Conference proceeding
Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare
AMIA ... Annual Symposium proceedings, Vol.2022, pp.552-559
2022
PMCID: PMC10148365
PMID: 37128448
Abstract
Home healthcare (HHC) agencies provide care to more than 3.4 million adults per year. There is value in studying HHC narrative notes to identify patients at risk for deterioration. This study aimed to build machine learning algorithms to identify "concerning" narrative notes of HHC patients and identify emerging themes. Six algorithms were applied to narrative notes (n = 4,000) from a HHC agency to classify notes as either "concerning" or "not concerning." Topic modeling using Latent Dirichlet Allocation bag of words was conducted to identify emerging themes from the concerning notes. Gradient Boosted Trees demonstrated the best performance with a F-score = 0.74 and AUC = 0.96. Emerging themes were related to patient-clinician communication, HHC services provided, gait challenges, mobility concerns, wounds, and caregivers. Most themes have been cited by previous literature as increasing risk for adverse events. In the future, such algorithms can support early identification of patients at risk for deterioration.
Details
- Title: Subtitle
- Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare
- Creators
- Mollie Hobensack - Columbia UniversityJiyoun Song - Columbia UniversitySena Chae - University of IowaErin Kennedy - University of PennsylvaniaMaryam Zolnoori - Columbia UniversityKathryn H Bowles - University of PennsylvaniaMargaret V McDonald - Visiting Nurse Service of New YorkLauren Evans - Visiting Nurse Service of New YorkMaxim Topaz - Visiting Nurse Service of New York
- Resource Type
- Conference proceeding
- Publication Details
- AMIA ... Annual Symposium proceedings, Vol.2022, pp.552-559
- PMID
- 37128448
- PMCID
- PMC10148365
- eISSN
- 1942-597X
- Grant note
- R01 HS027742 / AHRQ HHS F31 NR019919 / NINR NIH HHS T32 NR007969 / NINR NIH HHS
- Language
- English
- Date published
- 2022
- Academic Unit
- Nursing
- Record Identifier
- 9984400756402771
Metrics
24 Record Views