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Elements of Pathology Quality Dashboards

Elements of Pathology Quality Dashboards. Yael K. Heher, MD, MPH Director, Quality & Patient Safety Beth Israel Deaconess Medical Center Harvard Medical School Boston, MA.

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Elements of Pathology Quality Dashboards

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  1. Elements of Pathology Quality Dashboards Yael K. Heher, MD, MPHDirector, Quality & Patient Safety Beth Israel Deaconess Medical Center Harvard Medical School Boston, MA

  2. The individual below has responded that he/she has no relevant financial relationship(s) with commercial interest(s) to disclose: Yael K. Heher, MD, MPH

  3. Two Types of Dashboards • Quality indicators • 70 indicators • AP/CP • Mandated internally or externally • Event Management • When things go wrong • Standardized classification, workup, follow-up

  4. Two Types of Dashboards • Quality indicators • 70 indicators • AP/CP • Mandated internally or externally • Event Management • When things go wrong • Standardized classification, workup, follow-up

  5. How can we use dashboards to effect change? • Safety culture-Transparency, learning • Improvement culture—High Reliability • Preoccupation with failure • QC/QA versus growth, learning, workflow redesign • Data are compelling…or are they??

  6. Before: Quarterly Quality Meetings

  7. Before: Quarterly Quality Meetings • Only technologist managers attended, plus compliance officers and a single MD (CLIA license holder) • (Painful) reading aloud of excel spreadsheets • Narrative explanation for variation, error • Shame or punitive culture around failure to meet target • Metrics mainly externally mandated • Data not shared

  8. What is the current practice at BIDMC? 70+ Indicators 16 Lab areas

  9. Dashboard Overview

  10. METRIC & TARGET TREND TIME PERIOD SIGNAL

  11. Monthly

  12. Data Warehouse

  13. Statistical Process Control Special cause variation vs. Common cause variation

  14. Cultural shift: CURIOSITY quality data • Why are we collecting these data? • What is the clinical impact of this metric? • Why is the target what it is? • What is the cost of collecting quality data? • Should we add or remove any quality indicators? • What are root causes for performance variation? • What are the opportunities for QI projects? “TRUE NORTH” IMPROVE TRUST IN PROCESS DIMINISH FEELING OF ‘POINTLESS REGULATORY METRICS’

  15. Heher YK, Chen Y, VanderLaan PA. Measuring and assuring quality performance in cytology: a toolkit. Cancer 2017;125:502-507.

  16. DO DON’T Heher YK, Chen Y, VanderLaan PA. Measuring and assuring quality performance in cytology: a toolkit. Cancer 2017;125:502-507.

  17. DO DON’T Heher YK, Chen Y, VanderLaan PA. Measuring and assuring quality performance in cytology: a toolkit. Cancer 2017;125:502-507.

  18. DO DON’T Heher YK, Chen Y, VanderLaan PA. Measuring and assuring quality performance in cytology: a toolkit. Cancer 2017;125:502-507.

  19. DO DON’T Heher YK, Chen Y, VanderLaan PA. Measuring and assuring quality performance in cytology: a toolkit. Cancer 2017;125:502-507.

  20. DO DON’T Heher YK, Chen Y, VanderLaan PA. Measuring and assuring quality performance in cytology: a toolkit. Cancer 2017;125:502-507.

  21. DO DON’T Heher YK, Chen Y, VanderLaan PA. Measuring and assuring quality performance in cytology: a toolkit. Cancer 2017;125:502-507.

  22. DO DON’T Heher YK, Chen Y, VanderLaan PA. Measuring and assuring quality performance in cytology: a toolkit. Cancer 2017;125:502-507.

  23. What are the challenges for AP Quality review ? • Focus on (CAP) compliance versus safety • Highly manual processes-tough to measure • Benchmarking not always clear (doesn’t matter!) • Focus on analytic phase---but most errors pre-analytic

  24. How have things changed since the Dashboard was implemented? • All Medical Directors and techs attend, plus trainee representation • Statistical process control, root cause analysis, QI projects • Curiousity and accountability, not blame • Interactive, open discussion • Culture change!

  25. Two Types of Dashboards • Quality indicators • 70 indicators • AP/CP • Mandated internally or externally • Event Management • When things go wrong • Standardized classification, workup, follow-up

  26. Two Types of Dashboards • Quality indicators • 70 indicators • AP/CP • Mandated internally or externally • Event Management • When things go wrong • Standardized classification, workup, follow-up

  27. Events in Anatomic Pathology Heinrich Pyramid  Heinrich HW (1931). Industrial accident prevention: a scientific approach. McGraw-Hill.

  28. How do we learn from near miss and no-harm events in Pathology? Incident Management Dashboard

  29. Before Incident Dashboard • Emails to…??? • (Rare) hospital incident reporting system • ‘Curbsiding’ from clinical partners • Difficult to track, analyze, respond • How to escalate trends? • How to tackle complex risk?

  30. Benefits of Event Dashboard • Central documentation, workup, and tracking of events • Trending and broad data collection possible • Can be escalated/’fanned out’ to right groups

  31. SHARED ROOT CAUSES Temporary Harm 21 (10%) Near Misses 51 (24%) Unsafe Conditions No-Harm Events How do we compare?

  32. CP > 7,000,000 AP 90,000 What is AP’s part of the ‘problem’?

  33. What can be learned from an event dashboard?

  34. What can be learned from an event dashboard?

  35. What can be learned from an event dashboard?

  36. What can be learned from an event dashboard?

  37. Incident Dashboard: data aggregation • IT incidents: articulated, escalated/prioritized • Complex, multidiscliplinary incidents: workgroups formed • AP molecular sendout testing • PTH turnaround time • Hyperkalemia

  38. Incident Dashboard: Safety Culture • Reward those who call out safety concerns (do not penalize) • Investigate uniformly, fairly (weed through frustration and confusion) • Follow-up -- even if incomplete! • Involve clinician stakeholders—promotes trust and collaboration, not shame and blame.

  39. Before and After

  40. Dashboards: take home • A TOOL to aid with VISUAL representation of DATA • Dashboards can be used to continually assess any metric or data that aligns with organizational goals • Humans see, interpret, and synthesize data better when it is optimized visually.

  41. Acknowledgments BIDMC Pathology: Jeff Saffitz MD PhD Lynne Uhl MD Laura Collins MD Gina McCormack Paul VanderLaan MD PhD Cindy Dellicolli John McDonald Michelle Herman BIDMC Pathology Managers Yigu Chen MPH PSSB Process Improvement AnalystBIDMC

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