Dataset Information

Developing Methods of Repurposing Electronic Health Record Data for Identification of Older Adults at Risk of Unintentional Falls.

ABSTRACT: Nationally, nearly 40 percent of community-dwelling adults age 65 and older fall at least once a year, making unintentional falls the leading cause of both fatal and nonfatal injuries among this age group. Addressing this public health problem in primary care offers promise. However, challenges in incorporating fall risk screening into primary care result in a problem of missed opportunities for screening, counseling, intervention, and ultimately prevention. Given these barriers, this study examines the potential for the innovative use of routinely collected electronic health record data to provide enhanced clinical decision support in busy, often resource-thin primary care environments. Using de-identified data from a sample of West Virginia primary care centers, we find that it is both feasible and worthwhile to repurpose routinely collected data for the purpose of identification of older adults at risk of falls. Searching of both free-text and semistructured data was particularly valuable.

SUBMITTER: Baus A

PROVIDER: S-EPMC4832126 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Developing Methods of Repurposing Electronic Health Record Data for Identification of Older Adults at Risk of Unintentional Falls.

Baus Adam A Zullig Keith K Long Dustin D Mullett Charles C Pollard Cecil C Taylor Henry H Coben Jeffrey J

Perspectives in health information management 20160401

Nationally, nearly 40 percent of community-dwelling adults age 65 and older fall at least once a year, making unintentional falls the leading cause of both fatal and nonfatal injuries among this age group. Addressing this public health problem in primary care offers promise. However, challenges in incorporating fall risk screening into primary care result in a problem of missed opportunities for screening, counseling, intervention, and ultimately prevention. Given these barriers, this study exam ...[more]

PMID: 27134607

Similar Datasets

Project description:BackgroundFalls in older adults remain a pressing health concern. With advancements in data analytics and increasing uptake of electronic health records, developing comprehensive predictive models for fall risk is now possible. We aimed to systematically identify studies involving the development and implementation of predictive falls models which used routinely collected electronic health record data in home-based, community and residential aged care settings.MethodsA systematic search of entries in Cochrane Library, CINAHL, MEDLINE, Scopus, and Web of Science was conducted in July 2020 using search terms relevant to aged care, prediction, and falls. Selection criteria included English-language studies, published in peer-reviewed journals, had an outcome of falls, and involved fall risk modelling using routinely collected electronic health record data. Screening, data extraction and quality appraisal using the Critical Appraisal Skills Program for Clinical Prediction Rule Studies were conducted. Study content was synthesised and reported narratively.ResultsFrom 7,329 unique entries, four relevant studies were identified. All predictive models were built using different statistical techniques. Predictors across seven categories were used: demographics, assessments of care, fall history, medication use, health conditions, physical abilities, and environmental factors. Only one of the four studies had been validated externally. Three studies reported on the performance of the models.ConclusionsAdopting predictive modelling in aged care services for adverse events, such as falls, is in its infancy. The increased availability of electronic health record data and the potential of predictive modelling to document fall risk and inform appropriate interventions is making use of such models achievable. Having a dynamic prediction model that reflects the changing status of an aged care client is key to this moving forward for fall prevention interventions.

Dataset Information

Developing Methods of Repurposing Electronic Health Record Data for Identification of Older Adults at Risk of Unintentional Falls.

Publications

Developing Methods of Repurposing Electronic Health Record Data for Identification of Older Adults at Risk of Unintentional Falls.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets