Emergency Department Performance: BEHIND THE NUMBERS

1. Emergency Department Performance: BEHIND THE NUMBERS Todd Lang, MD, MBA Medical Director of Emergency Services Baptist Memorial Hospital-Memphis

2. Observations “Every system is perfectly designed to get the results it gets.” —Dr. Paul Batalden “It’s not luck.” —Eliyahu Goldratt “’Try Harder’ is not a tool for performance improvement.” —Dr. Todd Lang

3. Agenda • Paint a picture of a dynamic and organic organism that operates according to the goals set for it • Demonstrate Little’s Law for servers • Discuss some common ED metrics and their consequences • Set a simple path forward

4. Mountain bike parts: pick two • Light • Cheap • Durable • $8899 Healthcare: We want it all, yesterday, every time, and “all” grows every day.

5. Inverted U-shaped curve: Patient experience and speed of care Perfect Stay Overall Satisfaction “They didn’t listen” “It took forever!!” Total Length of Stay

6. Tensions • D2D vs. LOS • LWBS vs. LOS • Clinical care vs. patient satisfaction • Doctor work vs. nurse • Nurse vs. tech/paramedic • ED work vs. ICU or Hospitalist work • Perception: Listened to me vs. pushed me out the door • Speed and patient satisfaction • Hospital payroll vs. contract group payroll

7. The ED Care Team Pat Sat Readmission ED Care Team Sepsis, AMI, Stroke Core Measures Pain Control LOS D2D

8. Moral distress Moral distress occurs when one knows the ethically correct action to take but feels powerless to take that action. Do we cause this with our leadership and expectations?

9. Little’s Law: Match demand and capacity. • Server capacity x cycle time = Work In Process • Arrivals per hour x LOS = Patients in the ED • Apply it to the whole ED or Unit • Apply to each pod or doctor or nurse • Go to Wikipedia!

10. Sample doc calculation Using Little’s Law • LOS = 4 hours • Doc sees 2 pts/hr (on average) • 2 x 4 = 8, but if can see 3/hr then 3 x 4 = 12 • Need 8 beds to be busy IF AT AVERAGE • Doc work ends at admit order (not true for nurse) • Some patients are easier, and some days there are holds, so need a few extra beds • So, need 12 - 14 beds per doc to account for this • But, 16 is unmanageable in most situations

11. Can nurses do the work expected of them?

12. Queuing for a server: Avg wait time in hours It’s math—not luck! 96% Utilization 80% Utilization

13. Shift the curve: • Follow the value! • Map the stream and remove the waste • Give staff time to engage patients • Leverage fungible staff substitutions: • Lab/X-ray subs for ED staff! • Paramedic substitutes for RN • RN subs for Doctor • Hospitalist subs for ED doctor

14. Average patient collections • Know the cost of a walkout • Know the value of a bed-hour • Do the math on LOS changes and staffing • What is the value of shorter LOS in nursing cost? • What is the value of increased physician productivity?

15. The roving bottleneck server • Triage • Doctor • RN • Pharmacy • CT or X-ray • Read The Goal to understand bottlenecks.

16. Arrival curves • Demand:capacity matching • By day of week and hour • Differential scheduling by day? • Nurse and doc must match! • How about housekeeping and transport and CT servers?

17. D2D: Slice and dice it! • By day • By doctor • By arrival nurse • By shift worked • No excuses What do you get if you overdo D2D?

18. LOS • By doctor • By nurse • By area • By shift • By disease state or resource utilized What if you overdo LOS?

19. Patients per hour • By doctor • By shift • By day of week Is it low because of lack of patients?

20. Admission rate • By doctor • By disease and by doctor • CHF • Chest pain • Abdominal pain Why does it vary so much?

21. Utilization by doctor or by disease or both! • Lab • CT • Morphine equivalents • Total pharmacy cost per patient • Admit • Obs • Total cost Use the info to coach and ask the right questions! Let the high performers teach the rest.

22. Radiology • By CT groups: • brain/C-spine • Chest/belly w/IV only • X-ray: • Portable chest • Others • Study vs. reading on each of above • By hour of day

23. CT, in particular • A significant fraction of your patients are waiting for a CT result • CT rate often is in the neighborhood of admit rate • Cannot dispountil CT is done and read • This key server requires management • The radiologists probably won’t volunteer to perform better (see demand:capacitychart). The ED alone can rarely manage this process.

24. Time for pharmacy • Med pick and delivery times • Frequency of med send and what drugs • Frequency of stockouts in ED

25. Beyond Core Measures • Measure best care, not just the core measures • “consensus committee” or some other way to decide what good care looks like • Wildly different than CMS deciding for you • Easy examples: • imaging rules for head and neck • completion of NIHSS for stroke • time to pain meds for fractures • sepsis care

26. The biggest miss: pay for satisfaction • What does this incentivize? • Why is it so fundamentally at odds with “doctorhood”? • How does it fit with our national pill problem? • Do the people around you actually understand percentiles and how that works? • Does the hospital understand common cause variation?

27. On Call: measure it if you bought it! • Call back time? • ED physician satisfaction (particularly if paid) • Time to definitive care • Supply use for surgical cases • Measuring and reporting it is almost as good as putting it in the contract, but without the contention.

28. Readmissions vs. flow • Managing readmissions is at odds with ED flow • Is it easier to readmit than other options? • Social work? • Inpatient doctor consult? • Ethics consult? • Palliative care? • Talk to the NH doctor? • All take time and have limited resources! • Providing resources will move the needle

29. Dashboard: goals and colors • Conditional formatting works • (S)He who controls the dashboard file has considerable control! • Much like control of the meeting agenda!

30. ED Balanced Scorecard

31. Scorecard goals • Data • Focus each doc on 1-3 items • Rank performance compared to internal and external benchmarks of high performers. • Use color conditional formatting

32. “Physicians are quick to challenge performance data and to identify methodological problems with them. But the fact is that they are mesmerized by data and cannot look away.” - Dr. Thomas Lee: Turning Doctors Into Leaders, Harvard Business Review, April 2010.