Technology Services World Conference, Silicon Valley, CA May 4-6, Service Science Innovation Partnership Award Finalist Presentation

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Service Science in Hospitals: A Research-Based Partnership for Innovating and Transforming Patients Care IBM Research, Haifa Rambam Hospital Technion, IE&M

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Partners RambamRambam Hospital (1000 beds): Government Teaching hospital (research): clinical, managerial “We shall be your lab” for innovative research IBM IBM Research Lab, Haifa (500 researchers): Industry IS/IT/Healthcare, SSME; products OCR: “Spur innovation through university collaboration”OCR Technion IE&M (1500 students, 100+ faculty): Academic IE&M From OR & Stat, through IS & HFE, to Psychology SEE Lab: data repositories, analysis tools (online)SEE

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Project Goals Innovate and transform patients care Clinical Operational Financial Archive and disseminate research-based knowledge R&D of new products and services

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Service Science (in Hospitals) 7. Feedback 1. Measurements / Data 6. Improvement 5. Implementation 2. Modeling, Analysis 3. Validation 8. Novel needs, necessitating Science 4. Maturity enables Deployment

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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).

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Project 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

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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) Real Time Event Processing Network Rule-Based Analysis Statistical Inference Forecasting/Machine Learning Algorithms Analysis of Historical and Real-time Data Models: Math. Simulation Queueing (Flow) Theory, ED Simulator Data Collection Analysis Data Visualization Optimization / Control WFM, Priorities, Real- time Control, etc.

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Summary A lot has been achieved but no less yet to be done Foundational scientific impact Significant, innovative and potentially revolutionary improvements to patient care processes Enabled via true collaboration and lasting partnership: Industry, Government, Academia

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 2009 Service Science Innovation Partnership Award Support Material

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Dashboard (in Process) – Room Occupancy Level

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Efficiency vs. Fairness at the Internal Wards Ward AWard BWard CWard D ALOS (days) Mean Occupancy Rate 97.8%94.4%86.8%91.1% Mean # Patients per Month Standard capacity Mean # Patients per Bed per Month Return Rate (within 3 months) 16.4%17.4%19.2%17.6% 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

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Delays and Fairness in ED-to-IW Transfers Data-driven Theory

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Data-Driven Research is a Must + Fun Length of Stay (LOS), Internal Ward A (2004-8/2008), by Day

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Data-Driven Research is a Must + Fun Length of Stay (LOS), Internal Ward A (2004-8/2008), by 2 Hours

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Data-Driven Research is a Must + Fun Length of Stay (LOS), Internal Ward A (2004-8/2008), by 30 minutes

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Workload at the Internal Ward (In Progress): Arrivals, Departures, # Patients in Ward A, by Hour

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA The Business-case for RFID/US–based Tracking: Value Assessment at the Hospital ED (In Progress) Orthopedic (Orth for short) physician workload

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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 Healthcare seminars materialmaterial

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Empirical Analysis (Work in Progress) - ED : Activity (Flow) Chart

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Empirical Analysis (Work in Process) - ED: Resources (Flow) Chart

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Empirical Analysis (Work in Process) - ED: Activity – Resources (Flow) Chart

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Empirical Analysis (Work in Process) - ED: Information (Flow) Chart

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Empirical Analysis (Work in Process) – From ED to IW : Activity (Flow) Chart

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Empirical Analysis (Work in Process) – from ED to IW: Resources (Flow) Chart

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Empirical Analysis (Work in Process) – from ED to IW: Activity – Resources (Flow) Chart

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Empirical Analysis (Work in Process) – From ED to IW: Information (Flow) Chart

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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.

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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.

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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.

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Human Factors in the Design of a New Trauma Room Work procedures : 1.Preparation of a detailed list, with all the required instrumentation and inventory content of a bay. 2.Specification for general layout requirement of a bay. 3.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. Work was carried out in a participatory process that included all relevant people

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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.

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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).

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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).

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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.

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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.

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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.

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA 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.

May 4-6, 2009 Technology Services World Conference, Silicon Valley, CA Specification and Human Factors in Designing an Intelligent ED Dashboard Work method in three stages: 1. 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. 2. 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.