Inpatient Diagnosis of Delirium and Encephalopathy: Coding Trends 2011-2018

Requesting That Delirium Achieve Parity With Acute Encephalopathy in the MS-DRG System

Since 2007, the Medicare Severity Diagnosis Related Groups classification system has favored billing codes for acute encephalopathy over delirium codes in determining hospital reimbursement and several quality-of-care value metrics, despite broad overlap between these sets of diagnostic codes. Toxic and metabolic encephalopathy codes are designated as major complication or comorbidity, whereas causally specified delirium codes are designated as complication or comorbidity and thus associated with a lower reimbursement and lesser impact on value metrics. The authors led a submission to the U.S. Centers for Medicare and Medicaid Services requesting that causally specified delirium be designated major complication or comorbidity alongside toxic and metabolic encephalopathy. Delirium warrants reclassification because it satisfies U.S. Centers for Medicare and Medicaid Services' guiding principles for re-evaluating Medicare Severity Diagnosis Related Group severity levels. Delirium: (1) has a bidirectional relationship with the permanent condition of dementia (major neurocognitive disorder per DSM-5-TR), (2) indexes vulnerability across populations, (3) impacts healthcare systems across levels of care, (4) complicates postoperative recovery, (5) consigns patients to higher levels of care, (6) impedes patient engagement in care, (7) has several recent treatment guidelines, (8) often indicates neuronal/brain injury, and (9) represents a common expression of terminal illness. The proposal's impact was explored using the 2019 National Inpatient Sample, which suggested that increasing delirium's complexity designation would lead to an upcoding of less than 1% of eligible discharges. Parity for delirium is essential to enhancing awareness of delirium's clinical and economic costs. Appreciating delirium's impact would encourage delirium prevention and screening efforts, thereby mitigating its dire outcomes for patients, families, and healthcare systems.

Short-term outcomes of prehospital opioid pain management for older adults with fall-related injury

BACKGROUND

Opioids are recommended for pain management in patients being cared for and transported by emergency medical services, but no specific guidelines exist for older adults with fall-related injury. Prior research suggests prehospital opioid administration can effectively manage pain in older adults, but less is known about safety in this population. We compared short-term safety outcomes, including delirium, disposition, and length of stay, among older adults with fall-related injury according to whether they received prehospital opioid analgesia.

METHODS

We linked Medicare claims data with prehospital patient care reports for older adults (≥65) with fall-related injury in Illinois between January 1, 2014 and December 31, 2015. We used weighted regression models (logistic, multinomial logistic, and Poisson) to assess the association between prehospital opioid analgesia and incidence of inpatient delirium, hospital disposition, and length of stay.

RESULTS

Of 28,150 included older adults, 3% received prehospital opioids. Patients receiving prehospital opioids (vs. no prehospital opioids) were less likely to be discharged home from the emergency department (adjusted probability = 0.30 [95% CI: 0.25, 0.34] vs. 0.47 [95% CI: 0.46, 0.48]), more likely to be discharged to a non-home setting after an inpatient admission (adjusted probability = 0.43 [95% CI: 0.39, 0.48] vs. 0.30 [95% CI: 0.30, 0.31]), had inpatient length of stay 0.4 days shorter (p < 0.001) and ICU length of stay 0.7 days shorter (p = 0.045). Incidence of delirium did not vary between treatment and control groups.

CONCLUSIONS

Few older adults receive opioid analgesia in the prehospital setting. Prehospital opioid analgesia may be associated with hospital disposition and length of stay for older adults with fall-related injury. However, our findings do not provide evidence of an association with inpatient delirium. These findings should be considered when developing guidelines for prehospital pain management specific to the older adult population.

Identification of delirium from real-world electronic health record clinical notes

Introduction

We tested the ability of our natural language processing (NLP) algorithm to identify delirium episodes in a large-scale study using real-world clinical notes.

Methods

We used the Rochester Epidemiology Project to identify persons ≥ 65 years who were hospitalized between 2011 and 2017. We identified all persons with an International Classification of Diseases code for delirium within ±14 days of a hospitalization. We independently applied our NLP algorithm to all clinical notes for this same population. We calculated rates using number of delirium episodes as the numerator and number of hospitalizations as the denominator. Rates were estimated overall, by demographic characteristics, and by year of episode, and differences were tested using Poisson regression.

Results

In total, 14,255 persons had 37,554 hospitalizations between 2011 and 2017. The code-based delirium rate was 3.02 per 100 hospitalizations (95% CI: 2.85, 3.20). The NLP-based rate was 7.36 per 100 (95% CI: 7.09, 7.64). Rates increased with age (both p < 0.0001). Code-based rates were higher in men compared to women (p = 0.03), but NLP-based rates were similar by sex (p = 0.89). Code-based rates were similar by race and ethnicity, but NLP-based rates were higher in the White population compared to the Black and Asian populations (p = 0.001). Both types of rates increased significantly over time (both p values < 0.001).

Conclusions

The NLP algorithm identified more delirium episodes compared to the ICD code method. However, NLP may still underestimate delirium cases because of limitations in real-world clinical notes, including incomplete documentation, practice changes over time, and missing clinical notes in some time periods.