CoChat
Sign Up
Cited by 84Electronic Health Records SystemsHealthcare professionals’ stress and burnoutArtificial Intelligence in Healthcare and Education

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Open access
Jankovic, Ivana, Chen, Jonathan H.·2020·Yearbook of Medical Informatics
DOI 10.1055/s-0040-1701986PMC PMC7442505
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1055/s-0040-1701986

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 99

  1. [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 31940160
  2. [3]Gardner R L Cooper E Haskell J Harris D A Poplau S Kroth P J Physician stress and burnout: the impact of health information technology J Am Med Inform Assoc 2019 26 02 106 14 30517663 PMC7647171
  3. [5]Kroth P J Morioka-Douglas N Veres S Babbott S Poplau S Qeadan F Association of Electronic Health Record Design and Use Factors With Clinician Stress and Burnout JAMA Netw Open 2019 2 08 e199609 e199609 31418810 PMC6704736
  4. [6]Desai S V Asch D A Bellini L M Chaiyachati K H Liu M Sternberg A L 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 PMC6101652 29557719 · DOI
  5. [7]West C P Dyrbye L N Shanafelt T D Physician burnout: contributors, consequences and solutions J Intern Med 2018 283 06 516 29 29505159
  6. [8]Coiera E Ash J Berg M The Unintended Consequences of Health Information Technology Revisited Yearb Med Inform 2016Aug; 25 01 163 9 PMC5171576 27830246
  7. [9]Shanafelt T D Schein E Minor L B Trockel M Schein P Kirch D Healing the Professional Culture of Medicine Mayo Clin Proc 2019 94 08 1556 66 31303431
  8. [10]Weed L L Medical Records That Guide and Teach N Engl J Med 1968 278 12 652 7 5637250
  9. [11]Bates D W Kuperman G J Wang S Gandhi T Kittler A Volk L 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 12925543 PMC264429
  10. [13]Daly J Willis K Small R Green J Welch N Kealy M A hierarchy of evidence for assessing qualitative health research J Clin Epidemiol 2007 60 01 43 9 17161753
  11. [16]Marcial L H Richardson J E Lasater B Middleton B Osheroff J A Kawamoto K The Imperative for Patient-Centered Clinical Decision Support EGEMS Wash DC 2018 6 01 12 30094284 PMC6078148
  12. [17]Fant C Adelman D Too many medication alerts: How alarm frequency affects providers Nurse Pract 2018 43 11 48 52 30320636
  13. [18]Borum C Barriers for Hospital-Based Nurse Practitioners Utilizing Clinical Decision Support Systems: A Systematic Review Comput Inform Nurs 2018Apr; 36 04 177 82 29360699
  14. [19]Wilbanks B A McMullan S P A Review of Measuring the Cognitive Workload of Electronic Health Records Comput Inform Nurs 2018Dec; 36 12 579 88 30134256
  15. [20]Tolley C L Slight S P Husband A K Watson N Bates D W Improving medication-related clinical decision support Am J Health-Syst Pharm AJHP Off J Am Soc Health-Syst Pharm 2018 75 04 239 46 29436470
  16. [21]Hussain M I Reynolds T L 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 31206159 PMC6748819
  17. [22]Carroll A E Averting Alert Fatigue to Prevent Adverse Drug Reactions JAMA 2019 322 07 601 31429887
  18. [23]Legat L Van Laere S Nyssen M Steurbaut S Dupont A G Cornu P Clinical Decision Support Systems for Drug Allergy Checking: Systematic Review J Med Internet Res 2018 20 09 e258 30194058 PMC6231757
  19. [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 29367187 PMC5803531
  20. [25]Powers E M Shiffman R N Melnick E R 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 30239810 PMC6915824
  21. [26]Luo S Botash A S Designing and Developing a Mobile App for Clinical Decision Support: An Interprofessional Collaboration Comput Inform Nurs 2018Dec; 36 10 467 72 30300186
  22. [27]Mahabee-Gittens E M Dexheimer J W Tabangin M Khoury J C Merianos A L Stone L An Electronic Health Record-Based Strategy to Address Child Tobacco Smoke Exposure Am J Prev Med 2018Jan; 54 01 64 71 29102458 PMC5736447
  23. [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 29146562 PMC5766660
  24. [29]Bagri H Dahri K Legal M Hospital Pharmacists’ Perceptions and Decision-Making Related to Drug-Drug InteractionsCan J Hosp Pharm 2019 72(4) PMC6699872 31452540
  25. [30]Poncette A S Spies C Mosch L Schieler M Weber-Carstens S Krampe H Clinical Requirements of Future Patient Monitoring in the Intensive Care Unit: Qualitative Study JMIR Med Inform 2019 7 02 e13064 31038467 PMC6658223
  26. [31]Johansson-Pajala R M Conditions for the Successful Implementation of Computer-Aided Drug Monitoring From Registered Nurses’ Perspective-A Case Site Analysis Comput Inform Nurs 2018Oct; 37 04 196 202 30589648
  27. [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 30099935
  28. [33]Giuliano C A Binienda J Kale-Pradhan P B Fakih M G “I Never Would Have Caught That Before”: Pharmacist Perceptions of Using Clinical Decision Support for Antimicrobial Stewardship in the United States Qual Health Res 2018Apr; 28 05 745 55 29334865
  29. [34]Goodspeed A Kostman N Kriete T E Longtine J W Smith S M Marshall P Leveraging the utility of pharmacogenomics in psychiatry through clinical decision support: a focus group study Ann Gen Psychiatry 2019 18 13 31413721 PMC6688280
  30. [35]Asan O Nattinger A B Gurses A P Tyszka J T Yen T WF Oncologists’ Views Regarding the Role of Electronic Health Records in Care Coordination JCO Clin Cancer Inform 2018 2 1 12 30652555
  31. [36]Thoma-Lurken T Lexis M AS Bleijlevens M HC Hamers J PH 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 30091499 PMC7380290
  32. [37]Pet D B Holm I A Williams J L Myers M F Novak L L Brothers K B Physicians’ perspectives on receiving unsolicited genomic results Genet Med Off J Am Coll Med Genet 2018Jun; 21 02 311 8 PMC6294706 29904163
  33. [38]Abejirinde I OO Douwes R Bardaji A Abugnaba-Abanga R Zweekhorst M van Roosmalen J Pregnant women’s experiences with an integrated diagnostic and decision support device for antenatal care in Ghana BMC Pregnancy Childbirth 2018 18 01 209 29871596 PMC5989381
  34. [40]Nguyen K A Patel H Haggstrom D A Zillich A J Imperiale T F Russ A L 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 31623616 PMC6798472
  35. [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 2018Oct; 27 05 1392 400 29383820
  36. [42]Chokshi S K Belli H M Troxel A B Blecker S Blaum C Testa P 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 30820339 PMC6381676
  37. [43]Dehlendorf C Reed R Fitzpatrick J Kuppermann M Steinauer J Kimport KA 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 31404538
  38. [44]Pirnejad H Amiri P Niazkhani Z Shiva A Makhdoomi K Abkhiz S Preventing potential drug-drug interactions through alerting decision support systems: A clinical context based methodology. Int J Med Inform 2019 Jul;127:18–26 31128828
  39. [45]Alagiakrishnan K Ballermann M Rolfson D Mohindra K Sadowski C A Ausford A Utilization of computerized clinical decision support for potentially inappropriate medications Clin Interv Aging 2019 14 753 62 31118596 PMC6500432
  40. [46]Singh K Johnson L Devarajan R Shivashankar R Sharma P Kondal D 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 2018Dec; 35 12 1644 54 30142228
  41. [47]Vousden N Lawley E Nathan H L Seed P T Brown A Muchengwa T 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 29703254 PMC5924508
  42. [48]Cox J L Parkash R Abidi S S Thabane L Xie F MacKillop J 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 29807323
  43. [49]Austrian J S Jamin C T Doty G R 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 PMC7646853 29025165
  44. [50]Schild S Sedlmayr B Schumacher A K Sedlmayr M Prokosch H U 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 31033445 PMC6658227
  45. [51]Blecker S Pandya R Stork S Mann D Kuperman G Shelley D Interruptive Versus Noninterruptive Clinical Decision Support: Usability Study JMIR Hum Factors 2019 6 02 e12469 30994460 PMC6492060
  46. [52]Thayer J G Miller J M Fiks A G Tague L Grundmeier R W Assessing the Safety of Custom Web-Based Clinical Decision Support Systems in Electronic Health Records: A Case Study Appl Clin Inform 2019Mar; 10 02 237 46 30943572 PMC6447398
  47. [53]Winthereik A K Neergaard M A Jensen A B 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 29925332 PMC6011239
  48. [54]Baysari M T Zheng W Y Li L Westbrook J Day R O Hilmer S 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 PMC6701635 31427312
  49. [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 30753316
  50. [56]Grout R W Cheng E R Carroll A E Bauer N S Downs S M A six-year repeated evaluation of computerized clinical decision support system user acceptability Int J Med Inform 2018 112 74 81 29500025 PMC5836810
  51. [57]Nanji K C Seger D L Slight S P Amato M G Beeler P E Her Q L Medication-related clinical decision support alert overrides in inpatients J Am Med Inform Assoc 2018 25 05 476 81 29092059 PMC7646870
  52. [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 2018Apr; 26 02 112 8 28851026
  53. [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 29973225 PMC6031172
  54. [60]Yoshida E Fei S Bavuso K Lagor C Maviglia S The Value of Monitoring Clinical Decision Support Interventions Appl Clin Inform 2018Jan; 9 01 163 73 29514353 PMC5842071
  55. [62]Bubp J L Park M A Kapusnik-Uner J Dang T Matuszewski K Ly D Successful deployment of drug-disease interaction clinical decision support across multiple Kaiser Permanente regions J Am Med Inform Assoc 2019 26 10 905 10 30986823 PMC7647201
  56. [63]Saman D M Walton K M Harry M L Asche S E Truitt A R Henzler-Buckingham H A 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/ PMC6937782 31888630
  57. [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 2018Aug; 38 04 46 54 PMC6080211 30068720
  58. [66]Tu M H Chang P Lee Y L Avoiding Obsolescence in Mobile Health: Experiences in Designing a Mobile Support System for Complicated Documentation at Long-term Care Facilities Comput Inform Nurs 2018Oct; 36 10 501 6 30045129
  59. [67]Helmle K E Edwards A L Kushniruk A W Borycki E M 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 4640 29395845
  60. [68]Salz T Schnall R B McCabe M S Oeffinger K C Corcoran S Vickers A J Incorporating Multiple Perspectives Into the Development of an Electronic Survivorship Platform for Head and Neck Cancer JCO Clin Cancer Inform 2018 2 1 15 PMC6540969 30652547
  61. [69]Wright A Aaron S Seger D L Samal L Schiff G D Bates D W 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 29766382 PMC6206354
  62. [70]Grabel M Z Vaughan B L Dexheimer J W Kirkendall E S Mathematical Model for Computer-Assisted Modification of Medication Dosing Rules Biomed Inform Insights 2019 11 1.178222619829079E15 PMC6539576 31190853
  63. [71]Heringa M van der Heide A Floor-Schreudering A De Smet P AGM Bouvy M L Better specification of triggers to reduce the number of drug interaction alerts in primary care Int J Med Inform 2018 109 96 102 29195711
  64. [72]Saiyed S M Davis K R Kaelber D C Differences, Opportunities, and Strategies in Drug Alert Optimization—Experiences of Two Different Integrated Health Care Systems Appl Clin Inform 2019 10 05 777 82 31618781 PMC6795529
  65. [74]Khan S Richardson S Liu A Mechery V McCullagh L Schachter A Improving Provider Adoption With Adaptive Clinical Decision Support Surveillance: An Observational Study JMIR Hum Factors 2019 6 01 e10245 30785410 PMC6401673
  66. [75]Kawamanto K Flynn M C Kukhareva P El Halta D Hess R Gregory T 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 30815104 PMC6371304
  67. [76]Gunn L R Tunney R Kelly K Nonmodal Clinical Decision Support and Antimicrobial Restriction Effects on Rates of Fluoroquinolone Use in Uncomplicated Infections Appl Clin Inform 2018Jan; 9 01 149 55 29490408 PMC5830158
  68. [77]Khairat S Coleman C Ottmar P Bice T Koppel R Carson S S 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 31504578 PMC7647147
  69. [78]Rapoport M J Zucchero Sarracini C Kiss A Lee L Byszewski A Seitz D P Computer-Based Driving in Dementia Decision Tool With Mail Support: Cluster Randomized Controlled Trial J Med Internet Res 2018 20 05 e194 29802093 PMC5993977
  70. [79]Ramirez M Maranon R Fu J Chon J S Chen K Mangione C M 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 30060013 PMC6118864
  71. [80]Moja L PoloFriz H Capobussi M Kwag K Banzi R Ruggiero F 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 PMC6991299 31825499
  72. [81]Wasylewicz A TM Korsten E HM Egberts T CG Grouls R JE Clinical rule-guided pharmacists’ intervention in hospitalized patients with hypokalaemia: A time series analysis J Clin Pharm Ther 2020 45 03 520 9 31873951
  73. [82]Sperl-Hillen J M Crain A L Margolis K L Ekstrom H L Appana D Amundson G 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 29982627 PMC6658854
  74. [83]Cresswell K Callaghan M Mozaffar H Sheikh A NHS Scotland’s Decision Support Platform: a formative qualitative evaluationBMJ Health Care Inform 2019 26(1) PMC7062333 31160318
  75. [84]Canovas-Segura B Morales A Juarez J M 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 31071456
  76. [85]Divakaran S Singh A De Filippis E M Churchill T W Cuddy S Ge Y 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 PMC7231672 31741326 · DOI
  77. [86]Wulff A Montag S Steiner B Marschollek M Beerbaum P Karch A 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 PMC6588987 31221891
  78. [87]Rousseau J F Ip I K Raja A S Valtchinov V I Cochon L Schuur J D Can Automated Retrieval of Data from Emergency Department Physician Notes Enhance the Imaging Order Entry Process? Appl Clin Inform 2019 10 02 189 98 30895573 PMC6426724
  79. [88]Bakker T Klopotowska J E Eslami S de Lange D W van Marum R van der Sijs H 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 31409338 PMC6692933
  80. [89]Johansson-Pajala R M 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 29954263
  81. [90]Ankem K Cho S Simpson D Nurses’ perceptions and problems in the usability of a medication safety app Inform Health Soc Care 2019Jan; 44 01 48 69 29035624
  82. [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 30545312 PMC6293611
  83. [92]Thoma-Lurken T Bleijlevens M HC Lexis M AS Hamers J PH 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 2018Nov; 39 06 653 62 29858041
  84. [93]Mann D Hess R McGinn T Mishuris R Chokshi S McCullagh L Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research Digit Health 2019 5 2.055207619827716E15 PMC6376549 30792877
  85. [94]Fortin C van Schaik P Aubin-Fournier J F Bettany-Saltikov J Bernard J C Ehrmann Feldman D The acceptance of the clinical photographic posture assessment tool (CPPAT) BMC Musculoskelet Disord 2018 19 01 366 30309332 PMC6182862
  86. [95]Bertoni C B 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 31572886 PMC6708646
  87. [96]Ubanyionwu S Formea C M Anderson B Wix K Dierkhising R Caraballo P J 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 29436466
  88. [97]Biswas A Parikh C R Feldman H I Garg A X Latham S Lin H Identification of patients expected to benefit from electronic alerts for acute kidney injury Clin J Am Soc Nephrol 2018 13 06 842 9 29599299 PMC5989673
  89. [98]Kizzier-Carnahan V Artis K A Mohan V Gold J A Frequency of Passive EHR Alerts in the ICU: Another Form of Alert Fatigue? J Patient Saf 2019Sep; 15 03 246 50 27331600 PMC5820226
  90. [99]Arts D L Medlock S K van Weert H CPM Wyatt J C 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 29672521 PMC5908177
  91. [100]Roshanov P S Fernandes N Wilczynski J M Hemens B J You J J Handler S M 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 23412440
  92. [101]Moja L Kwag K H Lytras T Bertizzolo L Brandt L Pecoraro V 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 PMC4232126 25322302
  93. [102]Cimino J J Putting the “why” in “EHR”: capturing and coding clinical cognition J Am Med Inform Asso 2019 26 11 1379 84 PMC6798564 31407781
  94. [103]Liberati E G Ruggiero F Galuppo L Gorli M González-Lorenzo M Maraldi M What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation Implement Sci 2017 12 01 113 28915822 PMC5602839
  95. [104]Meslin S MM Zheng W Y Day R O Tay E MY Baysari M T Evaluation of Clinical Relevance of Drug–Drug Interaction Alerts Prior to Implementation Appl Clin Inform 2018 9 04 849 55 30485879 PMC6261735
  96. [105]Auerbach A D Khanna R Adler-Milstein JLetting 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 PMC7174516 31745851 · DOI
  97. [108]Fitzmaurice M G Wong A Akerberg H Avramovska S Smithburger P L Buckley M S Evaluation of Potential Drug–Drug Interactions in Adults in the Intensive Care Unit: A Systematic Review and Meta-Analysis Drug Saf 2019 1 10 31098917
  98. [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 31438114
  99. [110]Zeng-Treitler Q Nelson S JWill 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/ PMC6906615 31774409

Cited by

Sorted by citations

Authors
Jankovic, I., & Chen, J. H.
Affiliations
Stanford Medicine (US)
ORCID
Venue
Yearbook of Medical Informatics
Publisher
Georg Thieme Verlag KG
Published
2020-08
Volume
29
Issue
01
Pages
145-154
ISSN
  • 0943-4747 (ISSN-L)
  • 2364-0502
DOI
10.1055/s-0040-1701986
PMC
PMC7442505
OpenAlex
W3080876114
Cited by
84
References
8
Citations by year
2026: 11 · 2025: 20 · 2024: 20 · 2023: 10 · 2022: 21 · 2021: 1 · 2020: 1
FWCI
1.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Yearbook of Medical Informatics (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health
  • National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007217)
  • National Institute of Environmental Health Sciences (K01ES026837)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › General Health Professions › Healthcare professionals’ stress and burnout
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
clinical decision support, clinician burnout, clinician feedback, outcome measurement, clinician workload
MeSH terms
Alert Fatigue, Health Personnel, Burnout, Professional, Expert Systems, Humans, Decision Support Systems, Clinical, Precision Medicine, Electronic Health Records

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review