Journal article
Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model
Journal of the American Medical Informatics Association : JAMIA, Vol.30(10), pp.1622-1633
10/2023
DOI: 10.1093/jamia/ocad129
PMCID: PMC10531127
PMID: 37433577
Abstract
Abstract Objectives Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows. Materials and Methods We used data collected from 9362 patients from a large HHC agency. We iteratively developed risk models using both structured (eg, standard assessment tools, vital signs, visit characteristics) and unstructured data (eg, clinical notes). Seven specific sets of variables included: (1) the Outcome and Assessment Information Set, (2) vital signs, (3) visit characteristics, (4) rule-based natural language processing-derived variables, (5) term frequency-inverse document frequency variables, (6) Bio-Clinical Bidirectional Encoder Representations from Transformers variables, and (7) topic modeling. Risk models were developed for 18 time windows (1–15, 30, 45, and 60 days) before an ED visit or hospitalization. Risk prediction performances were compared using recall, precision, accuracy, F1, and area under the receiver operating curve (AUC). Results The best-performing model was built using a combination of all 7 sets of variables and the time window of 4 days before an ED visit or hospitalization (AUC = 0.89 and F1 = 0.69). Discussion and Conclusion This prediction model suggests that HHC clinicians can identify patients with HF at risk for visiting the ED or hospitalization within 4 days before the event, allowing for earlier targeted interventions.
Details
- Title: Subtitle
- Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model
- Creators
- Sena Chae - University of IowaAnahita DavoudiJiyoun Song - Columbia UniversityLauren EvansMollie Hobensack - Columbia UniversityKathryn H Bowles - University of PennsylvaniaMargaret V McDonaldYolanda BarrónSarah Collins Rossetti - Columbia UniversityKenrick Cato - University of PennsylvaniaSridevi SridharanMaxim Topaz - Columbia University
- Resource Type
- Journal article
- Publication Details
- Journal of the American Medical Informatics Association : JAMIA, Vol.30(10), pp.1622-1633
- DOI
- 10.1093/jamia/ocad129
- PMID
- 37433577
- PMCID
- PMC10531127
- NLM abbreviation
- J Am Med Inform Assoc
- ISSN
- 1067-5027
- eISSN
- 1527-974X
- Grant note
- DOI: 10.13039/100000133, name: Agency for Healthcare Research and Quality, award: R01 HS027742; DOI: 10.13039/100000056, name: National Institute for Nursing Research; name: Reducing Health Disparities, award: T32NR007969
- Language
- English
- Electronic publication date
- 07/11/2023
- Date published
- 10/2023
- Academic Unit
- Nursing
- Record Identifier
- 9984444765802771
Metrics
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