Multidisciplinary Sprint Program Achieved Specialty-Specific EHR Optimization in 20 Clinics

Amber Sieja; Eric Kim; Heather Holmstrom; Stephen Rotholz; Chen Tan Lin; Christine Gonzalez; Cortney Arellano; Sarah Hutchings; Denise Henderson; Katie Markley

doi:10.1055/s-0041-1728699

Applied Clinical Informatics, Inhaltsverzeichnis

Appl Clin Inform 2021; 12(02): 329-339
DOI: 10.1055/s-0041-1728699

Review Article

Multidisciplinary Sprint Program Achieved Specialty-Specific EHR Optimization in 20 Clinics

Authors

Amber Sieja

¹Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
Eric Kim

²Department of Family Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
Heather Holmstrom

²Department of Family Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
Stephen Rotholz

³Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, Colorado, United States
Chen Tan Lin

¹Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
Christine Gonzalez

⁴University of Colorado Health, Aurora, Colorado, United States
Cortney Arellano

⁴University of Colorado Health, Aurora, Colorado, United States
Sarah Hutchings

⁴University of Colorado Health, Aurora, Colorado, United States
Denise Henderson

⁴University of Colorado Health, Aurora, Colorado, United States
Katie Markley

⁵University of Colorado Health Medical Group and University of Colorado Health, Aurora, Colorado, United States

Abstract

Objective The objective of the study was to highlight and analyze the outcomes of software configuration requests received from Sprint, a comprehensive, clinic-centered electronic health record (EHR) optimization program.

Methods A retrospective review of 1,254 Sprint workbook requests identified (1) the responsible EHR team, (2) the clinical efficiency gained from the request, and (3) the EHR intervention conducted.

Results Requests were received from 407 clinicians and 538 staff over 31 weeks of Sprint. Sixty-nine percent of the requests were completed during the Sprint. Of all requests, 25% required net new build, 73% required technical investigation and/or solutions, and 2% of the requests were escalated to the vendor. The clinical specialty groups requested a higher percentage of items that earned them clinical review (16 vs. 10%) and documentation (29 vs. 23%) efficiencies compared with their primary care colleagues who requested slightly more order modifications (22 vs. 20%). Clinical efficiencies most commonly associated with workbook requests included documentation (28%), ordering (20%), in basket (17%), and clinical review (15%). Sprint user requests evaluated by ambulatory, hardware, security, and training teams comprised 80% of reported items.

Discussion Sprint requests were categorized as clean-up, break-fix, workflow investigation, or new build. On-site collaboration with clinical care teams permitted consensus-building, drove vetting, and iteration of EHR build, and led to goal-driven, usable workflows and EHR products.

Conclusion This program evaluation demonstrates the process by which optimization can occur and the products that result when we adhere to optimization principles in health care organizations.

Keywords

burnout - electronic medical record - electronic health record - training - optimization

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Referenzen

References
1 Berg S. Physician burnout: it's not you, it's your medical specialty. 2018 . Accessed April 2021 at: https://www.ama-assn.org/residents-students/specialty-profiles/physician-burnout-it-s-not-you-it-s-your-medical-specialty
2 Melnick ER, Dyrbye LN, Sinsky CA. et al. The association between perceived electronic health record usability and professional burnout among US physicians. Mayo Clin Proc 2019
3 Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med 2014; 12 (06) 573-576
4 Dillon EC, Tai-Seale M, Meehan A. et al. frontline perspectives on physician burnout and strategies to improve well-being: interviews with physicians and health system leaders. J Gen Intern Med 2020; 35 (01) 261-267
5 Gardner RL, Cooper E, Haskell J. et al. Physician stress and burnout: the impact of health information technology. J Am Med Inform Assoc 2019; 26 (02) 106-114
6 Panagioti M, Geraghty K, Johnson J. et al. Association between physician burnout and patient safety, professionalism and patient satisfaction: a systematic review and meta-analysis. JAMA Intern Med 2018; 178 (10) 1317-1331
7 Shanafelt TD, Dyrbye LN, Sinsky C. et al. Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clin Proc 2016; 91 (07) 836-848
8 Sinsky C, Colligan L, Li L. et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann Intern Med 2016; 165 (11) 753-760
9 Tai-Seale M, Olson CW, Li J. et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Aff (Millwood) 2017; 36 (04) 655-662
10 Young RA, Burge SK, Kumar KA, Wilson JM, Ortiz DF. A time-motion study of primary care physicians' work in the electronic health record era. Fam Med 2018; 50 (02) 91-99
11 Oshiro B. 6 proven strategies for engaging physicians- and 4 ways to fail. 2015 . Accessed January 2020 at: https://www.healthcatalyst.com/proven-physician-engagement-strategies
12 Pandhi N, Yang WL, Karp Z. et al. Approaches and challenges to optimising primary care teams' electronic health record usage. Inform Prim Care 2014; 21 (03) 142-151
13 Belden J, Grayson R, Barnes J. Defining and Testing EMR Usability: Principles and Proposed Methods of EMR Usability Evaluation and Rating. Chicago, IL: HIMMS Usability Task Force; 2009. . Accessed April 2021 at: https://mospace.umsystem.edu/xmlui/bitstream/handle/10355/3719/DefiningTestingEMR.pdf?sequence=1&isAllowed=y
14 Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2006; 13 (05) 547-556
15 Sittig DF, Wright A, Ash J, Singh H. New unintended adverse consequences of electronic health records. Yearb Med Inform 2016; 1 (01) 7-12
16 Jamoom EW, Heisey-Grove D, Yang N, Scanlon P. Physician opinions about EHR use by EHR experience and by whether the practice had optimized it's EHR use. J Health Med Inform 2016; 7 (04) 1000240
17 McAlearney AS, Song PH, Robbins J. et al. Moving from good to great in ambulatory electronic health record implementation. J Healthc Qual 2010; 32 (05) 41-50
18 Moon MC, Hills R, Demiris G. Understanding optimization processes of electronic health records (EHR) in select leading hospitals: a qualitative study. J Innov Health Inform 2018; 25 (02) 109-125
19 Blavin F. et al. Lessons from the literature on Electronic Health Record Implementation: A Study Funded by the Office of the National Coordinator for Health Information Technology of the U.S. Department of Health and Human Services; 2013. . Accessed November 20, 2015 at: http://www.healthit.gov/sites/default/files/hit_lessons_learned_lit_review_final_08-01-2013.pdf
20 Jones SS, Heaton PS, Rudin RS, Schneider EC. Unraveling the IT productivity paradox--lessons for health care. N Engl J Med 2012; 366 (24) 2243-2245
21 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11 (02) 104-112
22 DiAngi YT, Longhurst CA, Payne TH. Taming the EHR (electronic health record): there is hope. J Fam Med 2016; 3 (06) 1072
23 Longhurst CA, Davis T, Maneker A. et al; Arch Collaborative. Local investment in training drives electronic health record user satisfaction. Appl Clin Inform 2019; 10 (02) 331-335
24 Ratwani RM, Hettinger AZ, Fairbanks RJ. The Role of Health IT Developers in Improving Patient Safety in HRO's. Washington, DC: Office of the National Coordinator for Health Information Technology; 2014. . Accessed April 2021 at: https://www.healthit.gov/sites/default/files/medstar_hit_safety_1_29_v2.pdf
25 Leventhal R. Trend: EHR optimization. Post-implementation advancements. Leaders from various healthcare organizations explain how they have been moving forward with their EHRs following implementation. Healthc Inform 2014; 31 (02) 30-32
26 Swensen S, Kabcenell A, Shanafelt T. Physician-organization collaboration reduces physician burnout and promotes engagement: the Mayo Clinic experience. J Healthc Manag 2016; 61 (02) 105-127
27 Tutty MA, Carlasare LE, Lloyd S, Sinsky CA. The complex case of EHRs: examining the factors impacting the EHR user experience. J Am Med Inform Assoc 2019; 26 (07) 673-677
28 Sieja A, Markley K, Pell J. et al. Optimization sprints: improving clinician satisfaction and teamwork by rapidly reducing electronic health burden. Mayo Clin Proc 2019; 94 (05) 793-802
29 Robinson KE, Kersey JA. Novel electronic health record (EHR) education intervention in large healthcare organization improves quality, efficiency, time, and impact on burnout. Medicine (Baltimore) 2018; 97 (38) e12319
30 Stevens LA, DiAngi YT, Schremp JD. et al. Designing an individualized EHR learning plan for providers. Appl Clin Inform 2017; 8 (03) 924-935
31 Kadish SS, Mayer EL, Jackman DM. et al. Implementation to optimization: a tailored, data-driven approach to improve provider efficiency and confidence in use of the electronic medical record. J Oncol Pract 2018; 14 (07) e421-e428
32 Principles behind the Agile manifesto. Accessed April 2021 at: https://agilemanifesto.org/principles.html
33 Schwaber K, Sutherland J. The Scrum Guide (2020). Accessed April 2021 at: https://www.scrum.org/resources/scrum-guide
34 Hassett MJ. Usability considerations in oncology electronic medical records. J Oncol Pract 2017; 13 (08) 539-541