2009 Service Science Innovation Partnership Award Finalist Presentation

1. 2009 Service Science Innovation Partnership AwardFinalist Presentation

2. Service Science in Hospitals: A Research-Based Partnership for Innovating and Transforming Patients Care IBM Research, Haifa Rambam Hospital Technion, IE&M

3. Partners • RambamHospital (1000 beds): Government • Teaching hospital (research): clinical, managerial • “We shall be your lab” for innovative research • IBMResearch Lab, Haifa (500 researchers): Industry • IS/IT/Healthcare, SSME; products • OCR: “Spur innovation through university collaboration” • TechnionIE&M(1500 students, 100+ faculty): Academic • From OR & Stat, through IS & HFE, to Psychology • SEE Lab: data repositories, analysis tools (online)

4. Project Goals • Innovate and transform patients care • Clinical • Operational • Financial • Archive and disseminate research-based knowledge • R&D of new products and services

5. Service Science (in Hospitals) 1. Measurements / Data 7. Feedback 8. Novel needs, necessitating Science Science Management Engineering 4. Maturity enables Deployment 3. Validation 2. Modeling, Analysis 5. Implementation 6. Improvement

6. Methodology • Focus on central representative hospital units: • Emergency Department (ED): gate, window • Operating Rooms (OR): frontier, capital intensive • Neonatal: longest costly “projects” • Trauma: team to “save a life in 40 minutes” • Internal Ward: the hospital’s heart • Patient-centric processes: full scientific-cycle to some (ED, Trauma, Neonatal), in the midst of others (OR, Internal).

7. Project Outputs – Tangibles • Hospital: examples of tools and measurable improvements • ED: simulator (soon online), location-tracking in real-time • IW: least waits (quality) plus: shorter LOS have higher throughput (efficiency) yet lower occupancy (fairness) • Trauma: human-factor engineering of the new unit • Neonatal: team-shared models to improve info. transfer • Industry: research designed into products & services • University: teaching material (ServEng website) • PhD, MSc (locals, IBM, Rambam); students’ projects • Data-bases / repositories (future universal accessibility) • Innovation & transformation of patients care processes

8. Project Outputs – Knowledge • Research • Models: beds, staff, workload (operational, cognitive), ED-design • Education, training: Service Engineering course, ED experts • New technologies, beyond hospitals • e.g. telephone call centers: Workload forecasting; LWBS vs. Abandonment • Teaching: academia (students, colleagues), practitioners (hospital, industry), other hospitals (Hadassah, Jerusalem) • Potential for revolutionizing patient care processes

9. Real Time ED Monitoring and Control (Work in Progress) • RFID/US-based Location Tracking • Low level location tracking for patients and care personnel • Technology dependent capabilities • Hospital IT Systems • Admit, Discharge, Transfer • Electronic Health Records • Lab request/results • Picture Archive and Communication System (PACS) Data Collection Data Visualization Analysis Real Time Event Processing Network Rule-Based Analysis Statistical Inference Forecasting/Machine Learning AlgorithmsAnalysis of Historicaland Real-time Data Models: Math. Simulation Queueing (Flow) Theory, ED Simulator Optimization / Control WFM, Priorities, Real-time Control, etc.

10. Summary • A lot has been achieved but no less yet to be done • Foundational scientific impact • Significant, innovative and potentially revolutionaryimprovements to patient care processes • Enabled via true collaboration and lasting partnership: Industry, Government, Academia

11. 2009 Service Science Innovation Partnership AwardSupport Material

12. Dashboard (in Process) – Room Occupancy Level

13. Efficiency vs. Fairness at the Internal Wards • Data refer to period: 1/05/06-30/10/08 (excluding 1-3/07) • Smallest + “fastest” ward is subject to highest loads • Patients allocation unfair, as far as wards are concerned

14. Delays and Fairness in ED-to-IW TransfersData-driven Theory

15. Data-Driven Research is a Must + FunLength of Stay (LOS), Internal Ward A (2004-8/2008), by Day

16. Data-Driven Research is a Must + FunLength of Stay (LOS), Internal Ward A (2004-8/2008), by 2 Hours

17. Data-Driven Research is a Must + FunLength of Stay (LOS), Internal Ward A (2004-8/2008), by 30 minutes

18. Workload at the Internal Ward (In Progress): Arrivals, Departures, # Patients in Ward A, by Hour

19. The Business-case for RFID/US–based Tracking: Value Assessment at the Hospital ED (In Progress) Orthopedic (Orth for short) physician workload

20. Work in Progress • Systems: RFID / US tracking systems • Smart Equipment: Dashboard for monitoring & control • Education: ED Education via simulation (+ Hadasa) • Research: Theses and projects: PhD, MSc • Teaching: Service Engineering – existing, planned • SEE Center: data repositories, accessible server • Online ED simulator • Online accessible data interface • Platform for teaching and research

21. Project Output & Future Work • Working Papers (Conferences) • Toward Simulation-Based Real-Time Decision Support Systems For Emergency Departments (WSC09) • RFID-Based Business Process Transformation: Value Assessment in Hospital Emergency Department (BPM09) • InEDvance: Advanced IT in Support of Emergency Department Management (NGITS09) • Teaching • Service Engineering http://ie.technion.ac.il/serveng • Healthcare seminars material

22. Project Output & Future Work (continued) • Graduate theses (PhD, MSc) • Task Mental Models and Neonate Medical Status Maps of Doctors and Nurses in Neonatal Units • Queues in Hospitals: Semi-Open Queueing Networks in the QED Regime • The Workload Process: Modeling, Inference and Applications • Uncertainty in the Demand for Service: The Case of Call Centers and Emergency Departments • Queueing Systems with Heterogeneous Servers: Improving Patients' Flow in Hospitals • Improving quality of treatment in the Emergency Department

23. Project Output & Future Work (continued) • Students Project • Improving the Pre-surgical Process in the Hospital • Operational Aspects of Transfer the Rambam's ED to a Temporary Location • Choosing the Most Effective Operational Model for the new Rambam's ED • Patient Flow from ED to Internal Wards: Solving Bottlenecks and Operational Problems • Feasibility Test for Implementation of RFID system in Hospital • Comparison of Four possible operational models for ED • Simulation of Patients Routing from an Emergency Department to Internal Wards in Rambam Hospital

24. Project Output & Future Work (continued) • OCR projects in progress • Patient Quality of Care – Longitudinal observations and Analysis of Medical Records • Human Factors in the design of a New Trauma Room • Development of an advanced BI system for an ED, which involves a dashboard and forecasting capabilities • Development of a Virtual World Simulation for ED: Training Individuals and Teams in clinical and managerial issues • Empirical Analysis of an Emergency Department • Emergency Department, Hospitalization, and everything in between: using Simulation, Empirical and Theoretical Models for the Operational Analysis of Hospitals

25. Empirical Analysis (Work in Progress) - ED:Activity (Flow) Chart

26. Empirical Analysis (Work in Process) - ED:Resources (Flow) Chart

27. Empirical Analysis (Work in Process) - ED:Activity – Resources (Flow) Chart

28. Empirical Analysis (Work in Process) - ED:Information (Flow) Chart

29. Empirical Analysis (Work in Process) – From ED to IW:Activity (Flow) Chart

30. Empirical Analysis (Work in Process) – from ED to IW:Resources (Flow) Chart

31. Empirical Analysis (Work in Process) – from ED to IW:Activity – Resources (Flow) Chart

32. Empirical Analysis (Work in Process) – From ED to IW:Information (Flow) Chart

33. Human Factors in the Design of a New Trauma Room • The aim of the research: • Designing the layout of the new trauma room bays • The Trauma unit is currently under the process of doubling its capacity with new admitting room that would contain 6 bays. • Each bay is equipped for both surgical and internal trauma patients at all ages including children • Each bay is designed for the two side operation of a double trauma team with two surgeons and two nurses.

34. Human Factors in the Design of a New Trauma Room • Method: • construction of 1:1 carton-board Mockup of new cabinet • The mockup allowed representation and rearrangement of all drawers, shelves, medical equipment and communication devices, which are planned for the new workstations.

35. Human Factors in the Design of a New Trauma Room • Method: • construction of 1:1 carton-board Mockup of new cabinet • The mockup allowed representation and rearrangement of all drawers, shelves, medical equipment and communication devices, which are planned for the new workstations.

36. Human Factors in the Design of a New Trauma Room Work was carried out in a participatory process that included all relevant people • Work procedures : • Preparation of a detailed list, with all the required instrumentation and inventory content of a bay. • Specification for general layout requirement of a bay. • Mockup development and testing with the active participation and iterative inputs of the trauma medical team, and architects, as well as the emergency department and hospital management.

37. A 1:1 carton-board mockup The work was summarized in design sketches and a list of recommendations for building cabinets and specification of their measures and inventory.

38. Task models and neonate medical status maps of doctors and nurses in neonatal units • Patient care in Intensive Care Units (ICU) requires continuous and ongoing information transfer, collaboration and coordination between team members, at different times and locations. • There are unexpected events and gaps due to the dynamic nature of the process and the medical status of the patient, or at times works procedures and hand over that are not properly defined • These failures, and in particular those associated to impaired information transfer, are a serious cause of adverse events in the medical work environment • (Xiao, et al. 2003; Cook, et al. 2000; Bates & Gawande, 2003).

39. Task models and neonate medical status maps of doctors and nurses in neonatal units • When working in a team, it is not enough that each medical team member will develop a good representation of the situation from his own perspective. • To be efficient and work in coordination, teams should have an appropriate team-shared model (STM) of the patient and his medical status should be developed. • STM is the shared understanding and mental representation of team's task, knowledge and situation • When having a good STM, the team's performance will improve, the overall load will be better divided between team members and effective working strategies will be adopted • (Klimoski and Mohammed, 1994; Mohamammed & Dumville, 2001; Cooke, et al., 2000).

40. Task models and neonate medical status maps of doctors and nurses in neonatal units • The aim of the research: • To examine the differences and gaps between physicians and nurses models of their task, its influence on creating a medical status map of the neonates they treat and the resulting gaps in these maps. • The study of this problem may enhance our understanding of the ways to improve information transfer and create better shared maps among medical teams in health care procedures.

41. Task models and neonate medical status maps of doctors and nurses in neonatal units • The study has been conducted on the medical staff members of 3 neonatal units in Israel. • To derive their status map of a treated neonate, a simulation of information transfer was conducted during hand over (shift change). Simulation data has been collected on 13 doctors and 30 nurses and has been submitted to statistical analysis. • In the post simulation stage nurses and physician are given a detailed questionnaire that will help extrapolating their STM by allowing each member to describe his own tasks as well as those of the other member. Questionnaires are being administered these days.

42. Patient Quality of Care-Longitudinal observations and analysis of medical records • The aim of the research: • to specify and document the process that an arriving and treated patient undergoes • an attempt to uncover possible gaps in the treatment process. • Method: • 147 longitudinal, patient-centered observations, were conducted, on all shifts and all types of patient. • Observations covered all stages, stations and staff interactions that a patient goes through during his treatment. • Initial results show differences between patients' type and shifts on factors such as treatment time and waiting time.

43. Specification and Human Factors in Designing an Intelligent ED Dashboard • Research goal: • Development of a computer driven dashboard which will be • Real-time tool: providing each specific user type the specific information required for carrying out daily routines, treat patients, assure efficiency and reduce errors. • Forecasting tool: plan ahead and thus avoid congestion (e.g. via forecasting peaks of arrivals). This will be supported by mathematical models that forecast, based on historical data, future loads on bottlenecks of the ED.

44. Specification and Human Factors in Designing an Intelligent ED Dashboard Work method in three stages: Analysis of the existing state:learn the current way in which the ED team gathers information and makes decisions on care processes, by conducting interviews and observations. User-centered task analysis of objectives: expectations desired content and required information for each type of user. 3. User-centered design and beta testing of the dashboard via usability testing methods and prototyping techniques.