Emilio LuqueComputer Architecture & Operating Systems Department     University Autonoma of Barcelona (UAB)
Patients must be                                addressed with the bestEmergency Departments (ED)              quality.are...
Simulation:Optimization:    What if?The bestsolution for?
Supported by the MICINN Spain, under contract               TIN2007-64974 and the MINECO (MICINN) Spain, under contract   ...
Optimization Simulation
STATE Variables                                   Values                                Observability                     ...
1) Active Agents                                                         2) Passive AgentsPatients                        ...
The Environment                        Arrival/dismissal                         by ambulanceArrival/dismissal by own mean...
EDfunctionality         Agents      interactions   Agents
Arrival/dismissalb                               y ambulance        Arrival/dismissal         by own means   A   N   D
ED SimulatorInput     Patients arrival:            Could arrive every 3 min. , but with different probabilities:         ...
• Find the best/optimum solution from all the  possible solutions. Given any objective (index) function f :              ...
Is it always the "best solution" (theoptimum) the most interesting for us?
Methodology          Simulator:                     2nd version                                          Parameter conf...
Methodology: Computational complexity  Multidimensional            Discrete      DD       D                • Search space ...
ABM   SIMULATOR              PARAMETERS      DSS                I                         N                         D     ...
Quality Index:   Minimize patient “Length of Stay” (LoS)                 Constraint: Cost        <= 3500 €             20...
20% (4 pat/hr)            Cost constraint <= 3500 €                                                         Patient    40%...
Cost constraint <= 3500 €                                       Average patient “LoS”4 p/hr                              ...
Cost constraint <= 3500 €               Average patient “LoS”4 p/hr                                  9 p/hr13 p/hr       ...
Cost constraint <= 3500 €               Average patient “LoS”4 p/hr                                  9 p/hr              ...
La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias
La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias
La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias
La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias
La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias
La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias
La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias
La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias
La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias
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La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias

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Conferencia UAB llevada a cabo en el marco del Acto de inauguración del curso académico 2012-2013 celebrado el 25 de septiembre de 2012

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La informática en el ámbito de la salud una ayuda en la gestión del servicio de urgencias

  1. 1. Emilio LuqueComputer Architecture & Operating Systems Department University Autonoma of Barcelona (UAB)
  2. 2. Patients must be addressed with the bestEmergency Departments (ED) quality.are complex andquite dynamic systems.ED’s are overcrowded and workwith limited budget.
  3. 3. Simulation:Optimization: What if?The bestsolution for?
  4. 4. Supported by the MICINN Spain, under contract TIN2007-64974 and the MINECO (MICINN) Spain, under contract Emilio Luque TIN2011-24384 CAOS – HPC4EAS Manel Taboada GIMBERNAT Eduardo Cabrera CAOS – HPC4EAS Francisco Epelde PARC TAULÍ Ma. Luisa Iglesias PARC TAULÍ
  5. 5. Optimization Simulation
  6. 6. STATE Variables Values Observability Name/identifier <id> Unique per agent I Gender, Medical history (cardiology, pulmonology, neurological,…); Allergies (yes-no); Personal details Treatments that received (classified into therapeutic groups: I bronchodilators, vasodilators, etc.); Origin (national or immigrant) Entrance, Admissions, Waiting Room, Triage, Treatment Location E Zone. Idle, Requesting information from <id>, Giving information Action to <id>, Searching, Moving to <location> , Waiting for E ambulance. Healthy; Hemodynamic-Constant; Barthel Index (degree of Physical condition Variables Values E/I/N Observability dependence). Healthy, Cardiac/respiratory arrest, severe/moderate Symptoms (patients) E/I trauma, headache, vomiting, diarrhea Communication skills Low, Medium, High E Level of experience Resident (1 to 5); Junior (5-10); Senior (10 - 15) and E/ICurrent state Next state / (doctors) Consultant (over 15 years) Input / Output Output …. …. …. Level of experience E/I (triage Low, Medium, High Sx / Ox Ia (p1) Sy / Oy nurses) Level of experience E/I Sx / Ox Ia (p2) Sz / Oz (emergency nurses) Low, Medium, High Level of experience E/I Sx / Ox Ia (p3) Sx / Ox (admissions) Low, Medium, High …. …. ….
  7. 7. 1) Active Agents 2) Passive AgentsPatients Information systemCompanions of patientsAdmission personnel Loudspeaker systemSanitarian technicians Pneumatic pipesNurses (Triage, Emergency) Tests servicesDoctors (Emergency,Specialists) 1 to Zone: individuals in Zone1 to 1(One-to-One) 1 to n (Multicast) (Area- Restricted Broadcast)
  8. 8. The Environment Arrival/dismissal by ambulanceArrival/dismissal by own means The model should include the spatial topographical design from the ED
  9. 9. EDfunctionality Agents interactions Agents
  10. 10. Arrival/dismissalb y ambulance Arrival/dismissal by own means A N D
  11. 11. ED SimulatorInput Patients arrival:  Could arrive every 3 min. , but with different probabilities: 20% (4 pat/hr), 40% (9 pat/hr), 60% (13 pat/hr) , 80% (17 pat/hr) Configuration of the ED Staff Staff Number Junior Senior Admission 1-2 2 min. 1 min. 15 sec. Triage Nurse 1-3 8 min. 5 min. Doctor 1-4 20 min. 15 min.Output Patients:  How many arrive to the service  How many leave the service  Times of staying in each area What if?
  12. 12. • Find the best/optimum solution from all the possible solutions. Given any objective (index) function f : f :A max / min f x subject to x A A constraintset; xo A f xo f x f xo f x Maximize minimize xo A
  13. 13. Is it always the "best solution" (theoptimum) the most interesting for us?
  14. 14. Methodology  Simulator: 2nd version  Parameter configuration: A, N, D = > 3D + P => 4D  A  N  D  ~ 400 patients daily
  15. 15. Methodology: Computational complexity Multidimensional Discrete DD D • Search space – # Dimensions = Patients, B staff (D, N, A, …), T, B, P PP … NN N N – Each dimension=> range of possible A A AA T values – # Points = # simulations (indexes)(time) COMBINATORIAL!
  16. 16. ABM SIMULATOR PARAMETERS DSS I N D E + X constraints
  17. 17. Quality Index: Minimize patient “Length of Stay” (LoS)  Constraint: Cost <= 3500 € 20% (4 pat/hr) 14 D, 9 N, 9 A = 1,134 cases Patient 40% (9 pat/hr) Arrival 60% (13 pat/hr) 1,134 cases * 4 = 4,536 cases 80% (17 pat/hr) 25,000 ticks => 1 day  4,536 total cases => 2,408 cases under limit Staff Time (ticks) Quantity Cost (€) Senior Junior Senior Junior Doctors 260 350 1- 4 1000 500 Nurses 90 130 1- 3 500 350Admission personnel 20 35 1- 3 200 150
  18. 18. 20% (4 pat/hr) Cost constraint <= 3500 € Patient 40% (9 pat/hr) Arrival 60% (13 pat/hr)  Average patient “LoS” 80% (17 pat/hr) 4 p/hr 9 p/hr Time € # D N A (ticks) Staff 428 3,200 5 2S 2S 1S Optimum 428 2,900 5 2S 1S 2S 428 2,850 5 2S 1S 1 S, 1 J13 p/hr 17 p/hr
  19. 19. Cost constraint <= 3500 €  Average patient “LoS”4 p/hr 9 p/hr Time € # Staff D N A (ticks) 3,266 3,350 7 1 S, 3 J 2J 1J13 p/hr 17 p/hr Optimum
  20. 20. Cost constraint <= 3500 €  Average patient “LoS”4 p/hr 9 p/hr13 p/hr 17 p/hr
  21. 21. Cost constraint <= 3500 €  Average patient “LoS”4 p/hr 9 p/hr Optimal vs Suboptimal13 p/hr 17 p/hr

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