title: An information model for automated assessment of concordance between advance care preferences and care delivered near the end of life.
abstract: OBJECTIVE
  To develop an information model for automating evaluation of concordance between patient preferences and end-of-life care.
  
  
  METHODS
  We modeled and validated 15 end-of-life care preference option domains, to which we mapped preferences recorded in standardized advance care planning documents and 232 end-of-life care events defined by procedure and medication codes. Patient preferences and end-of-life care events were available in electronic health records. Data from Kaiser Permanente Southern California modeling and testing populations were evaluated for concordance between patients' preferences and the end-of-life care events they experienced.
  
  
  RESULTS
  The information model successfully assessed concordance between patient preferences and end-of-life care events. Among 388 expired patients in the modeling population, 4164 care events occurred, 4100 (98%) of which were preference-concordant, and 64 (2%) of which were preference-discordant. Including end-of-life care events that did not occur increased the number of observations to 6029; 99% were preference-concordant. At the level of individuals, 72% (278) of patients experienced only preference-concordant care events, 13% (50) experienced at least one preference-discordant care event, and 15% (60) experienced no preference-related care events.
  
  
  CONCLUSIONS
  Model limitations pertain to assumptions that are required to match advance care planning documents with patient preference options and exclusion of preferred care that did not occur. Further research is required to apply the model to larger populations and to investigate the need for additional preference options.
  
  
  CONCLUSIONS
  An information model for automating the assessment of the concordance between patients' advance care planning preferences and the end-of-life care they received was effective in a small population and has the potential to assess population-level preference-concordance on an ongoing basis.
authors.affiliation: Health Information Technology Transformation and Analytics, Kaiser Permanente, Portland OR 97210, USA marianne.c.turley@kp.org.
authors.forename: Marianne
authors.initials: M
authors.lastname: Turley
authors.affiliation: Southern California Permanente Medical Group, Los Angeles, CA 90027, USA.
authors.forename: Susan
authors.initials: S
authors.lastname: Wang
authors.affiliation: Health Information Technology Transformation and Analytics, Kaiser Permanente, Oakland, CA 94612, USA.
authors.forename: Di
authors.initials: D
authors.lastname: Meng
authors.affiliation: Southern California Permanente Medical Group, Pasadena, CA 91188, USA.
authors.forename: Michael H
authors.initials: MH
authors.lastname: Kanter
authors.affiliation: Health Information Technology Transformation and Analytics, Kaiser Permanente, Oakland, CA 94612, USA.
authors.forename: Terhilda
authors.initials: T
authors.lastname: Garrido
mesh_terms: D032722:Advance Care Planning; D057286:Electronic Health Records; D006801:Humans; D008962:Models, Theoretical; D057240:Patient Preference; D018802:Patient-Centered Care; D013727:Terminal Care
keywords: advance care planning; documentation; electronic health record; patient-centered care; population management