Right Care Right Place Right Time

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System Level Measures Right Care Right Place Right Time M15 I have nothing to disclose Frederick C. Ryckman, MD Professor of Surgery / Transplantation Sr.

1 System Level Measures Right Care Right Place Right Time M15 I have nothing to disclose Frederick C. Ryckman, MD Professor of Surgery / Transplantation Sr. Vice President Medical Operations Cincinnati Children s Hospital Cincinnati, Ohio James Anderson Center for IHI 26 th National um December 7, 2015 Health Care Delivery System Transformation Strategic Improvement Priorities and System Level Measures 2 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 1

January February March April May June July August September October November December # of Days # of Days Challenges of Growth Operating Above Optimal

occupancy) * System Census Days between 85-90% Occupancy System Census Days > 90% Occupancy 30 25 20 15 10 5 2010 2011 2012 2013 2014 2015 16 14 12 10 8 6 4

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

2 January February March April May June July August September October November December # of Days # of Days Challenges of Growth Operating Above Optimal Occupancy We are increasingly operating above optimal census of 460 (85% occupancy) and frequently operating above a system stressing census of 485 (90% occupancy) * System Census Days between 85-90% Occupancy System Census Days > 90% Occupancy 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 2

3 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 Value Equation for Healthcare Value = (Outcomes + Patient Experience) x Appropriateness Cost + Hassle Factor 3

4 JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN # of Patients with a New Failure Critical Flow Failure Recognition Critical Patient Flow Failures by Month Month of Fiscal Year Last Update: 9/14/2015 by M. Ponti-Zins, for Data Source: MPS 4

5 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, GARDiANS Decrease overutilization of hospital services 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 Shape or Reduce Demand Match Capacity and Demand Redesign the System IHI Theory on Flow Outcomes Primary Drivers Secondary Drivers Specific Change Ideas 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 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. 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 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 5

6 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, GARDiANS Evidence Based Care 12 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 6

7 Bronchiolitis 13 Population Infants 1 year or younger with bronchiolitis 3 years control data vs. 3 years post implementation Results Admissions 30% decrease LOS 17% decrease Nasal Washings (RSV) 52% decrease Chest X Ray 14% Decrease Respiratory Therapies 17% decrease, repeat Tx - 28% decrease Net Cost Reduction Total Costs 14% decrease Respiratory care services 72% decrease Re-Admissions No change Perlstein PH et al. Arch Pediatr Adolesc Med 2000; 154: Muething S et al. J Pediatr 2004;144: Cytomegalovirus Prophylaxis 14 75% Decrease in CMV infection liver/intestine transplants Decreased IV-IGG expense Liver - Intestine Danziger-Isakov, Lara et al. CCHMC Integrated Solid Organ Transplant 7

8 Yearly SSI Patients - CCHMC 774 SSI s 9 Years 387 SSI s Prevented 337 SSI s Yearly SSI Patients - CCHMC Case Average 10 days LOS $27, Business Case 3870 days LOS $10.5 million 774 SSI s 9 Years 387 SSI s Prevented 337 SSI s Sparling KW, RyckmanFC, Schoettker PJ et al. Qual Manag Health Care Jul-Sep;16(3): Financial impact of failing to prevent surgical site infection 8

7 SSIs/100 procedures 60% reduction Overall SPS - Estimated 3,699 fewer children harmed Since October 2009 - $79 million in

9 Standardization for Outcomes Merging Evidence and Practice Focus on Excellence SSI Spines SSI Accomplishments Baseline rate: 4.4 SSIs/100 procedures, Current Rate: 1.7 SSIs/100 procedures 60% reduction Overall SPS - Estimated 3,699 fewer children harmed Since October $79 million in health care costs saved 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 :

Inflammatory Bowel Disease Remission rate (PGA, Centers >75% registered) 71 Care Centers >19,500 patients >575 physicians >35% of all IBD patients Key Drivers for Capacity Management IHI Drivers

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

10 Inflammatory Bowel Disease Remission rate (PGA, Centers >75% registered) 71 Care Centers >19,500 patients >575 physicians >35% of all IBD patients 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, GARDiANS 10

Surgical Streams of Care Urgent / Emergent Surgery Predictable and Measurable Natural Variation Possible to Model Can be managed within the System with resource allocation Delay Increased risk and

11 Surgical Streams of Care Urgent / Emergent Surgery Predictable and Measurable Natural Variation Possible to Model Can be managed within the System with resource allocation Delay Increased risk and worse outcomes Elective Surgery Unpredictable Whim of Surgical Schedule High variability over time Delay Case specific risk Initial Design around Urgent Needs Goal No urgent cases in Block Time Allocate Block for Urgent Needs Traditional Block Reactive System Urgent Emergent Cases placed within Block Time as needed Elective Case Plan disrupted, prolonged waiting time for elective patients Inefficient (Unsafe) Access for Urgent Cases Push complex Elective Cases into the late hours Overtime Wrong Team in OR Not Ideal 11

12 Scheduling Guidelines A to E Acute Life and Death Emergencies A < 30 Minutes Airway emergency(upper airway obstruction) Cardiac surgery postop bleeding with tamponade Cardiorespiratory decompensation (severe) Liver transplant postoperative emergency Malrotation with volvulus Massive bleeding Mediastinal injury Multiple Trauma-unstable or O.R. resuscitation Neurosurgical condition w/imminent herniation Emergent, but not immediately life threatening B < 2 Hours Acute shunt malfunction Acute spinal cord compression Bladder rupture Bowel perforation, traumatic Cardiac congenital emergencies w/hemodynamic or pulmonary instabilities Compartment syndrome Donor harvest ECMO cannulation Ectopic pregnancy Embolization for acute hemorrhage Esophageal atresia with tracheoesophageal fistula Gastroschisis/omphalocele Heart, heart/lung, lung, liver and intestinal transplants Incarcerated hernias Intestinal obstruction with suspected vascular compromise Intussusception-irreducible Ischemic limb/cold extremity (compromised arterial flow) Liver/Multivisceral/SI Transplant (when organ available) Liver transplant with suspected thrombosis Newborn bowel obstruction Open globe Orbital abscess Pacemaker insertion for complete heart block Replant fingers Replant hand or arm Spontaneous abortion GUIDELINES FOR SURGICAL CASE GROUPING DIAGNOSES/PROCEDURES (guideline only: medical judgment required) Revised Master Urgent C < 4 Hours Abscess with sepsis Airway (non-urgent diagnostic L&B, flex bronch, non-symptomatic foreign body) Appendicitis-with sepsis/rapid progression Biliary obstruction non-drainable Cardiac ventricular assist device placement Cerebral angiogram for intracranial hemorrhage Chest tube placement in patient w/unstable vital signs, increased work of breathing and decreased O2 saturation Contaminated Wounds-Multiple Trauma Diagnostic/therapeutic airway intervention Hepatic angiogram w/suspected vascular thrombus Hip Dislocation Intestinal Obstruction-no suspected vascular compromise Kidney transplant (ORGAN AVAILABLE) Liver laparotomy Massive soft tissue injury Nephrostomy tube placement in patient w/sepsis Obstructed kidney (stones) with sepsis Older child with bowel obstruction PICC placement where patient has no access but needs fluids/medications urgently Progressive shunt malfunction Traumatic dislocation-hip Unstable neurosurgical condition Semi-Urgent D < 8 Hours Abscess drainage Appendicitis-stable/elective Caustic ingestion Chest tube in patient w/stable vital signs Chronic airway foreign bodies Closure abdomen-liver transplant Coarctation repair in newborn Esophageal foreign body without airway symptoms GJ tube/nj tube placement with no other nutrition access Hematuria with clot retention I & D abscess Health without Systems septicemia Excellence Joint aspiration or bone biopsy prior to starting antibiotic therapy Kidney transplant (ORGAN NOT YET AVAILABLE) Add-on case to elective schedule E < 24 Hours Needs to be done that day, but does not require the manipulation of the elective schedule, pyloromyotomy Broviac Closed reduction Eyelid/canalicular lacerations Facial nerve decompression Femoral neck fracture Liver biopsy Mastoidectomy Open fracture grade I/II Open reduction of fracture PICC placement-has other IV access Retinopathy of prematurity treatment Unstable slipped capital femoral epiphysis Options from Simulation # Cases Included # Rooms Average Waiting Times (minutes) Probability 1 Or More Rooms Will Be Available Utilization Rate Recommendations/Considerations 1 A, B, C, D, missing treated as B 1 A: 45 B + missing: 53 C: 72 D: % 40% NOT RECOMMENDED Mean wait for A cases would exceed stated limit 2 A, B, C, missing treated as B 1 A: 21 B + missing: 24 C: 30 76% 24% NOT RECOMMENDED Low utilization rate 3 A, B, C (No missing ) 1 A: 17 B: 19 C: 22 81% 19% NOT RECOMMENDED Low utilization rate Ignores missing cases 4 A E, divided; missing treated as D 2 rooms: 1 room for A- C, 1 room for D,E, & missing A: 18 B: 19 C: 24 D + missing: 70 E: 162 A C room: 80% D E room: 43% A C room: 20% D E room: 57% NOT RECOMMENDED Low utilization rate in A C room Some cases with missing urgency codes may be more urgent than D 5 A E together; missing treated as B 2 rooms that would take any A E case A: 7 B + missing: 8 C + D: 9 E: 17 83% 42%, each room RECOMMENDED Very good waiting times (Wait for A cases would exceed stated limit about 1X/112 weekdays (21.4 weeks )) Treats missing cases conservatively Highest utilization rate Not very sensitive to small increases in case duration or case volume 12

13 Block with Urgent Access Assured Predictive system Urgent Cases in Defined Rooms with Scheduled Teams Resources needed can be modeled Care based on Urgency / Medical Need B-E Case Access - % Successful OR Renovation 1 Add-On Room Closed 13

14 A Case Access Times Target 30 Minutes 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

# of Patients with a New Failure 7/16/2008 9/14/2008 11/13/2008 1/12/2009 3/13/2009 5/12/2009 7/11/2009 9/9/2009 11/8/2009 1/7/2010 3/8/2010 5/7/2010 7/6/2010 9/4/2010 11/3/2010 1/2/2011 3/3/2011

15 # 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 8/9/2015 '14/1 '14/4 '14/7 '14/10 '15/1 '15/4 '15/7 '15/10 # of Delays 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 9/8/2015 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 9/8/2015 # of Patients with a New Failure # of Patients with a New Failure ICU Admission Model Elective Cases Short Stay Cases Access Cap # Cases on Schedule / Day Long Stay Cases Fixed # Beds Critical Flow Failures 30 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 9 Available Daily Failures OR Delays Due to Bed Availability 15

16 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, GARDiANS Discharge Prediction P32 Various approaches to Discharge Management 1980 s Keep it a Secret 1990 s 2000 s Discharge goals Reactive AM before 11 > 30-40% Shift goals 4 hour time block goals with prediction of window Not Patient Centered Prediction Discharge when Medically Ready 16

Discharge when Medically Ready Criteria based entirely on completion of necessary treatment plan Discharge criteria are determined on admission by treating physician / service Standardization of

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

17 Discharge when Medically Ready Criteria based entirely on completion of necessary treatment plan Discharge criteria are determined on admission by treating physician / service Standardization of criteria for all common treatment protocols All Hospital Medicine Pediatrics Surgery Gen, ENT, Orthopedics, Cardiac Develop mechanism to execute 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 17

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

18 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) Discharge when Physiologically Ready P36 18

diagnosis 3 2.5 2.42 2 2.12 2.1 1.97 2.1 1.87 1.5 1 1.14 1.4 0.

19 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 Balancing Measures Readmission Rate 19

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, GARDiANS Prediction Model for the Future Static Analytics Performing a ONE TIME analysis of processes with historical data in order to PREDICT what s going to happen under certain circumstances. Critical Care Bed Modeling for Growth Real Time Analytics Performing ONGOING analysis of processes with latest available data in order to continuously PREDICT what s going to happen under certain circumstances. RN Bedside Nurse Staffing Model 20

Full Unit Models future status Allows for

21 Critical Care Bed Predictions Discrete Event Simulation Variable Input Growth, Length of Stay, Readmissions What if scenarios Sample Output Probability of Full Unit Models future status Allows for Safety Considerations Construct right size units 21

22 ecasted Bed Needs Advantages of Efficiency Estimated number of beds required for given probability of the unit being full. ecast Time Frame Probability of Full Unit PICU Beds CICU Beds ICU Bed Needs Combined ICUs Estimated Savings Year 2 10% % % % Year 5 10% % % % Year7 10% % % % POPULATION: Unscheduled Medical/Surgical, BMT, ENT Airway ICU Elective Cases, Heart Institute Patients Real World Impact of Business Analytics Bed demand predictions facilitate staffing and overflow planning right patient right team ED admit predictions improved from 40% to 70% accuracy resource allocation Encourages staff to more consistently predict and document estimated discharge date, which helps guide care system efficiency Uncovers scheduling issues efficiency and access One-stop source to determine where there is capacity (or lack thereof) to add services (infusions, etc.) efficiency and utilization 22

Environmental Impact Assessments Predict program demand on current institutional capacity and resources Utilize simulation modeling and data

Therapy, Bronchoscopy) Other (OR resources, pharmacy, blood products, lab) Understanding Capacity Needs & Variability for New/Growth Programs

23 Environmental Impact Assessments Predict program demand on current institutional capacity and resources Utilize simulation modeling and data analytics to project future capacity needs in the areas of: Inpatient beds (ICU, Step-down/Floor) Outpatient (Clinical, Testing, Radiology, Therapy, Bronchoscopy) Other (OR resources, pharmacy, blood products, lab) Understanding Capacity Needs & Variability for New/Growth Programs Utilize information from historic data, subject matter experts, market analysis, and outside sources to develop model that predicts future resource demands. 23

24 Quantify Model Results for Analysis & Planning Number of beds needed based on probability of having a full unit (5%, 2%, 1%, 0%) and the growth estimate. 1 Yr Low/Conservative Mid-Range/Most Likely High/Aggressive Growth 3 Yr 5 Yr 7 Yr 10 Yr 1 Yr 3 Yr 5 Yr 7 Yr 10 Yr 1 Yr 3 Yr 5 Yr 5% % % % Outpatient Clinic Needs Clinics/Week Year Year Year Year Year Yr 10 Yr 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, GARDiANS 24

Patients Correct Number and Competency Flexible with

25 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 25

26 GARDiANS GARDiANS 26

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

27 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. Operations and Prediction Meeting (Weekly) COO, RN Leadership, In-Chiefs, Sr. VP s, Safety Director, ED Director 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

Patient Satisfaction Only 3-4% of 1 Million outpatient visitors rank our care in the

Stadium 42,319 65,535 Lessons Learned Building Will to work on Flow is a challenge When

When I does not work, it is someone else s problem Linkage Safety and Flow Speed vs

28 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 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, it is not my problem. When I does not work, it is someone else s problem Linkage Safety and Flow Speed vs Efficiency Work Backwards not just ward Embrace Mathematics and Analytics Standardize processes and work flows 28

29 Thanks! 29

Shaping Demand: Managing Elective OR Schedules and Predicting Downstream Demand

This presenter has nothing to disclose. Shaping Demand: Managing Elective OR Schedules and Predicting Downstream Demand Flow Symposium Nov. 2016 Frederick C. Ryckman, MD Professor of Surgery / Transplantation