Clinical Optimization: One Approach to Integration Session #192, February 14, 2019 Dr. Hiloni Bhavsar, Associate Director, Quality & Safety, Rochester General Hospital James Williams, VP of Integration, Rochester Regional Health 1 Conflict of Interest Dr. Hiloni Bhavsar Has no real or apparent conflicts of interest to report James Williams, MHSA Has no real or apparent conflicts of interest to report 2 Agenda • Learning Objectives • Organization Overview • Clinical Optimization Program Discussion – Overview and Background – Performance Improvement Process: Heart Failure Example – Work Done to Date – Challenges – Future Areas of Focus • Open Discussion (Encourage Questions / Comments Throughout) 3 Learning Objectives • Describe an enterprise-wide approach to use clinical analytics to drive performance improvement • Describe a sustainable process to support clinical performance improvement projects • Discuss a methodology focused on understanding variations of care to drive clinical integration and improve clinical outcomes • List examples on how disparate clinical teams came together to establish standards of care 4 Organization Overview 5 Rochester Regional Health: Our Mission To enhance lives and preserve health by enabling access to a comprehensive, fully integrated network of the highest quality and most affordable care, delivered with kindness, integrity and respect. Source: Rochester Regional Health Website 6 Rochester Regional Health 5 16K+ 2,500+ 1,600+ system hospital medical system employees locations staff volunteers 936 8 370K 87+ long term senior living behavioral primary care care beds facilities health visits & ambulatory locations 1M+ 245K+ 54 clinical emergency Patient lab trials tests room visits testing sites 7 Source: Rochester Regional Health Right Care. Right Time. Right Place. Rochester Unity Hospital General Hospital Newark Wayne Community Hospital United Memorial Medical Center Clifton Springs Hospital & Clinic Source: Rochester Regional Health 8 8 Audience Polling #1 Has your organization undergone a recent merger? 1. Past 1 year 2. Past 2-3 years 3. Planning in next 12-24 months 4. None https://live.eventbase.com/polls?event=himss 19&polls=5147 9 Clinical Optimization Program Discussion 10 Audience Polling #2 Which of the following applies regarding EMR in your organization? 1. Single EMR system across facilities 2. Multiple EMR systems across facilities 3. EMR + Paper 4. Paper https://live.eventbase.com/polls?event=himss 19&polls=5148 11 Audience Polling #3 Where is your organization in the evolution of addressing variations in care? 1. Initial phases of discussion 2. Projects initiated but not fully integrated 3. Design and implementation 4. Sustained projects with results https://live.eventbase.com/polls?event=himss 19&polls=5149 12 Clinical Optimization Program Overview & Background • Reduce variation • Create a culture of High Reliability • Develop consistent clinical and patient experience across the continuum • Utilize and implement evidence-based standards of care • Yield sustainable results 13 Clinical Optimization Program Overview & Background Principles of High Reliability Clinical Optimization Clinical leads determine evidence based Deference to Expertise approach for standardization Understand current context to identify areas of Sensitivity of Operations opportunity Recognize value of reducing variation while Reluctance to Simplify appreciating inherent complexity Build in control plans to flex with future Preoccupation with Failure changes/modifications, new sources of variation Build in practice of rapid assessment and Commitment to Resilience improvement cycles that are data driven Clinical Optimization Program Overview & Background Example • Using internal data, reviewed cost-per-case distribution for each DRG • Establish internal peer- groups: system average cost-per-case and low-cost provider • Run statistics for performance ranges to identify focus area 15 Clinical Optimization Program Overview & Background DRG Grouping 1 SDV + Mean 1 SDV + Mean Excess AVG SDV % # PLM - Product Case Count (System Avg) (System Best) Days of Mean 1IP Other Medical 5,934 $3,666,263 $5,884,001 10,636 92% 2IP General/GI/Endocrine Surgery 2,776 $3,027,795 $8,101,527 8,606 66% 3IP Cardiac - Medical Volume 4,916 $2,029,466 $3,477,155 6,153 79% 4IP Respiratory 3,711 $1,799,741 $3,097,401 5,150 80% 5IP Orthopedic Surgery assessment4,665 $1,648,500 $4,084,207 4,309 38% 6IP Gastroenterology 3,190 $1,144,384 $2,002,984 3,787 78% 7IP Nervous System 2,475 $1,140,282 $2,338,849 4,307 78% 8IP Neurosurgery 1,235 $998,915 $2,225,799 1,585 53% 9IP CardiacEngaged Valve leaders 400 $941,129 $941,129 1,084 50% 10IP Oncology/Hematology 1,135 $900,603 $1,614,253 1,890 98% 11IP Nephrology 1,711 $880,301 $1,328,587 3,008 98% 12IP Acute Rehab 482 $767,959 $1,817,588 4,438 78% 13IP Acute Psych 1,395 $753,280 $3,229,544 6,187 114% 14IP Surgical Other 515 $549,881 $4,817,101 4,831 111% 15IP Cardiothoracic Surgery 305 $513,883 $687,814 719 64% SUBTOTAL 34,845 $20,762,383 $45,647,938 66,689 N/A GRAND TOTAL 53,040 $26,619,455 $58,523,681 81,257 72% OB (Special Care, Vaginal 9,058 1,327,322 4,345,980 1,965 59% Delivery, C-Section, Newborns) % of Total 2015 Statistics 100% 8% 19% 31% 16N/A Data Source: EPSI, FY 15, Jan-Sep FY 16 inpatients, no exclusions Performance Improvement Process Example: Heart Failure 17 Performance Improvement: Approach 18 1. Macro-Analysis: Heart Failure Example LOS largely driven by LOS largely driven by admit 30-day readmission rates discharge disposition day of the week higher than CMS targets 19 FY 15 – YTD Sep FY 16 IP LOS by Discharge Status MS DRG 291 Heart Failure Shock w/ MCC Data Source: EPSI, FY 15, Jan-Sep FY 16 inpatients, no exclusions 20 FY 15 – YTD Sep FY 16 IP LOS and Discharges by Day of Week Admit Day of the Week - ALOS Enc – CMS MS DRG ALOS Mon Tue Wed Thu Fri Sat Sun 293 3.1 2.75 2.60 2.65 2.80 3.46 2.51 2.40 292 4.5 4.27 4.64 4.69 4.74 4.50 4.47 3.67 291 5.9 5.12 5.83 5.75 6.17 6.21 6.32 5.85 % of Cases Admitted by Admit Date Enc - MS DRG Mon Tue Wed Thu Fri Sat Sun 293 18% 15% 14% 18% 15% 11% 10% 292 17% 15% 13% 14% 17% 13% 11% 291 19% 17% 13% 13% 15% 11% 13% % of Cases Discharged by Discharge Date Enc - MS DRG Mon Tue Wed Thu Fri Sat Sun 293 15% 18% 16% 17% 19% 7% 8% 292 15% 16% 19% 17% 18% 7% 9% 291 15% 18% 18% 19% 18% 8% 5% 21 Data Source: EPSI, FY 15, Jan-Sep FY 16 inpatients, no exclusions CMS Public Reporting : Hospital Readmissions Reduction Program (HRRP) for Heart Failure Hospital Discharge Period: July 1, 2012 through June 30, 2015 Predicted Expected Excess Readmission National Observed Hospital Readmission [a] Readmission [b] Ratio [c] Readmission [d] NWCH 26.5% 21.8% 1.2152 21.9% RGH 25.6% 21.8% 1.1732 21.9% UNITY 21.8% 21.4% 1.0220 21.9% UMMC 24.4% 20.8% 1.1709 21.9% CLIFTON 22.9% 22.7% 1.0094 21.9% [a] The 30-day readmission rate predicted on the basis of your hospital’s performance with its observed case mix and your hospital’s estimated effect on readmissions (provided in your hospital discharge-level data). The Predicted Readmission Rate is also referred to as "Adjusted Actual Readmissions" in Section 3025 of the Affordable Care Act. [b] The 30-day readmission rate expected on the basis of average hospital performance with your hospital’s case mix and the average hospital effect (provided in your hospital discharge-level data). [c] Ratio of the predicted readmission rate [d] to the expected readmission rate [e]. (Note: Due to rounding the Excess Readmission Ratio may not be the exact ratio of the numbers in columns D and E; see the replication instructions for how to exactly replicate the results in column F). The Excess Readmission Ratio (also referred to as the Standardized Readmission Ratio [SRR]) is the measure that will be used to determine the payment adjustment for the Program. If a hospital performs better than an average hospital that admitted similar patients (that is, patients with similar risk factors for readmission such as age and comorbidities), the ratio will be less than 1.0000. If a hospital performs worse than average, the ratio will be greater than 1.0000. Excess Readmission Ratios greater than 1.0000 will be included in the payment adjustment formula. 22 [d] The number of eligible unplanned 30-day readmissions nationally divided by the number of eligible discharges nationally. 2. Prioritization and Roadmap: Heart Failure Example Based on the data, the team identified areas where heart failure care can be improved. We prioritized and mobilized four workgroups. Work Groups Wave 1: March - June a. Standard Care Process b. Medication Reconciliation c. High-Risk Care Management Wave 2: September d. Home Health & SNF Care 23 3. 90 Day Improvement Cycle: Heart Failure Example Once the three workgroups were identified and mobilized, they each went through a 90-day improvement cycle. Completed SWOT analysis Identified gaps between Developed Heart Failure to develop current state current/future states initiative roadmap 24 Current State – SWOT Analysis Strengths Weaknesses • Physician Champion on-board • Poor identification and tracking of high-risk • Existing HF care pathway at UH patients • Institutional Support • Lack of adequate O/P care management resources • Collaboration with eHealth at Home • Inefficient med rec process at admission • Telemedicine support • Non-standardized handoffs at transitions of care • Gap in patient medication knowledge • No evidence standard HF care pathway in I/P (built into care connect) and O/P setting Opportunities Threats • Introduction of new positions (i.e, Nurse • ED Avoidance/Admission – Navigator, Practice Manager, Data Decrease in value Analyst positions) • PCP potentially view clinic and • Further education of community and patients eHealth at home O/P care • Improve patient and family experience strategies as a threat through care process (i.e.