Advanced analytics for supporting public policy, bracketology, and beyond!
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Analytics talk at the 2017 WISCNET conference
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Advanced analytics for supporting public policy, bracketology, and beyond!
- 4. The road map • What is the analytics landscape? • How have analytical methods been applied to problems in the public sector and bracketology? • Where is the analytics discipline going? • Where does the analytics discipline need to go? WiscNet 2017 Laura Albert 4
- 7. Our world is becoming increasingly complex and increasingly connected Systems matter! Mathematical models and systems thinking help us study systems and navigate the complex, interconnected world we live in. WiscNet 2017 Laura Albert 7
- 13. A brief history of passenger screening • Dawn of time until 1970 • Not much! • 1970 • Surveillance equipment, air marshals • Feb 1972 • Armed guards to make people feel safe • Screened by behavioral profile, metal detector, and ID check • Dec. 1972 • Metal detector / magnometer WiscNet 2017 Laura Albert 13
- 14. A brief history of passenger screening • 1996 • Checked baggage for high-risk passengers screened for explosives (run by airlines) • Motivated by Pan Am Flight 103 (1988) and TWA Flight 800 (1996) • November 2001 – Aviation Transportation and Security Act • Created the TSA • Required all checked baggage to be screened for explosives, Dec. 2002 deadline WiscNet 2017 Laura Albert 14
- 15. Checked baggage challenges in 2001 State of the art • Existing explosive detection system devices from two manufacturers Limitation: Current technology is slow and not effective • Not enough space for security and security lines in airports or for in-line screening Ultimate goal: Good screening devices that work fast (< 10 minutes) • Do all this in < 14 months!! What happened: haphazard & piecemeal security • Labor intensive explosive trace detection methods used (swabs) WiscNet 2017 Laura Albert 15
- 16. Security is a moving target Changes in passenger and baggage screening • December 2001 • Remove shoes • 2002 + • CAPPS II, Secure Flight, etc. for risk-based screening • August 2006 • No more liquids • 2009 - 2010 • Explosive trace portals • September 2012 • Less screening for seniors (75+) and children (<12) • December 2013 • TSA Precheck: reduced securityWiscNet 2017 Laura Albert 16
- 17. Why are homeland security problems good analytics problems? • Limited resources • Passenger risk assessments • Tradeoffs among criteria (efficiency, security, cost) • Note: TSA has a goal of <10 minutes waiting for screening • System and goals are always changing: security is a moving target We will always have security challenges, and systems engineering/analytical tools will always help us address these challenges. WiscNet 2017 Laura Albert 17
- 32. Performance standards National Fire Protection Agency (NFPA) standard evaluates EMS systems based on their response times Most common response time threshold (RTT): 9 minutes for 80% of calls - Get credit for responding in 8:59 but not 9:00 • Easy to measure • Intuitive • Unambiguous WiscNet 2017 Laura Albert 32
- 35. What is the best response time threshold? • Guidelines suggest 9 minutes WiscNet 2017 Laura Albert 35
- 36. What is the best response time threshold? • Guidelines suggest 9 minutes • Medical research suggests ~5 minutes • But this would disincentive 5-9 minute responses Responses no longer “count” WiscNet 2017 Laura Albert 36
- 37. 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? WiscNet 2017 Laura Albert 37
- 38. What is the best response time threshold based on retrospective survival rates? Decision context is locating and dispatching ALS ambulances • Integer programming model to locate ambulances * • Markov decision process model to dispatch ambulances * McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 - 136 WiscNet 2017 Laura Albert 38
- 39. Survival and dispatch decisions Across different ambulance configurations 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 dispatch decisions WiscNet 2017 Laura Albert 39 Less risk in system
- 40. Operationalizing recommendations Priority dispatch: … but which ambulance when there is a choice? …and how to make decisions with imperfect information? Type Capability Response Time Priority 1 Advanced Life Support (ALS) Emergency Send ALS and a fire engine/BLS E.g., 9 minutes (first unit) Priority 2 Basic Life Support (BLS) Emergency Send BLS and a fire engine if available E.g., 13 minutes Priority 3 Not an emergency Send BLS E.g., 16 minutes WiscNet 2017 Laura Albert 40
- 41. Sequential decision-making for finding the optimal dispatch policies when patient risk levels are uncertain 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 HT or LT Information changes over the course of a call Decisions made based on classified priority. Performance metrics based on true priority. Optimal policies: Ration ambulances in the areas with the most calls for higher priority calls WiscNet 2017 Laura Albert 41
- 42. Optimal solutions shed light on how to manage risk Over-prioritize patients and treat most patients like high-priority patients when informational accuracy is low. Under-prioritize patients and treat fewer patients like high-priority patients when informational accuracy is high. • Allows more effective rationing of critical resources In both cases, service is improved by improving backup service and response to low-priority patients. WiscNet 2017 Laura Albert 42
- 44. Should we replace an ambulance (2 EMTp/EMT) with two ALS quick response vehicles (QRVs) (1 EMTp)? • Double response = both ALS and BLS units dispatched • Downgrades / upgrades for Priority 1 / 2 calls WiscNet 2017 Laura Albert 44 Coordinating multiple types of vehicles with prioritized patients is not intuitive Mix of vehicles Emergency medical technician (EMT) vs. Paramedic (EMTp)
- 53. Simple yet powerful idea Automatically rate and ranks teams by taking advantage of the network structure of the match ups • Use Markov chains to account for strength of schedule • Do not need a human in the loop Simple data requirements: 1. Game outcomes (score differentials), 2. Home/away status Takes difficulty of future games into account in football playoff forecasts • Polls give the ranking right now, only gives insight a playoff held today WiscNet 2017 Laura Albert 53
- 57. Transitions Rutgers 52 @ Wisconsin 72 Wisconsin Rutgers 1 − 𝑊 𝑊 𝑊 1 − 𝑊 How much credit should Wisconsin get for beating Rutgers by 20 at home? 𝑊 = effective wins (fraction of a vote), which help us compute our Markov chain transition probabilities WiscNet 2017 Laura Albert 57
- 61. College football playoff forecasting 0. Observe a few (7-8) weeks of game outcomes 1. Ranking. • Assign a rating to each team to rank the teams. • Similar to what we had before but with college football data 2. Game simulation. • Determine who wins a game based on the team ratings. Simulate the next week’s game outcomes. • Combine these: • Re-rate and re-rank after each week of games. • Simulate the remainder of the season. • Report teams most likely to be in the top 4 WiscNet 2017 Laura Albert 61
- 63. What you do with data makes all the difference • Data are just data • We want to turn data into information for it to be useful • If we apply advanced analytics to that information and data, then we can make better decisions WiscNet 2017 Laura Albert 63
- 71. Analytics will continue to be human-centered Decisions are usually made in conjunction with humans Analytical models will supplement decisions more often than they replace decisions For example: • Ambulance dispatch algorithms must be implemented in real- time by humans WiscNet 2017 Laura Albert 71