Anesthesiology Control Tower: Feasibility Assessment to Support Translation (ACT-FAST)-a feasibility study protocol

Teresa M Murray-Torres, Frances Wallace, Mara Bollini, Michael S Avidan, Mary C Politi, Teresa M Murray-Torres, Frances Wallace, Mara Bollini, Michael S Avidan, Mary C Politi

Abstract

Background: Major postoperative morbidity and mortality remain common despite efforts to improve patient outcomes. Health information technologies have the potential to actualize advances in perioperative patient care, but failure to evaluate the usability of these technologies may hinder their implementation and acceptance. This protocol describes the usability testing of an innovative telemedicine-based intra-operative clinical support system, the Anesthesiology Control Tower, in which a team led by an attending anesthesiologist will use a combination of established and novel information technologies to provide evidence-based support to their colleagues in the operating room.

Methods: Two phases of mixed-methods usability testing will be conducted in an iterative manner and will evaluate both the individual components of the Anesthesiology Control Tower and their integration as a whole. Phase I testing will employ two separate "think-aloud" protocol analyses with the two groups of end users. Segments will be coded and analyzed for usability issues. Phase II will involve a qualitative and quantitative in situ usability and feasibility analysis. Results from each phase will inform the revision and improvement of the Control Tower prototype throughout our testing and analysis process. The final prototype will be evaluated in the form of a pragmatic randomized controlled clinical trial.

Discussion: The Anesthesiology Control Tower has the potential to revolutionize the standard of care for perioperative medicine. Through the thorough and iterative usability testing process described in this protocol, we will maximize the usefulness of this novel technology for our clinicians, thus improving our ability to implement this innovation into the model of care for perioperative medicine.

Trial registration: The study that this protocol describes has been registered in clinicaltrials.gov as NCT02830126.

Keywords: Clinician decision support; Feasibility; Health information technology; Human-computer interaction; Telemedicine; Usability.

Conflict of interest statement

Approval for this protocol was obtained from the Washington University Institutional Review Board (IRB #201611035).Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
AlertWatch Tower Mode census view. From this view, clinicians in the ACT can obtain a brief overview of all the patients in the ORs. Alerts and abnormal physiologic and laboratory parameters are represented by squares and triangles, respectively; checkmarks indicate alerts that must be addressed by the ACT. These groups of alerts are unique to the AW Tower Mode and will be refined based on the results of the present study. Clicking on an OR accesses the detailed information for that OR
Fig. 2
Fig. 2
AlertWatch Tower Mode patient display. Organ systems are depicted and labeled with relevant physiologic variables and values. Colors outlining organs indicate normal (green), marginal (yellow), or abnormal function (red). The left side of the display shows patient characteristics and case information. Information regarding the actual patient’s comorbidities can be accessed by selecting the organ system or laboratory study of interest. Text alerts are present on the right-hand side of the screen. The black checkmark at the bottom of the left panel indicates that there is an active alert for the ACT clinicians to address; clicking on the checkmark opens the case review dialogue (Figure 3)
Fig. 3
Fig. 3
Case review. This popup window allows physicians in the ACT (ACTors) to document their assessment of alerts and what actions they would recommend. This is a feature of AlertWatch that is unique to the ACT Tower Mode platform. ACTors successfully assess and address an alert by documenting their assessment of the significance of the alert and by documenting what action they would recommend taking, if any

References

    1. Lee TH, et al. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation. 1999;100(10):1043–1049. doi: 10.1161/01.CIR.100.10.1043.
    1. Turrentine FE, et al. Surgical risk factors, morbidity, and mortality in elderly patients. J Am Coll Surg. 2006;203(6):865–877. doi: 10.1016/j.jamcollsurg.2006.08.026.
    1. Bilimoria KY, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5):833-42.e1-3. doi: 10.1016/j.jamcollsurg.2013.07.385.
    1. Kheterpal S, et al. Predictors of postoperative acute renal failure after noncardiac surgery in patients with previously normal renal function. Anesthesiology. 2007;107(6):892–902. doi: 10.1097/01.anes.0000290588.29668.38.
    1. Aronson S, et al. Intraoperative systolic blood pressure variability predicts 30-day mortality in aortocoronary bypass surgery patients. Anesthesiology. 2010;113(2):305–312. doi: 10.1097/ALN.0b013e3181e07ee9.
    1. Biccard BM, Rodseth RN. What evidence is there for intraoperative predictors of perioperative cardiac outcomes? A systematic review. Perioper Med (Lond) 2013;2(1):14. doi: 10.1186/2047-0525-2-14.
    1. Walsh M, et al. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology. 2013;119(3):507–515. doi: 10.1097/ALN.0b013e3182a10e26.
    1. Nair BG, et al. Anesthesia information management system-based near real-time decision support to manage intraoperative hypotension and hypertension. Anesth Analg. 2014;118(1):206–214. doi: 10.1213/ANE.0000000000000027.
    1. Lipton JA, et al. Impact of an alerting clinical decision support system for glucose control on protocol compliance and glycemic control in the intensive cardiac care unit. Diabetes Technol Ther. 2011;13(3):343–349. doi: 10.1089/dia.2010.0100.
    1. Sathishkumar S, et al. Behavioral modification of intraoperative hyperglycemia management with a novel real-time audiovisual monitor. Anesthesiology. 2015;123(1):29037.
    1. Morris AH, Hirshberg E, Sward KA. Computer protocols: how to implement. Best Pract Res Clin Anaesthesiol. 2009;23(1):51–67. doi: 10.1016/j.bpa.2008.09.002.
    1. Richardson WC, et al. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: Institute of Medicine, National Academy Press; 2001.
    1. The Hypothermia after Cardiac Arrest Study Group. Mild therapeutic hypothermia to improve the neurologic outcome after cardiac arrest. N Engl J Med. 2002;346(8):549–56.
    1. DesRoches CM, et al. Electronic health records in ambulatory care—a national survey of physicians. N Engl J Med. 2008;359(1):50–60. doi: 10.1056/NEJMsa0802005.
    1. HIMSS Usability Task Force, Defining and Testing EMR Usability: Principles and Proposed Methods of EMR Usability Evaluation and Rating, Healthcare Information and Management Systems Society, Chicago, IL; 2009.
    1. Rose AF, et al. Using qualitative studies to improve the usability of an EMR. J Biomed Inform. 2005;38(1):51–60. doi: 10.1016/j.jbi.2004.11.006.
    1. Hornbæk K. Current practice in measuring usability: challenges to usability studies and research. International Journal of Human-Computer Studies. 2006;64(2):79–102. doi: 10.1016/j.ijhcs.2005.06.002.
    1. Daniels J, et al. A framework for evaluating usability of clinical monitoring technology. J Clin Monit Comput. 2007;21(5):323–330. doi: 10.1007/s10877-007-9091-y.
    1. Zahabi M, Kaber DB, Swangnetr M. Usability and safety in electronic medical records interface design a review of recent literature and guideline formulation. Hum Factors. 2015;57(5):805–834. doi: 10.1177/0018720815576827.
    1. Seffah A, et al. Usability measurement and metrics: a consolidated model. Softw Qual J. 2006;14(2):159–178. doi: 10.1007/s11219-006-7600-8.
    1. Rubin J. Handbook of Usability Testing. NewYork: Wiley; 1994.
    1. Standardization, I.O.f . ISO 9241-11: ergonomic requirements for office work with visual display terminals (VDTs): part 11: guidance on usability. 1998.
    1. Abran A, et al. Usability meanings and interpretations in ISO standards. Softw Qual J. 2003;11(4):325–338. doi: 10.1023/A:1025869312943.
    1. Kushniruk AW, Patel VL. Cognitive and usability engineering methods for the evaluation of clinical information systems. J Biomed Inform. 2004;37(1):56–76. doi: 10.1016/j.jbi.2004.01.003.
    1. Nielsen J. Estimating the number of subjects needed for a thinking aloud test. International Journal of Human-Computer Studies. 1994;41:385–397. doi: 10.1006/ijhc.1994.1065.
    1. Hart SG, Staveland LE. Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv Psychol. 1988;52:139–183. doi: 10.1016/S0166-4115(08)62386-9.
    1. Ahmed A, et al. The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance. Crit Care Med. 2011;39(7):1626–1634. doi: 10.1097/CCM.0b013e31821858a0.
    1. Young G, Zavelina L, Hooper V. Assessment of workload using NASA Task Load Index in perianesthesia nursing. Journal of PeriAnesthesia Nursing. 2008;23(2):102–110. doi: 10.1016/j.jopan.2008.01.008.
    1. Brooke J. SUS-A quick and dirty usability scale. Usability Evaluation in Industry. 1996;189(194):4–7.
    1. Lewis JR. IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. International Journal of Human-Computer Interaction. 1995;7(1):57–78. doi: 10.1080/10447319509526110.
    1. Bangor A, Kortum PT, Miller JT. An empirical evaluation of the system usability scale. Intl Journal of Human–Computer Interaction. 2008;24(6):574–594. doi: 10.1080/10447310802205776.
    1. Strauss AL, Corbin J, editors. Basics of qualitative research: grounded theory procedures and techniques. Thousand Oaks: Sage Publications; 1990.
    1. Patton MQ. Qualitative research & evaluation methods. Thousand Oaks: 2001 Sage Publications; 2014.
    1. Bevan N, Macleod M. Usability measurement in context. Behav Inform Technol. 1994;13(1–2):132–145. doi: 10.1080/01449299408914592.
    1. Quesenbery W. Balancing the 5Es of usability. Cutter IT Journal. 2004;17(2):4–11.
    1. Yen PY, Bakken S. Review of health information technology usability study methodologies. J Am Med Inform Assoc. 2012;19(3):413–422. doi: 10.1136/amiajnl-2010-000020.
    1. Jaspers MW. A comparison of usability methods for testing interactive health technologies: methodological aspects and empirical evidence. Int J Med Inform. 2009;78(5):340–353. doi: 10.1016/j.ijmedinf.2008.10.002.
    1. Leslie SJ, et al. Clinical decision support software for management of chronic heart failure: development and evaluation. Comput Biol Med. 2006;36(5):495–506. doi: 10.1016/j.compbiomed.2005.02.002.
    1. Li AC, et al. Integrating usability testing and think-aloud protocol analysis with “near-live” clinical simulations in evaluating clinical decision support. Int J Med Inform. 2012;81(11):761–772. doi: 10.1016/j.ijmedinf.2012.02.009.
    1. Kushniruk AW, Patel VL, Cimino JJ. Usability testing in medical informatics: cognitive approaches to evaluation of information systems and user interfaces. Proc AMIA Symp. 1997. p. 218–22.
    1. Jaspers MW, et al. The think aloud method: a guide to user interface design. Int J Med Inform. 2004;73(11):781–795. doi: 10.1016/j.ijmedinf.2004.08.003.
    1. Willis GB. Cognitive interviewing : a tool for improving questionnaire design. Thousand Oaks: Sage Publications. xii; 2005.
    1. Middleton B, et al. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc. 2013;20(e1):e2–e8. doi: 10.1136/amiajnl-2012-001458.
    1. Kushniruk AW, et al. Emerging approaches to usability evaluation of health information systems: towards in-situ analysis of complex healthcare systems and environments. Studies in Health Technology and Informatics. 2010;169:915–919.
    1. Lundgren-Laine H, Salantera S. Think-aloud technique and protocol analysis in clinical decision-making research. Qual Health Res. 2010;20(4):565–575. doi: 10.1177/1049732309354278.
    1. Zhang J, Walji MF. TURF: toward a unified framework of EHR usability. J Biomed Inform. 2011;44(6):1056–1067. doi: 10.1016/j.jbi.2011.08.005.

Source: PubMed

3
Tilaa