AI Article Synopsis

  • The Cleveland Clinic Health System developed a readmission risk score in their electronic medical records to predict 30-day hospital readmissions, as this is crucial for improving healthcare outcomes and managing costs.
  • A study evaluated the effectiveness of this risk score by analyzing data from over 600,000 patients discharged between April 2017 and September 2020 across various hospitals, discharge diagnoses, and specialties.
  • Results showed that the readmission risk score had a c-statistic of 0.6875, indicating moderate predictive accuracy, with performance varying by hospital site and type of discharge, particularly noting lower scores for certain conditions like congenital anomalies and COVID-19.

Article Abstract

Background: Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR).

Objective: We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity.

Design: Retrospective cohort study.

Participants: Adult patients discharged from a CCHS hospital April 2017-September 2020.

Main Measures: Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic.

Results: The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories.

Conclusions: The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8821785PMC
http://dx.doi.org/10.1007/s11606-021-07277-4DOI Listing

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