Fall Ascertainment and Development of a Risk Prediction Model Using Electronic Medical Records

OBJECTIVES To examine the use of electronic medical record (EMR) data to ascertain falls and develop a fall risk prediction model in an older population. DESIGN Retrospective longitudinal study using 10 years of EMR data (2004‐2014). A series of 3‐year cohorts included members continuously enrolled...

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Bibliographic Details
Published in:Journal of the American Geriatrics Society (JAGS) 2019-07, Vol.67 (7), p.1417-1422
Main Authors: Oshiro, Caryn E. S., Frankland, Timothy B., Rosales, A. Gabriela, Perrin, Nancy A., Bell, Christina L., Lo, Serena H. Y., Trinacty, Connie M.
Format: Article
Language:English
Subjects:
Accidental Falls - statistics & numerical data
adults
Aged
Aged, 80 and over
Anticonvulsants
Electronic Health Records
Electronic medical records
EMR
Falls
Female
Humans
Male
Mathematical models
Medical records
Mental depression
Middle Aged
Movement disorders
Neurodegenerative diseases
Osteoarthritis
Osteoporosis
Parkinson's disease
Population studies
Prediction models
Retrospective Studies
risk
Risk Assessment - methods
Risk Factors
Statistical analysis
Urinary incontinence
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Summary:OBJECTIVES To examine the use of electronic medical record (EMR) data to ascertain falls and develop a fall risk prediction model in an older population. DESIGN Retrospective longitudinal study using 10 years of EMR data (2004‐2014). A series of 3‐year cohorts included members continuously enrolled for a minimum of 3 years, requiring 2 years pre‐fall (no previous record of a fall) and a 1‐year fall risk period. SETTING Kaiser Permanente Hawaii, an ambulatory setting. PARTICIPANTS A total of 57 678 adults, age 60 years and older. MEASUREMENTS Initial EMR searches were guided by current literature and geriatricians to understand coding sources of falls as our outcome. Falls were captured by two coding sources: International Classification of Diseases, Ninth Revision (ICD‐9) codes (E880‐889) and/or a fall listed as a “primary reason for visit.” A comprehensive list of EMR predictors of falls were included into prediction models enabling statistical subset selection from many variables and modeling by logistic regression. RESULTS Although 72% of falls in the training data set were coded as “primary reason for visit,” 22% of falls were coded as ICD‐9 and 6% coded as both. About 80% were reported in face‐to‐face encounters (eg, emergency department). A total of 2164 individuals had a fall in the risk period. Using the 13 key predictors (age, comorbidities, female sex, other mental disorder, walking issues, Parkinson's disease, urinary incontinence, depression, polypharmacy, psychotropic and anticonvulsant medications, osteoarthritis, osteoporosis) identified through LASSO regression, the final model had a sensitivity of 67%, specificity of 69%, positive predictive value of 8%, negative predictive value of 98%, and area under the curve of .74. CONCLUSION This study demonstrated how the EMR can be used to ascertain falls and develop a fall risk prediction model with moderate sensitivity/specificity. Concurrent work with clinical providers to enhance fall documentation will improve the ability of the EMR to capture falls and consequently may improve the model to predict fall risk.
ISSN:0002-8614
1532-5415
DOI:10.1111/jgs.15872