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A Proposed Method for the Measurement of Anesthetist Care Variability

This document explores a new method for measuring variability in anesthetist care, as presented by Paul King and collaborators. Definitions crucial to understanding anesthesiology include the differentiations between anesthesiologists and anesthetists. The significance of the study is underscored by the prevalence of anesthetics administered in the U.S. and the importance of reducing human error in the surgical environment. The proposed method utilizes sophisticated simulators to evaluate anesthetists' abilities to maintain patient homeostasis during procedures and aims to enhance safety through standardized assessments of anxiety management.

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A Proposed Method for the Measurement of Anesthetist Care Variability

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  1. A Proposed Method for the Measurement of Anesthetist Care Variability Paul King

  2. Definitions: • Anesthesiology = the practice of medicine dedicated to the relief of pain and total care of the surgical patient during and after surgery. • Anesthesiologist = MD trained (4+4+4) • Anesthetist = MD, CRNA (4+3), …

  3. Statistics • 40 Million + anesthetics/year USA • 90% by MD Anesthesiologists

  4. Role of Anesthesiologist • Perioperative care = • Preop evaluation • Intraoperative care • Postoperative care

  5. Intraoperative Role: • Provide continuous medical assessment • Monitor & control vital life functions • Control Pain & level of consciousness • safe surgery

  6. Intraoperative Role Reworded: • NO Pain • NO Memory/Consciousness • NO Movement

  7. A Proposed Method for the Measurement of Anesthetist Care Variability Paul King

  8. Who/Where • Paul King, PhD, PE. Bme/me/anesth. • Don Pierce MD, PhD. Anesth. HPS & Pre. OP • Mike Higgins MD Anesth., Peri. OP • Charles Beattie PhD, MD Chairman, $ • Russ Waitman, MS PhD candidate, data mining • … all at Vanderbilt

  9. What? When? • A Proposed Method (demo/technique) for the Measurement of Anesthetist (resident anesthesiologist– novice to final, faculty, CRNA, others) Care Variability ( controllability) • Testing done at VU, ~ 1 year ago, to be published (JOCM).

  10. Why? • To Err Is Human:Building a Safer Health System (2000) – National Academy Press (anesthetic only) • ~1 death/2-300,000 v 2/10,000 (80’s) pg 32. • Human error ~82% of preventable pg 53. • 72 year lifespan = ~ 1 death/630,720 hours.

  11. How 2/10,000  1/(2-300,000)? • Technological changes (new dev, std.) • Guidelines & strategies • Use of human factors, including simulators • APSF • Leaders (Pierce, Cooper, Schwid, …)

  12. Why? • U. S. Anesthesiologists are ~ 100% certain of at least one major lawsuit during their careers…

  13. Maintain? • Continue the above… • Increase/improve training (MD v CRNA). • Morbidity/Mortality conferences. • Periodic Reviews of cases & records. • Test. Test for competency. Test safely. Test in an unbiased fashion. Test.

  14. Hypotheses • A challenging protocol may be developed using a simulator that tests anesthetists' skills at maintaining patient homeostasis within limits, and • An analytical technique may be demonstrated that will suggest that "skill level" may be inferred from the data collected from the simulator.

  15. Method: METI Simulator

  16. Method: METI Simulator

  17. Why a simulator? • Standardization of “cases.” • Standardization of “patient.” • Data collection q 5 sec, not circa 5 min. (20+ variables, important HR, BP, pOx) • Other (biased?) modalities possible – observation, taping, etc. • Safe, not sorry.

  18. Simulation Method • Inform examinee who the patient is (Stan, normal young male) • Operation type: low anterior bowel resection • SOP please … • Inform re stage of surgery… • Start!

  19. And we are off…

  20. The protocol (“Stable Anesthesia”) • Induction  Intubation (epi)  Maintenance •  Incision (epi) Fluid loss(~ 3L)  •  Maintenance  Ischemia & Desaturation ( & lung changes) •  Maintenance  Emergence •  Extubation ( adequacy)

  21. This Scenario was designed to discriminate between subjects at different levels of anesthesia training • Events range from minor to severe • Events and responses (drug & gas admin.) are recorded real time • Maintenance periods for reality • Instructor available for simple requests only, but does forewarn per real OR

  22. Data Analysis Criteria • Blood pressure wrt preop. +/- 20% • +/- 20%  hypertensive/hypotensive  cardiac/renal disorders. • HR wrt preop.+/- 20% • Probably need to set +60%/-30%, give me a reference? • pOx wrt preop. +/- 5% • Based upon thoughts about significant changes…

  23. Literature re limits & analysis? • Reich, et al, “Validation of an Algorithm for Assessing Intraoperative Mean Arterial Pressure Lability” Anesthesiology 87:156-161 • … rolling 2 min map values exceeding +/-6% swing

  24. Analysis Method • Fractional time out of range (King) • +/- 20% BP • +/- 20% HR • +/- 5% pOx

  25. Subjects • First year new student – “novice” • Second year - “PGY2” • Graduate/Faculty – “PGA” • All physician data from outpatient clinic, cases > ~60 samples, 1543 cases

  26. Results: Fraction out of range – Heart Rate • Simulator: PGA .310 • Simulator: PGY2 .328 • Simulator: Novice .685 • Outpatient data set: .311

  27. Results: Fraction out of range – Systolic Blood Pressure • Simulator: PGA .036 • Simulator: PGY2 .145 • Simulator: Novice .236 • Outpatient data set: .318

  28. Results: Fraction out of range – Diastolic Blood Pressure • Simulator: PGA .131 • Simulator: PGY2 .224 • Simulator: Novice .236 • Outpatient data set: .642

  29. Results: Fraction out of range – Pulse Oximeter Data • Simulator: PGA .158 • Simulator: PGY2 .197 • Simulator: Novice .170 • Outpatient data set: .081

  30. Conclusion • The human patient simulator may be used as a testing device to do inter-individual comparison of anesthetist response to simulated stresses during anesthetic procedures. • A simple measure of competency of intervention may be derived by a “time out of range” measure as discussed here.

  31. Thank you for your attention, from Dr. King & patient… Questions?

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