Shaping Demand: Managing Elective OR Schedules and Predicting Downstream Demand

Transcription

1 This presenter has nothing to disclose. Shaping Demand: Managing Elective OR Schedules and Predicting Downstream Demand Flow Symposium Nov Frederick C. Ryckman, MD Professor of Surgery / Transplantation Sr. Vice President Medical Operations Cincinnati Children s Hospital Cincinnati, Ohio James Anderson Center for Health Systems Excellence

2 550 Bed Medical Center Admissions/Year 30,848 Opt Visits 1.02 M Surgical Procedures 32,000 cases 28 OR s, 2 IR suites, Hybrid Cath lab 8 OR Outpatient Surgery Center 1.4 M sq. ft. Research Space 15,000 Employees Cincinnati Children s Hospital

3 System Level Measures Health Care Delivery System Transformation Strategic Improvement Priorities and System Level Measures ACCESS FLOW PATIENT SAFETY CLINICAL EXCELLENCE REDUCE HASSLES TEAM WELLBEING FAMILY CENTERED CARE 3 rd Next available appointment % of eligible patients with delays Discharge Prediction and Execution Growth Prediction Adverse drug events (ADE) per 1,000 doses Nosocomial infection rates: Bloodstream infection rate Surgical site infection rate infection rates: VAP Safe Practices Serious Safety Events Codes outside the ICU rate/1000 days Standardized PICU Mortality Ratio Expected/ Actual % use of Evidence- Based Care for eligible patients Functional Health Status Touch Time for Providers Employee Satisfaction Staffing Effectiveness Staff Physician Satisfaction Voluntary staff turnover rate Accident rate for staff with Work days lost Overall Rating: Patient Experience Functional Health Status Risk Adjusted Cost per Discharge

4 What Do Patients Hire Us to Provide What do they call Value Make the Right Diagnosis Deliver the Correct Therapy / Treatment Outcomes Prevent Complications or Errors in Care Deliver Safe Care regardless of the Inherent Risks Safety Get Me Home, Keep me at Home Respect my needs Give me my Money s Worth Patient / Family Experience Value This is all FLOW management it is essential for SAFETY, PATIENT / FAMILY EXPERIENCE and QUALITY DELIVERY

5 Flow is a Safety Initiative Prediction Framework for Safety Getting the Rights Right Right Diagnosis and Treatment Right Patient in Right Bed Location Right Nursing Staff and Staffing Expertise Disease Specific Expertise Equipment Expertise Best Care Model Requires ability to Predict future needs, and manage present capacity control variability Operations Management techniques to understand and manage variability are the key to success

6 Value Equation for Healthcare Value = (Outcomes + Patient Experience) x Appropriateness Cost + Hassle Factor

7 10/5/ /12/ /19/ /26/ /2/ /9/ /16/ /23/ /30/ /7/ /14/ /21/ /28/2015 1/4/2016 1/11/2016 1/18/2016 1/25/2016 2/1/2016 2/8/2016 2/15/2016 2/22/2016 2/29/2016 3/7/2016 3/14/2016 3/21/2016 3/28/2016 4/4/2016 4/11/2016 4/18/2016 4/25/2016 5/2/2016 5/9/2016 5/16/2016 5/23/2016 5/30/2016 6/6/2016 6/13/2016 6/20/2016 6/27/2016 7/4/2016 7/11/2016 7/18/2016 7/25/2016 8/1/2016 8/8/2016 8/15/2016 8/22/2016 8/29/2016 9/5/2016 9/12/2016 9/19/2016 9/26/ /3/2016 # of New Patient Failures Total # of Bed Days Critical Flow Failure Recognition Weekly Critical Flow Failures Over the last 52 weeks Week Beginning # of New Failures Total Failures (Bed Days) Last Update: 2/2/2015 by Michael Ponti-Zins, for Data Source: MPS Type of Control Chart: P Chart

8 JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN # of Patients with a New Failure Our Growth Requires Efficiency Improvement 300 Critical Patient Flow Failures by Month Month of Fiscal Year Last Update: 3/4/2016 by M. Ponti-Zins, for Data Source: MPS

9 Key Drivers for Capacity Management IHI Drivers CCHMC Initiative Operations Possibilities Shape / Reduce Demand Predictable Care Delivery Management of Variability Best Practices, Analysis of ALOS and outliers, Standardize then Customize, Eliminate unnecessary care Identify Patient Streams Inpatient/Outpatient/OR Manage System Variation D/C Match Optimization of Flow Delivery Capacity Prediction Placement initiatives D:C Matching plans Discharge prediction and planning, Home Care, Parent Initiatives Integration of simulation modeling and planning Environmental Impact Reports for growth programs System Re-Design Capacity Management Flow:Safety Matching Simulation for design and patient placement Environments Impact Planning Flow Failure Analysis, Predictive Risk Analysis

10 IHI Theory on Flow Outcomes Primary Drivers Secondary Drivers Specific Change Ideas Decrease overutilization of hospital services Shape or Reduce Demand Relocate care in ICUs in accordance with patients EOL wishes Relocate care in Med/Surg Units to community-based care settings Relocate low-acuity care in EDs to community-based care settings Decrease demand for hospital beds through delivering appropriate care Decrease demand for hospital beds by reducing hospital acquired conditions 1. Proactive advanced illness planning 2. Development of palliative care programs (hospital-based and community-based) 3. Reduce readmissions for high risk populations 4. Extended hours in primary care practices 5. Urgent Care and Retail Clinics 6. Enroll patients in community-based mental health services 7. Paramedics & EMTs triaging & treating patients at home 8. Greater use of clinical pathways and evidence-based medicine 9. Care management for vulnerable/high risk patient populations 10. Decrease complications/harm (HAPU, CAUTI, SSI, falls with harm) and subsequent LOS 11. Redesign surgical schedules to create an predictable flow of patients to downstream ICUs and inpatient units Optimize patient placement to insure the right care, in the right place, at the right time Increase clinician and staff satisfaction Demonstrate a ROI for the systems moving to bundled payment arrangements Match Capacity and Demand Redesign the System Decrease variation in surgical scheduling Oversight system for hospital-wide operations to optimize patient flow Real-time demand and capacity management processes Flex capacity to meet hourly, daily and seasonal variations in demand Early recognition for high census and surge planning Improve efficiencies and throughput in the OR, ED, ICUs and Med/Surg Units Service Line Optimization (frail elders, SNF residents, stroke patients, etc.) Reducing unnecessary variations in care James and managing M. Anderson LOS outliers Center 1. Assess seasonal variations and changes in demand patterns and proactively plan for variations 2. Daily flow planning huddles (improve predictions to synchronize admissions, discharges and discharges) 3. Real-time demand and capacity problem-solving (managing constraints and bottlenecks) 4. Planning capacity to meet predicted demand patterns 5. High census protocols to expedite admissions from the ED and manage surgical schedules. 1. Redesign surgical schedules to improve throughput and to improve smooth flow of patients to downstream ICUs and inpatient units 2. Separate scheduled and unscheduled flows in the OR 3. ED efficiency changes to decrease LOS 4. Decrease LOS in ICUs (timely consults, tests and procedures) 5. Decrease LOS on Med/Surg Units (case management for patients with complex medical and social needs) 6. Advance planning for transfers to community-based care settings 7. Cooperative agreements with rehab facilities, SNFs and nursing homes

11 Key Drivers for Capacity Management IHI Drivers CCHMC Initiative Operations Possibilities Shape / Reduce Demand Predictable Care Delivery Management of Variability Evidence Based Best Practices, Analysis of ALOS/outliers, Standardize then Customize, Eliminate unnecessary care Identify Patient Streams Inpatient/Outpatient/OR Manage System Variation D/C Match Optimization of Flow Delivery Capacity Prediction Placement initiatives D:C Matching plans Discharge prediction and planning, Home Care, Parent Initiatives Integration of simulation modeling and planning Environmental Impact Reports for growth programs System Re-Design Capacity Management Flow:Safety Matching Simulation for design and patient placement Environments Impact Planning Flow Failure Analysis, Predictive Risk Analysis

12 Evidence Based Care Evidence Based Care Guidelines serve as an interface between rapidly evolving scientific information and busy clinical practices Developed by Inter-disciplinary teams experts Implementation Awareness of recommendation to facilitate change Easy access to the Evidence Feedback on Outcomes Feedback on further improvements Culture of Improvement / Evidence Based Care

13 Integration Priority Practice - Plan Prioritization Goal = Exceptional, Safe, Affordable Care Every Child Owner Executive Leadership Practice What we Do Essential Steps, Decisions and Actions Owner Clinical Leadership Teams Departments / Divisions Processes How we Do It Processes to execute to the goal Owner Operational Leaders Sites of Care Plan Implement the Processes - plan through application of process steps Owner Sites of Care leaders and clinical staff (MD / RN) Front Line Implementation Just Do It Every day for every child

14 Cytomegalovirus Prophylaxis 75% Decrease in CMV infection liver/intestine transplants Decreased IV-IGG expense Liver - Intestine Danziger-Isakov, Lara et al. CCHMC Integrated Solid Organ Transplant

15 Yearly SSI Patients - CCHMC Total SSI Pa ents Fiscal Year 1032 SSI s 12 Years 540 SSI s Prevented 492 SSI s

16 Yearly SSI Patients - CCHMC Total SSI Pa ents Case Average 10 days LOS $27, Business Case 5400 days LOS $145.8 million Fiscal Year 1032 SSI s 12 Years 540 SSI s Prevented 492 SSI s

17 Standardization for Outcomes Merging Evidence Based Care and Practice Focus on Excellence SSI Spines

18 SSI Accomplishments Baseline rate: 4.4 SSIs/100 procedures, Current Rate: 1.7 SSIs/100 procedures 60% reduction, 32% reduction in past 3 years - $17.4 Million from SSI alone Overall SPS - Estimated 6,686 fewer children harmed Since October $121.4 Million saved in SPS Network Toltzis P, O Riordan M, Cunningham DJ, Ryckman FC, Bracke TM, Olivea J, Lyren A. A statewide collaborative to reduce pediatric surgical site infections. Pediatrics :

19 Inflammatory Bowel Disease 92 Care Centers 26,000 patients 795 physicians

20 Key Drivers for Capacity Management IHI Drivers CCHMC Initiative Operations Possibilities Shape / Reduce Demand Predictable Care Delivery Management of Variability Evidence Based Best Practices, Analysis of ALOS and outliers, Standardize then Customize, Eliminate unnecessary care Identify Patient Streams Inpatient/Outpatient/OR Manage System Variation D/C Match Optimization of Flow Delivery Capacity Prediction Placement initiatives D:C Matching plans Discharge prediction and planning, Home Care, Parent Initiatives Integration of simulation modeling and planning Environmental Impact Reports for growth programs System Re-Design Capacity Management Flow:Safety Matching Simulation for design and patient placement Environments Impact Planning Flow Failure Analysis, Predictive Risk Analysis

21 ICU Bed Availability ICU Scheduling Category Case Statistics by Category Total PICU Days Case Count ALOS Short (61%) 1.27 (27%) Medium (28%) 3.72 (37%) Long (11%) 9.76 (36%) Grand Total

22 ICU Admission Model Elective Cases Short Stay Cases Access Cap # Cases on Schedule / Day Long Stay Cases Fixed # Beds

23 Predicting ICU Discharge

24 7/16/2008 9/14/ /13/2008 1/12/2009 3/13/2009 5/12/2009 7/11/2009 9/9/ /8/2009 1/7/2010 3/8/2010 5/7/2010 7/6/2010 9/4/ /3/2010 1/2/2011 3/3/2011 5/2/2011 7/1/2011 8/30/ /29/ /28/2011 2/26/2012 4/26/2012 6/25/2012 8/24/ /23/ /22/2012 2/20/2013 4/21/2013 6/20/2013 8/19/ /18/ /17/2013 2/15/2014 4/16/2014 6/15/2014 8/14/ /13/ /12/2014 2/10/2015 4/11/2015 6/10/2015 8/9/ /8/ /7/2015 2/5/2016 4/5/2016 6/4/2016 8/3/ /2/ /1/2016 1/30/2017 3/31/2017 5/30/2017 7/29/2017 9/27/ /26/2017 1/25/2018 3/26/2018 # of Patients with a New Failure 7/16/2008 9/14/ /13/2008 1/12/2009 3/13/2009 5/12/2009 7/11/2009 9/9/ /8/2009 1/7/2010 3/8/2010 5/7/2010 7/6/2010 9/4/ /3/2010 1/2/2011 3/3/2011 5/2/2011 7/1/2011 8/30/ /29/ /28/2011 2/26/2012 4/26/2012 6/25/2012 8/24/ /23/ /22/2012 2/20/2013 4/21/2013 6/20/2013 8/19/ /18/ /17/2013 2/15/2014 4/16/2014 6/15/2014 8/14/ /13/ /12/2014 2/10/2015 4/11/2015 6/10/2015 8/9/ /8/ /7/2015 2/5/2016 4/5/2016 6/4/2016 8/3/ /2/ /1/2016 1/30/2017 3/31/2017 5/30/2017 7/29/2017 9/27/ /26/2017 1/25/2018 3/26/2018 # of Patients with a New Failure 7/16/ /14/2008 1/12/2009 4/12/2009 7/11/ /9/2009 1/7/2010 4/7/2010 7/6/ /4/2010 1/2/2011 4/2/2011 7/1/2011 9/29/ /28/2011 3/27/2012 6/25/2012 9/23/ /22/2012 3/22/2013 6/20/2013 9/18/ /17/2013 3/17/2014 6/15/2014 9/13/ /12/2014 3/12/2015 6/10/2015 9/8/ /7/2015 3/6/2016 6/4/2016 9/2/ /1/2016 3/1/2017 5/30/2017 8/28/ /26/2017 2/24/2018 7/16/ /14/2008 1/12/2009 4/12/2009 7/11/ /9/2009 1/7/2010 4/7/2010 7/6/ /4/2010 1/2/2011 4/2/2011 7/1/2011 9/29/ /28/2011 3/27/2012 6/25/2012 9/23/ /22/2012 3/22/2013 6/20/2013 9/18/ /17/2013 3/17/2014 6/15/2014 9/13/ /12/2014 3/12/2015 6/10/2015 9/8/ /7/2015 3/6/2016 6/4/2016 9/2/ /1/2016 3/1/2017 5/30/2017 8/28/ /26/2017 2/24/2018 # of Patients with a New Failure # of Patients with a New Failure Critical Flow Failures 24 Delayed or Canceled Surgery Due to Bed Capacity PICU Bed Not Available for Urgent Use Daily Failures Patients who Utilize an ICU bed b/c an Appropriate Bed is Not Available Daily Failures Psychiatry Patients Placed Outside of their Primary Unit Daily Failures

25 Key Drivers for Capacity Management IHI Drivers CCHMC Initiative Operations Possibilities Shape / Reduce Demand Predictable Care Delivery Management of Variability Evidence Based Best Practices, Analysis of ALOS and outliers, Standardize then Customize, Eliminate unnecessary care Identify Patient Streams Inpatient/Outpatient/OR Manage System Variation D/C Match Optimization of Flow Delivery Capacity Prediction Placement initiatives D:C Matching plans Discharge prediction and planning, Home Care, Parent Initiatives Integration of simulation modeling and planning Environmental Impact Reports for growth programs System Re-Design Capacity Management Flow:Safety Matching Simulation for design and patient placement Environments Impact Planning Flow Failure Analysis, Predictive Risk Analysis

26 Strategies for Patient Placement Early Day Beds PICU, CICU Critical Units Later Day Beds All units Demand : Capacity Match Opportunistic with ethought Specific Bed D:C Match Unit Bed Awareness

27 Discharge Prediction P27 Various approaches to Discharge Management 1980 s Keep it a Secret 1990 s 2000 s Discharge goals AM before 11 > 30-40% Shift goals 4 hour time block goals with prediction of window Reactive Not Patient Centered Prediction Discharge when Medically Ready

28 Timeline for DC when Medically Ready Admission to Floor Discharge Criteria Set Treatment Protocol Followed Discharge Criteria Met Nurse Notifies Staff 2 Hrs Discharge Home Re-Adm & LOS Tracked Standardized Criteria Buy-In by Staff Standardized Protocols for most Tx Evaluation Criteria Modify Rounding Clear Discharge Criteria Communication Family Criteria established at admission Nurse at bedside notifies service when Medical discharge criteria are met Discharge from floor in < 2 hours Review Length of Stay and Re-Admissions as balancing measures Not about Speed Now about Efficiency

29 Discharge When Medically Ready Karen Tucker, Angela Statile, Diane Herzog, and Christy White Increase percentage of all HM patients who have met* Medically ready criteria who will be discharged within two hours of reaching that goal* on A6S, A6N, LA1W from 75%to 80% by June 30, 2014 Productivity: Optimize use of facilities and staff and improve patient flow to achieve 20% greater utilization of existing assets by June 30, 2015 Criteria for Medically Ready Defined at Admission Shared Ownership/ Accountability and Buy-In Among Physicians and Nurses Communication regarding prediction of discharge and defined goals is ongoing through the hospital stay Potential Barriers to Discharge are Clearly Articulated and Mitigation Plans Established Performance by team is transparent Evidence of Preoccupation with Failure Clear expectations for Parents/ Families Agreement among HM attendings and nursing staff of discharge criteria for order set diagnoses and general admissions (LOR 2) 1) 8 pm Huddle discussion re: early discharges (LOR 2) 2) 0630 notification of patients ready for discharge (LOR 1) Performance Management (LOR 1) Standardized and modifiable order sets (LOR 2) Identify and Mitigate Plans: 1) Transportation- census based (LOR 1) 2) Pharmacy- priority fills (LOR 2), Outpt delivery to patient room (LOR 1) 3) Consults- proactive evaluation (LOR 2) 4) RT- process in PICU (LOR 1) 5) Home Health Care Daily Feedback reports to RNs and MD s with ID and mitigation of process and outcome measure failures (LOR 2) Feedback of data by HM team In conference room and by (LOR 1) Auto notification to resident team that patient has met all criteria (LOR 2)

30 Discharge when Medically Ready All Units

31 Service Level DC when Medically Ready

32 Average Length of Stay (Days) Balancing Measures Length of Stay Hospital Medicine Average Length of Stay patients with selected diagnosis Unit 1 Unit 2 Unit 3 All FY11 FY13

33 Balancing Measures Readmission Rate

34 Key Drivers for Capacity Management IHI Drivers CCHMC Initiative Operations Possibilities Shape / Reduce Demand Predictable Care Delivery Management of Variability Evidence Based Best Practices, Analysis of ALOS and outliers, Standardize then Customize, Eliminate unnecessary care Identify Patient Streams Inpatient/Outpatient/OR Manage System Variation D/C Match Optimization of Flow Delivery Capacity Prediction Placement initiatives D:C Matching plans Discharge prediction and planning, Home Care, Parent Initiatives Integration of simulation modeling and planning Environmental Impact Reports for growth programs System Re-Design Capacity Management Flow:Safety Matching Simulation for design and patient placement Environments Impact Planning Flow Failure Analysis, Predictive Risk Analysis

35 Predicting Future Programmatic Needs Environmental Impact Studies LUNG TRANSPLANTATION

36 ICU Bed Needs Number of beds needed based on probability of having a full unit (5%, 2%, 1%, 0%) and the growth estimate. Low/Conservative Mid-Range/Most Likely High/Aggressive Growth 1 Yr 3 Yr 5 Yr 7 Yr 10 Yr 1 Yr 3 Yr 5 Yr 7 Yr 10 Yr 1 Yr 3 Yr 5 Yr 7 Yr 5% % % % Yr

37 Predicted to Actual ICU Bed Demand ICU Prediction Mid Level Conservative Aggressive Year Year Year Year Year Year ICU demand appears to be dependent on pre-transplant severity and much higher than originally anticipated Actual

38 Predicted to Actual Cardiology Bed Demand Heart Prediction Mid Level Conservative Aggressive Year Year Year Year Year Year Cardiology demand appears to be lower than original projections Actual

39 Predicted to Actual Pulmonary Bed Demand Pulmonary Prediction Mid Level Conservative Aggressive Year Year Year Year Year Year Pulmonary demand appears to be in line with original projections Actual

40 Outpatient Demand

41 Outcomes and Observations - Environmental Impact Assessment provided valuable information allowing for assessment and agreement across the hospital before program was initiated - Answer questions about patient flow and placement - Assess potential stress on existing resources - Quantify demand and capacity needs (staffing, beds, outpatient clinic rooms, PFT demand, OR demand) - Requires assumptions and research for new programs - As always your results are only accurate if your assumptions are correct

42 Staffing Prediction Proactive Planning Data to Front Line Leaders Updated daily Right Staff for the Right Patients Correct Number and Competency Flexible with Changing Environment Prediction of Needs Be Prepared Be Resilient

43 Staffing Prediction Proactive Planning

44 Key Drivers for Capacity Management IHI Drivers CCHMC Initiative Operations Possibilities Shape / Reduce Demand Predictable Care Delivery Management of Variability Evidence Based Best Practices, Analysis of ALOS and outliers, Standardize then Customize, Eliminate unnecessary care Identify Patient Streams Inpatient/Outpatient/OR Manage System Variation D/C Match Optimization of Flow Delivery Capacity Prediction Placement initiatives D:C Matching plans Discharge prediction and planning, Home Care, Parent Initiatives Integration of simulation modeling and planning Environmental Impact Reports for growth programs System Re-Design Capacity Management Flow:Safety Matching Simulation for design and patient placement Environments Impact Planning Flow Failure Analysis, Predictive Risk Analysis

45 GARDiANS

46 GARDiANS

47 Hospital Wide System for Safety 3 Times - Every Day Individual Room / Floor / System Predictions Capacity and Safety Floor Huddles PeriOp Huddle Outpt, Home, Psych ED Huddle ICU Huddles Institutional Wide Bed Huddle Capacity Management Pharmacy Pt. Transport Facilities Institutional Daily Operations Brief System Prediction Mitigation Strategy Security Housekeeping P.F.E.

48 Flow Dashboard Sites of Care

49 Patient Satisfaction Only 3-4% of 1 Million outpatient visitors rank our care in the lower half (0-6 of 10 pts) 35,000 patient per year Great American Ballpark Paul Brown Stadium 42,319 65,535

50 Understanding Outliers I thought: If I can get 80-85% of this under good control, that will solve at least 85% of the problem

51 N=297 cases < 62 days (85.6%) Total N=347 N=50 >62 Days N=25 >100 Days N=14 >200 Days N=6 >365 Days

52 1,795 bed days 1,339 bed days 2,131 bed days 2,340 bed days 7,595 Bed Days 50 Patients 342 Acute Care Beds 22.2 Days Total Hospital Census 6.08% Yearly Hospital Census

53 Observations on Outliers In these predictive models, it is important to be right. What is really important is the cumulative magnitude of your errors. The errors are often the result of big surprises, not multiple small issues. Failure to meet the predictive model leads to progressive and increasing cumulative error, (the more you are off, the more you are off.) It is hard to offset the surprise errors with great prediction of the expected.

54 Lessons Learned Building Will to work on Flow is a challenge When it works, it is not on anyone s radar If it works for me, your problem is not my problem. When it does not work, somebody else should solve it Linkage Safety and Flow Speed vs Efficiency Work Backwards not just ward Embrace Mathematics and Analytics Standardize processes and work flows

55 Make it Personal Don t let the Data Drown out the Dream Stories not Statistics Names and Faces Accountability is Personal & Group Responsibility Collective Mission/Vision Cincinnati Children s Hospital Medical Center 2013

56 Thanks!