title: Patient-clinician concordance, face-time and access.
abstract: OBJECTIVE
  People in socially disadvantageous positions may receive less time with their clinicians and consequently reduced access to healthcare resources, potentially magnifying health disparities. Socio-cultural characteristics of clinicians and patients may influence the time spent together. The purpose of this paper is to explore the relationship between clinician/patient time and clinician and patient characteristics using real-time location systems (RTLS).
  
  
  METHODS
  In the MGH/MGPO Outpatient RFID (radio-frequency identification) project clinicians and patients wore RTLS tags during the workday to measure face-time (FT), the duration patients and clinicians are co-located, wait time (WT); i.e. from registration to clinical encounter and flow time (FLT) from registration to discharge. Demographic data were derived from the health system's electronic medical record (EMR). The RTLS and EMR data were synthesized and analyzed using standard structured-query language and statistical analytic methods.
  
  
  RESULTS
  From January 1, 2009 to January 1, 2011, 1,593 clinical encounters were associated with RTLS measured FTs, which differed with socioeconomic status and gender: women and lower income people received greater FT. WT was significantly longer for lower socioeconomic patients and for patients seeing trainee clinicians, women or majority ethnic group clinicians (Caucasian). FLT was shortest for men, higher socioeconomic status and for attending physician patients. Demographic concordance between patient and clinician did not significantly affect process times.
  
  
  CONCLUSIONS
  The study demonstrates the feasibility of using RTLS to capture clinically relevant process measures and suggests that the clinical delivery system surrounding a clinical encounter may more significantly influence access to clinician time than individual patient and clinician characteristics.
  
  
  CONCLUSIONS
  Applying RTLS to healthcare is coming. We can now successfully install and run these systems in healthcare settings and extract useful information from them. Interactions with the clinical delivery system are at least as important as interactions with clinicians for providing access to care: measure FT, WT and FLT with RTLS; link clinical behavior, e.g. FT, with patient characteristics; explore how individual characteristics interact with system behavior.
authors.forename: James E
authors.initials: JE
authors.lastname: Stahl
authors.forename: Mark A
authors.initials: MA
authors.lastname: Drew
authors.forename: Alexa Boer
authors.initials: AB
authors.lastname: Kimball
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D006302:Health Services Research; D006801:Humans; D008297:Male; D008875:Middle Aged; D011369:Professional-Patient Relations; D011787:Quality of Health Care / Q000706:statistics & numerical data*; D057233:Radio Frequency Identification Device; D012737:Sex Factors; D012959:Socioeconomic Factors; D013997:Time Factors; D014850:Waiting Lists; D057188:Workflow; D055815:Young Adult