentation inaccuracies contribute to inaccurate measurement of the quality of care being provided and underreport the positive outcomes patients are achieving.
Healthcare scrutiny and evaluations—while necessary—contribute to the problem as there is no “gold standard” for quality. Therefore, organizations are forced to satisfy multiple quality ratings systems at once, each with its unique rating formula. According to an article featured by the American Hospital Association, this can “offer conflicting results, which may mislead stakeholders relying on the ratings to identify top-performing hospitals.”¹
Additionally, the ratings systems may be flawed. In fact, according to an NEJM Catalyst report, none of the major ratings systems earned an ‘A.’ Each of the ratings systems had a deficiency that could cause inaccuracies in the reporting of a healthcare organization’s performance.²
To address poor performance in these reports, healthcare organizations should first understand the most common root causes of inaccuracies by ensuring that the documentation of care provided is consistently accurate. Indeed, many organizations have attempted to solve the appearance of poor quality of care by implementing clinical documentation integrity programs.
However, it’s difficult to know whether there are real quality of care issues within the organization or if there is a problem with documentation integrity because there is no standard metric available today that can reflect the accuracy of documentation. So, how does an organization know when the documentation will translate into an accurate picture of the quality of care being provided?
There is no quick and easy answer…yet. But technological advancements in the measurement of accuracy are on the horizon. In the meantime, understanding how documentation accuracy can help improve a healthcare organization holistically is paramount. If we look at the state of documentation accuracy today, we can better understand the impact and why it will be important to measure going forward.
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About the Author
Fran Jurcak
MSN, RN, CCDS, CCDS-O
Iodine Software – Chief Clinical Strategist
Fran Jurcak is an accomplished senior executive with over 30 years of success in healthcare practice, education, consulting and technology. As a healthcare consultant, Fran leveraged her clinical and coding knowledge to support process improvement in the mid-revenue cycle, particularly in the clinical documentation integrity space. These process improvements allowed her clients to successfully minimize mid-cycle leakage and accurately report outcomes of care. She is currently the Chief Clinical Strategist at Iodine Software, where she has worked to bring artificial intelligence and machine learning technology to concurrent CDI workflow. Fran is active in ACDIS, received the 2017 ACDIS award for Professional Achievement, and is the author of the CCDS Study Guide. She is recognized as a national speaker and author for ACDIS and AHIMA and is currently serving a 3-year term on NAHRI’s advisory board.
¹ https://www.aha.org/news/headline/2019-08-15-study-hospital-quality-rating-systems-need-improvement
² https://www.hpnonline.com/patient-satisfaction/article/21092979/inaccuracies-revealed-in-hospital-quality-rating-systems