Session #20 How to Drive Clinical Improvement That Get Results Tom Burton And the Catalyst Academy Education Team What is a Clinical Program? • Organized around care delivery processes • Permanent integrated team of clinical and analytics staff • Creates a iterative continuous learning environment • Focus is on sustained clinical outcome improvement (not revenue growth) • Not a Clinical Service Line (although you can Leverage Service Lines as a good start) 2 Organizational AGILE Teams • Permanent teams that meet weekly • Integrated clinical and technical members • Supports multiple care process families Women & Children’s Clinical Program Guidance Team MD Lead RN SME Pregnancy Knowledge Manager MD Lead RN SME Normal Newborn MD Lead RN SME Gynecology Data Architect Guidance Team MD lead RN, Clin Ops Director Application Administrator Subject Matter Expert = Data Capture = Data Provisioning & Visualization = Data Analysis = 3 Incorporating the most effective learning methods Teach Others - 90% Practice by Doing- 75% Discussion Group- 50% Demonstration- 30% Audiovisual- 20% Reading- 10% Lecture- 5% % represents average 0 information retained through the particular learning method 50 100 ‒ Duke University 4 Session Objective 4 Learning Experiences Clinical Programs that Get Results Principles Choose the right initiative Understand variation Improve data quality Choose the right influencers 5 Choose the right initiative 6 Deal or No Deal Exercise 7 DEAL or NO DEAL 8 First Principle • Picking an improvement opportunity randomly is like playing traditional DEAL or NO DEAL • You might get lucky • Choosing the loudest physician or the choosing based on non-data driven reason can dis-engages other MDs and use scarce analytical resources on projects that may not be the best investment • It takes about as much effort to work on a large process as it does on a small process 9 Analytic System Pareto Example: Resources Consumed Key Findings: • 50% of all in-patient resources are represented by 7 Care Process Families • 80% of all in-patient resources are represented by 21 Care Process Families 80% Cumulative % 50% % of Total Resources Consumed for each clinical work process 7 CPFs 21 CPFs Number of Care Process Families (e.g., ischemic heart disease, pregnancy, bowel disorders, spine, heart failure) 10 10 Analytic System Mean Cost per Case = $20,000 Dr. J. 15 Cases $60,000 Avg. Cost Per Case $35,000 x 25x cases = = $40,000 15 cases $875,000 opportunity $600,000 opportunity Total Opportunity = $600,000 Total Opportunity = $1,475,000 Total Opportunity = $2,360,000 Total Opportunity = $3,960,000 Cost Per Case, Vascular Procedures 11 Improvement Approach - Prioritization High 3 1 Variability # of Cases # of Cases Poor Outcomes Excellent Outcomes 4 Poor Outcomes Excellent Outcomes 2 # of Cases # of Cases Poor Outcomes Excellent Outcomes Poor Outcomes Excellent Outcomes Low Low 12 Resource Consumption High 12 Improvement Approach - Prioritization High 3 1 Variability # of Cases # of Cases Poor Outcomes Excellent Outcomes 4 Poor Outcomes Excellent Outcomes 2 # of Cases # of Cases Poor Outcomes Excellent Outcomes Poor Outcomes Excellent Outcomes Low Low 13 Resource Consumption High 13 Y- Axis = Internal Variation in Resources Consumed Internal Variation versus Resource Consumption 3 1 4 2 Bubble Size = Resources Consumed X Axis = Resources Consumed Bubble Color = Clinical Domain 14 DEAL or BETTER DEAL 15 15 Understand Variation 16 The Popsicle Bomb Exercise Timer 1M 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 10 11 0 1 2 3 4 5 6 7 8 9 When you’re finished note your time and enter it in the HAS app – Poll Question 1 17 Variation in Results • Corp Analytics – shows results 18 Less Effective Approach to improvement: “Punish the Outliers” Focus on Minimum Standard Metric Mean # of Cases # of Cases Poor Outcomes Excellent Outcomes Poor Outcomes Excellent Outcomes Current Condition Option 1: “Punish the Outliers” or “Cut Off the Tail” • • Strategy • Set a minimum standard of quality • Focus improvement effort on those not meeting the minimum standard Significant Volume Significant Variation 1 box = 100 cases in a year 19 Effective Approach to improvement: Focus on “Better Care” Focus on Best Practice Care Process Model Mean # of Cases # of Cases Poor Outcomes Excellent Outcomes Poor Outcomes Excellent Outcomes Current Condition Option 2: Identify Best Practice “Narrow the curve and shift it to the right” • • Strategy • Identify evidenced based “Shared Baseline” • Focus improvement effort on reducing variation by following the “Shared Baseline” • Often those performing the best make the greatest improvements Significant Volume Significant Variation 1 box = 100 cases in a year 20 Round 2 Timer 1M 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 10 11 0 1 2 3 4 5 6 7 8 9 When you’re finished note your time and enter it in the HAS app – Poll Question 2 21 Reduced Variation in Results • Corp Analytics – shows results 22 Improve Data Quality 23 The Water Stopper Exercise 24 Information Management Fix it Here = Subject Matter Expert DATA CAPTURE = Data Capture • Acquire key data elements • Assure data quality • Integrate data capture into operational workflow Knowledge Managers (Data quality, data stewardship and data interpretation) = Data Provisioning = Data Analysis Application Administrators (optimization of source systems) DATA ANALYSIS DATA PROVISIONING • Interpret data • Discover new information in the data (data mining) • Evaluate data quality • Move data from transactional systems into the Data Warehouse • Build visualizations for use by clinicians • Generate external reports (e.g., CMS) Not Here Data Architects (Infrastructure, visualization, analysis, reporting) Not Here 25 25 25 Data Capture Quality Principles • Accuracy Does the data match reality? Example: Operating Room Time Stamps • Timeliness What is the latency of the data capture? Example: Billing data delay; end of shift catch-up • Completeness How often is critical data missing? Example: HF Ejection Fraction 26 Challenges with Data “Scrubbing” Analyst time spent on re-working scrubbing routines Root cause never identified Early binding vs. late binding – what you consider dirty data may actually be useful for others analyzing process failures. Using data to punish vs. data to learn – punish strategy promotes hiding the problem so clinicians don’t look bad 27 Choose the right influencers 28 Paul Revere's ride Exercise 29 Revere vs. Dawes Paul Revere "Revere knew exactly which doors to pound on during his ride on Brown Beauty that April night. As a result, he awakened key individuals, who then rallied their neighbors to take up arms against the British.” William Dawes "In comparison, Dawes did not know the territory as well as Revere. As he rode through rural Massachusetts on the night of April 18, he simply knocked on random doors. The occupants in most cases simply turned over and went back to sleep." Diffusion of Innovations (Free Press, 2003) by Everett M. Rogers 30 Early adopters. Recruit early adopters to chair improvement and to lead implementation at each site. (key individuals who can rally support) Innovators. Recruit innovators to redesign care delivery processes (like Revere) N = number of individuals in group N = number needed to influence group (but they must be the right individuals) late majority early majority Innovators N The Chasm early adopters laggards (never adopters) * Adapted from Rogers, E. Diffusion of Innovations. New York, NY: 1995. 31 Guidance Team (Prioritizes Innovations) Early Adopters W&N Innovators OB Innovators Newborn GYN • • • Meet quarterly to prioritize allocation of technical staff Approves improvement AIMs Reviews progress and removes road blocks • • • Meet weekly in iteration planning meeting Build DRAFT processes, metrics, interventions Present DRAFT work to Broader Teams • • • Broad RN and MD representation across system Meet monthly to review, adjust and approve DRAFTs Lead rollout of new process and measurement W&N Small Teams (Designs Innovation) Innovators OB Broad Teams (Implements Innovation) Early Adopters W&N Innovators OB Early Adopters W&N W&N 32 Organizational AGILE Teams • • • • Permanent teams Integrated clinical and technical members Supports multiple care process families Choose innovators and early adopters to lead Early Adopters Women & Children’s Clinical Program Guidance Team MD Lead RN SME Innovators Pregnancy Knowledge Manager MD Lead RN SME Normal Newborn MD Lead RN SME Gynecology Data Architect Guidance Team MD lead RN, Clin Ops Director Application Administrator Subject Matter Expert = Data Capture = Data Provisioning & Visualization = Data Analysis = 33 How to identify innovators and early adopters • Ask Innovators (inventors) - Who are the top three MDs in our group who are likely to invent a better way to deliver care? Early Adopters (thought leaders) - When you have a tough case who are the top three MDs you trust and would go to for a consult? • Fingerprinting selection process Invite innovators to choose identify their top three MD choices from the early adopters to lead the Clinical Program 34 Conclusion – TEACH OTHERS 35 Teach Others Exercise Timer Deal or No Deal - Choose the right initiative - Prioritize based on process size and variation Popsicle Bomb - Understand variation - Measure variation and standardize processes 1M 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 10 11 0 1 2 3 4 5 6 7 8 9 Water Stopper - Improve data quality - Fix the problem at the source Paul Revere’s Ride - Choose the right influencers - Identify Innovators and Early adopters to accelerate diffusion of innovation 1M 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 10 11 0 1 2 3 4 5 6 7 8 9 Take 1 minute and describe the purpose of each exercise to your neighbor, then swap and let them teach you 36 Exercise Effectiveness Q1 Overall, how effective were the exercises in explaining the principles? 1) Not effective 2) Somewhat effective 3) Moderately effective 4) Very effective 5) Extremely effective 37 Exercise Effectiveness Q2 How effective was the Deal or No Deal Exercise at teaching the principle of prioritizing based on process size and variation? 1) Not effective 2) Somewhat effective 3) Moderately effective 4) Very effective 5) Extremely effective 38 Exercise Effectiveness Q3 How effective was the Popsicle Bomb Exercise at teaching the principle of understanding variation and standardizing processes? 1) Not effective 2) Somewhat effective 3) Moderately effective 4) Very effective 5) Extremely effective 39 Exercise Effectiveness Q4 How effective was the Water Stopper Exercise at teaching the principle of fixing data quality issues at the source? 1) Not effective 2) Somewhat effective 3) Moderately effective 4) Very effective 5) Extremely effective 40 Exercise Effectiveness Q5 How effective was the “Paul Revere Ride” exercise at teaching the principle of choosing the right influencers based on their capabilities as innovators and early adopters? 1) Not effective 2) Somewhat effective 3) Moderately effective 4) Very effective 5) Extremely effective 41 Exercise Effectiveness Q6 Are you interested in running these same exercises in your organizations? a) Yes b) No 42 Analytic Insights Questions & Answers A Session Feedback Survey 1. On a scale of 1-5, how satisfied were you overall with this session? 1) 2) 3) 4) 5) Not at all satisfied Somewhat satisfied Moderately satisfied Very satisfied Extremely satisfied 2. What feedback or suggestions do you have? 3. On a scale of 1-5, what level of interest would you have for additional, continued learning on this topic (articles, webinars, collaboration, training)? 1) 2) 3) 4) 5) No interest Some interest Moderate interest Very interested Extremely interested 44