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Staffing Strategy
- 1. How Can Data Science Reduce Wait and Cost in an Emergency Room? A Service Disabled Veteran Owned Small Business
- 2. 2 Emergency Rooms Have Highly Uncertain Demand Leading to Chronic Understaffing and Overstaffing Which Demand Percentile Balances the Risk of Understaffing against Cost? PROBABILITY Average Demand Increasing Risk of Chronic Overstaffing, High Cost Increasing Risk of Chronic Understaffing, Long Waits, LWBS Predicted Patient Demand . PATIENT DEMAND
- 3. RPG’s Team of Data Scientists Predict Demand, Simulate Performance and Develop a Staffing Strategy . A Staffing Strategy maintains or improves operational effectiveness while minimizing cost Staffing Strategies guide scheduling, enabling departments to: Support performance goals the right staff at the right time Manage costs minimize FTE & hours worked STRATEGIC TACTICAL 3 Remove “Guesstimation” From Planning Planning Operational Staffing Detailed Scheduling 1 2 3 4 5 RPG Model of Staffing and Scheduling Activities
- 4. 4 Understanding Current State Planning • Staffing & Scheduling Policies • Manpower Requirements . Planning Operational Staffing Detailed Scheduling 1 2 3 4 5 What are your goals? Do you have a substantiated plan in place to achieve them? • Performance Goals • Financial Goals & Staffing Budget
- 5. 5 Model and Predict Patient Demand Predict Future Patient Demand Collect Historical Data V11139950091 Apply Data Science Techniques • Hour-of-Day • Day-of-Week • Month • Season • Holidays • Special Events • Long-term Trends Key Factors: Historical Data and Advanced Data Science Techniques Reduce the Uncertainty of Future Demand • Operational Staffing is driven by patient demand • RPG method: Historical data + data science techniques = accurate demand predictions . Planning Operational Staffing Detailed Scheduling 1 2 3 4 5
- 6. 6 Develop Scheduling Targets Scheduling Targets Understaffing Overstaffing Comparing an actual schedule against scheduling requirements typically reveals both over and understaffing . Planning Operational Staffing Detailed Scheduling 1 2 3 4 5 • Patient demand and Staffing and Scheduling Policies enable the calculation of scheduling targets for each staff type • Scheduling targets are compared against actual schedules Scheduling Performance Metrics •% Overstaffed •% Understaffed •Schedule Efficiency
- 7. # Constraint FTE Impact Cost 1 12 Hour Shifts 11 $1232k 2 Midshift start times 6.5 $878k 3 10 Hour Shifts 5 $643k 55 3 consecutive day rule 1.2 $84.4k 56 Day shift start times 0.8 $49.6k 7 Analyze Cost of Business Rules, Define Shifts & FTE Requirements Each constraint: • Increases the total number of staff required to fulfill a schedule (increases fixed cost) • Decreases scheduling efficiency (increases total hours worked higher variable costs) . Planning Operational Staffing Detailed Scheduling 1 2 3 4 5 … … … … FTE Requirements Shifts Definitions Advanced Analytical Model
- 8. 8 Finalize Staffing Strategy Staffing Strategy Includes: • Performance Goal Analysis • Budget & Manpower Recommendations • Revised Staffing & Scheduling Policies • Detailed Implementation Plan . Planning Operational Staffing Detailed Scheduling 1 2 3 4 5 RPG Delivers a substantiated plan aligned with achievable goals Sample Staffing Strategy
- 9. 9 Case Summary . • An Emergency Department in the Southeastern United States seeks to improve performance • RPG identified that staffing and scheduling activities were not aligned to demand, and optimized them resulting in significant performance gains while reducing costs Performance Indicators Before Target After Door-to-Doc (min) 52 30 Length-of-Stay (min) 141 120 LWBS (%) 0.71 < 2.0 105 0.08 Initial Annual Staffing Cost: $2,536,000 Projected Annual Staffing Cost: $2,208,000 Projected Annual Savings $328,000 27 12.9% Savings See a Dr. 25 min Faster! Note: Initial annual staffing cost and savings calculated to show costs savings and cost avoidance. The site in this case study was not meeting demand prior to changes. Given existing policies it would cost the site 2.536M annually to meet demand. Actual annual spend at site while not meeting demand was $2.375M. Savings versus actual spend is 7% annually
- 10. Contact David Gilinson Chief Data Scientist dgilinson@staffsked.com dgilinson@reefpointgroup.com 914.714.8252 A Service-Disabled, Veteran-Owned Small Business 410.267.0413 3168 Braverton St Suite 280 Edgewater, MD 21037
Notes de l'éditeur
- Overview Data science and advanced analytics Personal Introduction Senior management consultant Systems engineer, experience using data to improve processes in a number of industries Will talk more at end
- If you staff to the average demand, you should expect to be understaffed about half the time, and overstaffed about half the time If you staff to your worst case scenario, you will be chronically overstaffed, and spend far too much We help quantify So, you need to staff somewhere in between. We have found that generally this is around the 80th percentile, however each department has its own risk tolerance, and it is important to calibrate staffing decisions based on this tolerance. Stratified – ESI 1 vs ESI 5 Enables communication ---- The graphic to the shows that there is a probability distribution associated with our predictions of future demand. If we schedule to average or (really median) demand, the EDs would be understaffed half of the time. So clearly, we need to staff to above that level. But how far above it? If we staff to the worst case scenario, say the 100th percentile, the busiest day we think is possible, we would be chronically overstaffed and will incur very high costs. We have found that from our experience that the 80th percentile of demand is a best practice staffing level. That way we are adequately staffed the vast majority of the time, while balancing the risk of overstaffing. However this level can be tailored to the risk tolerance of individual departments. DiGiorgio: may still not understand following the explanation provide an example to illustrate (staffing to 100 percentile, worst day, always enough staff but almost always too many people and inherently wasteful); ask if there are questions following the presentation of this slide
- Talking points for walking through RPG Model: DETAILED SCHEDULING Individual Schedules Daily Overage/Underage Management OPERATIONAL STAFFING Scheduling Targets Constraint Analysis Shift Definition PLANNING Performance Goals, ie. KPIs Budget Manpower Staffing and Scheduling Policies
- The first step is understanding what you want to achieve We do this as a consulting service, not just some tool For example there is tradeoff between performance goals and budget. do you have physicians available to reduce your door-to-doc time? Or will this number of docs cost more than you have targeted to spend? Per DiGiorgio: Speak to examples for performance goals (ie performance throughput) and financial goals (ie budget); are they working against each other, etc. Jim: Add examples to voicetrack – tradeoff between performance goals and budgeting
- Use historical data and data science to predict future demand Many EDs “predict demand” by running reports from their EMR and looking at simple averages, which tend to be inaccurate We’ve done market research to see where we are at, there are a lot of tools out there that use lr/poisson distributions We have experts in applied mathematics that take this a step furthe ---- However, simple averages are not the best predictions we can make. Using data science, we can evaluate demand patterns and develop more accurate estimates of future demand. For example, demand may vary weekly, monthly or seasonally. Additionally, special events and holidays may drive patient demand. And, patient demand may vary differently for different subpopulations segmented for example on acuity or age. It is valuable to consider these and other factors when predicting future demand.
- We can quantify how well you are scheduling Physicians, nurses, and PAs As we make improvements, we can see these metrics improve Comparing an actual schedule against scheduling requirements typically reveals both over and understaffing % Overstaffed: The percent of hours per week that too many resources are scheduled % Understaffed: The percent of hours per week that too few resources are scheduled Schedule Efficiency: Required / Total Man-Hours Schedule
- Define constraints For example – if you only allow 12 hour shifts Constraints are not always obvious, you probably have more than you realize Every rule adds cost, we help you understand how much For example how much would you save if you changed your shift lengths? Would it be worth it? Example: nurse start times ------ Additionally, we perform an in-depth analysis of scheduling constraints so that departments can better understand their impact. In reality, any rules about how you schedule staff will cause some misalignment with demand, and decrease your staffing efficiency. Generally because understaffing must be controlled to acceptable levels you will need additional FTE every time you apply a scheduling constraint. This translates directly to higher costs for the department. Therefore, eliminating staffing constraints is an effective way to increase margins. Its not practical to eliminate all scheduling constraints. As you know, it is very difficult to change schedules without significant pushback from staff. This is because schedules have more than a professional impact on people, schedules effect their personal lives. RPG, as part of our staffing and scheduling service offering performs an in-depth review of all scheduling constraints in your department. Our analysis enables you to make targeted changes that will have a large impact, rather than potentially upsetting staff over scheduling changes that have little or no impact on your bottom line. People live with constraints. They are not always obvious
- Once all of this analysis is complete, we document our findings as a Staffing Strategy. This document provides a guide for Departments to capture the value identified by our data-driven techniques.
- https://www.ena.org/SiteCollectionDocuments/Position%20Statements/StaffingandProductivity.pdf https://www.informs.org/ORMS-Today/Public-Articles/August-Volume-38-Number-4/OR-in-the-ER http://www.ihi.org/resources/Pages/ImprovementStories/ImprovementReportEliminateOvercrowdingintheEmergencyDepartment.aspx http://www.ruckelshauscenter.wsu.edu/projects/documents/NurseStaffingfinal.pdf