title: Using EHR data to predict hospital-acquired pressure ulcers: a prospective study of a Bayesian Network model.
abstract: BACKGROUND
  Hospital-acquired pressure ulcers (HAPU) are common among inpatients and create substantial morbidity, mortality, and costs, but prevention strategies have been only variably effective.
  
  
  OBJECTIVE
  To develop and assess the impact of a decision support intervention to predict HAPU on the prevalence of ulcers and length of stay in an intensive care unit (ICU), and on the user adoption rate and attitudes.
  
  
  METHODS
  We compared the HAPU prevalence before and after introducing the intervention, and surveyed the users. We used a Bayesian Network model that was validated in previous studies and linked to the electronic health record system in an application called Pressure Ulcer (PU) Manager. The intervention group included 866 at-risk patients in the surgical ICUs of a tertiary teaching hospital over a 6-month period in 2009 and 2010; the controls were 348 patients from a 6-month baseline period in 2006 and 2007.
  
  
  RESULTS
  In the intervention group, the overall HAPU prevalence rate fell from 21% to 4.0% and the ICU length of stay shortened from 7.6 to 5.2 days. After adjustment for primary diagnoses and illness severity, the intervention group was significantly less likely than the baseline group to develop HAPU [odds ratio (OR)=0.1, p<0.0001] and had a shorter ICU length of stay (OR=0.67, p<0.0001). Data entry regarding ulcer severity and body site increased, and the participants used PU Manager more than once a day for over 80% of eligible cases. Attitudes toward PU Manager were positive.
  
  
  CONCLUSIONS
  This decision support approach reduced the prevalence of HAPU tenfold and the ICU length of stay by about one-third. Furthermore, the nurses had favorable attitudes toward using it.
authors.affiliation: Inha University, Incheon, Republic of Korea; Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: insook.cho@inha.ac.kr.
authors.forename: Insook
authors.initials: I
authors.lastname: Cho
authors.forename: Ihnsook
authors.initials: I
authors.lastname: Park
authors.forename: Eunman
authors.initials: E
authors.lastname: Kim
authors.forename: Eunjoon
authors.initials: E
authors.lastname: Lee
authors.forename: David W
authors.initials: DW
authors.lastname: Bates
mesh_terms: D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D057286:Electronic Health Records; D005260:Female; D006760:Hospitalization; D006784:Hospitals, Teaching; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D003668:Pressure Ulcer / Q000453:epidemiology / Q000209:etiology*; D015995:Prevalence; D011446:Prospective Studies; D056910:Republic of Korea / Q000453:epidemiology; D012307:Risk Factors
keywords: Clinical decision support; Electronic health records; Hospital-acquired pressure ulcers; Prevalence; Prevention