Exploring the Value of Clinical Data Standards to Predict Hospitalization of Home Care Patients

Abstract

SummaryBackground: There is a critical need to reduce hospitalizations for Medicare patients and electronic health record (EHR) home care data provide new opportunities to evaluate risk of hospitalization for patients. Objectives: The objectives of this study were to 1) develop a measure to predict risk of hospitalization among home care patients, the Hospitalization Risk Score (HRS), and 2) compare it with an existing severity of illness measure, the Charlson Index of Comorbidity (CIC). Methods: A convenience sample of clinical data from 14 home care agencies' EHRs, representing 1,643 home care patient episodes was used for the study. The development of the HRS was based on review of the literature, and expert panel evaluation to construct the HRS. Descriptive statistics and generalized linear models were used for comparative analysis; areas under curve (AUC) values were compared for receiver operating curves (ROC), and cut points predicting hospitalization were evaluated. Results:The HRS for this sample ranged from 0 to 5.6, with a median of 1.25. The CIC for this sample ranged from 0 to 9 and with a median of 0. Nearly three fourths of the sample was hospitalized at an HRS of 2, and a CIC of 1. AUC values for ROC were 0.63 for HRS and 0.59 for the CIC. The ROC curves were significantly different (t = -7.59, p <0.003). Conclusions: This preliminary study demonstrates the potential value of the HRS using Omaha System EHR data. There was a statistically significant difference for predicting hospitalization of home care patients with the HRS versus the CIC; however the AUC values for both were low. Continued research is needed to further refine the HRS, determine whether it is more sensitive for particular subgroups of patients, and combine it with additional risk factors in understanding rehospitalization.

Transitions From Skilled Nursing Facility to Home: The Relationship of Early Outpatient Care to Hospital Readmission

“…What is not clear from this secondary data analysis is what specific aspects of home health visits are helping patients to remain at home, especially since this group is high risk for hospital admission. 32 Future research is needed to explore this important question.…”

Activities of Daily Living of Home Healthcare Patients

“…The following variables were covariates: age, race (Black/African American, Hispanic, White, and other minority race/ethnicity), living condition (alone or with others), HHC length of stay (LOS), insurance status (Medicaid, Medicare and dual eligibility), Prior inpatient stay within 14 days of HHC admission (hospital, skilled nursing facility (SNF) or other inpatient unit. The weighted Charlson Comorbidity Index (CCI) was used to identify co-morbidities at HHC admission (Monsen et al, 2012). Impaired decision making based on single OASIS item (M1740).…”

Detecting Language Associated With Home Healthcare Patient’s Risk for Hospitalization and Emergency Department Visit

“…Compared to other standardized terminologies such as the International Classification of Diseases, Ninth Revision, Clinical Modification that encompasses mostly medical diagnoses, the Omaha System was developed to describe the healthcare provided in community settings, like HHC and public health. Previously, the Omaha System has been identified as a better candidate for predicting hospitalization in HHC patients compared to the International Classification of Diseases, Ninth Revision, Clinical Modification (Monsen et al, 2012).…”