2009 Service Science Innovation Partnership Award Finalist Presentation

Technology Services World Conference, Silicon Valley, CAMay 4-6, 2009

2009 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 HospitalTechnion, IE&M


Partners

Rambam Hospital (1000 beds): Government• Teaching hospital (research): clinical, managerial• “We shall be your lab” for innovative research

IBM Research Lab, Haifa (500 researchers): Industry• IS/IT/Healthcare, SSME; products• OCR: “Spur innovation through university

collaboration”

Technion IE&M (1500 students, 100+ faculty): Academic• From OR & Stat, through IS & HFE, to Psychology • SEE Lab: data repositories, analysis tools (online)


Project Goals

Innovate and transform patients care• Clinical• Operational• Financial

Archive and disseminate research-based knowledge

R&D of new products and services


Service Science (in Hospitals)

7. Feedback 1. Measurements / Data

6. Improvement 5. Implementation2. Modeling,

Analysis3. Validation

8. Novel needs, necessitating Science

4. Maturity enables Deployment


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).


Project Outputs – TangiblesHospital: 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


Project Outputs – KnowledgeResearch

• 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


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 AnalysisStatistical

Inference Forecasting/Mac

hine Learning AlgorithmsAnalysis of Historical

and Real-time DataModels: Math.

Simulation Queueing (Flow)

Theory, ED Simulator

Data Collection Analysis Data Visualization

Optimization / Control WFM, Priorities, Real-time Control, etc.


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


2009 Service Science Innovation Partnership Award

Support Material


Dashboard (in Process) – Room Occupancy Level


Efficiency vs. Fairness at the Internal WardsWard A Ward B Ward C Ward D

ALOS (days) 6.368 4.474 5.358 5.562

Mean Occupancy Rate 97.8% 94.4% 86.8% 91.1%

Mean # Patients per Month 205.5 187.6 210.0 209.6

Standard capacity 45 30 44 42

Mean # Patients per Bed per Month 4.57 6.25 4.77 4.77

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 loadsPatients allocation unfair, as far as wards are concerned


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


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


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


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


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


The Business-case for RFID/US–based Tracking: Value Assessment at the Hospital ED (In Progress)

Define Required Process

Change(s)

Define Sensor Related Data

Define Additional

Data

Model-based Metric-Evaluation

Define Required Process

Change(s)

Define Sensor Related Data

Define Additional

Data

Model-based Metric-Evaluation

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Ideal RFID Orth

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Passive RFID Orth

Orthopedic (Orth for short) physician workload


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


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


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


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


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


Empirical Analysis

(Work in Progress) - ED: Activity (Flow) Chart


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


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


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


Empirical Analysis (Work in Process)

– From ED to IW: Activity (Flow) Chart


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


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


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


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.



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.



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.



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


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.


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).



•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).



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.



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.


Patient Quality of Care-Longitudinal observations and analysis of

medical recordsThe 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.


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.


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.

Documents

2009 Service Science Innovation Partnership Award Finalist Presentation