Ascertaining Design Requirements for Postoperative Care Transition InterventionsFunding This study was funded by Washington University School of Medicine.
Background Handoffs or care transitions from the operating room (OR) to intensive care unit (ICU) are fragmented and vulnerable to communication errors. Although protocols and checklists for standardization help reduce errors, such interventions suffer from limited sustainability. An unexplored aspect is the potential role of developing personalized postoperative transition interventions using artificial intelligence (AI)-generated risks.
Objectives This study was aimed to (1) identify factors affecting sustainability of handoff standardization, (2) utilize a human-centered approach to develop design ideas and prototyping requirements for a sustainable handoff intervention, and (3) explore the potential role for AI risk assessment during handoffs.
Methods We conducted four design workshops with 24 participants representing OR and ICU teams at a large medical academic center. Data collection phases were (1) open-ended questions, (2) closed card sorting of handoff information elements, and (3) scenario-based design ideation and prototyping for a handoff intervention. Data were analyzed using thematic analysis. Card sorts were further tallied to characterize handoff information elements as core, flexible, or unnecessary.
Results Limited protocol awareness among clinicians and lack of an interdisciplinary electronic health record (EHR)-integrated handoff intervention prevented long-term sustainability of handoff standardization. Clinicians argued for a handoff intervention comprised of core elements (included for all patients) and flexible elements (tailored by patient condition and risks). They also identified unnecessary elements that could be omitted during handoffs. Similarities and differences in handoff intervention requirements among physicians and nurses were noted; in particular, clinicians expressed divergent views on the role of AI-generated postoperative risks.
Conclusion Current postoperative handoff interventions focus largely on standardization of information transfer and handoff processes. Our design approach allowed us to visualize accurate models of user expectations for effective interdisciplinary communication. Insights from this study point toward EHR-integrated, “flexibly standardized” care transition interventions that can automatically generate a patient-centered summary and risk-based report.
Keywordcontinuity of care - care transition - requirements analysis and design - handoffs - surgery - anesthesia - intensive and critical care - machine learning
Protection of Human and Animal Subjects
The study design was approved by the Washington University School of Medicine Institutional Review Board and consents were obtained from all participants.
Eingereicht: 20. August 2020
Angenommen: 10. November 2020
24. Februar 2021 (online)
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- 1 Segall N, Bonifacio AS, Schroeder RA. et al; Durham VA Patient Safety Center of Inquiry. Can we make postoperative patient handovers safer? A systematic review of the literature. Anesth Analg 2012; 115 (01) 102-115
- 2 Nagpal K, Arora S, Abboudi M. et al. Postoperative handover: problems, pitfalls, and prevention of error. Ann Surg 2010; 252 (01) 171-176
- 3 Catchpole KR, de Leval MR, McEwan A. et al. Patient handover from surgery to intensive care: using Formula 1 pit-stop and aviation models to improve safety and quality. Paediatr Anaesth 2007; 17 (05) 470-478
- 4 Agarwal HS, Saville BR, Slayton JM. et al. Standardized postoperative handover process improves outcomes in the intensive care unit: a model for operational sustainability and improved team performance*. Crit Care Med 2012; 40 (07) 2109-2115
- 5 Petrovic MA, Aboumatar H, Baumgartner WA. et al. Pilot implementation of a perioperative protocol to guide operating room-to-intensive care unit patient handoffs. J Cardiothorac Vasc Anesth 2012; 26 (01) 11-16
- 6 McElroy LM, Collins KM, Koller FL. et al. Operating room to intensive care unit handoffs and the risks of patient harm. Surgery 2015; 158 (03) 588-594
- 7 France DJ, Slagle J, Schremp E. et al. Impact of patient handover structure on neonatal perioperative safety. J Perinatol 2019; 39 (03) 453-467
- 8 France DJ, Slagle J, Schremp E. et al. Defining the epidemiology of safety risks in neonatal intensive care unit patients requiring surgery. J Patient Saf 2020
- 9 Northway T, Krahn G, Thibault K. et al. Surgical suite to pediatric intensive care unit handover protocol: implementation process and long-term sustainability. J Nurs Care Qual 2015; 30 (02) 113-120
- 10 Shah ACO, Oh DC, Xue AH, Lang JD, Nair BG. An electronic handoff tool to facilitate transfer of care from anesthesia to nursing in intensive care units. Health Informatics J 2019; 25 (01) 3-16
- 11 Yang J-G, Zhang J. Improving the postoperative handover process in the intensive care unit of a tertiary teaching hospital. J Clin Nurs 2016; 25 (7,8): 1062-1072
- 12 Gleicher Y, Mosko JD, McGhee I. Improving cardiac operating room to intensive care unit handover using a standardised handover process. BMJ Open Qual 2017; 6 (02) e000076
- 13 Lane-Fall MB, Pascual JL, Peifer HG. et al. A partially structured postoperative handoff protocol improves communication in 2 mixed surgical intensive care units: findings from the Handoffs and Transitions in Critical Care (HATRICC) prospective cohort study. Ann Surg 2018
- 14 Kamath SS, Helmers L, Otto A, Kirk D, Erdahl J, Wayling B. Operating room to pediatric intensive care unit handoff: improving communication and team relations while driving process improvement. . Accessed November 25, 2020 at: https://pdfs.semanticscholar.org/9af8/79d9c2679f30cff587e365d3dae03fcbc6fc.pdf?_ga=2.147604151.1480360050.1606310602-492149727.1565926388
- 15 Faiz T, Saeed B, Ali S, Abbas Q, Malik M. OR to ICU handoff: theory of change model for sustainable change in behavior. Asian Cardiovasc Thorac Ann 2019; 27 (06) 452-458
- 16 Gleich SJ, Nemergut ME, Stans AA. et al. Improvement in patient transfer process from the operating room to the PICU using a lean and six sigma-based quality improvement project. Hosp Pediatr 2016; 6 (08) 483-489
- 17 Chenault K, Moga M-A, Shin M. et al. Sustainability of protocolized handover of pediatric cardiac surgery patients to the intensive care unit. Paediatr Anaesth 2016; 26 (05) 488-494
- 18 Joy BF, Elliott E, Hardy C, Sullivan C, Backer CL, Kane JM. Standardized multidisciplinary protocol improves handover of cardiac surgery patients to the intensive care unit. Pediatr Crit Care Med 2011; 12 (03) 304-308
- 19 Krimminger D, Sona C, Thomas-Horton E, Schallom M. A multidisciplinary QI initiative to improve OR-ICU handovers. Am J Nurs 2018; 118 (02) 48-59
- 20 Mukhopadhyay D, Wiggins-Dohlvik KC, MrDutt MM. et al. Implementation of a standardized handoff protocol for post-operative admissions to the surgical intensive care unit. Am J Surg 2018; 215 (01) 28-36
- 21 Tun KS, Wai KS, Yin Y, Thein MK. Postoperative handover among nurses in an orthopedic surgical setting in Myanmar: a best practice implementation project. JBI Database Syst Rev Implement Reports 2019; 17 (11) 2401-2414
- 22 Karakaya A, Moerman AT, Peperstraete H, François K, Wouters PF, de Hert SG. Implementation of a structured information transfer checklist improves postoperative data transfer after congenital cardiac surgery. Eur J Anaesthesiol 2013; 30 (12) 764-769
- 23 Friesen MA, Herbst A, Turner JW, Speroni KG, Robinson J. Developing a patient-centered ISHAPED handoff with patient/family and parent advisory councils. J Nurs Care Qual 2013; 28 (03) 208-216
- 24 Maddox TM, Rumsfeld JS, Payne PRO. Questions for artificial intelligence in health care. JAMA 2019; 321 (01) 31-32
- 25 Spencer D, Warfel T. Card sorting: a definitive guide. . Accessed November 25, 2020 at: https://boxesandarrows.com/card-sorting-a-definitive-guide/
- 26 Kunjan K, Doebbeling B, Toscos T. Dashboards to support operational decision making in health centers: a case for role-specific design. Int J Hum Comput Interact 2019; 35 (09) 742-750
- 27 Cooper JB, Singer SJ, Hayes J. et al. Design and evaluation of simulation scenarios for a program introducing patient safety, teamwork, safety leadership, and simulation to healthcare leaders and managers. Simul Healthc 2011; 6 (04) 231-238
- 28 Fritz BA, Cui Z, Zhang M. et al. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth 2019; 123 (05) 688-695
- 29 King CR, Fritz BA, Escallier K. et al. Association between preoperative obstructive sleep apnea and preoperative positive airway pressure with postoperative intensive care unit delirium. JAMA Netw Open 2020; 3 (04) e203125-e203125
- 30 Cui Z, Fritz BA, King CR, Avidan MS, Chen Y. A factored generalized additive model for clinical decision support in the operating room. AMIA Annu Symp Proc 2019; 2019: 343-352
- 31 Lundberg SM, Nair B, Vavilala MS. et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2018; 2 (10) 749-760
- 32 Crowe M, Inder M, Porter R. Conducting qualitative research in mental health: Thematic and content analyses. Aust N Z J Psychiatry 2015; 49 (07) 616-623
- 33 National Patient Safety Goals. . Accessed November 25, 2020 at: https://www.jointcommission.org/standards/national-patient-safety-goals/
- 34 Breuer RK, Taicher B, Turner DA, Cheifetz IM, Rehder KJ. Standardizing postoperative PICU handovers improves handover metrics and patient outcomes. Pediatr Crit Care Med 2015; 16 (03) 256-263
- 35 Anwari JS. Quality of handover to the postanaesthesia care unit nurse. Anaesthesia 2002; 57 (05) 488-493
- 36 Herasevich V, Ellsworth MA, Hebl JR, Brown MJ, Pickering BW. Information needs for the OR and PACU electronic medical record. Appl Clin Inform 2014; 5 (03) 630-641
- 37 Andreae MH, Maman SR, Behnam AJ. An electronic medical record-derived individualized performance metric to measure risk-adjusted adherence with perioperative prophylactic bundles for health care disparity research and implementation science. Appl Clin Inform 2020; 11 (03) 497-514
- 38 Abraham J, Kannampallil TG, Patel VL. Bridging gaps in handoffs: a continuity of care based approach. J Biomed Inform 2012; 45 (02) 240-254
- 39 Mehdipour Ghazi M, Nielsen M, Pai A. et al; Alzheimer's Disease Neuroimaging Initiative. Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling. Med Image Anal 2019; 53: 39-46
- 40 Ghorbani A, Zou JY. Embedding for informative missingness: deep learning with incomplete data. . Accessed November 25, 2020 at: https://proceedings.allerton.csl.illinois.edu/2018/media/files/0202.pdf
- 41 Jerez JM, Molina I, García-Laencina PJ. et al. Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artif Intell Med 2010; 50 (02) 105-115
- 42 Roy G, Stewart M. Let's gamble: uncovering the impact of visualization on risk perception and decision-making. . Accessed November 25, 2020 at: http://visualdata.wustl.edu/files/lets-gamble.pdf
- 43 Ottley A, Metevier B, Han PKJ, Chang R. Visually communicating Bayesian statistics to laypersons. . Accessed November 25, 2020 at: https://www.eecs.tufts.edu/~alvittao/files/techReport.pdf
- 44 Hakone A, Harrison L, Ottley A. et al. PROACT: Iterative design of a patient-centered visualization for effective prostate cancer health risk communication. IEEE Trans Vis Comput Graph 2017; 23 (01) 601-610
- 45 Dwivedi S, Upadhyay S, Tripathi A. A working framework for the user-centered design approach and a survey of available methods. International Journal of Scientific and Research Publications. 2012; 2 (04) 12-19