Projects per year
Abstract
Background
As the prevalence of multimorbidity grows, provision of effective healthcare is more challenging. Both multimorbidity and complexity in healthcare delivery may be associated with worse outcomes.
Methods
We studied consecutive, unique emergency non-surgical hospitalisations for patients over 50 years old to three hospitals in Scotland, UK between 2016 and 2024 using linked primary care and hospital records to define multimorbidity (2+ long-term conditions), and timestamped hospital electronic health record (EHR) contacts with nursing and rehabilitation providers to describe intensity of inpatient care. The primary outcome was emergency hospital readmission within 30 days of discharge, analysed using multivariable logistic regression.
Results
Across 98,242 consecutive admissions, 84% of the study population had multimorbidity, 50% had 4+ long-term conditions, and 37% had both physical and mental health conditions. Both higher condition count and contacts (nursing and rehabilitation) were independently associated with the primary outcome in fully adjusted models (example adjusted odds ratio [aOR] 1.62, 95% CI 1.52 to 1.73 for 4+ conditions compared to no multimorbidity, p<0.001; aOR 1.35, 95% CI 1.28 to 1.42 for >8 nursing contacts compared to 1-3, p<0.001). While multimorbidity was associated with longer hospital stays with more nursing and rehabilitation contacts, the distribution of contacts and activity did not differ by multimorbidity or subsequent emergency readmission status.
Conclusions
Higher count multimorbidity was associated with an increased risk of readmission, but we observed uniformity in care despite differential outcomes across multimorbidity groups. This may 3 suggest that EHR data-driven approaches could inform person-centred care and improve hospital resource allocation.
As the prevalence of multimorbidity grows, provision of effective healthcare is more challenging. Both multimorbidity and complexity in healthcare delivery may be associated with worse outcomes.
Methods
We studied consecutive, unique emergency non-surgical hospitalisations for patients over 50 years old to three hospitals in Scotland, UK between 2016 and 2024 using linked primary care and hospital records to define multimorbidity (2+ long-term conditions), and timestamped hospital electronic health record (EHR) contacts with nursing and rehabilitation providers to describe intensity of inpatient care. The primary outcome was emergency hospital readmission within 30 days of discharge, analysed using multivariable logistic regression.
Results
Across 98,242 consecutive admissions, 84% of the study population had multimorbidity, 50% had 4+ long-term conditions, and 37% had both physical and mental health conditions. Both higher condition count and contacts (nursing and rehabilitation) were independently associated with the primary outcome in fully adjusted models (example adjusted odds ratio [aOR] 1.62, 95% CI 1.52 to 1.73 for 4+ conditions compared to no multimorbidity, p<0.001; aOR 1.35, 95% CI 1.28 to 1.42 for >8 nursing contacts compared to 1-3, p<0.001). While multimorbidity was associated with longer hospital stays with more nursing and rehabilitation contacts, the distribution of contacts and activity did not differ by multimorbidity or subsequent emergency readmission status.
Conclusions
Higher count multimorbidity was associated with an increased risk of readmission, but we observed uniformity in care despite differential outcomes across multimorbidity groups. This may 3 suggest that EHR data-driven approaches could inform person-centred care and improve hospital resource allocation.
Original language | English |
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Article number | 8522 |
Number of pages | 10 |
Journal | Scientific Reports |
Volume | 15 |
DOIs | |
Publication status | Published - 12 Mar 2025 |
Keywords / Materials (for Non-textual outputs)
- multimorbidity
- electronic health records
- readmission
- rehabilitation
Fingerprint
Dive into the research topics of 'Understanding hospital activity and outcomes for people with multimorbidity using electronic health records'. Together they form a unique fingerprint.Projects
- 1 Finished
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Systems Engineering and Thinking To Transform Transitions (SET4) in health and care for people with multiple long-term conditions
Anand, A. (Principal Investigator), Guthrie, B. (Co-investigator) & Underwood, I. (Co-investigator)
National Institute for Health Research
1/07/23 → 31/12/24
Project: Research