A Proposed Method for the Measurement of Anesthetist Care Variability

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).

11. 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.

12. 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, …)

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

14. 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.

15. 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.

16. Method: METI Simulator

17. Method: METI Simulator

18. 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.

19. 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!

20. And we are off…

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

22. 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

23. 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…

24. 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

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

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

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

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

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

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

34. 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.

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