Discharge Function Score: The Hidden Target

Why it matters for your home health agency

A patient makes meaningful progress during a home health episode. The clinician sees it. The patient and family feel it. Yet the episode does not count toward the agency’s Discharge Function measure. 

That is the challenge with Discharge Function: agencies can see the final percentage, but often cannot see the patient-level target behind it. A patient can improve significantly and still fall short of the score CMS expected at discharge. 

To understand performance, agencies need visibility into both sides of the comparison: where the patient finished and where CMS expected that patient to finish. 

Improvement and meeting expectations are different questions 

The Discharge Function measure reflects the percentage of eligible home health episodes in which a patient’s observed function score at discharge meets or exceeds an expected score. CMS calculates that expectation using information captured at start or resumption of care, including the patient’s function and other characteristics. 

This means improvement does not, by itself, tell an agency whether an episode met the measure. 

In our webinar, we examined two patients who each had an admission function score of 29. One had an expected discharge score of 58. The other had an expected score of 48. They started with the same function score, but CMS expected different outcomes. 

That difference reflects risk adjustment. CMS uses patient characteristics to calculate an expected score for each episode. Some characteristics raise that score, while others lower it—meaning two patients with the same admission function can have different targets. 

The practical lesson for agency teams is clear: the information captured at admission helps shape the expectation used at discharge. 

Assessment accuracy has consequences downstream 

Risk adjustment can sound like a statistical issue removed from day-to-day care. In practice, it gives assessment accuracy a direct role in how an episode is evaluated. 

When a Section GG activity used in the measure is coded Activity Not Attempted (ANA), CMS does not simply leave that item out. Instead, CMS estimates, or imputes, a value using its statistical methodology. The value used in the calculation may therefore differ from what a clinician would have observed had the activity been assessed. 

ANA codes are appropriate when the coding criteria are met. But when an activity can be assessed, capturing the patient’s actual performance gives the measure more direct information about that patient’s function. 

Imputed values can affect the patient’s calculated function score, including the admission score used to establish the expected discharge score. That makes it important for agencies to know when imputation occurred—not just their final Discharge Function percentage. 

This does not mean every missed target can be traced to an ANA code. It means agencies benefit from being able to see when imputation occurred and understand its potential effect. 

A percentage should lead to better questions 

An agency-level Discharge Function score is useful for tracking performance over time. It cannot, on its own, explain what happened within the episodes that produced it. 

For that, quality and clinical leaders need to ask more specific questions: 

• What was the patient’s function score at admission? 
• Which patient characteristics influenced the expected score? 
• Were any Section GG values imputed at admission or discharge? 
• What discharge score did CMS expect? 
• What was the patient’s actual discharge score, and how far above or below expectation did the patient finish? 
• Do patterns appear across activities, clinicians or branches? 

The purpose of this review is to help teams distinguish among opportunities for more accurate assessment, clearer documentation and clinical improvement. Those are different problems, and they call for different responses. 

Make the expectation visible 

Simple is building patient-level Discharge Function visibility into SimpleAnalyzer—connecting admission function, imputed values, expected discharge scores and actual discharge performance so agencies can better understand the results behind their overall percentage. 

The broader opportunity is to change the conversation from “Why isn’t our percentage higher?” to “Which episodes are driving the result, and what can we learn from them?” 

Home health teams already work to help patients achieve meaningful functional gains. Giving those teams a clearer view of how the Discharge Function measure evaluates each episode helps them understand where their results come from—and where to focus next. 

Want a clearer view of expected versus actual Discharge Function performance? Request a demo of SimpleAnalyzer.