CC BY-NC-ND 4.0 · Yearb Med Inform 2020; 29(01): 145-154
DOI: 10.1055/s-0040-1701986
Section 5: Decision Support
Survey
Georg Thieme Verlag KG Stuttgart

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Ivana Jankovic
1   Division of Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
,
Jonathan H. Chen
2   Center for Biomedical Informatics Research and Division of Hospital Medicine, Stanford University School of Medicine, Stanford, CA, USA
› Institutsangaben
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Publikationsverlauf

Publikationsdatum:
21. August 2020 (online)

Summary

Objectives: This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout.

Methods: We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science™. Themes were manually extracted from publications that met inclusion criteria.

Results: Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS.

Conclusions: CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.

 
  • References

  • 1 Melnick ER, Dyrbye LN, Sinsky CA, Trockel M, West CP, Nedelec L. et al. The Association Between Perceived Electronic Health Record Usability and Professional Burnout Among US Physicians. Mayo Clin Proc [Internet]. 2019 Nov 14 [cited 2019 Nov 15]; Available from: http://www.sciencedirect.com/science/article/pii/S0025619619308365
  • 2 National Academies of Sciences Engineering, and Medicine; National Academy of Medicine. Taking Action Against Clinician Burnout: A Systems Approach to Professional Well-Being [Internet]. Washington, DC: The National Academies Press; 2019. Available from https://www.nap.edu/catalog/25521/taking-action-against-clinician-burnout-a-systems-approach-to-professional
  • 3 Gardner RL, Cooper E, Haskell J, Harris DA, Poplau S, Kroth PJ. et al. Physician stress and burnout: the impact of health information technology. J Am Med Inform Assoc 2019; 26 (02) 106-14
  • 4 Shanafelt TD, Dyrbye LN, Sinsky C, Hasan O, Satele D, Sloan J. et al. Relationship Between Clerical Burden and Characteristics of the Electronic Environment With Physician Burnout and Professional Satisfaction. Mayo Clin Proc 2016; 91 (07) 836-48
  • 5 Kroth PJ, Morioka-Douglas N, Veres S, Babbott S, Poplau S, Qeadan F. et al. Association of Electronic Health Record Design and Use Factors With Clinician Stress and Burnout. JAMA Netw Open 2019; 2 (08) e199609-e199609
  • 6 Desai SV, Asch DA, Bellini LM, Chaiyachati KH, Liu M, Sternberg AL. et al. Education Outcomes in a Duty-Hour Flexibility Trial in Internal Medicine. N Engl J Med [Internet]. 2018 Mar 20 [cited 2019 Nov 15]; Available from: https://www-nejm-org.laneproxy.stanford.edu/doi/10.1056/NEJMoa1800965
  • 7 West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med 2018; 283 (06) 516-29
  • 8 Coiera E, Ash J, Berg M. The Unintended Consequences of Health Information Technology Revisited. Yearb Med Inform 2016; Aug; 25 (01) 163-9
  • 9 Shanafelt TD, Schein E, Minor LB, Trockel M, Schein P, Kirch D. Healing the Professional Culture of Medicine. Mayo Clin Proc 2019; 94 (08) 1556-66
  • 10 Weed LL. Medical Records That Guide and Teach. N Engl J Med 1968; 278 (12) 652-7
  • 11 Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L. 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) 523-30
  • 12 Glogowski J. GSU Library Research Guides: Literature Reviews: Types of Clinical Study Designs [Internet]. [cited 2020 May 1]. Available from: https://research.library.gsu.edu/c.php?g=115595&p=755213
  • 13 Daly J, Willis K, Small R, Green J, Welch N, Kealy M. et al. A hierarchy of evidence for assessing qualitative health research. J Clin Epidemiol 2007; 60 (01) 43-9
  • 14 Evans D. Hierarchy of evidence: a framework for ranking evidence evaluating healthcare interventions. J Clin Nurs 2003; 12 (01) 77-84
  • 15 Osheroff JA, Teich J, Levick D, Saldana L, Valesco F, Sittig D. et al. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide. 2nd ed. Chicago, IL: HIMSS; 2012
  • 16 Marcial LH, Richardson JE, Lasater B, Middleton B, Osheroff JA, Kawamoto K. et al. The Imperative for Patient-Centered Clinical Decision Support. EGEMS Wash DC 2018; 6 (01) 12
  • 17 Fant C, Adelman D. Too many medication alerts: How alarm frequency affects providers. Nurse Pract 2018; 43 (11) 48-52
  • 18 Borum C. Barriers for Hospital-Based Nurse Practitioners Utilizing Clinical Decision Support Systems: A Systematic Review. Comput Inform Nurs 2018; Apr; 36 (04) 177-82
  • 19 Wilbanks BA, McMullan SP. A Review of Measuring the Cognitive Workload of Electronic Health Records. Comput Inform Nurs 2018; Dec; 36 (12) 579-88
  • 20 Tolley CL, Slight SP, Husband AK, Watson N, Bates DW. Improving medication-related clinical decision support. Am J Health-Syst Pharm AJHP Off J Am Soc Health-Syst Pharm 2018; 75 (04) 239-46
  • 21 Hussain MI, Reynolds TL, Zheng K. Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review. J Am Med Inform Assoc 2019; 26 (10) 1141-9
  • 22 Carroll AE. Averting Alert Fatigue to Prevent Adverse Drug Reactions. JAMA 2019; 322 (07) 601
  • 23 Legat L, Van Laere S, Nyssen M, Steurbaut S, Dupont AG, Cornu P. Clinical Decision Support Systems for Drug Allergy Checking: Systematic Review. J Med Internet Res 2018; 20 (09) e258
  • 24 Carli D, Fahrni G, Bonnabry P, Lovis C. Quality of Decision Support in Computerized Provider Order Entry: Systematic Literature Review. JMIR Med Inform 2018; 6 (01) e3
  • 25 Powers EM, Shiffman RN, Melnick ER, Hickner A, Sharifi M. Efficacy and unintended consequences of hard-stop alerts in electronic health record systems: a systematic review. J Am Med Inform Assoc 2018; 25 (11) 1556-66
  • 26 Luo S, Botash AS. Designing and Developing a Mobile App for Clinical Decision Support: An Interprofessional Collaboration. Comput Inform Nurs 2018; Dec; 36 (10) 467-72
  • 27 Mahabee-Gittens EM, Dexheimer JW, Tabangin M, Khoury JC, Merianos AL, Stone L. et al. An Electronic Health Record-Based Strategy to Address Child Tobacco Smoke Exposure. Am J Prev Med 2018; Jan; 54 (01) 64-71
  • 28 Brown B, Balatsoukas P, Williams R, Sperrin M, Buchan I. Multi-method laboratory user evaluation of an actionable clinical performance information system: Implications for usability and patient safety. J Biomed Inform 2018; 77: 62-80
  • 29 Bagri H, Dahri K, Legal M. Hospital Pharmacists’ Perceptions and Decision-Making Related to Drug-Drug Interactions. Can J Hosp Pharm 2019 72. (4)
  • 30 Poncette AS, Spies C, Mosch L, Schieler M, Weber-Carstens S, Krampe H. et al. Clinical Requirements of Future Patient Monitoring in the Intensive Care Unit: Qualitative Study. JMIR Med Inform 2019; 7 (02) e13064
  • 31 Johansson-Pajala RM. Conditions for the Successful Implementation of Computer-Aided Drug Monitoring From Registered Nurses’ Perspective-A Case Site Analysis. Comput Inform Nurs 2018; Oct; 37 (04) 196-202
  • 32 Nilsson L, Fagerstrom C. Decision-makers and mediators in a home healthcare digitisation process: nurses’ experiences of implementation and use of a decision support system. Contemp Nurse 2018; 54 (4–5): 511-21
  • 33 Giuliano CA, Binienda J, Kale-Pradhan PB, Fakih MG. “I Never Would Have Caught That Before”: Pharmacist Perceptions of Using Clinical Decision Support for Antimicrobial Stewardship in the United States. Qual Health Res 2018; Apr; 28 (05) 745-55
  • 34 Goodspeed A, Kostman N, Kriete TE, Longtine JW, Smith SM, Marshall P. et al Leveraging the utility of pharmacogenomics in psychiatry through clinical decision support: a focus group study. Ann Gen Psychiatry 2019; 18: 13
  • 35 Asan O, Nattinger AB, Gurses AP, Tyszka JT, Yen TWF. Oncologists’ Views Regarding the Role of Electronic Health Records in Care Coordination. JCO Clin Cancer Inform 2018; 2: 1-12
  • 36 Thoma-Lurken T, Lexis MAS, Bleijlevens MHC, Hamers JPH. Perceived added value of a decision support App for formal caregivers in community-based dementia care. J Clin Nurs 2019; 28 (1–2): 173-81
  • 37 Pet DB, Holm IA, Williams JL, Myers MF, Novak LL, Brothers KB. et al. Physicians’ perspectives on receiving unsolicited genomic results. Genet Med Off J Am Coll Med Genet 2018; Jun; 21 (02) 311-8
  • 38 Abejirinde IOO, Douwes R, Bardaji A, Abugnaba-Abanga R, Zweekhorst M, van Roosmalen J. et al. Pregnant women’s experiences with an integrated diagnostic and decision support device for antenatal care in Ghana. BMC Pregnancy Childbirth 2018; 18 (01) 209
  • 39 Banks J, Farr M, Salisbury C, Bernard E, Northstone K, Edwards H. et al. Use of an electronic consultation system in primary care: a qualitative interview study. Br J Gen Pract J R Coll Gen Pract 2018; 68 (666): e1-8
  • 40 Nguyen KA, Patel H, Haggstrom DA, Zillich AJ, Imperiale TF, Russ AL. Utilizing a user-centered approach to develop and assess pharmacogenomic clinical decision support for thiopurine methyltransferase. BMC Med Inform Decis Mak 2019; 19 (01) 194
  • 41 Bjorkman A, Salzmann-Erikson M. When all other doors are closed: Telenurses’ experiences of encountering care seekers with mental illnesses. Int J Ment Health Nurs 2018; Oct; 27 (05) 1392-400
  • 42 Chokshi SK, Belli HM, Troxel AB, Blecker S, Blaum C, Testa P. et al Designing for implementation: user-centered development and pilot testing of a behavioral economic-inspired electronic health record clinical decision support module. Pilot Feasibility Stud 2019; 5: 28
  • 43 Dehlendorf C, Reed R, Fitzpatrick J, Kuppermann M, Steinauer J, Kimport K. A mixed-methods study of provider perspectives on My Birth Control: a contraceptive decision support tool designed to facilitate shared decision making. Contraception 2019 Aug 9
  • 44 Pirnejad H, Amiri P, Niazkhani Z, Shiva A, Makhdoomi K, Abkhiz S. et al. Preventing potential drug-drug interactions through alerting decision support systems: A clinical context based methodology. Int J Med Inform 2019 Jul;127:18–26
  • 45 Alagiakrishnan K, Ballermann M, Rolfson D, Mohindra K, Sadowski CA, Ausford A. et al. Utilization of computerized clinical decision support for potentially inappropriate medications. Clin Interv Aging 2019; 14: 753-62
  • 46 Singh K, Johnson L, Devarajan R, Shivashankar R, Sharma P, Kondal D. et al. Acceptability of a decision-support electronic health record system and its impact on diabetes care goals in South Asia: a mixed-methods evaluation of the CARRS trial. Diabet Med J Br Diabet Assoc 2018; Dec; 35 (12) 1644-54
  • 47 Vousden N, Lawley E, Nathan HL, Seed PT, Brown A, Muchengwa T. et al. Evaluation of a novel vital sign device to reduce maternal mortality and morbidity in low-resource settings: a mixed method feasibility study for the. BMC Pregnancy Childbirth 2018; 18 (01) 115
  • 48 Cox JL, Parkash R, Abidi SS, Thabane L, Xie F, MacKillop J. et al. Optimizing primary care management of atrial fibrillation: The rationale and methods of the Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) study. Am Heart J 2018; 201: 149-57
  • 49 Austrian JS, Jamin CT, Doty GR, Blecker S. Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay. J Am Med Inform Assoc 2017; 25 (05) 523-9
  • 50 Schild S, Sedlmayr B, Schumacher AK, Sedlmayr M, Prokosch HU, St Pierre M. A Digital Cognitive Aid for Anesthesia to Support Intraoperative Crisis Management: Results of the User-Centered Design Process. JMIR MHealth UHealth 2019; 7 (04) e13226
  • 51 Blecker S, Pandya R, Stork S, Mann D, Kuperman G, Shelley D. et al. Interruptive Versus Noninterruptive Clinical Decision Support: Usability Study. JMIR Hum Factors 2019; 6 (02) e12469
  • 52 Thayer JG, Miller JM, Fiks AG, Tague L, Grundmeier RW. Assessing the Safety of Custom Web-Based Clinical Decision Support Systems in Electronic Health Records: A Case Study. Appl Clin Inform 2019; Mar; 10 (02) 237-46
  • 53 Winthereik AK, Neergaard MA, Jensen AB, Vedsted P. Development, modelling, and pilot testing of a complex intervention to support end-of-life care provided by Danish general practitioners. BMC Fam Pract 2018; 19 (01) 91
  • 54 Baysari MT, Zheng WY, Li L, Westbrook J, Day RO, Hilmer S. et al. Optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug-drug interaction alerts. BMJ Open 2019; 9 (08) e026034
  • 55 Muhlenkamp R, Ash N, Ziegenbusch K, Rampe N, Bishop B, Adane E. Effect of modifying dose alerts in an electronic health record on frequency of alerts. Am J Health Syst Pharm 2019; ;76(Supplement_1): S1-8
  • 56 Grout RW, Cheng ER, Carroll AE, Bauer NS, Downs SM. A six-year repeated evaluation of computerized clinical decision support system user acceptability. Int J Med Inform 2018; 112: 74-81
  • 57 Nanji KC, Seger DL, Slight SP, Amato MG, Beeler PE, Her QL. et al. Medication-related clinical decision support alert overrides in inpatients. J Am Med Inform Assoc 2018; 25 (05) 476-81
  • 58 Ollerenshaw A, Wong Shee A, Yates M. Towards good dementia care: Awareness and uptake of an online Dementia Pathways tool for rural and regional primary health practitioners. Aust J Rural Health 2018; Apr; 26 (02) 112-8
  • 59 Porter A, Dale J, Foster T, Logan P, Wells B, Snooks H. Implementation and use of computerised clinical decision support (CCDS) in emergency pre-hospital care: a qualitative study of paramedic views and experience using Strong Structuration Theory. Implement Sci 2018; 13 (01) 91
  • 60 Yoshida E, Fei S, Bavuso K, Lagor C, Maviglia S. The Value of Monitoring Clinical Decision Support Interventions. Appl Clin Inform 2018; Jan; 9 (01) 163-73
  • 61 Wong JC, Izadi Z, Schroeder S, Nader M, Min J, Neinstein AB. et al. A Pilot Study of Use of a Software Platform for the Collection, Integration, and Visualization of Diabetes Device Data by Health Care Providers in a Multidisciplinary Pediatric Setting. Diabetes Technol Ther 2018; Dec; 20 (12) 806-16
  • 62 Bubp JL, Park MA, Kapusnik-Uner J, Dang T, Matuszewski K, Ly D. et al. Successful deployment of drug-disease interaction clinical decision support across multiple Kaiser Permanente regions. J Am Med Inform Assoc 2019; 26 (10) 905-10
  • 63 Saman DM, Walton KM, Harry ML, Asche SE, Truitt AR, Henzler-Buckingham HA. et al. Understanding primary care providers’ perceptions of cancer prevention and screening in a predominantly rural healthcare system in the upper Midwest. BMC Health Serv Res [Internet] 2019 Dec 30 [cited 2020 Apr 29];19. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937782/
  • 64 Long D, Capan M, Mascioli S, Weldon D, Arnold R, Miller K. Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis. Crit Care Nurse 2018; Aug; 38 (04) 46-54
  • 65 Page NJ, Baysari MT, Westbrook JI. Selection and use of decision support alerts in electronic medication management systems in Australian hospitals: a survey of implementers. J Pharm Pract Res 2019; 49 (02) 142-9
  • 66 Tu MH, Chang P, Lee YL. Avoiding Obsolescence in Mobile Health: Experiences in Designing a Mobile Support System for Complicated Documentation at Long-term Care Facilities. Comput Inform Nurs 2018; Oct; 36 (10) 501-6
  • 67 Helmle KE, Edwards AL, Kushniruk AW, Borycki EM. Qualitative Evaluation of the Barriers and Facilitators Influencing the Use of an Electronic Basal Bolus Insulin Therapy Protocol to Improve the Care of Adult Inpatients With Diabetes. Can J Diabetes 2018; 42 (05) 459-464e1
  • 68 Salz T, Schnall RB, McCabe MS, Oeffinger KC, Corcoran S, Vickers AJ. et al. Incorporating Multiple Perspectives Into the Development of an Electronic Survivorship Platform for Head and Neck Cancer. JCO Clin Cancer Inform 2018; 2: 1-15
  • 69 Wright A, Aaron S, Seger DL, Samal L, Schiff GD, Bates DW. Reduced effectiveness of interruptive drug-drug interaction alerts after conversion to a commercial electronic health record. J Gen Intern Med 2018; 33 (11) 1868-76
  • 70 Grabel MZ, Vaughan BL, Dexheimer JW, Kirkendall ES. Mathematical Model for Computer-Assisted Modification of Medication Dosing Rules. Biomed Inform Insights 2019; 11: 1178222619829079
  • 71 Heringa M, van der Heide A, Floor-Schreudering A, De Smet PAGM, Bouvy ML. Better specification of triggers to reduce the number of drug interaction alerts in primary care. Int J Med Inform 2018; 109: 96-102
  • 72 Saiyed SM, Davis KR, Kaelber DC. Differences, Opportunities, and Strategies in Drug Alert Optimization—Experiences of Two Different Integrated Health Care Systems. Appl Clin Inform 2019; 10 (05) 777-82
  • 73 Horn J, Ueng S. The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study. Ann Pharmacother 2019; 53 (11) 1087-92
  • 74 Khan S, Richardson S, Liu A, Mechery V, McCullagh L, Schachter A. et al. Improving Provider Adoption With Adaptive Clinical Decision Support Surveillance: An Observational Study. JMIR Hum Factors 2019; 6 (01) e10245
  • 75 Kawamanto K, Flynn MC, Kukhareva P, El Halta D, Hess R, Gregory T. et al. A Pragmatic Guide to Establishing Clinical Decision Support Governance and Addressing Decision Support Fatigue: a Case Study. AMIA Annu Symp Proc AMIA Symp 2018; 2018: 624-33
  • 76 Gunn LR, Tunney R, Kelly K. Nonmodal Clinical Decision Support and Antimicrobial Restriction Effects on Rates of Fluoroquinolone Use in Uncomplicated Infections. Appl Clin Inform 2018; Jan; 9 (01) 149-55
  • 77 Khairat S, Coleman C, Ottmar P, Bice T, Koppel R, Carson SS. Physicians’ gender and their use of electronic health records: findings from a mixed-methods usability study. J Am Med Inform Assoc 2019; 26 (12) 1505-14
  • 78 Rapoport MJ, Zucchero Sarracini C, Kiss A, Lee L, Byszewski A, Seitz DP. et al. Computer-Based Driving in Dementia Decision Tool With Mail Support: Cluster Randomized Controlled Trial. J Med Internet Res 2018; 20 (05) e194
  • 79 Ramirez M, Maranon R, Fu J, Chon JS, Chen K, Mangione CM. et al. Primary care provider adherence to an alert for intensification of diabetes blood pressure medications before and after the addition of a “chart closure” hard stop. J Am Med Inform Assoc 2018; 25 (09) 1167-74
  • 80 Moja L, PoloFriz H, Capobussi M, Kwag K, Banzi R, Ruggiero F. et al. Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes. JAMA Netw Open [Internet] 2019 Dec 11 [cited 2020 May 4];2(12). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6991299
  • 81 Wasylewicz ATM, Korsten EHM, Egberts TCG, Grouls RJE. Clinical rule-guided pharmacists’ intervention in hospitalized patients with hypokalaemia: A time series analysis. J Clin Pharm Ther 2020; 45 (03) 520-9
  • 82 Sperl-Hillen JM, Crain AL, Margolis KL, Ekstrom HL, Appana D, Amundson G. et al. Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial. J Am Med Inform Assoc 2018; 25 (09) 1137-46
  • 83 Cresswell K, Callaghan M, Mozaffar H, Sheikh A. NHS Scotland’s Decision Support Platform: a formative qualitative evaluation. BMJ Health Care Inform 2019 26. (1)
  • 84 Canovas-Segura B, Morales A, Juarez JM, Campos M, Palacios F. Impact of expert knowledge on the detection of patients at risk of antimicrobial therapy failure by clinical decision support systems. J Biomed Inform 2019; 94: 103200
  • 85 Divakaran S, Singh A, De Filippis EM, Churchill TW, Cuddy S, Ge Y. et al. Appropriateness of inpatient stress testing: Implications for development of clinical decision support mechanisms and future criteria. J Nucl Cardiol [Internet] 2019 Nov 18 [cited 2020 May 4]; Available from: https://doi.org/10.1007/s12350-019-01955-x
  • 86 Wulff A, Montag S, Steiner B, Marschollek M, Beerbaum P, Karch A. et al. CADDIE2—evaluation of a clinical decision-support system for early detection of systemic inflammatory response syndrome in paediatric intensive care: study protocol for a diagnostic study. BMJ Open 2019; 9 (06) e028953
  • 87 Rousseau JF, Ip IK, Raja AS, Valtchinov VI, Cochon L, Schuur JD. et al. Can Automated Retrieval of Data from Emergency Department Physician Notes Enhance the Imaging Order Entry Process?. Appl Clin Inform 2019; 10 (02) 189-98
  • 88 Bakker T, Klopotowska JE, Eslami S, de Lange DW, van Marum R, van der Sijs H. et al. The effect of ICU-tailored drug-drug interaction alerts on medication prescribing and monitoring: protocol for a cluster randomized stepped-wedge trial. BMC Med Inform Decis Mak 2019; 19 (01) 159
  • 89 Johansson-Pajala RM, Martin L, Jorsater Blomgren K. Registered nurses’ use of computerised decision support in medication reviews. Int J Health Care Qual Assur 2018; 31 (06) 531-44
  • 90 Ankem K, Cho S, Simpson D. Nurses’ perceptions and problems in the usability of a medication safety app. Inform Health Soc Care 2019; Jan; 44 (01) 48-69
  • 91 Skyttberg N, Chen R, Koch S. Man vs machine in emergency medicine - a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires. BMC Emerg Med 2018; 18 (01) 54
  • 92 Thoma-Lurken T, Bleijlevens MHC, Lexis MAS, Hamers JPH. Evaluation of a decision support app for nurses and case managers to facilitate aging in place of people with dementia. A randomized controlled laboratory experiment. Geriatr Nurs N Y N 2018; Nov; 39 (06) 653-62
  • 93 Mann D, Hess R, McGinn T, Mishuris R, Chokshi S, McCullagh L. et al Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research. Digit Health 2019; 5: 2055207619827716
  • 94 Fortin C, van Schaik P, Aubin-Fournier JF, Bettany-Saltikov J, Bernard JC, Ehrmann Feldman D. The acceptance of the clinical photographic posture assessment tool (CPPAT). BMC Musculoskelet Disord 2018; 19 (01) 366
  • 95 Bertoni CB, Prusakov P, Merandi J, Bartman T. Clinical Decision Support to Improve Dosing Weight Use in Infants with Neonatal Abstinence Syndrome. Pediatr Qual Saf 2019; 4 (04) e184
  • 96 Ubanyionwu S, Formea CM, Anderson B, Wix K, Dierkhising R, Caraballo PJ. Evaluation of prescriber responses to pharmacogenomics clinical decision support for thiopurine S-methyltransferase testing. Am J Health-Syst Pharm AJHP Off J Am Soc Health-Syst Pharm 2018; 75 (04) 191-8
  • 97 Biswas A, Parikh CR, Feldman HI, Garg AX, Latham S, Lin H. et al. Identification of patients expected to benefit from electronic alerts for acute kidney injury. Clin J Am Soc Nephrol 2018; 13 (06) 842-9
  • 98 Kizzier-Carnahan V, Artis KA, Mohan V, Gold JA. Frequency of Passive EHR Alerts in the ICU: Another Form of Alert Fatigue?. J Patient Saf 2019; Sep; 15 (03) 246-50
  • 99 Arts DL, Medlock SK, van Weert HCPM, Wyatt JC, Abu-Hanna A. Acceptance and barriers pertaining to a general practice decision support system for multiple clinical conditions: A mixed methods evaluation. PloS One 2018; 13 (04) e0193187
  • 100 Roshanov PS, Fernandes N, Wilczynski JM, Hemens BJ, You JJ, Handler SM. et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ [Internet] 2013 Feb 14 [cited 2020 Apr 29];346. Available from: https://www.bmj.com/content/346/bmj.f657
  • 101 Moja L, Kwag KH, Lytras T, Bertizzolo L, Brandt L, Pecoraro V. et al. Effectiveness of Computerized Decision Support Systems Linked to Electronic Health Records: A Systematic Review and Meta-Analysis. Am J Public Health 2014; 104 (12) e12-22
  • 102 Cimino JJ. Putting the “why” in “EHR”: capturing and coding clinical cognition. J Am Med Inform Asso 2019; 26 (11) 1379-84
  • 103 Liberati EG, Ruggiero F, Galuppo L, Gorli M, González-Lorenzo M, Maraldi M. et al. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci 2017; 12 (01) 113
  • 104 Meslin SMM, Zheng WY, Day RO, Tay EMY, Baysari MT. Evaluation of Clinical Relevance of Drug–Drug Interaction Alerts Prior to Implementation. Appl Clin Inform 2018; 9 (04) 849-55
  • 105 Auerbach AD, Khanna R, Adler-Milstein J. Letting a Good Crisis Go to Waste. J Gen Intern Med [Internet] 2019 Nov 19 [cited 2019 Nov 21]; Available from: https://doi.org/10.1007/s11606-019-05552-z
  • 106 Panagioti M, Panagopoulou E, Bower P, Lewith G, Kontopantelis E, Chew-Graham C. et al. Controlled Interventions to Reduce Burnout in Physicians: A Systematic Review and Meta-analysis. JAMA Intern Med 2017; 177 (02) 195-205
  • 107 West CP, Dyrbye LN, Erwin PJ, Shanafelt TD. Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis. Lancet 2016; 388 10057 2272-81
  • 108 Fitzmaurice MG, Wong A, Akerberg H, Avramovska S, Smithburger PL, Buckley MS. et al. Evaluation of Potential Drug–Drug Interactions in Adults in the Intensive Care Unit: A Systematic Review and Meta-Analysis. Drug Saf 2019; 1-10
  • 109 Gesner E, Gazarian P, Dykes P. The Burden and Burnout in Documenting Patient Care: An Integrative Literature Review. Stud Health Technol Inform 2019; 264: 1194-8
  • 110 Zeng-Treitler Q, Nelson SJ. Will Artificial Intelligence Translate Big Data Into Improved Medical Care or Be a Source of Confusing Intrusion? A Discussion Between a (Cautious) Physician Informatician and an (Optimistic) Medical Informatics Researcher. J Med Internet Res [Internet] 2019 Nov 27 [cited 2020 May 4];21(11). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6906615/