title: Improving prediction of fall risk among nursing home residents using electronic medical records.
abstract: OBJECTIVE
  Falls are physically and financially costly, but may be preventable with targeted intervention. The Minimum Data Set (MDS) is one potential source of information on fall risk factors among nursing home residents, but its limited breadth and relatively infrequent updates may limit its practical utility. Richer, more frequently updated data from electronic medical records (EMRs) may improve ability to identify individuals at highest risk for falls.
  
  
  METHODS
  The authors applied a repeated events survival model to analyze MDS 3.0 and EMR data for 5129 residents in 13 nursing homes within a single large California chain that uses a centralized EMR system from a leading vendor. Estimated regression parameters were used to project resident fall probability. The authors examined the proportion of observed falls within each projected fall risk decile to assess improvements in predictive power from including EMR data.
  
  
  RESULTS
  In a model incorporating fall risk factors from the MDS only, 28.6% of observed falls occurred among residents in the highest projected risk decile. In an alternative specification incorporating more frequently updated measures for the same risk factors from the EMR data, 32.3% of observed falls occurred among residents in the highest projected risk decile, a 13% increase over the base MDS-only specification.
  
  
  CONCLUSIONS
  Incorporating EMR data improves ability to identify those at highest risk for falls relative to prediction using MDS data alone. These improvements stem chiefly from the greater frequency with which EMR data are updated, with minimal additional gains from availability of additional risk factor variables.
authors.affiliation: Abt Associates, U.S. Health Division, Bethesda, MD, USA Allison_Marier@abtassoc.com.
authors.forename: Allison
authors.initials: A
authors.lastname: Marier
authors.affiliation: Abt Associates, U.S. Health Division, Cambridge, MA, USA.
authors.forename: Lauren E W
authors.initials: LE
authors.lastname: Olsho
authors.affiliation: Abt Associates, U.S. Health Division, Cambridge, MA, USA.
authors.forename: William
authors.initials: W
authors.lastname: Rhodes
authors.affiliation: Agency for Healthcare Research & Quality, U.S. Department of Health & Human Services, Rockville, MD, USA.
authors.forename: William D
authors.initials: WD
authors.lastname: Spector
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data; D000368:Aged; D002140:California; D066264:Datasets as Topic; D057286:Electronic Health Records; D006707:Homes for the Aged; D006801:Humans; D062527:Meaningful Use; D009735:Nursing Homes; D012307:Risk Factors
keywords: electronic medical records; meaningful use; minimum data set 3.0; nursing home falls; prediction