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Delivering emergency medical services: research, application, and outreach

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Laura McLay's slides from the German Operations Research Society Conference for the presentation entitled "Delivering emergency medical services: research application, and outreach"

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Delivering emergency medical services: research, application, and outreach

  1. 1. Delivering emergency medical services: research, application, and outreach Laura A. McLay Industrial & Systems Engineering University of Wisconsin-Madison lmclay@wisc.edu punkrockOR.wordpress.com @lauramclay 1 This work was in part supported by the U.S. Department of the Army under Grant Award Number W911NF-10-1-0176 and by the National Science Foundation under Award No. CMMI -1054148.
  2. 2. The road map •How do emergency medical service (EMS) systems work? •How do we know when EMS systems work well? •How can we improve how well EMS systems work? •Where is EMS OR research going? •Where does EMS OR research need to go? 2
  3. 3. Emergency medical service (EMS) systems in a nutshell •Originally designed to transport patients to hospital •Medical advances allowed for more treatment of patients at the scene •E.g., Cardiopulmonary resuscitation and automated external defibrillation for cardiac arrest patients •OR application areas: 3 Healthcare Transportation Public sector
  4. 4. Anatomy of a 911 call Response time Service provider: Emergency 911 call Unit dispatched Unit is en route Unit arrives at scene Service/care provided Unit leaves scene Unit arrives at hospital Patient transferred Unit returns to service 4 Response time from the patient’s point of view
  5. 5. EMS design at the local level •Design varies by community •Fire and EMS vs. EMS •Paid staff vs. volunteers •Publicly run vs. privately run •Emergency medical technician (EMT) vs. Paramedic (EMTp) •Mix of vehicles •Operations vary by community •Ambulance location, relocation, and relocation on-the-fly •Operational guidelines (send closest unit) •Jurisdictional issues regarding mutual aid …one size doesn’t fit all 5 McLay, L.A., 2011. Emergency Medical Service Systems that Improve Patient Survivability. Encyclopedia of Operations Research in the area of “Applications with Societal Impact,” John Wiley & Sons, Inc., Hoboken, NJ (published online: DOI: 10.1002/9780470400531.eorms0296)
  6. 6. Operationalizing recommendations Additional recommendations from different national agencies regarding: •Time to answer 911 call •Time to send (dispatch) a unit to a call •Response time / travel time •The types of vehicles to send Priority dispatch: Does not indicate which specific units to send 6 Type Capability Response Time Priority 1 Advanced Life Support (ALS) EmergencySend ALSand a fire engine/BLS E.g.,9 minutes (first unit) Priority2 Basic Life Support (BLS) Emergency Send BLS and a fire engine ifavailable E.g., 13 minutes Priority 3 Not an emergency Send BLS E.g., 16minutes
  7. 7. Performance standards •National Fire Protection Agency (NFPA) 1710 guidelines for departments with paid staff •5 minute response time for first responding vehicle •9 minute response time for first advanced life support vehicle •Must achieve these goals 90% of the time for all calls •Similar guidelines for volunteer agenciesin NFPA 1720 allow for 9-14 minute response times •Guidelines based on medical research for cardiac arrest patients and time for structural fires to spread •Short response times only critical for some patient types: cardiac arrest, shock, myocardial infarction •Most calls are lower-acuity •Many communities use different response time goals 7
  8. 8. Objective functions •NFPA standard yields a coverage objective function for response time threshold (RTT) •Most common RTT: nine minutes for 80% of calls •A call with response time of 8:59 is covered •A call with response time of 9:00 is not covered Why RTTs? •Easy to measure •Intuitive •Unambiguous 8
  9. 9. Response times vs. cardiac arrest survival 9 0 1.1 0 2 4 6 8 10 12 14 Response time (minutes) Probability of survival Larsen et al. 1993 Valanzuela et al. 1997 Waaelwijn et al. 2001 De Maio et al. 2003 9 Minute Standard CDF of calls for service covered Response time (minutes) 9 80%
  10. 10. What is the best response time threshold? •Guidelines suggest 9 minutes •Medical research suggests ~5 minutes •But this would disincentive 5-9 minute responses •Which RTT is best for design of the system? 10
  11. 11. What is the best response time threshold? Research Goal: find the best RTT based on corresponding patient survival rates •RTTs drive resource utilization decisions •Optimize 4, 5, …, 12 minute RTT for high-priority patients Decision context is locating and dispatching ALS ambulances •Discrete optimization model to locate ambulances •Markov decision process model to dispatch ambulances 11
  12. 12. Ambulance Locations, N=7Best for patient survival / 8 Minute RTT = one ambulance = two ambulances McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 -136 Suburban area –> (vs. rural areas) <–Interstates 12
  13. 13. Ambulance Locations, N=710 Minute RTT = one ambulance = two ambulances McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 -136 13
  14. 14. Ambulance Locations, N=74-5 Minute RTT = one ambulance = two ambulances McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 -136 14
  15. 15. Survival and dispatch decisions 15 Across different ambulance configurations Across different call volumes McLay, L.A., Mayorga, M.E., 2011. Evaluating the Impact of Performance Goals on Dispatching Decisions in Emergency Medical Service. IIE Transactions on Healthcare Service Engineering 1, 185 –196 Minimize un-survivability when altering dispatchdecisions
  16. 16. Insights •Response time thresholds are a good proxy for patient survival •…but some response time thresholds (e.g., 7-9 minutes) are better than others •Short response time thresholds based on what is best for individualpatient survival are do not improve survival of the system 16
  17. 17. Location and dispatching models 17
  18. 18. Optimal dispatching policiesusing Markov decision process models 911 call Unit dispatched Unit is en route Unit arrives at scene Service/care provided Unit leaves scene Unit arrives at hospital Patient transferred Unit returns to service Determine which ambulance to send based on classified priority Classified priority (H or L) True priority HTor LT 18 Information changes over the course of a call Decisions made based on classifiedpriority. Performance metrics based on truepriority. Classified customer risk Map Priority 1, 2, 3 call types to high-priority (퐻) or low-priority (퐿) Calls of the same type treated the same True customer risk Map all call types to high-priority (퐻푇) or low-priority (퐿푇)
  19. 19. Under-or over-prioritize •Assumption: No priority 3 calls are truly high-priorityCase 1: Under-prioritizewith different classification accuracyCase 2: Over-prioritize Pr1 Pr2 Pr3 Pr1 Pr2 Pr3 HT HT Pr1 Pr2 Pr3 HT Pr1 Pr2 Pr3 HT Informational accuracy captured by: 훼= 푃퐻푇퐻 푃(퐻푇|퐿) 19 Classified high-priority Classified low-priority Improved accuracy
  20. 20. Coverage 010203040500.4050.410.4150.420.4250.430.4350.440.445  Expected coverage Optimal Policy, Case 1Optimal Policy, Case 2Closest Ambulance 20
  21. 21. Low and high priority calls Conditional probability that the closest unit is dispatched given initial classification 0 Hig1h0-prior2i0ty cal3ls0 40 50 Low-priority calls 0.98 0.985 0.99 0.995 1 1.005  Proportion closest ambulance is dispatched Closest Ambulance Optimal Policy, Case 1 Optimal Policy, Case 2 0 10 20 30 40 50 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1  Proportion closest ambulance is dispatched Closest Ambulance Optimal Policy, Case 1 Optimal Policy, Case 2 Classified high-priority Classified low-priority 21
  22. 22. Case 1 (훼 = ∞), Case 2 policies High-priority calls Case 2: First to send to high-priority calls Station 1 2 3 4 Case 2: Second to send to high-priority calls Station 1 2 3 4 Service can be improved via optimization of backup service and response to low-priority patients Rationed for high-priority calls Rationed for low-priority calls 22
  23. 23. Server busy probabilities 1 2 3 4 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Server busy probability Server Closest Server Policy Optimal Policy 1 2 3 4 District 훼 = ∞ 23
  24. 24. Equity in OR models •EMS systems are public processes where there is an expectation of equity •We want to balance equity with efficiency/effectiveness •Giving no one pie is equitable but it is not very efficient •Twenty equity measures used in models for locating public assets* •Not all are “good” equity measures •Equity measures often selected for computational tractability •All focus equity from customer point of view •Need equity measures for •(Spatial) queueing systems •Service providers and stakeholders other than customers * Marsh, M. T., & Schilling, D. A. (1994). Equity measurement in facility location analysis: A review and framework. European Journal of Operational Research, 74(1), 1-17. 24
  25. 25. Equity and Markov decision processes Goal: Balance coverage (efficiency) and an equity model •Constrained MDP that optimizes coverage subject to equity constraints •Solve MDP via linear programming Equity constraints from the customerpoint of view 1.Ex ante equity: are resourced allocated fairly up front? Fraction of patients serviced by ambulance at “home” station. 2.Ex post equity: was equity achieved? Minimum utility achieved at each node (e.g., survival). Equity constraints from the service providerpoint of view 3. Min/max ambulance busy probabilities 4.Rate at which each ambulance is dispatched to high-priority patients. 25
  26. 26. Implications of choosing equitable policies Observation (not surprising): •Not possible to satisfy all notions of equity Observation: •Not always possible to equalize a single notion of equity •E.g., patient survival Observation: •Sometimes we can achieve equity only at an enormous cost •E.g., Rate at which each ambulance dispatched to high-priority patients Observation: •Sometimes it is easily to equalize a notion of equity •E.g., ambulance busy probabilities 26
  27. 27. Coordinating multiple types of units 27
  28. 28. Coordinating multiple types of vehicles •Not intuitive how to use multiple types of vehicles •ALS ambulances / BLS ambulances (2 EMTp/EMT) •ALS quick response vehicles (QRVs) (1 EMTp) •Double response = both ALS and BLS units dispatched •Downgrades / upgrades for Priority 1 / 2 calls •Who transports the patient to the hospital? •Research goal: operationalize guidelines for sending vehicle types to prioritized patients •(Linear) integer programming model for a two vehicle-type system: ALS Non-transport QRVs and BLS ambulances 28
  29. 29. Results quantify impact of using QRVs 29
  30. 30. Application in a real setting 30 Achievement Award Winner for Next-Generation Emergency Medical Response Through Data Analysis & Planning (Best in Category winner), National Association of Counties, 2010. McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 Calls. Interfaces42(4), 380-394.
  31. 31. Severe weather and disasters 31
  32. 32. Emergency response during severe weather events •Resource allocation decisions—such as staffing levels—is important for system performance and patient outcomes. •First, we have to understand what is different during severe weather: •the volume and nature of calls for service may be different, •critical infrastructure is impaired or destroyed, and •there are cascading failures in the system. •…these issues are not as predictable as they would be on a “normal” day •In a blizzard scenario: •System flooded with low-priority calls •Amount of work (offered load) between fire and EMS increases by 41% 32
  33. 33. Staffing during blizzards •Study the number of calls that arrive when no units are available (NUA scenario). •How many ambulances are needed such that NUA scenario occurs less than 1% of the time? •How does this change based on response policies and system-wide adaptation? •Model parameters vary according to the traffic in the system: 1.Probability that a patient needs to go to the hospital. 2.Service times conditioned on whether a patient needs hospital transport. •Simulation goal: •>99% of patients receive an immediate response •Four queuing disciplines considered for priority queueing Kunkel, A., McLay, L.A. 2013. Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability model. Health Care Management Science 16(1), 14 –26. 33
  34. 34. How many ambulances are needed to immediately respond to 99% of calls? Taking system adaptation into account is often like having one additional ambulance in the system, particularly when the system is busy. Queueing Discipline System Adaptation Normal weather Snow flurries Leftover snow Blizzard conditions Queue excess No 6 7 7 8 Yes 6 6 7 7 Priority-specific excess No 6 7 7 8 Yes 6 6 7 7 Drop excess No 6 6 6 8 Yes 5 6 6 7 Drop low priority No 5 5 5 7 Yes 5 5 5 6 34
  35. 35. Where do EMS systems need to go? 35
  36. 36. EMS response during/after extreme events •Two main research streams exist: •Normal operations •Disaster operations •More guidance needed for “typical” emergencies and mass casualty events •Health risks during/after hurricanes: •Increased mortality •Traumatic injuries •Low-priority calls •Carbon monoxide poisoning* Caused by power failures •Electronic health devices* Caused by power failures •Decisions may be very different during disasters •Ask patients to wait for service •Evacuate patients from hospitals •Massive coordination with other agencies (mutual aid) 36
  37. 37. EMS = Prehospitalcare Operations Research •Efficiency •Optimality •Utilization •System-wide performance Healthcare •Efficacy •Access •Resources/costs •“Patient centered outcomes” 37 Healthcare Transportation Public sector Common ground?
  38. 38. More thoughts on patient centered outcomes Operational measures used to evaluate emergency departments •Length of stay •Throughput Increasing push for more health metrics •Disease progression •Recidivism Many challenges for EMS modeling •Health metrics needed •Information collected at scene •Equity models a good vehicle for examining health measures (access, cost, efficacy) 38 Healthcare Transportation Public sector
  39. 39. Thank you! 39 1.McLay, L.A., Mayorga, M.E., 2013. A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities. IIE Transactions 45(1), 1—24. 2.McLay, L.A., Mayorga, M.E., 2011. Evaluating the Impact of Performance Goals on Dispatching Decisions in Emergency Medical Service. IIE Transactions on Healthcare Service Engineering 1, 185 –196 3.McLay, L.A., Mayorga, M.E., 2014. A dispatching model for server-to-customer systems that balances efficiency and equity. To appear in Manufacturing & Service Operations Management, doi:10.1287/msom.1120.0411 4.Ansari, S., McLay, L.A., Mayorga, M.E., 2014. A maximum expected covering problem for locating and dispatching servers. Technical Report, Virginia Commonwealth University, Richmond, VA. 5.Kunkel, A., McLay, L.A. 2013. Determining minimum staffing levels during snowstorms using an integrated simulation, regression, andreliability model. Health Care Management Science 16(1), 14 –26. 6.McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 Calls. Interfaces 42(4), 380-394. 7.Leclerc, P.D., L.A. McLay, M.E. Mayorga, 2011. Modeling equity for allocating public resources. Community-Based Operations Research: Decision Modeling for Local Impact and Diverse Populations, Springer, p. 97 –118. 8.McLay, L.A., Brooks, J.P., Boone, E.L., 2012. Analyzing the Volume and Nature of Emergency Medical Calls during Severe Weather Events using Regression Methodologies. Socio-Economic Planning Sciences 46, 55 –66. 9.McLay, L.A., 2011. Emergency Medical Service Systems that Improve Patient Survivability. Encyclopedia of Operations Research in the area of “Applications with Societal Impact,” John Wiley & Sons, Inc., Hoboken, NJ (published online: DOI: 10.1002/9780470400531.eorms0296) 10.McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 -136 lmclay@wisc.edu punkrockOR.wordpress.com @lauramclay

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