Download PDF
Appl Clin Inform 2018; 09(02): 248-260
DOI: 10.1055/s-0038-1641594
Research Article
Schattauer GmbH Stuttgart

Examining Workflow in a Pediatric Emergency Department to Develop a Clinical Decision Support for an Antimicrobial Stewardship Program

Mustafa Ozkaynak
1   College of Nursing, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States
,
Danny T. Y. Wu
2   Department of Biomedical Informatics and Pediatrics, University of Cincinnati, Cincinnati, Ohio, United States
,
Katia Hannah
3   Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States
,
Peter S. Dayan
4   Division of Pediatric Emergency Medicine, Department of Pediatrics, Columbia University College of Physicians and Surgeons, New York, New York, United States
,
Rakesh D. Mistry
5   Section of Emergency Medicine, Department of Pediatrics and Emergency Medicine, School of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States
› Author Affiliations Funding This study was funded by grants from Emergency Medicine Foundation, University of Colorado School of Medicine and Children's Hospital Colorado.
Further Information

Publication History

02 October 2017

21 February 2018

Publication Date:
11 April 2018 (online)

Also available at
   Permissions and Reprints

Abstract

Background Clinical decision support (CDS) embedded into the electronic health record (EHR), is a potentially powerful tool for institution of antimicrobial stewardship programs (ASPs) in emergency departments (EDs). However, design and implementation of CDS systems should be informed by the existing workflow to ensure its congruence with ED practice, which is characterized by erratic workflow, intermittent computer interactions, and variable timing of antibiotic prescription.

Objective This article aims to characterize ED workflow for four provider types, to guide future design and implementation of an ED-based ASP using the EHR.

Methods Workflow was systematically examined in a single, tertiary-care academic children's hospital ED. Clinicians with four roles (attending, nurse practitioner, physician assistant, resident) were observed over a 3-month period using a tablet computer-based data collection tool. Structural observations were recorded by investigators, and classified using a predetermined set of activities. Clinicians were queried regarding timing of diagnosis and disposition decision points.

Results A total of 23 providers were observed for 90 hours. Sixty-four different activities were captured for a total of 6,060 times. Among these activities, nine were conducted at different frequency or time allocation across four roles. Moreover, we identified differences in sequential patterns across roles. Decision points, whereby clinicians then proceeded with treatment, were identified 127 times. The most common decision points identified were: (1) after/during examining or talking to patient or relative; (2) after talking to a specialist; and (3) after diagnostic test/image was resulted and discussed with patient/family.

Conclusion The design and implementation of CDS for ASP should support clinicians in various provider roles, despite having different workflow patterns. The clinicians make their decisions about treatment at different points of overall care delivery practice; likewise, the CDS should also support decisions at different points of care.

Keywords

clinical decision support systems - workflow - emergency department - antimicrobial stewardship

Protection of Human and Animal Subjects

This study was approved as a quality improvement activity by the Organizational Research Risk and Quality Improvement Review Panel, under agreement with the local Institutional Review Board.


 

  • References

  • 1 Shaw B, Cheater F, Baker R. , et al. Tailored interventions to overcome identified barriers to change: effects on professional practice and health care outcomes. Cochrane Database Syst Rev 2005; (03) CD005470
    PubMedGoogle Scholar
  • 2 Ozkaynak M, Brennan PF. Characterizing patient care in hospital emergency departments. Health Syst (Basingstoke) 2012; 1 (02) 104-117
    CrossrefPubMedGoogle Scholar
  • 3 Ozkaynak M, Dziadkowiec O, Mistry R. , et al. Characterizing workflow for pediatric asthma patients in emergency departments using electronic health records. J Biomed Inform 2015; 57: 386-398
    CrossrefPubMedGoogle Scholar
  • 4 Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med 2002; 9 (11) 1184-1204
    CrossrefPubMedGoogle Scholar
  • 5 Goldberg RM, Kuhn G, Andrew LB, Thomas Jr HA. Coping with medical mistakes and errors in judgment. Ann Emerg Med 2002; 39 (03) 287-292
    CrossrefPubMedGoogle Scholar
  • 6 May L, Cosgrove S, L'Archeveque M. , et al. A call to action for antimicrobial stewardship in the emergency department: approaches and strategies. Ann Emerg Med 2013; 62 (01) 69-77
    CrossrefPubMedGoogle Scholar
  • 7 Schenkel S. Promoting patient safety and preventing medical error in emergency departments. Acad Emerg Med 2000; 7 (11) 1204-1222
    CrossrefPubMedGoogle Scholar
  • 8 Shiffman RN, Michel G, Essaihi A, Thornquist E. Bridging the guideline implementation gap: a systematic, document-centered approach to guideline implementation. J Am Med Inform Assoc 2004; 11 (05) 418-426
    CrossrefPubMedGoogle Scholar
  • 9 Sim I, Gorman P, Greenes RA. , et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 2001; 8 (06) 527-534
    CrossrefPubMedGoogle Scholar
  • 10 Sirajuddin AM, Osheroff JA, Sittig DF, Chuo J, Velasco F, Collins DA. Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support. Effective CDS is essential for addressing healthcare performance improvement imperatives. J Healthc Inf Manag 2009; 23 (04) 38-45
    PubMedGoogle Scholar
  • 11 Waitman LR, Miller RA. Pragmatics of implementing guidelines on the front lines. J Am Med Inform Assoc 2004; 11 (05) 436-438
    CrossrefPubMedGoogle Scholar
  • 12 Barlam TF, Cosgrove SE, Abbo LM. , et al. Executive summary: implementing an antibiotic stewardship program: guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America. Clin Infect Dis 2016; 62 (10) 1197-1202
    CrossrefPubMedGoogle Scholar
  • 13 Kullar R, Goff DA. Transformation of antimicrobial stewardship programs through technology and informatics. Infect Dis Clin North Am 2014; 28 (02) 291-300
    CrossrefPubMedGoogle Scholar
  • 14 Kullar R, Goff DA, Schulz LT, Fox BC, Rose WE. The “epic” challenge of optimizing antimicrobial stewardship: the role of electronic medical records and technology. Clin Infect Dis 2013; 57 (07) 1005-1013
    CrossrefPubMedGoogle Scholar
  • 15 Sintchenko V, Coiera E, Gilbert GL. Decision support systems for antibiotic prescribing. Curr Opin Infect Dis 2008; 21 (06) 573-579
    CrossrefPubMedGoogle Scholar
  • 16 Moja L, Liberati EG, Galuppo L. , et al. Barriers and facilitators to the uptake of computerized clinical decision support systems in specialty hospitals: protocol for a qualitative cross-sectional study. Implement Sci 2014; 9: 105
    CrossrefPubMedGoogle Scholar
  • 17 Wright A, Sittig DF, Ash JS. , et al. Lessons learned from implementing service-oriented clinical decision support at four sites: a qualitative study. Int J Med Inform 2015; 84 (11) 901-911
    CrossrefPubMedGoogle Scholar
  • 18 Spellberg B, Blaser M, Guidos RJ. , et al; Infectious Diseases Society of America (IDSA). Combating antimicrobial resistance: policy recommendations to save lives. Clin Infect Dis 2011; 52 (Suppl. 05) S397-S428
    CrossrefPubMedGoogle Scholar
  • 19 Donnelly JP, Baddley JW, Wang HE. Antibiotic utilization for acute respiratory tract infections in U.S. emergency departments. Antimicrob Agents Chemother 2014; 58 (03) 1451-1457
    CrossrefPubMedGoogle Scholar
  • 20 Gonzales R, Camargo Jr CA, MacKenzie T. , et al; IMPAACT Trial Investigators. Antibiotic treatment of acute respiratory infections in acute care settings. Acad Emerg Med 2006; 13 (03) 288-294
    CrossrefPubMedGoogle Scholar
  • 21 Kroening-Roche JC, Soroudi A, Castillo EM, Vilke GM. Antibiotic and bronchodilator prescribing for acute bronchitis in the emergency department. J Emerg Med 2012; 43 (02) 221-227
    CrossrefPubMedGoogle Scholar
  • 22 Paul IM, Maselli JH, Hersh AL, Boushey HA, Nielson DW, Cabana MD. Antibiotic prescribing during pediatric ambulatory care visits for asthma. Pediatrics 2011; 127 (06) 1014-1021
    CrossrefPubMedGoogle Scholar
  • 23 Society for Healthcare Epidemiology of America; Infectious Diseases Society of America; Pediatric Infectious Diseases Society. Policy statement on antimicrobial stewardship by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Diseases Society of America (IDSA), and the Pediatric Infectious Diseases Society (PIDS). Infect Control Hosp Epidemiol 2012; 33 (04) 322-327
    CrossrefPubMedGoogle Scholar
  • 24 Mistry RD, Newland JG, Gerber JS. , et al. Current state of antimicrobial stewardship in children's hospital emergency departments. Infect Control Hosp Epidemiol 2017; 38 (04) 469-475
    CrossrefPubMedGoogle Scholar
  • 25 Shebl NA, Franklin BD, Barber N. Clinical decision support systems and antibiotic use. Pharm World Sci 2007; 29 (04) 342-349
    CrossrefPubMedGoogle Scholar
  • 26 Bates DW, Kuperman GJ, Wang S. , et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530
    CrossrefPubMedGoogle Scholar
  • 27 Christakis DA, Zimmerman FJ, Wright JA, Garrison MM, Rivara FP, Davis RL. A randomized controlled trial of point-of-care evidence to improve the antibiotic prescribing practices for otitis media in children. Pediatrics 2001; 107 (02) E15
    CrossrefPubMedGoogle Scholar
  • 28 Gerber JS, Prasad PA, Fiks AG. , et al. Effect of an outpatient antimicrobial stewardship intervention on broad-spectrum antibiotic prescribing by primary care pediatricians: a randomized trial. JAMA 2013; 309 (22) 2345-2352
    CrossrefPubMedGoogle Scholar
  • 29 Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998; 280 (15) 1339-1346
    CrossrefPubMedGoogle Scholar
  • 30 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330 (7494): 765
    CrossrefPubMedGoogle Scholar
  • 31 Ansari F, Gray K, Nathwani D. , et al. Outcomes of an intervention to improve hospital antibiotic prescribing: interrupted time series with segmented regression analysis. J Antimicrob Chemother 2003; 52 (05) 842-848
    CrossrefPubMedGoogle Scholar
  • 32 Carling P, Fung T, Killion A, Terrin N, Barza M. Favorable impact of a multidisciplinary antibiotic management program conducted during 7 years. Infect Control Hosp Epidemiol 2003; 24 (09) 699-706
    CrossrefPubMedGoogle Scholar
  • 33 Jenkins TC, Knepper BC, Sabel AL. , et al. Decreased antibiotic utilization after implementation of a guideline for inpatient cellulitis and cutaneous abscess. Arch Intern Med 2011; 171 (12) 1072-1079
    PubMedGoogle Scholar
  • 34 Linder J, Schnipper JL, Volk LA. , et al. Clinical decision support to improve antibiotic prescribing for acute respiratory infections: results of a pilot study. AMIA Annu Symp Proc 2007; 11: 468-472
    PubMedGoogle Scholar
  • 35 Metlay JP, Camargo Jr CA, MacKenzie T. , et al; IMPAACT Investigators. Cluster-randomized trial to improve antibiotic use for adults with acute respiratory infections treated in emergency departments. Ann Emerg Med 2007; 50 (03) 221-230
    CrossrefPubMedGoogle Scholar
  • 36 Parnes B, Fernald D, Coombs L. , et al. Improving the management of skin and soft tissue infections in primary care: a report from State Networks of Colorado Ambulatory Practices and Partners (SNOCAP-USA) and the Distributed Ambulatory Research in Therapeutics Network (DARTNet). J Am Board Fam Med 2011; 24 (05) 534-542
    CrossrefPubMedGoogle Scholar
  • 37 Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson SA. Computerized clinical decision support for prescribing: provision does not guarantee uptake. J Am Med Inform Assoc 2010; 17 (01) 25-33
    CrossrefPubMedGoogle Scholar
  • 38 Sittig DF, Wright A, Osheroff JA. , et al. Grand challenges in clinical decision support. J Biomed Inform 2008; 41 (02) 387-392
    CrossrefPubMedGoogle Scholar
  • 39 Zheng K, Ciemins EL, Lanham HJ, Lindberg C. Examining the Relationship Between Health IT and Ambulatory Care Workflow Redesign (Prepared by Billings Clinic under Contract No. 290–2010–0019I–1). Rockville, MD; 2015
    PubMedGoogle Scholar
  • 40 Agrawal R, Srikant R. . Fast Algorithms for Mining Association Rules in Large Databases, in Proceedings of the 20th International Conference on Very Large Data Bases; 1994, Morgan Kaufmann Publishers Inc.:487–499
    PubMed
  • 41 Han J, Cheng H, Xin D, Yan X. Frequent pattern mining: current status and future directions. Data Min Knowl Discov 2007; 15 (01) 55-86
    CrossrefPubMedGoogle Scholar
  • 42 Chung P, Scandlyn J, Dayan PS, Mistry RD. Working at the intersection of context, culture, and technology: provider perspectives on antimicrobial stewardship in the emergency department using electronic health record clinical decision support. Am J Infect Control 2017; 45 (11) 1198-1202
    CrossrefPubMedGoogle Scholar
  • 43 Baysari MT, Del Gigante J, Moran M. , et al. Redesign of computerized decision support to improve antimicrobial prescribing. A controlled before-and-after study. Appl Clin Inform 2017; 8 (03) 949-963
    Article in Thieme ConnectPubMedGoogle Scholar
  • 44 Hollingsworth JC, Chisholm CD, Giles BK, Cordell WH, Nelson DR. How do physicians and nurses spend their time in the emergency department?. Ann Emerg Med 1998; 31 (01) 87-91
    CrossrefPubMedGoogle Scholar
  • 45 Yen K, Shane EL, Pawar SS, Schwendel ND, Zimmanck RJ, Gorelick MH. Time motion study in a pediatric emergency department before and after computer physician order entry. Ann Emerg Med 2009; 53 (04) 462-468
    CrossrefPubMedGoogle Scholar
  • 46 Ozkaynak M, Unertl KM, Johnson SA, Brixey JJ, Haque SN. Clinical workflow analysis, process redesign, and quality improvement. In Finnel J, Dixon B. , eds. Clinical Informatics Study Guide. New York: Springer International Publishing; 2016: 135-161
    CrossrefGoogle Scholar
  • 47 Zheng K, Guo MH, Hanauer DA. Using the time and motion method to study clinical work processes and workflow: methodological inconsistencies and a call for standardized research. J Am Med Inform Assoc 2011; 18 (05) 704-710
    CrossrefPubMedGoogle Scholar
  • 48 Zheng K, Haftel HM, Hirschl RB, O'Reilly M, Hanauer DA. Quantifying the impact of health IT implementations on clinical workflow: a new methodological perspective. J Am Med Inform Assoc 2010; 17 (04) 454-461
    CrossrefPubMedGoogle Scholar