Why American Hospitals Face Benchmarking Challenges

Many hospitals nationwide are facing significant challenges in benchmarking and quality reporting. While it may seem like these issues stem from complex clinical variations, the actual root cause often lies elsewhere – specifically in the inconsistent ways hospitals define and apply admission types.

This inconsistency leads to discrepancies that ripple across data accuracy, performance metrics, and even patient safety reporting.

To clarify some of the confusion we’ve identified in the industry, I’d like to start by explaining the difference between admission type and admission status – two often misunderstood terms.

• Admission type refers to the urgency of the admission – whether it’s elective, urgent, or emergent – and is based on the patient’s clinical needs.

• Admission status, on the other hand, is an administrative classification. It designates whether a patient is classified as an inpatient, outpatient, or under observation. This classification is primarily used for billing and regulatory purposes.

While admission status affects operational workflows, the admission type directly impacts quality reporting, especially regarding Patient Safety Indicators, or PSIs.

The National Uniform Billing Committee (NUBC) provides standardized definitions for assigning a patient’s admission type to help reduce variability.

 According to the NUBC:

• Emergency admissions are defined as cases requiring immediate medical intervention for life-threatening conditions.
• Urgent admissions are cases that need prompt attention for physical or mental disorders, where the patient is admitted to the first available suitable accommodation.
• Elective admissions refer to conditions where the patient’s situation allows time to schedule the admission, based on availability.

Despite these precise definitions, hospitals often interpret them differently, which leads to significant discrepancies in reporting and benchmarking.

We’ve observed striking variations in how admit types are classified through benchmarking research. For example:

• In one benchmarking organization’s data, the top-performing hospital’s admission rates were reported as 4 percent elective, 48 percent urgent, and 45 percent emergent.
• Now, compare that to the 10th-ranked hospital, which reported 42 percent elective, 22 percent urgent, and 35 percent emergent.

This raises a critical question: Are these differences due to patient populations?

The answer is – it’s unlikely. Instead, these discrepancies reflect how hospitals apply admit type definitions inconsistently.

Our current focus is on the variability between urgent and elective admissions. Inconsistent classifications have the most significant impact on benchmarking and quality metrics, particularly when it comes to Patient Safety Indicators (PSIs)

Developed by AHRQ, PSIs aim to achieve the following:

1. Promote Patient Safety by identifying potential complications like infections or surgical errors;
2. Support Quality Improvement with data-driven insights to reduce preventable harm;
3. Enable Benchmarking to compare hospital performance nationally; and
4. Inform Public Reporting, influencing hospital rankings and accountability measures.

However, the validity of PSIs relies heavily on accurately classifying admission types. If admission types are misclassified, PSI rates become distorted, affecting not just performance metrics but also patient safety trends.

I’ll explore these issues further at the upcoming National Physician Advisor Conference in Chicago, April 7–10, 2025.

Then, in May, I’ll also present with Cheryl Ericson at the National ACDIS Conference in Orlando, where we’ll explore how inconsistent admission type definitions impact patient safety metrics and regulatory compliance.

Both conferences will focus on the urgent need for standardization within AHRQ guidelines. We’ll highlight variations that are compromising the integrity of benchmarking efforts.

In conclusion, the challenges we’re seeing in benchmarking and quality reporting aren’t due to differences in patient populations.

They stem from inconsistent admission type definitions at the institutional level.

By focusing on the consistent application of national standards, we can improve data accuracy, support more meaningful benchmarking, and ultimately enhance patient outcomes.