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Usability and Human Factors Unit 10: Designing for Safety Lecture b This material (Comp 15 Unit 10) was developed by Columbia University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 1U24OC000003. This material was updated by The University of Texas Health Science Center at Houston under Award Number 90WT0006. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/.
Designing for Safety Lecture b – Learning Objectives • Apply the cognitive taxonomy of errors (Lecture b) • Define “workflow analysis” and methods for examining and addressing human errors (Lecture b) 2
Woods and Colleagues: Resilience Engineering • Challenger disaster an example of: – Drift toward failure as defenses erode in the face of production pressure. – An organization that takes past success as a reason for confidence instead of investing in anticipating the changing potential for failure. – Fragmented problem solving process that clouds the big picture. – Failure to revise assessments as new evidence accumulates. – Breakdowns at the boundaries of organizational units that impedes communication and coordination. (Hollnagel, E., Woods, D.D., and Leveson, N., 2006) 3
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Woods and Colleagues: Challenger Analysis • Interpretation of past “success”. The absence of failure is taken as positive indication that hazards are not present or that countermeasures are effective. • An organization usually is unable to change its model of itself unless and until overwhelming evidence accumulates that demands revising the model. (Hollnagel, E., Woods, D.D., and Leveson, N., 2006) 4
‘Failure of Foresight’ • Focus on differences people see no lessons for own operations, narrow well bounded responses • Crux is to notice info that changes past models, without clear cut evidence • Provide ‘fresh’ view by: – New people, interactions across diverse groups, knowledge, tools, new visualizations which capture big picture, reorganize data into different perspectives 5
Woods and Colleagues: Detecting Danger • Cross-checking, collaborations • Display safety margin indicators • “Errors will always be there” – can’t have 100% perfection, but anticipate and avoid risk situations, or handle appropriately 6
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Woods – Resilience Engineering (Cont’d – 1) 7 • Based on insights from above 5 patterns • Assessing organization risk, i.e. that holes in decision making will produce unrecognized drift to failure boundary • Assessing technical hazards, but goal is to monitor decision making • Balance production pressures with protection pressures • Management commitment to above • Truly open and encouraged reporting
Resilience Engineering • Learning culture v. culture of denial reflected in incident response • Preparedness / Anticipation • Opacity / Observability • Flexibility / Stiffness • Revise / Fixated 8
Resilience Engineering – 3 Basics 1. Detecting signs of increasing organizational risk, especially when production pressures are intense or increasing; 2. Having the resources and authority to make extra investments in safety at precisely these times when it appears least affordable; 3. Having a means to recognize when and where to make targeted investments to control rising signs of organizational risk and re-balance the safety and production tradeoff. 9
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Reason’s Swiss Cheese Model Duke University,   ("Anatomy of an Error", 2016) 10
Catastrophes Duke University,   ("Anatomy of an Error", 2016) 11
Failure Factors and Recovery Zhang, J., Patel, L.V., Johnson, R. T., &. Shortliffe, H.E. (2004). 12
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Patel, Cohen – Error in Critical Care • Distribution of cognition can result in vulnerabilities • Increased complexity, interruptions (Laxmisan: every 9 min for attendings, 14 min for residents) Patel, V.L, , & Cohen, T. (2008). 13
Time Course of Medical Errors Cohen, T., Blatter, B., Almeida, C., Patel VL. (2007). 14
Error Detection and Correction • Poorly understood, but integral to all cognitive work • Bounds of acceptable practice violated, usually corrected (e.g. prescribe neglected med) • Failure to detect this > cross another boundary > adverse event • Maximal productivity > strains system, shifts balance between error commission and correction (due to cognitive capacity strain) 15
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Workflow Analysis and Modeling (Malhotra and Colleagues: 2006) • Detailed characterization of individual workflows • ID critical events • Reconstruct collective workflow from events connected in space or time, done collaboratively • Delineate the workflow, role players, devices, protocols, and communications so that we can identify and focus on areas where cognitive aids, technology or interventions may be of assistance. • Develop a generalizable cognitive model to represent the intricate workflow applicable to other health care settings 16
Schematic Layout of the Cardio Thoracic Intensive Care Unit (CTICU) & Key Activities (Malhotra et al 2007) Malhotra, S., Jordan, D., Shortliffe, E., Patel, V.L. (2007). 17
CTICU Critical Zones - Examples Critical zones with descriptions and examples from CTICU ethnographic study. Critical zone (CZ) Examples of activities Description Re-orientation and preliminary planning Resident change (handoff) Morning sign-out by (night) nurses and residents to their in-coming counterparts. Nurse change (handoff) Re-orientation to patients and condition, assessment of criticality “ICU assessment” by attending Preliminary determination of rounding sequence and future admission/discharge plans Goal formulation Morning clinical rounds Morning rounds (patient management goals determined); Procedures and patient care activities conducted during rounds. Finalization of admission/discharge plan Goal execution Post clinical round resident activity Documentation of management plan, orders for patients, charting carried out Post clinical round nurse activity Patient care activities based on management plan discussed in clinical round are carried out; Consults from health personal external to the clinical team are obtained Transfers Patient transfer from OR to ICU Patients ready for step down care are prepared and transferred Transfers Transfer of existing patient in ICU to floors (wards); Transfer summary continued management plan documented, initiated and communicated to the clinical unit receiving the patient. Jiajie, Z., Vimla, P.L., Johnson, T.R., Shortliffe. E.H. (2004). 18
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Intensive Care Unit (ICU) and Critical Care • Almost all patients admitted to ICU suffer adverse events • 10% average mortality • 5 milllion ICU admissions/year (US) • 30% of hospital costs, $180B / year • Priority for Joint Commission and Leapfrog 19
Factors in ICU Care • Staffing: Intensivists reduce mortality (3x reduction, Pronovost, JAMA 1999) • Fewer ICU nurses increased length of stay (LOS), pulmonary complications (Pronovost ECP 2001) • Pharmacists: daily rounds: 66% reduction in ADEs (10.4/1000 pt days -> 3.5 • Principles: staff accountable, reduce complexity, independent redundancies for key processes 20
Care Goal Sheet (Pronovost) • Instituting care goals dramatically reduced length of stay from 2.2 to 1, increasing revenue from new admissions Pronovost, (2005). 21
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Critical Care Environments • Methods include ethnographic data collection, observations, surveys and questionnaires coupled with cognitive task analysis of the processes • Vankipuram (2010): RID tags: dual method, like ‘black box’ of aviation • Data trained and analyzed, visualized using virtual world replay • Israeli study: avg pt has 178 actions/day; errors 1% (Gawande, 2007); 2/day/pt 22
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Virtual World Replay Vankipuram, M., Kahol, K., Cohen, T., Patel, V.L. (2010). 23
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Cognitive Taxonomy of Error (Zhang and Colleagues: 2004) • Systematically categorize medical errors (individual level) • Better understand cognitive mechanisms of medical error • Framework to guide future studies • Interventions to decrease errors • Foundation for reporting system (to categorize, ID and generate solutions 24
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Errors • Reason: “an error is a failure of achieving the intended outcome in a planned sequence of mental or physical activities when that failure is not due to Chance” • Not all adverse events caused by error – E.g. device malfunction, – System problems (e.g. delays in care caused by organizational policies); not caused by individuals • Non-preventable: e.g. unpredictable drug reaction 25
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Cognitive Taxonomy of Error Zhang, J., Patel, L.V., Johnson, R. T., &. Shortliffe, H.E. (2004). 26
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Example of an Error and Questions It Raises (from Zhang, 2004) • Nurse tries to program infusion pump to deliver 130.1 ml/h, presses “1 3 0 . 1” • Nurse is unaware that decimal point only works for numbers up to 99.9 • Pump ignores decimal point key press and is programmed to deliver 1301 ml/h • Error blamed on user, ‘solved’ by more training 27
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Error Example (Cont’d – 1) • Questions: why did decimal point only work up to 99.9? (design flaw)? • Why does device just ignore decimal key press, rather than alerting user? • Why is nurse unaware of this flaw? • Was this problem covered in training? • Why was order written for 130.1, which pump cannot deliver? • Why did nurse not see 1301 on display? – Tiredness? Display hard to read? Understaffing? 28
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Error Taxonomy • 2 types: – slips that result from the incorrect execution of a correct action sequence and – mistakes that result from the correct execution of an incorrect action sequence. o Zhang et al: based on Norman’s 7-stage theory of action; errors can be on evaluation side as well as execution side 29
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Taxonomy Zhang, J., Patel, L.V., Johnson, R. T., &. Shortliffe, H.E. (2004). 30
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Examples From Zhang, 2004 Slip Stage in Action cycle Examples Execution slip Goal slips Doctor was called out of the room to answer an urgent call and afterwards went to the room of a different patient who was next in the queue. (Loss of activation) Execution slip Intention slip A nurse intended to enter the rate of infusion using the up-down arrow keys, because this is the technique required on the pump she most frequently uses; however, on this pump the arrow keys move the selection region instead of changing the selected number (capture) Execution slip Action specification slips A nurse intends to decrease a value using the decrement function, but pushes the down arrow key (which moves to the next field) instead of the minus key. (Associative activation) Execution slip Action execution slips “I meant to turn off the antibiotics IV only, but turned off the infusion pump completely” (Double capture) 1.1 Table: Patel, V.L, , & Cohen, T. (2008). 31
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Examples From Zhang, 2004 (Cont’d – 1) Slip Stage in Action cycle Examples ( From Zhang, 2004) Execution slip Goal slips Doctor was called out of the room to answer an urgent call and afterwards went to the room of a different patient who was next in the queue. (Loss of activation) Execution slip Intention slip A nurse intended to enter the rate of infusion using the up-down arrow keys, because this is the technique required on the pump she most frequently uses; however, on this pump the arrow keys move the selection region instead of changing the selected number (capture) Execution slip Action specificatio n slips A nurse intends to decrease a value using the decrement function, but pushes the down arrow key (which moves to the next field) instead of the minus key. (Associative activation) Execution slip Action execution slips “I meant to turn off the antibiotics iv only, but turned off the infusion pump completely” (Double capture) 1.2 Table: Patel, V.L, , & Cohen, T. (2008). 32
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Examples From Zhang, 2004 (Cont’d – 2) Mistakes Stage in action cycle Examples (From Zhang, 2004) Execution mistakes Goal mistakes Incorrect diagnosis due to neglect of base rate information (Biases) Intention mistakes A physician treating a patient with oxygen set the flow control knob between 1 and 2 liters per minute, not realizing that the scale numbers represented discrete, rather than continuous settings (Incomplete knowledge) Action specification mistakes Strange burn scars appear in post-operative patients in a hospital. The problem was caused by electric discharge of a device that was not grounded. The device has a blinking red light to signal the problem, but the device operators did not know the meaning of the signal. (Incomplete knowledge) Action specification mistakes For example, a perfect knowledge of a surgical procedure may not lead to a successful surgical operation if the operator has not extensively practiced the procedure. (Dissociation between knowledge and rules) 1.3 Table: Patel, V.L, , & Cohen, T. (2008). 33
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Examples From Zhang, 2004 (Cont’d – 3) Evaluation Mistakes Perception mistakes A pharmacists filling prescription for Lamisil (an antifungal) mistakenly perceived Lamictal (an anticonvulsant) as Lamisil because he mistakenly expected it since he was looking for Lamisil. (Misperception) Evaluation Mistakes Interpretation mistakes A steady green light on an infusion pump means the device is ready, and a flashing green light indicates an infusion is in progress. The device user did not know the meaning of the steady green light, and correctly interpreted it as an indication that the infusion had begun. (Incorrect knowledge) Evaluation Mistakes Action evaluation mistakes In the infusion pump example the user may not know that the device has accepted the volume, and may then assume that the goal (‘set volume to be infused at 1000cc’) has not been accomplished, leading to a search for additional buttons (such as ‘enter’) to complete the goal (Incomplete knowledge) 1.4 Table: Patel, V.L, , & Cohen, T. (2008). 34
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Cognitive Interventions • Depend on type of slip or mistake – e.g. education, decision support, representational aid, information reduction, display design, device redesign • Aids for perceptual systems – Example: if intention slip due to loss of activation in memory, give memory aid (‘Press Volume to enter volume to be infused’). 35
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Errors - Context • Contrary to expectation, most errors can happen at times of low productivity • Personnel will arrange items for maximal functioning during peak times (Xiao, 1995) 36
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Designing for Safety Summary – Lecture b • This unit examine cognitive taxonomies in error and reviewed various studies on source of errors 37
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Designing for Safety References – Lecture b References Cohen T, Blatter B, Almeida C, Patel VL. (2007). Reevaluating recovery: perceived violations and preemptive interventions on emergency psychiatry rounds. J Am Med Inform Assoc. 2007 May- Jun;14(3):312-9.) Gawande A. (2007). The checklist.  Retrieved  on September 10th, 2010 from The New Yorker, December 10 2007.  Available at  http://www.newyorker.com/reporting/2007/12/10/071210fa_fact_gawande?currentPage=2 Hollnagel, E., Woods, D.D., and Leveson, N. (2006). Resilience engineering: concepts and precepts. Publisher, Ashgate Publishing Limited, Burlington, VT. Hollnagel, E., Woods, D.D., and Leveson, N. (2006). Resilience engineering: concepts and precepts. Publisher, Ashgate Publishing Limited, Burlington, VT. Malhotra S, Jordan D, Shortliffe E, Patel VL. Workflow modeling in critical care: piecing together your own puzzle. J Biomed Inform. 2007 Apr;40(2):81-92. Patel VL, Cohen T. (2008). Error in Critical Care. Curr Opin Crit Care. 2008 Aug;14(4):456-9 Pronovost, PJ,Jenckes, MW,Dorman, T., Garrett, E., Breslow, MJ, Rosenfeld, BA, Lipsett, PA, Bass, E. (19990. Introduction to patient safety research. JAMA. 1999;281(14):1310-1317. http://www.slideshare.net/changezkn/pronovost-ppt-918kb . Vankipuram M, Kahol, K, Cohen, T, Patel, VL. Toward automated workflow analysis and visualization in clinical environments. J Biomed Inform (2010) Xiao Y. Artifacts and collaborative work in healthcare: methodological, theoretical, and technological implications of the tangible. Journal of Biomedical Informatics. 2005 February 2005;38(1):26-33. Zhang, J., Patel, L.V., Johnson, R. T., &. Shortliffe, H.E. (2004). A cognitive taxonomy of medical errors. Journal of Biomedical Informatics 37:193–204 38
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Designing for Safety References – Lecture b (Cont’d – 1) Images Slides 10, 11: Anatomy of an Error . (2016).  Patientsafetyed.duhs.duke.edu . Retrieved 30 June 2016, from http://patientsafetyed.duhs.duke.edu /module_e/swiss_cheese.html Slide 12: Zhang, J., Patel, L.V., Johnson, R. T., &. Shortliffe, H.E. (2004). A cognitive taxonomy of medical errors. Journal of Biomedical Informatics 37:193–204 Slide 13: Patel, V.L, , & Cohen, T. (2008). Error in Critical Care. Curr Opin Crit Care. 2008 Aug;14(4):456-9. Slide 14: Cohen, T., Blatter, B., Almeida, C., Patel VL. (2007). Reevaluating recovery: perceived violations and preemptive interventions on emergency psychiatry rounds. J Am Med Inform Assoc. 2007 May-Jun;14(3):312-9. Slide 17: Malhotra, S., Jordan, D., Shortliffe, E., Patel, V.L. (2007). Workflow modeling in critical care: piecing together your own puzzle. J Biomed Inform. 2007 Apr;40(2):81-92. Slide 18: Jiajie, Z., Vimla, P.L., Johnson, T.R., Shortliffe. E.H. (2004).A cognitive taxonomy of medical errors. Journal of Biomedical Informatics 37 (2004) 193–204 Slide 21: Pronovost, PJ,Jenckes, MW,Dorman, T., Garrett, E., Breslow, MJ, Rosenfeld, BA, Lipsett, PA, Bass, E. (19990. Introduction to patient safety research. JAMA. 1999;281(14):1310-1317. http://www.slideshare.net/changezkn/pronovost-ppt-918kb . Slide 23: Vankipuram, M., Kahol, K., Cohen, T., Patel, V.L. (2010). Toward automated workflow analysis and visualization in clinical environments. J Biomed Inform(2010). Slide 26, 30: Zhang, J., Patel, L.V., Johnson, R. T., &. Shortliffe, H.E. (2004). A cognitive taxonomy of medical errors. Journal of Biomedical Informatics 37:193–204 39
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Usability and Human Factors Designing for Safety Lecture b This material was developed by Columbia University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 1U24OC000003. This material was updated by The University of Texas Health Science Center at Houston under Award Number 90WT0006. 40
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