Improving Staffing Effectiveness & Quality Outcomes with Predictive Labor Management (PLM) OHA Annual Conference June 8, 2015

Improving Staffing Effectiveness & Quality Outcomes with Predictive Labor Management (PLM) OHA Annual Conference June 8, 2015 Hank Drummond, PhD, MDiv, BA, RN Chief Clinical Officer Cross Country Healthcare Bernadette Rotea, MS, BSN, RN Director of Standards, Quality Management, & Operational Improvement Cross Country Healthcare Scott Duncan President Agile Healthcare

PROGRAM OVERVIEW Leveraging PLM How to leverage Predictive Labor Management to get the right staff with the right skill sets at the right bedside at the right time through a combination of predictive analytics and nursing operations process improvements. Learning Objectives: Relationship between staffing effectiveness, staffing models, & quality outcomes Direct process improvements to improve HCAHPS & Star Ratings Using Predictive Analytics to control costs and improve coverage with better staffing effectiveness 2

PROGRAM OVERVIEW Besides Providing Quality Care and Saving Lives 1. Total Labor Costs 2. Quality and HCAHPS Scores 3. Revenue Generation 4. Redistribution of Care 5. Marketing 3

PROGRAM OVERVIEW Issues Aging Workforce High Labor Costs Challenges in Healthcare Today Challenges with Patient Safety High Demand Low Supply Decreased Efficiency Nursing Recruitment & Retention Challenges Challenges with Quality Outcomes Challenges with Patient Experience Challenges with Reimbursements Decreased Productivity Lack of Transparency JUNE 2015 4

LEARNING OBJECTIVE Learning Objective: Staffing Effectiveness Relationship Between Staffing Models Quality & Safety Outcomes 5

Staffing Effectiveness

STAFFING EFFECTIVENESS Staffing Effectiveness Appropriate level of nurse staffing that will provide for the best possible outcome of individual patients throughout a particular facility The Joint Commission Standards LD.04.04.05, EP13: at least once per year, the hospital/organization must provide written reports on all system or process failures, the number and types of sentinel events, information provided to families/patients about the events, and actions taken to improve patient safety. PI.02.01.01, EPs 12 14: process improvement efforts Mandated Patient-to-Staff Ratio Reference: The Joint Commission, 2009 Interim Standards for Staffing Effectiveness 7

Staffing Models

STAFFING MODELS Staffing Quantity Levels Staffing Models Match staffing levels with work requirements Staffing Quality Person/Job Match Match employee personality and talents with job requirements Staffing Quality Person/Organization Match Is the candidate a good fit? Staffing Organizations Model Use mission, vision, and goals Variable Workload Staffing Model Dynamic staffing model approach that accounts for workload variability to more closely match staffing resources to the demand for patient care Reference: Heneman, H.G. & Judge, T.A. (2009). Staffing Organizations, 6 th Edition. McGraw-Hill/Irwin. Boston, MA. 9

Quality & Safety Outcomes

QUALITY & SAFETY OUTCOMES Quality & Safety Outcomes Nursing- Sensitive Quality Indicators Hospital- Acquired Conditions Patient Safety Indicators Sentinel Events Patient Experience 11

QUALITY & SAFETY OUTCOMES What Is The Relationship? Is There a Correlation? The higher the RN skill mix, the lower the incidence of adverse occurrences. A higher proportion of RN hours and RNHPPD associated with better inpatient care and patient outcomes. 12

LEARNING OBJECTIVE Learning Objective: Direct Process Improvements to Improve Star Ratings 13

How HCAHPS Are Driving The Need To Right-Size Staff

QUALITY OUTCOMES Hospital Consumer Assessment of Healthcare Providers & Systems (HCAHPS) National and Publicly reported standardized survey instrument & data collection methodology for measuring patients perspectives regarding their hospital care. Valid and reliable tool that a l l o w s co m p a r i s o n s across all hospitals. 32 item survey instrument for measuring patients perceptions regarding their hospital experience. Standardized tool that is credible, reliable, valid, practical, and actionable. JUNE 2015 15

QUALITY OUTCOMES Hospital Consumer Assessment of Healthcare Providers & Systems (HCAHPS) HCAHPS Star Ratings CMS to add Hospital Compare website in April 2015 Allows consumers to easily compare hospitals Star Ratings spotlight EXCELLENCE in healthcare quality 12 HCAHPS star ratings Based on discharges between July 1, 2013 June 30, 2014 Summarizes one (1) aspect of hospital quality: PATIENTS EXPERIENCE OF CARE 16

QUALITY OUTCOMES Valued-Based Purchasing (VBP) Program Centers for Medicare & Medicaid Services (CMS) program Hospital VBP program links a portion of IPPS hospitals payment from CMS to performance on a set of quality measures. Three (3) Domains Total to 100% of a hospital s Total Performance Score or TPS The 3 Domains 1. Clinical Process of Care Domain Accounts for 45% of a hospital s TPS 2. Patient Experience of Care Domain Accounts for 30% of a hospital s TPS; HCAHPS survey is the basis for this domain. 3. Outcome Domain Accounts for 25% of a hospital s TPS 17

QUALITY OUTCOMES Affordable Care Act of 2010 (ACA) Signed by President Obama on March 23, 2010 New Patient s Bill of Rights gives individuals stability and flexibility needed to make informed choices regarding their healthcare Key Features of ACA Improve quality and lower costs of healthcare Improve access to healthcare New consumer protection Strengthen Medicare Hold insurance companies accountable Create jobs by addressing primary care work force needs 18

QUALITY OUTCOMES Improving Patient Experience Leadership Engaged and Passionate Patient & Family Focused Care Caregivers Competent and Compassionate Process & Care Patient-Centered Care Focused Initiatives 19

LEARNING OBJECTIVE Learning Objective: Using Predictive Analytics to Control Costs and Improve Coverage with Better Staffing Effectiveness 20

VARIABLE WORKLOAD STAFFING Variable Workload Staffing A dynamic staffing model approach that accounts for workload variability to more closely match staffing resources to the demand for patient care Possibly a different schedule every M o n t h / We e k / D a y Requires flexibility in the availability of staff Idea derived from JIT operations management in manufacturing Quickly becoming a requirement in today s environment of regulation and cost controls 2011 HFM Article* optimizing staffing in a way that best reflects demand Intermountain ED savings opportunity A cost savings of over 16% within the RN budget * Bryce, David J.; Christensen, Taylor J. Finding the Sweet Spot: How to get the right staffing for variable workloads. hfm Magazine March 2011 21

VARIABLE WORKLOAD STAFFING Variable Workload Staffing Not a New Concept Staffing Grids Patient/Nurse Ratios HPPD or HPPV Flexible Staffing: On-call, call-offs, PRN, Float, Agency Real-World Considerations Census Alone In Not A Good Indicator Of Workload Acuity, ADT Activities, Non-patient Care Activities Staff Scheduling Restrictions Exist Union rules, Employee expectations, Minimal coverage VWS Doesn t Always Help Low Variability / Highly Scheduled 22

PREDICTIVE LABOR MANAGEMENT Predictive Labor Management An improved version of Variable Workload Staffing Considers all measurable patient-care workload components Census, ADT activity, Acuity, etc. Forecasts patient-care workload, including confidence interval Utilizes department-specific staffing policies to determine needed staffing levels from the workload forecast Considers other scheduling impacts non-patient care activities, union rules, minimal coverage, etc. Compares to actual & planned schedules, and p r o v i d e s g u i d a n c e on how to improve them A view of needs across multiple departments Can be applied to non-scheduling staffing activities Roster sizing, position control, budgeting, etc. 23

PREDICTIVE LABOR MANAGEMENT Primary Benefits Predictive Labor Management Reduces Costs Improves Coverage Staff Satisfaction Patient Satisfaction Improved Quality of Care Consistency in scheduling policy application Less time managing the schedule 24

Opportunity Analysis CASE STUDY Case Study: 400-bed Academic Medical Center Variable Workload Staffing Schedule Staff levels to Expected Workload Issues Goal High inpatient RN overtime costs Anecdotal information suggested some units were not correctly staff Limited ability to make day of staffing adjustments (Union Shop) Several units were chronically over- or under-staffed Higher than expected overtime costs Use forecasting to dynamically set staffing levels of the monthly schedules before staff sign-up Data Collection Modeling Analysis Worked with client s decision support group to submit 6 years of patient levels by unit and 1 year of staffing levels by unit Created forecasting model relating needed staffing levels to patient levels using client s staffing policies (coverage, call-offs, etc.) Ran a simulation for 12 months Compared resulting costs and coverage to actual costs and coverage Illustrated possible efficiency using VWS to guide monthly schedules 25

CASE STUDY Case Study: 400-bed Midwest Academic Medical Center continued Significant efficiency improvement opportunity The study showed an issue with coverage Some departments were chronically understaffed despite large overtime & agency costs RN HPPD Patient Actual % Days Overstaffestaffed Under- 12+ Hrs RN 12+ Hrs RN Department Target Actual Hours Unit Flex Float OT/AG Extra Short In Coverage Rehab/Surg Spec 6.62 5.35 187,387 30,037 2,744 4,858 4,363 215 9,901 1% 77% 22% Neuro SD 7.34 6.8 187,002 47,780 1,070 3,636 2,085 1,600 4,221 14% 25% 61% Med Surg SD 7.92 7.48 192,564 49,054 2,103 7,909 4,264 3,284 3,501 31% 15% 55% Med Surg 6.34 5.68 321,020 50,634 8,424 11,123 8,138 2,332 8,815 18% 45% 37% Transplant 10.96 10.54 114,428 42,439 4,251 7,976 5,152 9,580 2,017 78% 3% 19% Nephrology 6.58 5.59 195,018 32,422 3,940 4,984 4,427 338 8,033 3% 64% 32% Observation 7.5 7.03 101,371 27,508 1,415 2,865 2,054 4,146 1,983 25% 8% 66% Oncology 6.78 5.87 200,590 36,146 4,232 3,921 5,486 730 7,611 5% 58% 37% Adolesc Psych 4.8 3.28 73,294 7,894 665 126 1,505 158 4,627 1% 49% 50% Adult Psych 4.8 3.74 257,720 25,203 9,267 1,679 4,530 567 11,431 5% 70% 24% Stem Cell 8.34 7.5 73,531 18,493 778 2,512 2,660 1,474 2,583 13% 18% 69% Labor/Delivery 19.67 18.73 80,918 84,635 5,356 3,873 6,857 37,564 3,161 90% 6% 3% Postpartum 6.91 6.64 169,895 44,151 6,254 3,625 7,534 14,569 1,935 90% 4% 5% Med Surg ICU 14.28 13.72 125,384 59,834 4,154 7,310 10,433 10,065 2,937 74% 5% 21% Neonatal ICU 12.4 11.74 251,588 116,701 5,029 5,497 9,045 13,174 6,916 59% 23% 18% Neuro ICU 14.88 14.15 147,099 80,220 2,877 4,283 5,893 6,552 4,481 52% 14% 34% Pediatrics 7 5.29 122,890 22,660 1,942 1,965 1,735 1,208 8,749 11% 58% 31% Peds ICU 13.48 12.69 80,296 42,283 2,646 6,317 5,079 13,858 2,634 69% 12% 19% Totals 8.06 7.26 2,881,995 818,093 67,149 84,458 91,240 121,413 95,535 36% 31% 34% 26

CASE STUDY Case Study: 400-bed Academic Medical Center continued Significant efficiency improvement opportunity Several scenarios were analyzed to show different trade-offs between lowering costs and improving coverage RN Hours RN HPPD RN Costs Improvements Unit Flex Float OT/AG Overstaff Understaff Target Achieve Total Cost Savings Understaff Savings Actual 818,093 67,149 84,458 91,240 121,413 95,535 8.06 7.26 $54,645,102 Coverage Matched using VWS Some Understaffing Using VWS No Understaffing using VWS No Understaffing w/o VWS 888,640 67,149 38,833 0 55,072 95,535 8.06 7.49 $49,760,461 $4,884,641 0.00% 8.90% 888,640 59,412 71,816 22,523 55,072 47,765 8.06 7.73 $51,988,847 $2,656,254 50.00% 4.90% 888,640 67,149 81,621 52,747 55,072 0 8.06 8.06 $54,789,825 ($144,723) 100.00% -0.26% 818,093 67,149 84,458 186,775 121,413 0 8.06 8.06 $60,960,219 ($6,315,117) 100.00% 11.56% 27

Case Study: 400-bed Academic Medical Center continued Significant efficiency improvement opportunity Variability in Patient Volume forecasts is a strong indicator of appropriate levels of supplemental staffing CASE STUDY Department Scheduled Supplemental % Supp Rehab/Surg Spec 51,854 6,304 12% Neuro SD 61,944 4,801 8% Med Surg SD 63,448 7,888 12% Med Surg 84,161 11,685 14% Transplant 55,110 5,159 9% Nephrology 56,650 5,255 9% Observation 36,110 3,802 11% Oncology 59,122 5,099 9% Adolesc Psych 17,562 454 3% Adult Psych 57,785 4,195 7% Stem Cell 28,494 2,583 9% Labor/Delivery 64,862 13,131 20% Postpartum 53,363 5,170 10% Med Surg ICU 78,712 8,190 10% Neonatal ICU 132,289 23,019 17% Neuro ICU 92,667 12,844 14% Pediatrics 40,093 4,097 10% Peds ICU 47,317 10,691 23% Totals 1,081,546 134,368 12% 28

PREDICTIVE ANALYTICS Predictive Analytics Example ED Visits Historical Analysis & Demand Forecast 1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 Actual Actual Model Trend Trend85%Fcst 29

Service Levels Example PREDICTIVE ANALYTICS ED Visits: Demand Forecasting by Service Level 1600 1500 1400 1300 1200 1100 1000 Trend Forecast 80% SL 90% SL 30

PREDICTIVE ANALYTICS Common Workload to Staffing Levels Calculations Staffing Grids # PATIENTS CHG RN RN NT CLERKS 1 1 1 0 1 2 1 1 0 1 - - - - 25 1 3 1 2 26 1 4 2 2 27 1 4 2 2 IP Acuity Coverage PATIENT ACUITY P/N RATIO NURSE HPPD Level 1 6 4 Level 2 4 6 Level 3 3 8 Level 4 2 12 Level 5 1 24 Productivity Targets DEPARTMENT MEASURE TARGET Emergency HPPV 2.9 Med/Surg HPPD 6.8 VISIT SEVERITY ICU HPPD 17.5 ED Severity Coverage DURING FIRST HOUR (MIN) DURING SUBSEQUENT HOURS (MIN PER HOUR) ESI 5 15 15 ESI 4 30 15 ESI 3 45 30 ESI 2 60 45 ESI 1 120 60 31

CASE STUDY Case Study Suburban Medical Center Forecasting usefulness confirmed Analysis of the forecast usefulness focused on RNs needed by shift FORECAST vs. POLICY vs. ACTUAL PRACTICE UNIT A (AVG. CENSUS 27) MATCHED NEED OVER SCHEDULED UNDER SCHEDULED NOTES Forecast 58% 28% 15% 60% Service Level, 3 months pilot Core schedule 52% 45% 3% Actual schedule 41% 49% 10% 2 months immediately prior 6 weekdays, 5 weeknights, 5 weekend days, 4 weekend nights UNIT B (AVG. CENSUS 8) MATCHED NEED OVER SCHEDULED UNDER SCHEDULED NOTES Forecast 36% 38% 27% 60% Service Level, 2 months pilot Core schedule 14% 77% 9% 4 all shifts Actual schedule 30% 49% 21% 3 months immediately prior 32

PLM REPORT PLM Report Example Current Schedule compared to Forecasted Need 33

PLM REPORT PLM Report Example Enterprise-wide View 34

PLM REPORT PLM Report Example Forecasted Staffing Plan / Roster Size 35

PLM REPORT PLM Report Example Monthly Forecast with Confidence interval 36

PLM REPORT PLM Report Example Workload Time Of Day Analysis 37

PLM REPORT PLM Report Example 7 Day / 4 Hour Block Forecast w/ two Service Levels 38

SUMMARY Summary 1) If you know the future you can prepare for it All good planning requires a forecast, so take the guesswork out 3) Excellence in Nursing requires Excellence in Staffing Your patients deserve the best 2) Predictive Labor Management increases your preparedness Get the right staff with the right skill sets at the right bedside at the right time 4) Exciting Time Ahead for Nursing New technologies coming to make your job easier, so you can focus on patient care 39

any QUESTIONS?