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Emergency Department Load EstimationOff line and on line load monitoring (and More)
Boaz Carmeli, IBM Haifa Research Laboratory & the Technion
IBM Haifa Research Laboratory 2
Agenda Emergency Department Crowding:
Consensus Development of Potential Measures
Measuring and Forecasting Emergency DepartmentCrowding in Real Time
Real Time ED Monitoring and Control System Initial thought
The Rambam, Technion and IBM Open Collaborative Research Project (creative project name – anyone ??)
®
Emergency Department Crowding:Consensus Development of Potential Measures
IBM Haifa Research Laboratory 4
Paper Summary
Authors Leif I. Solberg, MDF; HealthPartners Medical Group and Clinics Minneapolis, MN; Brent R. Asplin, MD, MPH Department of Emergency Medicine, Regions Hospital, St.
Paul, MN Robin M. Weinick, PhD; Agency for Healthcare Research and Quality, Rockville, MD; David J. Magid, MD, MPH; Colorado Permanente Medical Group, Denver, CO;
What is already known on this topic Although emergency department (ED) crowding is a topic of increasing public and
professional concern, there is no standardized definition of it and little agreement on what factors may contribute to it
What question this study addressed To use a broad-based and thorough expert process to identify all measures of ED and
hospital workflow that may be useful in understanding, monitoring, and managing crowding
What this study adds to our knowledge A panel of 74 national experts assessed 113 measures, and chose 38 through a
discussion and rating process
How this might change clinical practice The 38 measures should serve as a resource for research to determine which ones
are related to crowding, and eventually to develop tools to predict and modifycrowding
IBM Haifa Research Laboratory 5
ED Conceptual Model
Emergency Care•Seriously ill and injured
patients from the community
•Referral of patients with emergency conditions from other providers
Unscheduled urgent care
•Desire for immediate care
•Lack of capacity for unscheduled care in the ambulatory care system
Safety net care•Vulnerable populations (eg,
Medicaid beneficiaries, the uninsured) care
•Access barriers (eg, financial, transportation, insurance, lack of usual source of care)
AmbulanceDiversion
Demand forED Care
Patient Arriveat ED
Triage and room placement
Diagnostic evaluationand ED treatment
ED boarding of inpatients
Leaves without
treatmentcomplete
Patientdisposition
Ambulatorycare
system
Transfer to other facility
Admit to hospital
Input Throughput Output
IBM Haifa Research Laboratory 6
ED Conceptual Model The model is based on engineering principles from queuing
theory and compartmental models of flow, dividing ED function into input, throughput, and output stages
The input-throughput-output model permits most factors affecting use and crowding to be grouped into 1 of these 3 stages Input or demand for ED services depends on the volume of ill and
injured people in the community and the capability of the rest of the health care system to address the needs of individuals not requiring emergency care
Throughput includes factors that affect the efficiency of an ED to cope with its input, ranging from ED beds and staffing to the efficiency of ancillary services and consultant access
output factors include the ability of the inpatient system to admit patients requiring hospital care and of the ambulatory care system to provide timely post-discharge care
IBM Haifa Research Laboratory 7
The KPI* Selecting ProcessCore investigators (Authors and additional 6 people)
in response to a request for task order proposals from the Agency for Healthcare Research and Quality
Request to “select a group of content experts with expertise representing clinical care, data, emergency medical services, ED staff, hospital administration, information technology, and other relevant areas”
A group of expert reviewers with varied expertise and experience Independent of the core investigator group The final group of reviewers includes experts from 58 organizations in 21
states The majority (72%) are emergency physicians
*KPI – Key Performance Indicators
IBM Haifa Research Laboratory 8
The KPI Selecting Criteria
1. Feasibility How feasible would it be for operational staff to collect the data needed for this
measure routinely (or as frequently as would be needed) in the rater’s ED system or in one known to the rater?
2. Early warning potential How well would this measure provide warning about impending capacity
problems within the next 2 to 24 hours?
3. Planning value How well would this measure provide information about trends and changes in
ED business and crowding throughout a period of weeks to months?
4. Cost-efficiency How affordable would the data collection be for this measure?
5. Summary rating of operational usefulness According to a combination of the above criteria, how useful would this measure
be for clinical and administrative operations?
6. Usefulness for research Entirely apart from the aforementioned criteria, how much would this measure
helpto improve our general understanding of the causes and consequencesof ED crowding?
IBM Haifa Research Laboratory 9
KPI CategoriesTo clarify their purpose, the KPI have been grouped within
each stage by the main concept they represent
1. Patient demand (6 items) The volume of patients presenting to the ED for medical care
2. Patient complexity (3 items) Patient factors such as the urgency and potential seriousness of the
presenting complaint, the stability of the clinical condition, and the baseline medical and psychosocial burden of illness
3. ED capacity (6 items) The ability of the ED to provide timely care for the level of patient
demand according to the adequacy of physical space, equipment, personnel, and the organizational system.
IBM Haifa Research Laboratory 10
KPI Categories (Cont.)
4. ED workload (6 items) The demand and complexity of patient care that is undertaken by the
ED within a given period
5. ED efficiency (3 items) The ability of the ED to provide timely, high-quality emergency care
while limiting waste of equipment, supplies, and effort
6. Hospital capacity (6 items) The ability of the hospital to provide timely inpatient care for ED
patients who require hospitalization according to the adequacy of physical space, equipment, personnel, and the organizational system
7. Hospital efficiency (8 items) The ability of the hospital to provide timely, high-quality inpatient care
while limiting waste of equipment, supplies, and effort
IBM Haifa Research Laboratory 11
The Rating Process This revised set of measures was then rated by 56 of the 64
reviewers and the core investigators on an Internet Web site by using a magnitude estimation technique.
This technique permits averaging of ratings across many raters on a ratio level scale by asking each respondent to provide a relative score from 0 to infinity for each item in comparison with a measure used as a standard. The standard score was set at 100.
For example, if the feasibility of a measure was believed to be twice that of the standard in the mind of a reviewer, a score of 200 would be assigned. Likewise, if it were half as feasible, the reviewer would assign a score of 50.
Theoretically and empirically, the distribution of scores from a magnitude likelihood task are log linear, and thus the geometric mean rather than the more common arithmetic mean is the appropriate measure of central tendency, which results in much less clustering of scores than often occurs with rating scales using the more traditional Likert scale.
It also makes it easier to interpret the ratings because a rating of 200 for a measure means that the reviewers as a group thought that the measure was literally twice as good as one that was rated 100 in the same category.
IBM Haifa Research Laboratory 12
Input KPIs
Input Measure Concept Operational
Definition
1. ED patient volume, standardized for bed hours
Patient demand
Number of new patients registered within a defined period (hour, shift, day) ÷ number of ED bed hours within this period
2. ED patient volume, standardized for annual average
Patient demand
Number of new patients registered within a defined period ÷ annual mean number new patients registered within this period
3. ED ambulance patient volume, standardized for bed hours
Patient demand
Number of new ambulance patients registered within a defined period ÷ number of ED bed hours within this period
4. ED ambulance patient volume, standardized for annual average
Patient demand
Number of new ambulance patients within a defined period ÷ annual average of new ambulance patients registered within this period
5. Patient source Patient demand
Time, arrival mode, reason, referral source, and usual care for each patient registering at an ED in a defined period (hour/shift/day)
* Leave without treatment complete includes those patients who leave without being seen, leave before being finished, and leave against medical advice.
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Input KPIs
Input Measure Concept Operational
Definition
6. Percentage of open appointments
Patient demand
Percentage of open appointments at the beginning of a day in ambulatory care clinics that serve an ED’s patient population
7. Percentage of patients who leave without treatment complete*
ED capacity Number of registered patients who leave the ED without treatment complete ÷ total number ofpatients who register during this period
8. Leave without treatment complete severity*
ED capacity Average severity of patients who leave the ED without treatment complete within a defined period (shift/day/week)
9. Ambulance diversion episodes
ED capacity Number and duration of all diversion episodes at EDs within the EMS system within a defined period (week/month/year)
10. Ambulance diversion requests denied and forced openings
ED capacity Number of diversion requests denied or forced openings within a defined period (week/month/year)
* Leave without treatment complete includes those patients who leave without being seen, leave before being finished, and leave against medical advice.
IBM Haifa Research Laboratory 14
Input KPIs
Input Measure Concept Operational
Definition
11. Diverted ambulance patient description
ED capacity Chief complaints and final destination of diverted EMS patients within a defined period (week/month/year)
12. Average EMS waiting time
ED efficiency Total time at hospital for ambulances delivering patients to ED during a defined period (shift/day/week/month) ÷ number of ambulance deliveries within that period
13. Patient complexity as assessed at triage
Patient complexity
Mean complexity level as assessed at triage (using local criteria) for all
14. Patient complexity as the percentage of ambulance patients
Patient complexity
Percentage of patients registering at an ED in adefined period (shift/day/week/month) whoarrived by ambulance
15. Patient complexity as assessed by coding
Patient complexity
Mean complexity level as coded at the end of the visit for all patients completed in a defined period (shift/day/week/month)
* Leave without treatment complete includes those patients who leave without being seen, leave before being finished, and leave against medical advice.
IBM Haifa Research Laboratory 15
Throughput KPIs
Throughput Measure
Concept Operational
Definition
1. ED throughput time
ED efficiency Average time between patient sign-in and departure (separately for admitted vs discharged patients) within a defined period (day/week/month)
2. ED bed placement time
ED efficiency Mean interval between patient sign-in and placement in a treatment area within a defined period (shift/day/week/month)
3. ED ancillary service turnaround time
ED efficiency Average time between physician order and result report (separately for each service area) within a defined period (shift/day/week/month)
4. Summary workload, standardized for ED bed hours
ED workload Summary of (patients treated ׳ acuity) in a defined period (shift/day/week) ÷ number of ED bed hours within this period
5. Summary workload, standardized for registered nurse staff hours
ED workload Summary of (patients treated ׳acuity) in a defined period(shift/day/week) ק total ED staff registered nurse hours within this period
IBM Haifa Research Laboratory 16
Throughput KPIs
Throughput Measure
Concept Operational
Definition
6. Summary workload, standardized for physician staff hours
ED workload Summary of (patients treated xacuity) in a defined period (shift/day/week) ÷ total ED staff physician hours within this period
7. ED occupancy rate
ED workload Total number of ED patients registered at a defined time ÷ number of staffed treatment areas at that time
8. ED occupancy ED workload Total number of patients present in the ED at a defined time ÷ number of staffed treatment areas at that time
9. Patient disposition to physician staffing ratio
ED workload Number of patients admitted or discharged per staff physician during a defined period (shift/day/week)
IBM Haifa Research Laboratory 17
Output KPIs
Output Measure Concept Operational
Definition
1. ED boarding time
Hospital efficiency
Mean time from inpatient bed request to physical departure of patients from the ED overall and by bed type within a defined period (shift/day/week)*
2. ED boarding time components
Hospital efficiency
Mean time from inpatient bed request to physical departure of patients from the ED by bed type by component (bed assignment, bed cleaning, transfer arrival) within a defined period*
3. Boarding burden
Hospital efficiency
Mean number of ED patients waiting for an inpatient bed within a defined period ÷ number of staffed ED treatment areas
4. Hospital admission source, standardized
Hospital efficiency
Number of requests for admission within a defined period (shift/day) overall and by admission source ÷ annual mean requests for admission during that period and adjusted for day of week and season of year†
5. ED admission transfer rate
Hospital efficiency
Number of patients transferred from ED to another facility who would normally have been admitted within a defined period ÷ number of ED admissions within this period
*Bed type=ICU/telemetry/psychiatry/ward.
†Admission source=ED/operating room/catheterization laboratory/outpatient/other.
IBM Haifa Research Laboratory 18
Output KPIs
Output Measure Concept Operational
Definition
6. Hospital discharge potential
Hospital efficiency
Number of inpatients ready for discharge at or within a defined period ÷ number of hospital inpatients at that time
7. Hospital discharge process interval
Hospital efficiency
Mean interval from discharge order to patient departure from a unit in a defined period (shift/day/week/month)
8. Inpatient cycling time
Hospital efficiency
Mean amount of time required to discharge an inpatient and admit a new patient to the same bed within this period
9. Hospital census Hospital capacity
Mean number of inpatient beds available by bed type at a defined time ÷ number of staffed inpatient beds by bed type*
10. Hospital occupancy rate
Hospital capacity
Number of occupied inpatient beds overall and by bed type ÷ number of staffed inpatient beds overall and by bed type*
*Bed type=ICU/telemetry/psychiatry/ward.
†Admission source=ED/operating room/catheterization laboratory/outpatient/other.
IBM Haifa Research Laboratory 19
Output KPIs
Output Measure Concept Operational
Definition
11. Hospital supply/demand status forecast
Hospital capacity
Forecast of expected hospital admissions and discharges as reported daily at 6 AM and compared with hospital census
12. Observation unit census
Hospital capacity
Mean number of available ED observation beds at a defined time ÷ number of staffed ED observation beds
13. ED volume/hospital capacity ratio
Hospital capacity
Number of new ED patients within a defined period (shift/day) ÷ number of available hospital beds at the beginning of analysis period overall and by bed type*
14. Agency nursing expenditures
Hospital capacity
Registered nurse agency nursing expenditures (ED/overall) within a defined period ÷ total nursing expenditures (ED/overall) within this period
*Bed type=ICU/telemetry/psychiatry/ward.
†Admission source=ED/operating room/catheterization laboratory/outpatient/other.
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Measuring and Forecasting Emergency Department Crowding in Real Time
IBM Haifa Research Laboratory 21
Paper Context
Authors – Vanderbilt University Medical Center, Nashville, TN Nathan R. Hoot, MS; Department of Biomedical Informatics Chuan Zhou, PhD; Department of Biostatistics Ian Jones, MD; Department of Emergency Medicine & Biomedical Informatics Dominik Aronsky, MD, PhD; Department of Biomedical Informatics & Biomedical
Informatics
What is already known on this topic In the absence of an accepted definition of emergency department (ED) crowding,
multiple scores have been proposed to measure this phenomenon
What question this study addressed How 5 metrics for measuring current and impending ED crowding fared in predicting
ambulance diversion status during an 8-week period in a single adult ED
What this study adds to our knowledge All measures performed reasonably well at measuring crowding in real time, but
none outperformed the simplest measure, ED occupancy level. None of the measures was particularly useful as a short-term warning system for future crowding
How this might change clinical practice This study will not change clinical practice but suggests that ED occupancy,
the simplest metric for measuring ED crowding, performs just as well asmore complex methods
IBM Haifa Research Laboratory 22
Paper Summary
Study objective: To quantifying the potential for monitoring current and near-future
emergency department (ED) crowding by using 4 measures: the Emergency Department Work Index (EDWIN) the National Emergency Department Overcrowding Scale
(NEDOCS) the Demand Value of the Real-time Emergency Analysis of
Demand Indicators (READI) the Work Score
Methods:Study calculated the 4 measures at 10-minute intervals during an 8-
week study period (2006)Ambulance diversion status was the outcome variable for crowdingOccupancy level was the performance baseline measureEvaluation of discriminatory power for current crowding was calculated
by the area under the receiver operating characteristic curve (AUC)To assess forecasting power, activity monitoring operating
characteristic curves was applied, which measure the timeliness of early warnings at various false alarm rates
IBM Haifa Research Laboratory 23
Paper Summary (Cont.)
Results: 7,948 observations were recorded during the study period. The ED was on ambulance diversion during 30% of the observations The AUC (Area Under Curve) was:
0.81 for the EDWIN 0.88 for the NEDOCS 0.65 for the READI Demand Value 0.90 for the Work Score 0.90 for occupancy level
In the activity monitoring operating characteristic analysis, only the occupancy level provided more than an hour of advance warning (median 1 hour 7 minutes) before crowding, with 1 false alarm per week
Conclusion: The EDWIN, the NEDOCS, and the Work Score monitor current ED crowding with
high discriminatory power None of them exceeded the performance of occupancy level across the range
of operating points None of the measures provided substantial advance warning before crowding
at low rates of false alarms
IBM Haifa Research Laboratory 24
Emergency Department Work IndexEDWIN was calculated by
EDWIM = Σniti / (Na X (Bt – Pboard))
where ni – number of non-boarding patients in triage category i
ti – reversed triage category i, where 5 denotes the sickest patients and 1 denotes the least sick patients
Na – number of attending physicians on duty
Bt – number of licensed treatment beds in the ED
Pboard – number of boarding patients
Offered load ??
Number of physicians
Spare treatment cycles
IBM Haifa Research Laboratory 25
National Emergency Department OverCrowding Scale
NEDOCS was calculated by
NEDOCS = (Pbed ⁄ Bt) X 85.8 + (Padmit ⁄ Bh) X 600 + (Wtime X 5.64) + (Atime X 0.93) + (Rn X 13.4) – 20
Where Pbed – number of patients in licensed beds and overflow locations, such as
hallway beds or chairs
Bt – number of licensed treatment beds
Padmit – number of admitted patients
Bh – number of hospital beds
Wtime – waiting time for the last patient put into bed
Atime – longest time since registration among boarding patients
Rn – number of respirators in use, maximum of 2*
* The respirator variable (Rn) did not generalize to the study setting, because patients ill enough to require mechanical ventilation are stabilized and transferred immediately to a critical care unit. As a surrogate, the number of trauma beds was used in place of the number of respirators.
Patient Index- number of
patients within ED
Admitted Index – number of
patients within the hospital
Registration Time – time from
registration to triage for the last patient
Admission Time – the longest time
an admitted patient is staying
at the EDNumber of Respirators – an
indication for additional (non-
linear) load
IBM Haifa Research Laboratory 26
NEDOCS Nomogram
IBM Haifa Research Laboratory 27
Real-time Emergency Analysis of Demand Indicators
The Demand Value of the Real-time Emergency Analysis of Demand Indicators (READI) was calculated by
DV = (BR + PR) X AR
BR = (Ptotal + Apred – Dpred) ⁄ Bt
AR = Σniti ⁄ Ptriage
PR = Ahour ⁄ΣPPH
Where DV – Demand Value, BR – bed ratio, PR – provider ratio, AR – acuity ratio; Ptotal – number of ED patients, Apred – number of predicted arrivals,
Dpred – number of predicted departures, Bt – number of licensed treatment beds;
ni – number of patients in triage category i, ti – reversed triage category i,
Ptriage – number of patients in the ED with an assigned triage category,
Ahour – number of arrivals in the past hour, PPH – average patients seen per hour for each attending physicianand resident on duty.
The demand for care – patients, care givers and
acuityPatient Index –
number of (expected) patients within the hospital
Provider Ratio – the number of patients a provider is treated in an
hour
IBM Haifa Research Laboratory 28
READI Calculation – Additional Info The predicted number of arrivals (Apred) and departures
(Dpred) for each hour of the day was calculated using 9 months of ED data
The original READI instrument used a 4-level triage system, so the 5-level Emergency Severity Index was condensed into 4 categories by combining the 2 least severe acuity levels.
The number of patients treated per hour was calculated for residents at each level of training and for attending physicians who treated patients without a resident, using 9 months of ED data
IBM Haifa Research Laboratory 29
Work Score The Work Score was calculated using the following formula:
Work Score = 3.23 X Pwait ⁄ Bt + 0.097 Σniti ⁄ Nn + 10.92 X Pboard ⁄ Bt
where Pwait – number of waiting patients
Bt – number of licensed treatment beds
ni – number of patients under evaluation in triage category I
ti – triage category I
Nn – number of nurses on duty
Pboard – number of boarding patients
IBM Haifa Research Laboratory 30
ED Occupancy Level The ED occupancy level was used as a control measure for
baseline comparison
The occupancy level was calculated using the following formula:
Occupancy level = 100 X Pbed ⁄ Bt
Where Pbed – number of patients in licensed beds and overflow locations such
as hallway beds or chairs Bt - number of licensed treatment beds
IBM Haifa Research Laboratory 31
Calculation Results
Time series plots of the crowding measures during the study period The plots shown here are smoothed using cubic splines Episodes of ambulance diversion are marked by the shaded areas.
IBM Haifa Research Laboratory 32
Receiver Operating Characteristic Curves
IBM Haifa Research Laboratory 33
Activity Monitoring Operating Characteristic Curves
IBM Haifa Research Laboratory 34
Further Research
Future research should focus on improving the forecasting power of crowding measures
The use of historical data to predict changes in the next few hours may allow for substantial improvements in the performance of an early warning system
Advanced modeling techniques such as neural networks, applied specifically for the purpose of forecasting, may result in improved forecasting power
The development of a good forecasting model for ED crowding will pave the way to studying intervention policies, which may allow researchers to identify ways of sustaining health care quality and access in the face of crowding
Other researchers have discussed strategies including the use of reserve physicians and nurses and deferring care of low-acuity patientseither of which could be initiated, given a few hours of advancewarning before crowding
IBM Haifa Research Laboratory 35
Paper Summary
The findings demonstrate the feasibility of implementing 4 measures for real-time monitoring of ED crowding
Occupancy level showed discriminatory power similar to or greater than the 4 other measures for measuring current ED crowding
In terms of timely forecasting, none of the measures showed a clear advantage over occupancy level
These findings suggest new directions for the measurement and management of ED crowding.
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Real Time ED Monitoring and Control SystemWork in (its very early stages but in) progress
IBM Haifa Research Laboratory 37
Real Time ED Monitoring and Control SystemImproving the Forecasting Power of Crowding Measures
Data Collection Adding RFID based location tracking system for Physicians, Nurses,
Patients and other relevant personnel Collect real-time relevant information from hospital IT systems such
as PACS, EHR, ADT, LAB etc Better utilize historical EHR and operational data from existing IT
systems within the hospital
Data Visualization Operational dashboard Provide sophisticated data
Analysis Techniques Machine learning - neural networks Mathematical models – service engineering Other ??
IBM Haifa Research Laboratory 38
System Architecture
ED Simulator•Based on observation
•Will be used, mainly, for design phase e.g. to mimic the RFID system
RFID based LocationTracking
•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
Machine LearningAlgorithmsAnalysis of Historical
And Real-time Data
Mathematical Models
e.g. Queuing Theory
Data Collection Analysis Data Visualization
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OCR project – Next Generation HospitalRambam/Technion/IBM open collaborative research
IBM Haifa Research Laboratory 40
An IBM funded program to support open collaborative research between IBM and universities in Computer Science (including related disciplines in Electrical Engineering and Math) and its applications
Implements the Open Collaboration Principles established under IBM’s leadership in 2005 - IP openly published or available in royalty free “public commons”, software available as open source
Choose a limited number of strategically defined topics where open collaborative innovation would benefit IBM and the world at large – endorsed by Research area strategists and VP strategists
Subject to approval in advance by the OSSC
Piloted in US in 2006 Research topics and universities: Software Quality (Rutgers, UC Berkeley), Privacy & Security
Policy Management (CMU, Purdue), Clinical Decision Support (Columbia, Georgia Tech), Mathematical Optimization (CMU, UC Davis)
Joint announcement and publicity with universities 12/14/2006
Expanded in 2007, including outside US Research topics and universities: Accessibility for an Aging Population (Dundee, Miami), NewGen
Hospital (Technion, Rambam Hospital), Service Professionals’ Social Network (Indian School of Business), Privacy & Security Policy Management (Imperial College)
What makes it work? Multi-year, so that faculty can take on new students and obligations Collaborative, allowing IBM and university participants to forge deep relationships Open, providing maximum opportunity for others to build on the results Challenging, research requiring considerable innovation Well-funded, large enough to make a difference (average $150K)
What is OCR?
IBM Haifa Research Laboratory 41
OCR Overview
Joint research project between Rambam hospital, Technion, and IBM Leverage Technion’s relationship with Rambam hospital
Goal: Combined multi-dimensional improvement of patient care process Clinical Operational Financial
Multi-disciplinary approach: Medical (Rambam) Statistics (IBM, Technion) Operations Research (IBM, Technion) Healthcare informatics (IBM, Rambam) Process improvement (IBM, Technion) Human factors engineering (Technion) Financial (Rambam) Domain specific knowledge in above areas – IBM & Technion
Participation Rambam hospital: Top management including hospital general manager, Prof. Rafi Bayar,
and ER manager, Dr. Dagan Schwartz Technion: Prof. Avishai Mandelbaum, Prof. Danny Gopher, Prof. Avi Shtub, Prof. Eitan Naveh IBM: Pnina Vortman, Segev Wasserkrug, Boaz Carmeli, Ohad Greenshpan and Sergey Zeltyn
IBM Haifa Research Laboratory 42
Processes at the Healthcare Domain
Business & Operational processes
financial, HR, assets aspects and indicators)
Clinical processes
(Mostly health aspects and indicators)
Organization centric
Interested role: Management
Example: Procurement, training
Interested role: Care Personnel
Example: Protocols and procedures
Patient centric
Interested role: Patient & Management
Example: Obtaining reimbursement for medical procedures
Interested role: Patient & Care Personnel
Example: Arrival and treatment at ER
IBM Haifa Research Laboratory 43
OCR Approach and Status Approach
Pick four high intensity department ER Operating room Neonatal Trauma
Map patient centric processes from various dimensions Focus and implement specific research projects based on initial
analysis
HRL work mode Hands on (joint work) Mentoring student projects at the Technion Collaborating work carried out by Technion graduate and
undergraduate students
IBM Haifa Research Laboratory 44
Current status Work carried out In ER
Some initial discussions with second department (OR)
Metrics: Detailed Metrics document was developed collaboratively between the 3 parties
Longitudinal observations: Around 200 observations were taken
Horizontal observations (Work sampling) Compared to 2001 observations (later to 2004-2007/8) Data analysis and improvement ideas (Lean Manufacturing in Healthcare)
Observations were used to develop the following analytical views: Process Maps (Activities, Resources, Information) Demo of the Monitoring Console – Command & Control Forecasting Model - forecast arrival flow to the ED, based on short term
historical data Online Statistics – integrating Technion’s SEE-Stat tool, which takes an
Operations Research viewpoint
IBM Haifa Research Laboratory 45
Current status (Cont.)
Layout planning Simulation-based analysis of the temporary ER, continuing into Future ER Layout of new trauma unit
Flow from ER to wards Projects on Fairly moving patients from the ED to the Wards (Fairness
Table)
RFID: Highly accurate measurement of actual patient flow using RTLS is being discussed Pilots with three companies Additional options being considered
Focused research topics: Measure, forecast, predict, and optimize intraday performance
Combination of Forecasting, OR models, and Cognos ED vs. ER
Cognos based measurement, forecasting and improvement pilot being planned
IBM Haifa Research Laboratory 46
Intraday measurement, forecast and optimization
Process: Measure multi dimensional
metrics Forecast near term future Enable optimization and
decision making using advanced analytics
Based on: Cognos BI platform ER patient process simulator
created by Technion Jointly created load
forecasting models Jointly created optimization
algorithms
®
Thank You