An alternative to Red-Yellow-Green Board Reports
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1 Tuesday, December 12 Session A9/B9 An alternative to Red-Yellow-Green Board Reports Dan Watson, MBA Valerie Craig, RN, MSN, MAOM Richard Scoville, PhD
2 Objectives Describe how MHS transitioned to a systems-based approach for selecting/prioritizing key metrics Describe our systems-based approach for selecting and prioritizing key metrics Walk through how MHS integrated systems-based data analysis to support their Board Committee These presenters have nothing to disclose
3 Methodist Health System 10 Hospitals 31 Family Health Centers 6 Ambulatory Sites (imaging, urgent care) 1,500 beds 3
4 About Methodist Health System Founded in ,500+ employees 1,500+ physicians on staff 290+ affiliated physicians 536,000 patient visits $140 million in unreimbursed charity care One of Dallas Business Journal s Top 10 Best Places to Work (13 years running) First and only member of the Mayo Clinic Care Network in Texas Fellowships Gastroenterology Nephrology Surgical critical care Hepato-pancreato-biliary surgery Residencies General surgery Internal medicine Obstetrics-gynecology Family medicine 4
5
6 THE SYSTEM QUALITY REVIEW COMMITTEE in 2014
7
8
9 to best quartile
10
11 Who s Who & Does it Matter?
12 Change to report
13 SYSTEMS-BASED MEASUREMENT as the cure to what ailed us
14 Goal-Driven View [with point-to-point coloration] Methodist Health System Performance Improvement Dashboard, October 2013 Q1,Q2, Q3, Q4 FYTD13 (Oct-August13) Privileged and Confidential Board Summary FY13 Q4 data includes Jul-Aug data only MCMC MDMC MMMC MRMC MHS Public Measure FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 Quality Process Domain Score (Composite for VBP indicators) Goal 91.04% 97.44% 88.83% 93.81% 90.50% 94.49% 93.99% 96.59% 90.42% 93.79% 91.12% 98.00% 93.78% 90.53% 95.28% 90.83% 96.96% 88.55% 94.43% 96.20% 96.02% 95.47% 95.26% 96.38% 95.37% 98.85% 97.04% 91.44% 97.01% 93.86% 90.75% 94.12% 91.32% 89.99% 92.06% 93.53% 89.70% 95.88% 94.99% 92.01% 91.90% 97.53% 95.41% 94.16% 98.40% 1,2,3,4 MCMC MDMC MMMC MRMC MHS Public Measure Mortality Hospital Compare Natl Avg (1) FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 FY13 Q1 FY13 Q2 FY13 Q3 FY13 Q4 FYTD13 Risk Adj Index (All payors) Acute Myocardial Infarction (AMI) Goal 1.0 All payors 4.65% 3.21% 6.34% 4.81% 4.87% 8.44% 8.62% 4.48% 0.00% 5.44% 5.29% 4.52% 5.07% 2.20% 4.66% 2.49% 6.66% 10.47% 9.95% 7.06% 5.53% 5.18% 6.01% 3.31% 5.05% 1,2,3,4,5,6 Risk Adj Index (M'care only) M'care only 15.5% 5.52% 3.21% 13.64% 14.40% 8.99% 22.78% 12.89% 8.04% 0.00% 10.51% 3.42% 0.00% 8.27% 0.00% 3.54% 0.00% 6.02% 8.60% 16.72% 6.96% 6.25% 5.54% 10.32% 9.51% 8.17% Risk Adj Index (All payors) Congestive Heart Failure (CHF) Goal 1.0 All payors 1.74% 2.61% 0.64% 0.00% 1.47% 1.87% 3.31% 2.89% 5.45% 3.18% 2.31% 0.91% 2.46% 1.44% 1.71% 2.26% 6.28% 0.00% 7.93% 3.89% 2.06% 2.82% 1.69% 2.77% 2.34% 1,2,3,4,5,6 Risk Adj Index (M'care only) M'care only 11.6% 0.00% 1.45% 1.78% 0.00% 0.87% 4.50% 5.86% 4.22% 6.90% 5.57% 5.87% 2.37% 3.14% 0.00% 3.14% 0.00% 6.83% 0.00% 0.00% 1.57% 2.59% 3.41% 2.57% 1.87% 2.76% Risk Adj Index (All payors) Pneumonia (PN) Goal 1.0 All payors 3.73% 4.29% 3.69% 2.43% 3.88% 1.15% 1.38% 3.08% 2.24% 2.02% 3.10% 1.75% 1.51% 4.62% 2.37% 4.74% 1.82% 0.00% 0.00% 2.12% 3.21% 2.36% 2.35% 2.77% 2.57% 1,2,3,4,5,6 Risk Adj Index (M'care only) M'care only 12.0% 8.29% 6.86% 7.61% 0.00% 6.71% 0.00% 0.00% 2.59% 0.00% 0.88% 5.69% 2.39% 0.00% 21.40% 4.35% 10.06% 3.41% 0.00% 0.00% 4.15% 6.03% 3.51% 2.55% 3.78% 3.96% Fails to provide actionable insight into overall performance, rather tends to overemphasize the significance of small, expected changes.
15 The Illusion of Change Month Oct 2016 Nov 2016 Dec 2016 Jan 2017 Feb 2017 Mar 2017 Apr 2017 May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017 KPI
16 Expected Variation is Not Difficult
17 Making decisions based on expected variation can be intuitive
18
19 Our daily commute How long does it take you to get to work? minutes? 15 minutes?
20 Managing our daily commute Would you change your route if it took 17 minutes one day? What about after an inclement weather day? How much time would you give yourself if you had a really important meeting?
21 No different is an acceptable & actionable conclusion
22
23 Math to the Rescue!
24 The Swiss Army Knife of Control Charts Ask not what your chart can do for you, but what you can do with your chart
25 Don t waste too much time on tools and techniques. You can learn the lot in 15 minutes ~Dr. Deming [from David Kerridge]
26 The value of the data doesn t come from the chart it comes from how we use it.
27 THE PATH TOWARDS OUR SOLUTION patience, picked battles, and persistence on process
28 A woodsman was once asked, What would you do if you had just five minutes to chop down a tree? I would spend the first two and a half minutes sharpening my axe.
29 Simple Summary Summarizing can obscure useful info But we can describe complexity simply We can differentiate performance to goal from system performance * Also, it s 5
30 Prioritize Performance Remove items we re not working on Support System Performance over Point-to-Point Comparison Define data well, but as reference
31 Consider Context Over Clarity Tone down designs that encourage knee-jerk conclusions Embrace and explain complexity Summarize AND analyze
32 Accentuate Action Prioritize key system initiatives Recognize hard work Avoid sharing effects without causes
33 First pass
34 Current
35 Status How did we do compared to last time? Absolute change is a coin flip Used Moving Range Analysis from SPC software Change is noted when +/- 2 sigma & color coded based on favorable direction Yes, I know. Not 3.
36 Sparkline* What is our pattern of results? Visual display of relative performance Uses most recent running data points Whole process is colorcoded based on system stability * Not a trend line. Because those are my enemy.
37 Data Definitions Where do these numbers come from? Practical data definitions Replaces multiple in-line legends Available & scalable, but not intrusive Parameters Indicator Name Unit of Measure Calculation [if applicable] Frequency Time Periods Current Value Sparkline Description Summary statement Key inclusion/exclusion Aggregation methods Benchmark Source Data Source
38 Entity Process Control Charts How is each campus doing?
39 The cutting room floor You say trend, I say shift. Let s call the whole thing off Special cause is special cause Your fancy symbols don t impress me Critical Values Managing KPI like lab values was a bridge too far
40 Executive Summary So what? Unburdens each tool to focus on its strength Very effective treatment for footnote-philia Enables analysis & action to be shared
41 Simple is hard Lessons Learned Focus on your stakeholders end goal Be patient and ready to compromise
42 Next Steps Center line assessment Routine re-assessment of content Bring Balance
43
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