title: Uncovering Hospitalists' Information Needs from Outside Healthcare Facilities in the Context of Health Information Exchange Using Association Rule Learning.
abstract: BACKGROUND
  Important barriers to health information exchange (HIE) adoption are clinical workflow disruptions and troubles with the system interface. Prior research suggests that HIE interfaces providing faster access to useful information may stimulate use and reduce barriers for adoption; however, little is known about informational needs of hospitalists.
  
  
  OBJECTIVE
  To study the association between patient health problems and the type of information requested from outside healthcare providers by hospitalists of a tertiary care hospital.
  
  
  METHODS
  We searched operational data associated with fax-based exchange of patient information (previous HIE implementation) between hospitalists of an internal medicine department in a large urban tertiary care hospital in Florida, and any other affiliated and unaffiliated healthcare provider. All hospitalizations from October 2011 to March 2014 were included in the search. Strong association rules between health problems and types of information requested during each hospitalization were discovered using Apriori algorithm, which were then validated by a team of hospitalists of the same department.
  
  
  RESULTS
  Only 13.7% (2 089 out of 15 230) of the hospitalizations generated at least one request of patient information to other providers. The transactional data showed 20 strong association rules between specific health problems and types of information exist. Among the 20 rules, for example, abdominal pain, chest pain, and anaemia patients are highly likely to have medical records and outside imaging results requested. Other health conditions, prone to have records requested, were lower urinary tract infection and back pain patients.
  
  
  CONCLUSIONS
  The presented list of strong co-occurrence of health problems and types of information requested by hospitalists from outside healthcare providers not only informs the implementation and design of HIE, but also helps to target future research on the impact of having access to outside information for specific patient cohorts. Our data-driven approach helps to reduce the typical biases of qualitative research.
authors.affiliation: Johns Hopkins University, Emergency Medicine , Baltimore, MD, United States.
authors.forename: D A
authors.initials: DA
authors.lastname: Martinez
authors.affiliation: Politecnico di Milano, Dipartimento di Ingegneria Gestionale, Milan, Italy.
authors.forename: E
authors.initials: E
authors.lastname: Mora
authors.affiliation: Politecnico di Milano, Dipartimento di Ingegneria Gestionale, Milan, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Gemmani
authors.affiliation: University of South Florida, Industrial and Management Systems Engineering , Tampa, FL, United States.
authors.forename: J
authors.initials: J
authors.lastname: Zayas-Castro
mesh_terms: D002947:Cities; D057225:Data Mining / Q000379:methods*; D004282:Documentation; D066275:Health Information Exchange / Q000706:statistics & numerical data*; D006801:Humans; D000069550:Machine Learning; D020380:Needs Assessment; D062606:Tertiary Care Centers
keywords: Health information exchange; medical decision making; medical informatics applications; medical record linkage; patient handoff