Using clinical data to predict high-cost performance coding issues associated with pressure ulcers: a multilevel cohort model

Abstract

Objective: Hospital-acquired pressure ulcers (HAPUs) have a mortality rate of 11.6%, are costly to treat, and result in Medicare reimbursement penalties. Medicare codes HAPUs according to Agency for Healthcare Research and Quality Patient-Safety Indicator 3 (PSI-03), but they are sometimes inappropriately coded. The objective is to use electronic health records to predict pressure ulcers and to identify coding issues leading to penalties.

Materials and methods: We evaluated all hospitalized patient electronic medical records at an academic medical center data repository between 2011 and 2014. These data contained patient encounter level demographic variables, diagnoses, prescription drugs, and provider orders. HAPUs were defined by PSI-03: stages III, IV, or unstageable pressure ulcers not present on admission as a secondary diagnosis, excluding cases of paralysis. Random forests reduced data dimensionality. Multilevel logistic regression of patient encounters evaluated associations between covariates and HAPU incidence.

Results: The approach produced a sample population of 21 153 patients with 1549 PSI-03 cases. The greatest odds ratio (OR) of HAPU incidence was among patients diagnosed with spinal cord injury (ICD-9 907.2: OR = 14.3; P < .001), and 71% of spinal cord injuries were not properly coded for paralysis, leading to a PSI-03 flag. Other high ORs included bed confinement (ICD-9 V49.84: OR = 3.1, P < .001) and provider-ordered pre-albumin lab (OR = 2.5, P < .001).

Discussion: This analysis identifies spinal cord injuries as high risk for HAPUs and as being often inappropriately coded without paralysis, leading to PSI-03 flags. The resulting statistical model can be tested to predict HAPUs during hospitalization.

Conclusion: Inappropriate coding of conditions leads to poor hospital performance measures and Medicare reimbursement penalties.

Keywords: Braden Scale; Medicare; electronic health record; mixed-effects regression model; predictive modeling; pressure ulcer; spinal cord injury.