title: Predicting improvement in urinary and bowel incontinence for home health patients using electronic health record data.
abstract: OBJECTIVE
  The purpose of the study was to discover which patient and support system characteristics and interventions documented by home health clinicians were associated with improvement in urinary and bowel incontinence contrasting logistic regression and data mining approaches.
  
  
  METHODS
  Seventeen hundred ninety-three patients in this study experienced 2072 episodes of care. The study sample comprised all nonmaternity patients aged 18 years or older receiving skilled home health services in 2004. Subjects were drawn from a convenience sample of 15 home health agencies .
  
  
  METHODS
  We completed a secondary analysis of data from 15 home health agencies' electronic health records. Data for this study were documented by home care clinicians using the Outcome and Assessment Information Set (OASIS) structured assessment form and the Omaha System interventions, which is a standardized terminology.
  
  
  RESULTS
  There were 684 patients with urinary incontinence and 187 with bowel incontinence. By discharge 38% improved in urinary incontinence and 45% improved their bowel incontinence. Using logistic regression, no patient or support system characteristics were identified that associated with improvement in either urinary or bowel incontinence, only a limited number of interventions were significant. A data mining decision tree was producible only for bowel incontinence, demonstrating a combination of patient and support system factors as well as selected interventions were important in determining whether patients would improve in bowel incontinence.
  
  
  CONCLUSIONS
  Home health patients have complex comorbid conditions requiring home care nurses to have broad, generalized knowledge. Future research is needed to determine if the inclusion of a certified WOC nurse would improve outcomes.
authors.affiliation: School of Nursing, University of Minnesota, 6-135 Weaver-Densford Hall, Minneapolis, MN 55455, USA. westr006@umn.edu
authors.forename: Bonnie L
authors.initials: BL
authors.lastname: Westra
authors.forename: Kay
authors.initials: K
authors.lastname: Savik
authors.forename: Cristina
authors.initials: C
authors.lastname: Oancea
authors.forename: Lynn
authors.initials: L
authors.lastname: Choromanski
authors.forename: John H
authors.initials: JH
authors.lastname: Holmes
authors.forename: Donna
authors.initials: D
authors.lastname: Bliss
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D057225:Data Mining / Q000379:methods; D003663:Decision Trees; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005242:Fecal Incontinence / Q000453:epidemiology / Q000451:nursing*; D005260:Female; D006699:Home Care Services / Q000458:organization & administration; D006801:Humans; D016015:Logistic Models; D008297:Male; D008910:Minnesota / Q000453:epidemiology; D009720:Nurse Clinicians; D024802:Nurse's Role; D009730:Nursing Assessment / Q000379:methods*; D015403:Nursing Evaluation Research; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011237:Predictive Value of Tests; D015995:Prevalence; D011379:Prognosis; D014549:Urinary Incontinence / Q000453:epidemiology / Q000451:nursing*