Appl Clin Inform 2019; 10(01): 001-009
DOI: 10.1055/s-0038-1676587
Research Article
Georg Thieme Verlag KG Stuttgart · New York

CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support

Barbara E. Jones
1   VA Salt Lake City IDEAS Center, VA Salt Lake City Healthcare System, Salt Lake City, Utah, United States
2   Division of Pulmonary and Critical Care Medicine, University of Utah, Salt Lake City, Utah, United States
,
Dave S. Collingridge
3   Intermountain Healthcare, Murray, Utah, United States
,
Caroline G. Vines
3   Intermountain Healthcare, Murray, Utah, United States
,
Herman Post
4   Homer Warner Center for Informatics, Intermountain Healthcare, Murray, Utah, United States
,
John Holmen
4   Homer Warner Center for Informatics, Intermountain Healthcare, Murray, Utah, United States
,
Todd L. Allen
5   Department of Emergency Medicine, Intermountain Healthcare, Murray, Utah, United States
,
Peter Haug
4   Homer Warner Center for Informatics, Intermountain Healthcare, Murray, Utah, United States
,
Charlene R. Weir
6   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
,
Nathan C. Dean
7   Division of Pulmonary and Critical Care Medicine, Intermountain Healthcare and University of Utah, Murray, Utah, United States
› Author Affiliations
Funding This work was supported by Intermountain Healthcare and the Intermountain Research and Medical Foundation. The Research Electronic Data Capture (REDCap) tool is funded by a grant from the National Institutes of Health (CTSA 3UL1RR025764–02). Dr. Jones is funded by a career development award from the Veterans Affairs Health Services Research & Development (# IK2HX001908).
Further Information

Publication History

30 August 2018

09 November 2018

Publication Date:
02 January 2019 (online)

Abstract

Background Local implementation of guidelines for pneumonia care is strongly recommended, but the context of care that affects implementation is poorly understood. In a learning health care system, computerized clinical decision support (CDS) provides an opportunity to both improve and track practice, providing insights into the implementation process.

Objectives This article examines physician interactions with a CDS to identify reasons for rejection of guideline recommendations.

Methods We implemented a multicenter bedside CDS for the emergency department management of pneumonia that integrated patient data with guideline-based recommendations. We examined the frequency of adoption versus rejection of recommendations for site-of-care and antibiotic selection. We analyzed free-text responses provided by physicians explaining their clinical reasoning for rejection, using concept mapping and thematic analysis.

Results Among 1,722 patient episodes, physicians rejected recommendations to send a patient home in 24%, leaving text in 53%; reasons for rejection of the recommendations included additional or alternative diagnoses beyond pneumonia, and comorbidities or signs of physiologic derangement contributing to risk of outpatient failure that were not processed by the CDS. Physicians rejected broad-spectrum antibiotic recommendations in 10%, leaving text in 76%; differences in pathogen risk assessment, additional patient information, concern about antibiotic properties, and admitting physician preferences were given as reasons for rejection.

Conclusion While adoption of CDS recommendations for pneumonia was high, physicians rejecting recommendations frequently provided feedback, reporting alternative diagnoses, additional individual patient characteristics, and provider preferences as major reasons for rejection. CDS that collects user feedback is feasible and can contribute to a learning health system.

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed and approved by the Intermountain Healthcare Institutional Review Board (IRB #1017598). Implied consent was obtained from all surveyed physicians by completion of the survey, and was approved by the IRB; waiver of consent was approved by the IRB for tool data collection.


Supplementary Material

 
  • References

  • 1 Micek ST, Lang A, Fuller BM, Hampton NB, Kollef MH. Clinical implications for patients treated inappropriately for community-acquired pneumonia in the emergency department. BMC Infect Dis 2014; 14: 61
  • 2 Frei CR, Attridge RT, Mortensen EM. , et al. Guideline-concordant antibiotic use and survival among patients with community-acquired pneumonia admitted to the intensive care unit. Clin Ther 2010; 32 (02) 293-299
  • 3 Aliberti S, Faverio P, Blasi F. Hospital admission decision for patients with community-acquired pneumonia. Curr Infect Dis Rep 2013; 15 (02) 167-176
  • 4 Mandell LA, Wunderink RG, Anzueto A. , et al; Infectious Diseases Society of America; American Thoracic Society. Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis 2007; 44 (Suppl. 02) S27-S72
  • 5 Bunce AE, Gold R, Davis JV. , et al. “Salt in the Wound”: safety net clinician perspectives on performance feedback derived from EHR data. J Ambul Care Manage 2017; 40 (01) 26-35
  • 6 Capp R, Chang Y, Brown DF. Effective antibiotic treatment prescribed by emergency physicians in patients admitted to the intensive care unit with severe sepsis or septic shock: where is the gap?. J Emerg Med 2011; 41 (06) 573-580
  • 7 Jenkins TC, Stella SA, Cervantes L. , et al. Targets for antibiotic and healthcare resource stewardship in inpatient community-acquired pneumonia: a comparison of management practices with National Guideline Recommendations. Infection 2013; 41 (01) 135-144
  • 8 Jones BE, Brown KA, Jones MM. , et al. Variation in empiric coverage versus detection of methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa in hospitalizations for community-onset pneumonia across 128 US Veterans Affairs medical centers. Infect Control Hosp Epidemiol 2017; 38 (08) 937-944
  • 9 Busby J, Purdy S, Hollingworth W. A systematic review of the magnitude and cause of geographic variation in unplanned hospital admission rates and length of stay for ambulatory care sensitive conditions. BMC Health Serv Res 2015; 15: 324
  • 10 Dean NC, Jones JP, Aronsky D. , et al. Hospital admission decision for patients with community-acquired pneumonia: variability among physicians in an emergency department. Ann Emerg Med 2012; 59 (01) 35-41
  • 11 Eddy DM. Variations in physician practice: the role of uncertainty. Health Aff (Millwood) 1984; 3 (02) 74-89
  • 12 Halm EA, Atlas SJ, Borowsky LH. , et al. Understanding physician adherence with a pneumonia practice guideline: effects of patient, system, and physician factors. Arch Intern Med 2000; 160 (01) 98-104
  • 13 Committee on the Learning Health Care System in America, Institute of Medicine; Smith M, Saunders R, Stuckhardt L, McGinnis JM, eds. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington (DC): National Academies Press (US);2013
  • 14 Garg AX, Adhikari NK, McDonald H. , et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293 (10) 1223-1238
  • 15 Vines C, Dean NC. Technology implementation impacting the outcomes of patients with CAP. Semin Respir Crit Care Med 2012; 33 (03) 292-297
  • 16 Kellermann AL, Jones SS. What it will take to achieve the as-yet-unfulfilled promises of health information technology. Health Aff (Millwood) 2013; 32 (01) 63-68
  • 17 Dean NC, Jones BE, Jones JP. , et al. Impact of an electronic clinical decision support tool for emergency department patients with pneumonia. Ann Emerg Med 2015; 66 (05) 511-520
  • 18 Marrie TJ, Lau CY, Wheeler SL, Wong CJ, Vandervoort MK, Feagan BG. A controlled trial of a critical pathway for treatment of community-acquired pneumonia. CAPITAL Study Investigators. Community-Acquired Pneumonia Intervention Trial Assessing Levofloxacin. JAMA 2000; 283 (06) 749-755
  • 19 Jones BE, Jones JP, Vines CG, Dean NC. Validating hospital admission criteria for decision support in pneumonia. BMC Pulm Med 2014; 14: 149
  • 20 Evans RS, Lloyd JF, Pierce LA. Clinical use of an enterprise data warehouse. AMIA Annu Symp Proc 2012; 2012: 189-198
  • 21 Dean NC, Silver MP, Bateman KA, James B, Hadlock CJ, Hale D. Decreased mortality after implementation of a treatment guideline for community-acquired pneumonia. Am J Med 2001; 110 (06) 451-457
  • 22 Dean NC, Suchyta MR, Bateman KA, Aronsky D, Hadlock CJ. Implementation of admission decision support for community-acquired pneumonia. Chest 2000; 117 (05) 1368-1377
  • 23 Lanspa MJ, Jones BE, Brown SM, Dean NC. Mortality, morbidity, and disease severity of patients with aspiration pneumonia. J Hosp Med 2013; 8 (02) 83-90
  • 24 Jones BE, Jones J, Bewick T. , et al. CURB-65 pneumonia severity assessment adapted for electronic decision support. Chest 2011; 140 (01) 156-163
  • 25 Brown SM, Jones BE, Jephson AR, Dean NC. ; Infectious Disease Society of America/American Thoracic Society 2007. Validation of the Infectious Disease Society of America/American Thoracic Society 2007 guidelines for severe community-acquired pneumonia. Crit Care Med 2009; 37 (12) 3010-3016
  • 26 Aujesky D, McCausland JB, Whittle J, Obrosky DS, Yealy DM, Fine MJ. Reasons why emergency department providers do not rely on the pneumonia severity index to determine the initial site of treatment for patients with pneumonia. Clin Infect Dis 2009; 49 (10) e100-e108
  • 27 Schouten JA, Hulscher ME, Natsch S, Kullberg BJ, van der Meer JWM, Grol RPTM. Barriers to optimal antibiotic use for community-acquired pneumonia at hospitals: a qualitative study. Qual Saf Health Care 2007; 16 (02) 143-149
  • 28 Trochim W, Kane M. Concept mapping: an introduction to structured conceptualization in health care. Int J Qual Health Care 2005; 17 (03) 187-191
  • 29 Rogers EM. Diffusion of Innovations. New York: Free Press; 2003
  • 30 Yoshida E, Fei S, Bavuso K, Lagor C, Maviglia S. The value of monitoring clinical decision support interventions. Appl Clin Inform 2018; 9 (01) 163-173
  • 31 Kassakian SZ, Yackel TR, Gorman PN, Dorr DA. Clinical decisions support malfunctions in a commercial electronic health record. Appl Clin Inform 2017; 8 (03) 910-923
  • 32 Sahota N, Lloyd R, Ramakrishna A. , et al; CCDSS Systematic Review Team. Computerized clinical decision support systems for acute care management: a decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. Implement Sci 2011; 6: 91
  • 33 Coiera E. Technology, cognition and error. BMJ Qual Saf 2015; 24 (07) 417-422
  • 34 Bennett P, Hardiker NR. The use of computerized clinical decision support systems in emergency care: a substantive review of the literature. J Am Med Inform Assoc 2017; 24 (03) 655-668
  • 35 Ballard DW, Vemula R, Chettipally UK. , et al; KP CREST Network Investigators. Optimizing clinical decision support in the electronic health record. Clinical characteristics associated with the use of a decision tool for disposition of ED patients with pulmonary embolism. Appl Clin Inform 2016; 7 (03) 883-898
  • 36 Zhang J, Walji MF. TURF: toward a unified framework of EHR usability. J Biomed Inform 2011; 44 (06) 1056-1067
  • 37 Graham TA, Kushniruk AW, Bullard MJ, Holroyd BR, Meurer DP, Rowe BH. How usability of a web-based clinical decision support system has the potential to contribute to adverse medical events. AMIA Annu Symp Proc 2008; 2008: 257-261
  • 38 Miller A, Moon B, Anders S, Walden R, Brown S, Montella D. Integrating computerized clinical decision support systems into clinical work: a meta-synthesis of qualitative research. Int J Med Inform 2015; 84 (12) 1009-1018
  • 39 Werth GR, Connelly DP. Continuous quality improvement and medical informatics: the convergent synergy. Proc Annu Symp Comput Appl Med Care 1992; 631-635
  • 40 Chow AL, Lye DC, Arah OA. Patient and physician predictors of patient receipt of therapies recommended by a computerized decision support system when initially prescribed broad-spectrum antibiotics: a cohort study. J Am Med Inform Assoc 2016; 23 (e1): e58-e70
  • 41 Gundlapalli AV, Carter ME, Divita G. , et al. Extracting concepts related to homelessness from the free text of VA electronic medical records. AMIA Annu Symp Proc 2014; 2014: 589-598
  • 42 Gundlapalli AV, Redd A, Carter M. , et al. Validating a strategy for psychosocial phenotyping using a large corpus of clinical text. J Am Med Inform Assoc 2013; 20 (e2): e355-e364
  • 43 Welker JA, Huston M, McCue JD. Antibiotic timing and errors in diagnosing pneumonia. Arch Intern Med 2008; 168 (04) 351-356
  • 44 Kanwar M, Brar N, Khatib R, Fakih MG. Misdiagnosis of community-acquired pneumonia and inappropriate utilization of antibiotics: side effects of the 4-h antibiotic administration rule. Chest 2007; 131 (06) 1865-1869
  • 45 Jones BE, Jones M, Xi Z. , et al. The “Working” Diagnosis: Changes in the Pneumonia Diagnosis Among Hospitalized Veterans. Society for Medical Decision-Making Annual Conference; 2015 . Available at: https://smdm.confex.com/smdm/2015mo/webprogram/Paper9321.html . Accessed November 28, 2018
  • 46 Nisbett RE, Wilson TD. Telling more than we can know - verbal reports on mental processes. Psychol Rev 1977; 84 (03) 231-259
  • 47 Bradburn NM, Sudman S, Wansink B. Asking Questions: The Definitive Guide to Questionnaire Design–For Market Research, Political Polls, and Social and Health Questionnaires. Revised edition. San Francisco, CA: Jossey-Bass; 2004