Literature DB >> 27002075

Is the problem list in the eye of the beholder? An exploration of consistency across physicians.

John C Krauss1, Philip S Boonstra2, Anna V Vantsevich3, Charles P Friedman4.   

Abstract

OBJECTIVE: Quantify the variability of patients' problem lists - in terms of the number, type, and ordering of problems - across multiple physicians and assess physicians' criteria for organizing and ranking diagnoses.
MATERIALS AND METHODS: In an experimental setting, 32 primary care physicians generated and ordered problem lists for three identical complex internal medicine cases expressed as detailed 2- to 4-page abstracts and subsequently expressed their criteria for ordering items in the list. We studied variability in problem list length. We modified a previously validated rank-based similarity measure, with range of zero to one, to quantify agreement between pairs of lists and calculate a single consensus problem list that maximizes agreement with each physician. Physicians' reasoning for the ordering of the problem lists was recorded.
RESULTS: Subjects' problem lists were highly variable. The median problem list length was 8 (range: 3-14) for Case A, 10 (range: 4-20) for Case B, and 7 (range: 3-13) for Case C. The median indices of agreement - taking into account the length, content, and order of lists - over all possible physician pairings was 0.479, 0.371, 0.509, for Cases A, B, and C, respectively. The median agreements between the physicians' lists and the consensus list for each case were 0.683, 0.581, and 0.697 (for Cases A, B, and C, respectively).Out of a possible 1488 pairings, 2 lists were identical. Physicians most frequently ranked problem list items based on their acuity and immediate threat to health.
CONCLUSIONS: The problem list is a physician's mental model of a patient's health status. These mental models were found to vary significantly between physicians, raising questions about whether problem lists created by individual physicians can serve their intended purpose to improve care coordination.
© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  coordination of patient care; electronic health record; medical problem list; ranked lists; ranked-biased overlap

Mesh:

Year:  2016        PMID: 27002075      PMCID: PMC4997039          DOI: 10.1093/jamia/ocv211

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  26 in total

1.  Testing Three Problem List Terminologies in a simulated data entry environment.

Authors:  Kin Wah Fung; Junchuan Xu; S Trent Rosenbloom; David Mohr; Naveen Maram; Thomas Suther
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

2.  Comparative analysis of the VA/Kaiser and NLM CORE problem subsets: an empirical study based on problem frequency.

Authors:  Adam Wright; Joshua Feblowitz; Allison B McCoy; Dean F Sittig
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

3.  Can SNOMED CT fulfill the vision of a compositional terminology? Analyzing the use case for problem list.

Authors:  James R Campbell; Junchuan Xu; Kin Wah Fung
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

4.  EHRs in a web 2.0 world: time to embrace a problem-list Wiki.

Authors:  Neil Mehta; Nirav Vakharia; Adam Wright
Journal:  J Gen Intern Med       Date:  2014-03       Impact factor: 5.128

5.  The problem list beyond meaningful use. Part 2: fixing the problem list.

Authors:  Casey Holmes
Journal:  J AHIMA       Date:  2011-03

6.  Provider use of and attitudes towards an active clinical alert: a case study in decision support.

Authors:  J Feblowitz; S Henkin; J Pang; H Ramelson; L Schneider; F L Maloney; A R Wilcox; D W Bates; A Wright
Journal:  Appl Clin Inform       Date:  2013-03-27       Impact factor: 2.342

7.  Medical records that guide and teach.

Authors:  L L Weed
Journal:  N Engl J Med       Date:  1968-03-14       Impact factor: 91.245

8.  Computerized physician order entry of medications and clinical decision support can improve problem list documentation compliance.

Authors:  William L Galanter; Daniel B Hier; Chiang Jao; David Sarne
Journal:  Int J Med Inform       Date:  2008-07-02       Impact factor: 4.046

9.  Clinician attitudes toward and use of electronic problem lists: a thematic analysis.

Authors:  Adam Wright; Francine L Maloney; Joshua C Feblowitz
Journal:  BMC Med Inform Decis Mak       Date:  2011-05-25       Impact factor: 2.796

10.  Abstracts of the Society of General Internal Medicine 33rd Annual Meeting. Minneapolis, Minnesota, USA. April 28-May 1, 2010.

Authors: 
Journal:  J Gen Intern Med       Date:  2010-06       Impact factor: 5.128

View more
  7 in total

1.  Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.

Authors:  Timothy I Kennell; James H Willig; James J Cimino
Journal:  Appl Clin Inform       Date:  2017-12-21       Impact factor: 2.342

2.  Characterizing the Utilization of the Problem List for Pediatric Trauma Care.

Authors:  Ashimiyu B Durojaiye; Nicolette McGerorge; Webster Kristen; Cagla Oruc; James C Fackler; Ayse P Gurses
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

3.  Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data*. An Agile Framework.

Authors:  Vaishnavi Kannan; Jason S Fish; Jacqueline M Mutz; Angela R Carrington; Ki Lai; Lisa S Davis; Josh E Youngblood; Mark R Rauschuber; Kathryn A Flores; Evan J Sara; Deepa G Bhat; DuWayne L Willett
Journal:  Methods Inf Med       Date:  2017-06-14       Impact factor: 2.176

4.  Electronic Health Records in Ophthalmology: Source and Method of Documentation.

Authors:  Bradley S Henriksen; Isaac H Goldstein; Adam Rule; Abigail E Huang; Haley Dusek; Austin Igelman; Michael F Chiang; Michelle R Hribar
Journal:  Am J Ophthalmol       Date:  2019-12-05       Impact factor: 5.258

5.  Primary care physicians' electronic health record proficiency and efficiency behaviors and time interacting with electronic health records: a quantile regression analysis.

Authors:  Oliver T Nguyen; Kea Turner; Nate C Apathy; Tanja Magoc; Karim Hanna; Lisa J Merlo; Christopher A Harle; Lindsay A Thompson; Eta S Berner; Sue S Feldman
Journal:  J Am Med Inform Assoc       Date:  2022-01-29       Impact factor: 4.497

6.  Recording problems and diagnoses in clinical care: developing guidance for healthcare professionals and system designers.

Authors:  Anoop Dinesh Shah; Nicola J Quinn; Afzal Chaudhry; Ralph Sullivan; Julian Costello; Dermot O'Riordan; Jan Hoogewerf; Martin Orton; Lorraine Foley; Helene Feger; John G Williams
Journal:  BMJ Health Care Inform       Date:  2019-12

7.  Two algorithms for the reorganisation of the problem list by organ system.

Authors:  Daniel B Hier; Joshua Pearson
Journal:  BMJ Health Care Inform       Date:  2019-12
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.