title: Mining Patterns Associated With Mobility Outcomes in Home Healthcare.
abstract: BACKGROUND
  Mobility is critical for self-management. Understanding factors associated with improvement in mobility during home healthcare can help nurses tailor interventions to improve mobility outcomes and keep patients safely at home.
  
  
  OBJECTIVE
  The aims were to (a) identify patient and support system factors associated with mobility improvement during home care, (b) evaluate consistency of factors across groups defined by mobility status at the start of home care, and (c) identify patterns of factors associated with improvement and no improvement in mobility within each group.
  
  
  METHODS
  Outcome and Assessment Information Set data extracted from a national convenience sample of 270,634 patient records collected from October 1, 2008 to December 31, 2009 from 581 Medicare-certified, home healthcare agencies were used. Patients were placed into groups based on mobility scores at admission. Odds ratios were used to index associations of factors with improvement at discharge. Discriminative pattern mining was used to discover patterns associated with improvement of mobility.
  
  
  RESULTS
  Overall, mobility improved for 49.4% of patients; improvement occurred most frequently (80%) among patients who were able, at admission, to walk only with the supervision or assistance of another person at all times. Numerous factors associated with improvement in mobility outcome were similar across the groups (except for those who were chairfast but were able to wheel themselves independently); however, the number, strength, and direction of associations varied. In most groups, data mining-discovered patterns of factors associated with the mobility outcome were composed of combinations of functional and cognitive status and the type and amount of help required at home.
  
  
  CONCLUSIONS
  This study provides new data mining-based information about how factors associated with improvement in mobility group together and vary by mobility at admission. These approaches have potential to provide new insights for clinicians to tailor interventions for improvement of mobility.
authors.affiliation: Sanjoy Dey, BS, is PhD Student, Department of Computer Science and Engineering, University of Minnesota, Minneapolis. Jacob Cooner, MN, RN, is Graduate Student; and Connie W. Delaney, PhD, RN, FAAN, FACMI, is Professor and Dean, School of Nursing, University of Minnesota, Minneapolis. Joanna Fakhoury, is Undergraduate Student; and Vipin Kumar, PhD, is William Norris Professor and Head, Department of Computer Science and Engineering, University of Minnesota, Minneapolis. Gyorgy Simon, PhD, is Assistant Professor, Institute for Health Informatics, University of Minnesota, Minneapolis. Michael Steinbach, PhD, is Research Associate; and Jeremy Weed, is Undergraduate Student, Department of Computer Science and Engineering, University of Minnesota, Minneapolis. Bonnie L. Westra, PhD, RN, FAAN, FACMI, is Associate Professor, School of Nursing, University of Minnesota, Minneapolis.
authors.forename: Sanjoy
authors.initials: S
authors.lastname: Dey
authors.forename: Jacob
authors.initials: J
authors.lastname: Cooner
authors.forename: Connie W
authors.initials: CW
authors.lastname: Delaney
authors.forename: Joanna
authors.initials: J
authors.lastname: Fakhoury
authors.forename: Vipin
authors.initials: V
authors.lastname: Kumar
authors.forename: Gyorgy
authors.initials: G
authors.lastname: Simon
authors.forename: Michael
authors.initials: M
authors.lastname: Steinbach
authors.forename: Jeremy
authors.initials: J
authors.lastname: Weed
authors.forename: Bonnie L
authors.initials: BL
authors.lastname: Westra
mesh_terms: D000203:Activities of Daily Living; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D016000:Cluster Analysis; D057225:Data Mining; D016208:Databases, Factual; D005260:Female; D006699:Home Care Services; D006801:Humans; D008297:Male; D006278:Medicare; D008875:Middle Aged; D051346:Mobility Limitation; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D020127:Recovery of Function / Q000502:physiology; D012189:Retrospective Studies; D012307:Risk Factors; D014481:United States; D016138:Walking / Q000502:physiology*; D055815:Young Adult