Close care documentation gaps — Expert tips for better quality reporting

Between 46% and 60% of inpatient records are missing documentation that would reflect a more severe and complex patient1. Those are big numbers with big implications for health care providers. Inpatient records with missing documentation cloud the view of hospital leadership trying to understand their hospitals’ performance and can have a trickledown effect on industry measures utilized to evaluate quality and performance.

This includes rating and ranking measures such as the Centers for Medicare & Medicaid (CMS) Star Ratings, U.S. News & World Report, Leapfrog, Healthgrades and more. Health care professionals and organizations need a strategy to determine which industry measures meet their internal program goals. Careful consideration should be given to identifying these goals to ensure they align with accurate and complete documentation. 

Health care has long been focused on delivering quality clinical care that results in positive patient outcomes. Unfortunately, clinical excellence is not always accurately captured in quality ratings and rankings if sufficient attention is not paid to documentation and coding. Patient outcome methodologies must consider both the clinical risk and severity of the patient when they enter and depart the hospital. Barring complications, patients that enter the hospital in overall good health expect to leave the hospital with positive outcomes.

 It requires greater context to understand whether a hospital performed well when a sick patient is discharged. Hospitals need a strategy to ensure providers, ancillary clinicians, clinical documentation integrity (CDI) and coding are working to fully document the patient story and doing so efficiently to minimize the impact on thinly staffed teams. The silver lining is that the same efforts required to accurately document quality also support the solid documentation needed for full reimbursement. Here are three things your organization can evaluate today as part of a robust quality plan and one trend to follow in the future.

  1. Visibility to quality metrics is often challenged by workflows that cross many different technology platforms. This type of technology ecosystem also makes it easier to have a multitude of measurement approaches and makes improving, and ultimately automating processes, much more challenging.  For instance, an organization with a heavy focus on financial impact may increase documentation of diagnoses capturing multiple chronic conditions such as acute respiratory failure or sepsis in a post-operative patient. This could increase payment substantially, however will also be a quality ding with a reduction in payment and penalty on the backend. If you find yourself in an organization with competing priorities and disparate technology systems, it is important to incorporate checks and balances.

  2. When submitting claims to CMS, only the first 25 codes are recorded for payment. To accurately portray an organization’s quality performance with CMS, it is critical that codes with quality implications be submitted in the first 25 codes. Coders can manually review each code set and reorder codes impacting Elixhauser, length of stay, risk of mortality and severity of illness. Another technique is to use a modern encoder that completes the task of code reprioritization automatically using artificial intelligence (AI).

  3. A second quality strategy is rethinking where CDI fits in the documentation process. The CDI profession was born out of the need to bridge the gap between clinically minded physicians and the complex, dynamic medical coding rules. Historically, after a physician documents a case, a CDI specialist reviews it, sends a query to the physician, and then the physician reopens cases to provide greater clarity as an addendum or new document. AI has begun to augment CDI teams across the U.S. to nudge the physician in real time at the point of documentation in the electronic health record (EHR). When a physician acts on a nudge, more specific documentation is provided the first time and it saves rework for the physician and CDI specialist. Another benefit of utilizing real time nudges is that as physicians observe the same nudge, over time they learn better documentation practices and see less nudges with time. Ultimately, a clearer picture of the patient condition is documented, coded and the accuracy of quality metrics improves. 

  4. An exciting new technology is coding automation which can further improve the quality accounting process. The use of AI to nudge providers and uncover opportunities for CDI will shift your practice’s effort and spend from a mid-revenue cycle reliant on post-care review to a real time, point-of-care revenue cycle. This results in decreased documentation variability and more stable data sets capable of being utilized by deep learning AI models. These models have succeeded in automating the assignment of many codes based on AI’s interpretation of clinical documentation. Many coding departments across the U.S. will have the opportunity for the first time in 2023 to enhance their coders productivity by augmenting it with automated code assignment. There is still significant work ahead to match human capabilities in fully coding complex inpatient charts, but AI augmentation allows coding experts to focus on the most complicated aspects of coding required to receiving appropriate payment, avoid penalties and account for excellent care quality.


 1.Research based on a data analysis of over 60,000 inpatient cases in the US from 2020-2022.

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