Disability Impacts Length of Stay in General Internal Medicine Patients

Abstract

BACKGROUND: Disability is prevalent among patients treated in Internal Medicine (IM), but its impact on length of inpatient stay (LOS) is unknown. Current systems of patient management and resource allocation are disease-focused with scant attention paid to functional impairment. Earlier studies in selected cohorts suggest that disability prolongs LOS. OBJECTIVE: To investigate the relationship of disability with LOS in IM, controlling for comorbidity. DESIGN: Prospective cohort study. PATIENTS: We charted 448 patients from an IM team admitted between 2008 and 2012 for sociodemographic, disease, biochemical and functional characteristics. Each IM team is on duty for one month annually, and patients were hence recruited for one month each year. MAIN MEASURES: Disability was measured using the Functional Independence Measure (FIM) recorded at discharge. Comorbidity was measured using the Charlson Comorbidity Index (CCI). KEY RESULTS: Of the 448 patients, 57.4 % were male with mean age 68.6 years. The mean LOS was 9.58 days. The mean motor and cognitive FIM scores were 57.1 and 25.7, respectively. The mean CCI score was 2.69. Thirty-four percent had major social issues impacting discharge plans. The five most common diagnoses for admission were pneumonia (8.9 %), urinary tract infection (7.8 %), cellulitis (7.6 %), heart failure (7.1 %) and falls (6.0 %). Both cognitive and motor FIM scores were negatively correlated with longer LOS (P<0.001). On multivariate analysis, variables independently associated with longer LOS included the motor FIM score (P<0.001), presence of social issues such as caregiver unavailability (P<0.001), non-realistic patient expectations (P=0.001) and administrative issues impeding discharge (P=0.016). CONCLUSION: Disability predicts LOS in IM patients, and thus their comprehensive care should involve functional assessment. As social and administrative factors were also independently associated with LOS, there is a need to involve social workers and administrators in a multidisciplinary approach towards optimizing LOS.KEY WORDS: internal medicine; hospital medicine; disability; length of stay (LOS). J Gen Intern Med 29(6):885-90

Health information exchange systems and length of stay in readmissions to a different hospital

“…Our results also show that having HIE systems in both the index and readmitting hospitals does not “protect” against these negative outcomes, as there was no difference in the length of the readmission stay based on the availability of HIE systems. Factors that were found to be associated with longer readmission stays are well known indicators of the complexity of the patient's medical condition, such as the presence of disability, comorbidity, and ICU treatment during the readmission …”

“…Factors that were found to be associated with longer readmission stays are well known indicators of the complexity of the patient's medical condition, such as the presence of disability, comorbidity, and ICU treatment during the readmission. 24,27 The shorter LORS in SHRs may be due to the familiarity of physicians and other healthcare providers with the patient and his or her condition, especially as the policy in SHRs in Israel is to readmit to the same unit from which the patient was recently discharged. This same hospital familiarity is especially important as hospital care in Israel follows the hospitalist model, in which responsibility for patient care is transferred from the patient's primary care physician to the hospital's physician, resulting in increased need for integration through HIE systems, especially when patients are readmitted to a different hospital.…”

Bed-to-Bed Transfer Program Among Patients Who Need Hospitalization in a Crowded Emergency Department in Taiwan

“…Propensity score matching method was used to balance baseline characteristics between the two groups. 21,22 Factors related to transfer decision and outcomes of interest (eg, age, gender, CCI, Triage levels, Diagnosis) were included in a logistic regression model to predict the probability of transfer, namely, the propensity score for transfer [23][24][25][26] (Table S1, Supplementary file 1). The propensity was then assigned to each individual.…”