title: Provider attributes correlation analysis to their referral frequency and awards.
abstract: BACKGROUND
  There has been a recent growth in health provider search portals, where patients specify filters-such as specialty or insurance-and providers are ranked by patient ratings or other attributes. Previous work has identified attributes associated with a provider's quality through user surveys. Other work supports that intuitive quality-indicating attributes are associated with a provider's quality.
  
  
  METHODS
  We adopt a data-driven approach to study how quality indicators of providers are associated with a rich set of attributes including medical school, graduation year, procedures, fellowships, patient reviews, location, and technology usage. In this work, we only consider providers as individuals (e.g., general practitioners) and not organizations (e.g., hospitals). As quality indicators, we consider the referral frequency of a provider and a peer-nominated quality designation. We combined data from the Centers for Medicare and Medicaid Services (CMS) and several provider rating web sites to perform our analysis.
  
  
  RESULTS
  Our data-driven analysis identified several attributes that correlate with and discriminate against referral volume and peer-nominated awards. In particular, our results consistently demonstrate that these attributes vary by locality and that the frequency of an attribute is more important than its value (e.g., the number of patient reviews or hospital affiliations are more important than the average review rating or the ranking of the hospital affiliations, respectively). We demonstrate that it is possible to build accurate classifiers for referral frequency and quality designation, with accuracies over 85 %.
  
  
  CONCLUSIONS
  Our findings show that a one-size-fits-all approach to ranking providers is inadequate and that provider search portals should calibrate their ranking function based on location and specialty. Further, traditional filters of provider search portals should be reconsidered, and patients should be aware of existing pitfalls with these filters and educated on local factors that affect quality. These findings enable provider search portals to empower patients and to "load balance" patients between younger and older providers.
authors.affiliation: Department of Computer Science and Engineering, University of California, Riverside, CA, USA. mwile001@ucr.edu.|SmartDocFinder LLC, 3499 10th Street, Riverside, CA, USA. mwile001@ucr.edu.
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Wiley
authors.affiliation: Department of Computer Science and Engineering, University of California, Riverside, CA, USA.
authors.forename: Ryan L
authors.initials: RL
authors.lastname: Rivas
authors.affiliation: Department of Computer Science and Engineering, University of California, Riverside, CA, USA.
authors.forename: Vagelis
authors.initials: V
authors.lastname: Hristidis
mesh_terms: D000368:Aged; D016208:Databases, Factual; D005260:Female; D058005:General Practitioners; D006761:Hospitals; D006801:Humans; D008297:Male; D006278:Medicare; D008875:Middle Aged; D019984:Quality Indicators, Health Care; D012017:Referral and Consultation / Q000706:statistics & numerical data*; D014481:United States
keywords: Consumer health informatics; Patient decision making; Provider attributes; Provider quality designation; Provider referrals; Referral frequency correlation