Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Feb 6, 2020
Date Accepted: Jun 28, 2020
How Specialist Aftercare Impacts on Long-Term Readmission Risks in Elderly Patients with Metabolic, Cardiac, and Chronic Obstructive Pulmonary Diseases: Cohort Study Using Administrative Data
ABSTRACT
Background:
The health state of elderly patients is typically characterized by multiple cooccurring diseases requiring the involvement of several types of healthcare provider.
Objective:
We aim to quantify how multimorbid patients benefit from seeking specialist care in terms of long-term readmission risks.
Methods:
From an administrative database we identify 225,238 elderly patients with 97 different diagnosis (ICD10 codes) from hospital stays and contacts with 13 medical specialties. For each diagnosis associated with the first hospital stay, we use multiple logistic regression to quantify the sex-specific and age-adjusted long-term all cause readmission risk (hospitalizations occurring between 3 months and 3 years after the first admission) and how specialist contacts impact on these risks.
Results:
Males have higher readmission risks than females (mean difference over all first diagnoses 1.9%, p<.001), but similar readmission risk reductions after receiving specialist care. Specialist care can reduce readmission risks by almost 50%. We find the greatest risk reductions when the first hospital stay was associated with diagnoses corresponding to complex chronic diseases such as acute myocardial infarction (reduced readmission risk of 57.6% SE 7.6% for males [m], females [f]: 55.9% SE 9.8%), diabetic and other retinopathies (m: 62.3% SE 8.0, f: 60.1% SE 8.4%), chronic obstructive pulmonary disease (m: 63.9% SE 7.8%, f: 58.1% SE 7.5%), disorders of lipoprotein metabolism (m: 64.7% SE 3.7%, f: 63.8% SE 4.0%) and chronic ischemic heart diseases (m: 63.6% SE 3.1%, f: 65.4% SE 3.0%).
Conclusions:
Specialist care can greatly reduce long-term readmission risks for chronic and multimorbid patients. Further research is needed to identify the specific reasons of these findings, and to understand the sex-specific biases detected.
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