Quality Rankings and Documentation Accuracy

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. 

Learn more about the impact of clinical documentation accuracy on quality metrics with the instant download!


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 

Retrospective Reviews: The Last Line of Defense for Documentation Integrity?

By Fran Jurcak, MSN, RN, CCDS, CCDS-0, Chief Clinical Strategist

Executive summary:

Even before the COVID-19 pandemic, hospitals struggled with mid-revenue cycle leakage. In 2019 (pre-COVID), Medicare and Medicaid underpayments had reached $75.8 billion¹ and 84% of healthcare leaders cited inaccurate clinical documentation and coding as the root cause of lost or decreased revenue.² The pandemic has only exacerbated the problem.

There isn’t a single root cause for mid-cycle leakage—it remains difficult for healthcare leaders to manage due to competing priorities, a lack of clinical knowledge, and a scarcity of appropriate software solutions. But healthcare leaders have options for mitigating mid-cycle leakage, and one of the most compelling strategies is to implement a thorough retrospective review process.

Download this whitepaper to learn:

  • Why retrospective reviews are necessary
  • Strategies for prioritizing what to review and how to implement a retrospective review process at your organization
  • The impact of machine learning on retrospective reviews

¹ 2019 AHA Fact Sheet: Underpayment by Medicare and Medicaid January 2021

² HIMSS and Besler Revenue Cycle Management Research Report – Insights into Revenue Cycle Management October 2016

Mid-Revenue Cycle Management: How to Measure, Manage and Minimize Leakage

By Fran Jurcak

Executive summary:

Razor-thin profit margins are not a new problem for hospitals. In 2019, one study found that hospital profit margins had shrunk by 21% on average year-over-year.¹

Enter COVID-19. According to an August 2020 KaufmanHall report, hospital margins were down another 28% through Q3, even with funds from the CARES Act and Coronavirus Aid and Relief Funds. If it weren’t for the stimulus relief, hospital margins would have been down 96% on average in 2020.² 

Yes, COVID-19 exacerbated the problem. But it certainly didn’t cause it. 

For many healthcare leaders, the heart of the problem is systemic—leakage from their mid-revenue cycle. In fact, 84% cite inaccurate clinical documentation and coding as the root cause of lost or decreased revenue.³ This is not just a problem with struggling hospitals. Even “average performance” in the mid-revenue cycle was below optimal for those surveyed. In 2019 (pre-COVID), Medicare and Medicaid underpayments reached $75.8 billion.4 

Today, COVID-19 has transformed the landscape of hospitals and health systems. While leakage was a problem before, it was predictable and manageable—thanks to consistent revenue and expenses. But for many hospitals, revenue projections have been completely upended. 

Before COVID-19, mid-revenue cycle leakage impacted every hospital’s bottom line. Now, it can mean negative operating margins. If operating margins remain negative, it can mean anything from downsizing staff and services to diminishing quality clinical care. 

Thankfully, mid-revenue cycle leakage is not an insurmountable challenge. Financial leaders are turning to increasingly sophisticated and automated solutions to overcome leakage, building transformational solutions to ensure organizations are financially resilient for years to come.

Download this whitepaper to learn:

  • Leakage throughout the mid-revenue cycle
  • Strategies to better capture earned revenue
  • The impact of machine learning on mid-revenue cycle leakage 

¹ https://www.hfma.org/topics/news/2019/12/hospital-operating-margins-decline-21–in-2019–tracking-firm-fi.html

² https://kha-paywall.readz.com/executive-summary-august-2020?preview=139977

³ HIMSS and Besler Revenue Cycle Management Research Report – Insights into Revenue Cycle Management October 2016

4 2019 AHA Fact Sheet: Underpayment by Medicare and Medicaid January 2021

Examining the Unforeseen Financial and Clinical Impact of COVID-19

Leveraging Iodine Software’s Cognitive Emulation approach to better understand current and future state 

Iodine Software is a healthcare AI company that has pioneered a new machine learning approach— Cognitive Emulation—to help healthcare finance leaders build resilient organizations. To date, Iodine has partnered with over 600 hospitals in the United States to create a large and diverse clinical data set that can provide insights into the COVID-19 pandemic.

Iodine’s Cognitive Emulation approach analyzes the full clinical record for each patient much the way a clinician would, but on a massive scale. Coupled with proprietary technology that forecasts diagnosis-related groups (DRGs) for every patient still in the hospital, Iodine is able to look in real time at trends emerging in this data set without having to rely on final coded data that is typically only available post-discharge, after a considerable delay.

For this report, Iodine reviewed more than 60,000 COVID-19 cases from over 600 hospitals— spanning cases from the entire country, including both infection hot spots and emerging areas of concern. Using the Iodine CognitiveMLTM engine and Iodine ForecastTM product, the Iodine Data Science team identified likely COVID-19 patients and predicted the DRG for recently discharged, but not final-coded, cases. Both structured and unstructured data were leveraged to generate this analysis. The timeframe for this analysis is from March 4 through May 3, 2020. Within this data set, 50.1% of patients were male and 49.9% were female.

This information is meant to help healthcare providers nationwide more accurately forecast their resource needs (including staff, ICU beds, ventilators and other critical care equipment) and understand the demographics most vulnerable to COVID-19, as well as support healthcare finance leaders in determining the right strategies to ensure financial resilience both in the near- and long-term.

Download the full report here