Using Human Factors to Identify System Failures in the Cardiovascular Operating Room

Transcription

1 Using Human Factors to Identify System Failures in the Cardiovascular Operating Room A data-driven approach to understanding human error associated with adverse events and potential hazards in the CVOR Jennifer Cabrera, BS Doctoral Student, Embry-Riddle Aeronautical University Tara Cohen, BA Doctoral Student, Embry-Riddle Aeronautical University Olivia D. Crowe, BS Doctoral Student, Embry-Riddle Aeronautical University Erin Pohl, BA Doctoral Student, Embry-Riddle Aeronautical University Kristen Welsh, BA, BS Doctoral Student, Embry-Riddle Aeronautical University Albert J. Boquet, PhD Professor, Embry-Riddle Aeronautical University Scott Shappell, PhD Professor and Chair, Dept. of Human Factors and Systems, Embry-Riddle Aeronautical University

2 The Cardiovascular Operating Room one of the most complex and characteristically demanding environments of clinical medicine

3 The Cardiovascular Operating Room multiple disciplines/roles within disciplines numerous technologically advanced systems continuous communication among team members coordination of care acuity level of patient

4 One Size Doesn t Fit All lack of standardized taxonomies for adverse events, errors, interruptions, etc. copious amounts of data of an equivocal nature difficult to establish causal links to the events under investigation objective classification of the data understand why hazardous events occur in a quantifiable, data-driven manner

5 FOCUS Initiative Flawless Operative Cardiovascular Unified Systems collaborative project Society of Cardiovascular Anesthesiologists (SCA) SCA Foundation (SCAF) Johns Hopkins University (JHU) Quality and Safety Research Group (QSRG) substantially decrease the incidence and severity of human error in the operating room through scientific analysis leading to culture change

6 LENS Project Locating Errors through Networked Surveillance Health Services Research Industrial Psychology Human Factors Engineering Organizational Sociology Cardiovascular Clinical Care

7 LENS Project Locating Errors through Networked Surveillance Health Services Research Industrial Psychology Human Factors Engineering Organizational Sociology Cardiovascular Clinical Care

8 Purpose can we use HFACS to identify and classify errors and potential hazards within the operational environment of the CVOR? Human Factors Analysis and Classification System (HFACS)

9 HFACS Human Factors Analysis and Classification System Swiss cheese Model of Human Error* Organizational Factors Latent Conditions Unsafe Supervision Latent Conditions Preconditions for Unsafe Acts Active and Latent Conditions Unsafe Acts Active Conditions Failed or Absent Defenses *Adapted from Reason (1990) Accident & Injury

10 Organizational Process Resource Management Organizational Climate Operational Process Unsafe Supervision Inadequate Supervision Planned Inappropriate Operations Failure To Correct Known Problem Supervisory Violation Preconditions for Unsafe Acts Environmental Factors Condition of the Operator Personnel Factors Physical Environment Technological Environment Communication, Coordination, and Planning Fitness for Duty Adverse Mental State Adverse Physiological State Physical/Mental Limitations Unsafe Acts Errors Violations Decision Errors Skill-Based Errors Perceptual Errors Routine Violations Exceptional Violations

11 Methods systematically identify and classify distinct modalities of errors from an archival data set identify discrete problems disproportionately contributing to the overall hazardous incident rate

12 Methods error

13 Methods error potential hazards anything that poses a potential or real risk to the patient, including errors, near misses, and adverse events 1 1 Karsh, et. al. (2006)

14 Methods: Data Management LENS Database (14,070) - Good Practices - Preferences or Variations - Hazards - Time Hazards (5,290) Eliminated Duplicates (3,596) Categorized/Coded (1,334) - HFACS 36.81% (491)

15 Methods no error/hazard/adverse event Surgeon Can you shut off my light? His light was shut off. Thank you. no human error (e.g. mechanical) Their Echo is Phillips 5500 that they define as an old stable work horse. However, since it is too old, it has some software issues. Mainly it freezes up while using it. Probe tends to get hot quickly and shuts off automatically. Anesthesiology provider scrubs in, puts on gloves, and starts inserting a central line using the probe and Echo, but in 5 minutes the Echo shuts off.

16 Methods medical issues The anesthesia resident had trouble placing the central line. Made numerous attempts to place the line on the right side of the neck without success. The anesthesia fellow was teaching, stick the wire all the way in. Finally, the fellow took over and he too was unable to place the line despite multiple attempts. not enough information Anesthesia fellow left the room.

17 Methods opinion Surgeon communicated with CRNA about what his goals for BP are for post-op. Communication pattern was condescending. provider preferences Practice variation. Did not use occlusive dressing over the chest tube.

18 Methods no active failure They use Baxter pumps (not Smart IV pumps) that do not have drug libraries and very limited safety features. They tape the medication names on the pump as these cannot be electronically programmed. They are currently in the process of changing small volume pumps (outside the OR area) to Braun pump and will change these large volume pumps in the OR eventually, however limited financial resources (given how costly these new pumps are) is a big issue.

19 Methods an excellent point Surgeon asked perfusionist to tell him the time every 15 min (as opposed to 20 min due to patient s condition) for in-between cardioplegia. However, there is no reminder mechanism/alarm for the perfusionist other than watching the alarm closely (which she may be too busy to do or forget to do). So, she may forget to tell the surgeon the time.

20 Results Organizational Process Resource Management Organizational Climate Operational Process Unsafe Supervision Inadequate Supervision Planned Inappropriate Operations Failure To Correct Known Problem Supervisory Violation Preconditions for Unsafe Acts Environmental Factors Condition of the Operator Personnel Factors Physical Environment Technological Environment Communication & Coordination Planning Fitness for Duty Adverse Mental State Adverse Physiological State Physical/Mental Limitations Unsafe Acts Errors Violations Decision Errors Skill-Based Errors Perceptual Errors Routine Violations Exceptional Violations

21 Results: HFACS Analysis Frequency of Errors and Potential Hazards Organizational Influences Operational Process Organizational Climate Resource Management Supervisory Violation Unsafe Supervision Failure to correct known problem Planned Inappropriate Operations Inadequate Supervision Fitness for Duty Communication/Coordination/Planning 0.54 Preconditions for Unsafe Acts Physical-Mental Limitations Adverse Physiological State Adverse Mental State Technological Environment Physical Environment 0.04 Violations 0.29 Unsafe Acts Perceptual Errors Skill-Based Errors Decision Errors

22 Results: HFACS Analysis Frequency of Errors and Potential Hazards by Unsafe Acts and Preconditions Communication/Coordination/Planning e.g. Surgeon: Go back up. Perfusionist started to bring the flow up, surgeon had meant to bring the patient's head up

23 Results: HFACS Analysis Frequency of Errors and Potential Hazards by Unsafe Acts and Preconditions Violations e.g. Anesthesiologist did not wear gloves for intubation

24 Results: HFACS Analysis Frequency of Errors and Potential Hazards by Unsafe Acts and Preconditions Skill-Based Errors e.g. A second A-line was needed and the anesthesiologists added the A-line to original. He did not place a stopcock to obtain ABG. Had to disconnect, add stopcock, draw lab which was very cumbersome

25 HFACS: Fine-grained analysis What were the specific types of issues? develop nanocodes for various categories

26 Results: HFACS Fine-Grained Analysis Communication, Coordination, and Planning (CCP) Communication, Coordination, Planning lack of clarity e.g. Anesthesia attending to CRNA: You can give him a tad of sedation

27 Results: HFACS Fine-Grained Analysis Violations (V)

28 Results: HFACS Fine-Grained Analysis Violations (V) Violations - hand washing e.g. After manipulating the airway and throwing away the laryngoscope blade the operator of the central line did not wash his hands, put on the sterile gown and gloves

29 Results: HFACS Fine-Grained Analysis Violations (V)

30 Results: HFACS Fine-Grained Analysis Violations (V) skin antisepsis incorrect skin antisepsis incomplete/not at all skin antisepsis cross contamination skin antisepsis (drying) skin antisepsis application technique

31 Results: HFACS Fine-Grained Analysis Skill-based Errors (SBE) Skill-based Errors - memory failures e.g. Anesthesiologist: Can I get that gas? Perfusionst: I m sorry, I completely forgot about that. The first ABG of the case was never run attention memory technique

32 Conclusions We were able to classify 100% of those errors and potential hazards identified in the LENS data heavily represented in Unsafe Acts (41%) and Preconditions for Unsafe Acts (62%) tiers largest percentage accounted for by Communication, Coordination, and Planning (54%) disproportionate number of potential hazards in the form of Violations (29%) followed by Skill-based Errors (9%)

33 Discussion can migrate the framework; however, the failure modes are different e.g. HFACS in aviation industry SBE (GA 80%; CA 60%) V (15-20% typical) CCP (<20%)

34 Discussion if we go by this data, medicine is not as similar as we thought it was to aviation and other high-risk industries/complex systems can t just migrate over interventions; e.g. off-theshelf CRM off the shelf and apply to medicine need to really understand what the issues are by analyzing the data

35 Thank You Questions? Contacts: Jennifer Cabrera, Scott Shappell, Bert Boquet,