Understanding the Connection Between Observation Rate & CMI

By Tiffany Ferguson, LMSW, CMAC, ACM

EDITOR’S NOTE: The context for this article was conceptualized from a recent ACPA Town Hall meeting on observation metrics and a presentation by the author and Dr. Mohla at NPAC 2024 on observation metrics. Also used as a reference was the ACDIS white paper “Demystifying and Communicating Case-Mix Index,” published in 2022.

Those in healthcare roles frequently experience the stress associated with meeting key performance indicators (KPIs) compared to benchmark standards. While achieving these goals can be gratifying, there are instances when they can seem unattainable, often due to the arbitrary nature of how the metrics are set, relying on external benchmarks or ambitious targets without a comprehensive review of processes and data. Metrics like observation rate, denials rate, and case mix index (CMI), although interconnected, are frequently established with independent goals, further complicating the pursuit of comprehensive success.

Utilization management (UM) has long grappled with a historical bias, perceiving observation as unfavorable while favoring inpatient care for financial security – an orientation reflected in the overarching objective of hospitals and departments to “lower the observation rate.” While there may be valid opportunities to reduce the observation rate, it is crucial to carefully consider internal processes and target needs before establishing an ideal or benchmarked lower limits.  

Simultaneously, clinical documentation improvement (CDI) aims to accurately capture patient acuity, with benchmark objectives to achieve higher-weighted DRG classifications, resulting in a higher CMI for the hospital. Both objectives are financially significant and encouraged by hospital leaders, particularly in a fee-for-service environment.

However, they can be at odds; aggressively reducing the observation rate might prompt a shift towards designating more cases as inpatient care, leading to a higher proportion of lower-weighted DRG patients for CDI review and lowering the hospital’s CMI. Although the observation rate may decrease, so would the CMI.

This practice could also lead to an increase in post-discharge W2s and payer denials, potentially increasing accounts receivable (A/R) days and denial recovery, thus negatively impacting the financial goals for your revenue cycle and business office. It is plausible that cases initially pushed towards inpatient care, when observation might have been more appropriate, could eventually result in payment at the outpatient rate, often less than if observation was ordered properly at the outset, during the appeals process.

This lower-than-expected reimbursement would then hide in the patient accounting world, while the reported observation rate would remain low.

On the flip side, adopting an excessively conservative approach in determining level of care to preempt payer denials could lead to a situation in which more patients are retained in outpatient status with observation services, driven by apprehension of payer resistance. In this context, although the observation rate might be higher, the CMI could also rise, given that only patients unequivocally confirmed for inpatient care contribute to this metric. While denials may decrease, this cautious strategy carries the inherent risk of establishing a reimbursement structure that limits potential level-of-care opportunities for inpatient designation, despite the medical treatment delivered or clinical guidelines. Striking the right balance is not easy, but it is crucial to avoid the unintended consequence of limiting revenue for the sake of an overly conservative stance on care-level designations.

Determining level of care should by prioritized by doing what is right for the patient, considering factors such as medical necessity, patient acuity, and medical decision-making, rather than the pressures of meeting arbitrary benchmarks, which are often based on data from hospitals whose demographics and offerings are in distinct contrast to a hospital’s own.

Finding hospitals with similar patient populations, services, and physician behavior can allow developing appropriate benchmarks, which then could serve as a barometer to ensure that a health system aligns reasonably with their peers, rather than the sole driving force being operational efficiency.

Instead, consider a focus on understanding how data is collected and analyzed, with a collective goal that assesses hospital processes and performance opportunities across utilization management, CDI, and the denials/appeals team.