® Emergency Department Load Estimation Off line and on line load monitoring (and More) Boaz Carmeli, IBM Haifa Research Laboratory & the Technion [email protected].

®

Emergency Department Load EstimationOff line and on line load monitoring (and More)

Boaz Carmeli, IBM Haifa Research Laboratory & the Technion

[email protected]

[email protected]

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


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


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


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


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


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?


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.


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


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.


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.


Input KPIs


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)



Input KPIs


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)



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


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)


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.


Output KPIs


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*




Output KPIs


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



®

Measuring and Forecasting Emergency Department Crowding in Real Time


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


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


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


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


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


NEDOCS Nomogram


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


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


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


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


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.


Receiver Operating Characteristic Curves


Activity Monitoring Operating Characteristic Curves


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


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.

®

Real Time ED Monitoring and Control SystemWork in (its very early stages but in) progress


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 ??


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

®

OCR project – Next Generation HospitalRambam/Technion/IBM open collaborative research


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?


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


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


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


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


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


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

® Emergency Department Load Estimation Off line and on line load monitoring (and More) Boaz Carmeli, IBM Haifa Research Laboratory & the Technion [email protected].

Documents

ed care patient

emergency care throughput

measures of ed

unscheduled care

crowding slide

ibm haifa research laboratory

ed triage

ed services