Transcript Document

ED Overcrowding Solutions:
Reducing Variation
R. Scott Altman, MD, MPH, MBA
Managing Consultant,
Joint Commission International
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Overview
 Predicting variation
 Using data to plan ahead
 Reducing variation
 Smoothing and Queuing Theory
 Managing variation
 Who’s in charge
 Triggered tiered response plan
 All in advance
 New Accreditation standard
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Variation in The ER
 Demand management (input)
 Resource mobilization (throughput)
 Discharge planning (output)
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Demand Management
 Emergency Severity Index (ESI Triage)
 Wuerz, Eitel, et al. ESI Triage Category is Associated with
Six Month Survival. AEM. 2001; 8:61-4
 Manual available at http://www.ena.org/
 Smoothing theory
 Queuing theory
 Alternative creation and community education
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Emergency Severity Index
(ESI Triage)
yes
patient dying?
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no
yes
shouldn’t wait?
no
how many resources?
none
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one
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many
vital signs
consider
no
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Demand Management
Demand Prediction & Response
 Number of historical same-day
visits
this season
 Adjusted for recent trend
(eg: multiply by percent occupancy
of staffed available beds)
 Prepare for the expectation
(staff, supplies, capacity)
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Demand Management
(continued)
 Establish fixed triggers in advance for calling
in additional staff.
 Too often asking for help is seen as a failure
rather than an appropriate management tool.
 “ED volume ebbs and flows with consistency”
Mike Williams, President The Abaris Group
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Admissions
October 2000
3000
2500
Number of Admissions
2000
Em ergent
Urgent
Elective
1500
Newborn
Total
1000
500
0
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M
T
W
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Date and Day of Week
Source: MA DHCFP
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Ancillary Service Expansion
 Turn around time
 From sample/patient received until results available
to user
 Peak is more important than average
 Expectations for average and peak should be
mutually agreed upon
 Expectations should be based upon clinical need
 Tracking will be retrospective unless part of
computerized tracking system
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Ancillary Service Expansion
 Triggered responses
 Green: meeting average TAT expectations
 Yellow: sample exceeds average, but meets peak
 Example: ancillary resources shifted
 Red: Sample exceeds peak
 Example: extra ancillary resources mobilized
 Black: more than one sample exceeds peak
 Example: ED reviews orders for need; Ancillary
service opens backup operation(s)
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Bed Management
 Predict demand by hour of day
 Triggered responses
 Green: eight hours of beds are currently available
 Yellow: drop below historical peak
 Example: manual bed count, identify patients for
movement
 Red: drop below historical average
 Example: begin moving patients (discharges /
transfers)
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Bed Management
 Black: First bed request w/o identified bed
 Examples: Call in staff & prepare alternative site;
 contact neighbor hospital for potential direct
admit transfers;
 inform medical staff that office patients should
be admitted to an alternative site, not sent to the
ED;
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Bed Management
 Convert “Push” system to “Pull” system
 Track by root cause
 Delayed admission
 Patient waiting more than two hours for bed
assignment
 Example Response: Turn care responsibility
to inpatient medical staff
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Bed Management
 Boarding
 Patient still in the ER two hours after bed
assignment
 Example Responses: turn care
responsibility to floor team – financially,
physically, or managerially
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Tiered Triggered Response Plan
Copyright© 2003 ibex Healthdata Systems, Inc. All rights reserved.
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Smoothing Theory
Eugene Litvak, Ph.D.
Boston University School of Management
Program for Management of Variability in
Health Care Delivery
http://management.bu.edu/research/hcmrc/mvp/index.asp
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# of Patients
Demand vs. Capacity
Time
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Variability Methodology:
Litvak E., Long MC. Cost and Quality Under
Managed Care: Irreconcilable Differences?,
American Journal of Managed Care, 2000;
6:305-312
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What Makes Hospital Census
Variable?
 If ED cases are 50% of admissions
and…
 Elective-scheduled OR cases are 30% of
admissions
then…
 Which would you expect to be the largest
source of census variability?
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The Answer Is…
The ED and Elective-Scheduled OR have
approximately equal effects on census
variability.
 Why?
 Because of another (hidden) type of
variability...
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Artificial Variability
SPC: Special Causes of Variation
 Non-random
 Non-predictable (driven by unknown
individual priorities)
 Should not be managed, must be identified
and eliminated
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ED Diversions Study Under
DPH Grant
Litvak E, Long MC, Cooper AB, McManus
ML. Emergency Department Diversion:
Causes and Solutions. Academic Emergency
Medicine, November 2001, 8, No11, pp. 1108-1110
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ED Diversions Study Under
DPH Grant
 Between two hospitals
 42 days of information
 6000 admissions
 8000+ ED visits
 2000 staffing/capacity data points
 300,000+ patient movement/status data
points
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Results
Root Cause Analysis of ED Crowding and
Ambulance Diversion in Mass, BU, 2002:
 Correlation between # of ED arrivals (or ED
census) and average minutes of diversion is
either negative or insignificant.
 Correlation between time interval from
“time into slot” and “time admitting called”
(or time orders received) and diversions is
negative.
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Results
Root Cause Analysis of ED Crowding and
Ambulance Diversion in Mass, BU, 2002:
 Correlation between average number of ED
patients waiting for hospital beds and average
minutes of diversion is high.
 When the scheduled demand is significant,
there was much stronger correlation between
scheduled admissions and diversions than
between ED demand and diversions
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Elective Surgical Requests vs
Total Refusals
elective surgical patients seeking ICU admission
patients diverted or rejected from the ICU
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McManus, M.L., MD, MPH; Long, M.C., MD; Cooper, A; Mandell, J., MD; Litvak, E., Ph.D.
Impact of Variability in Surgical Caseload on Access to Intensive Care Services
ASA Meeting Abstracts; Oct 2002
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Smoothing Elective Case
Load: Benefits and Conditions
 Benefits:
 Better utilization of resources
 Reduced hours of ED diversions
 Staff and patient satisfaction
 More staffing resources: better tolerating peak loads
 Reduced medical errors
 Reduced length of stay
 Increased hospital throughput
 Increased surgical throughput
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Conditions
 Smoothing elective case volume requires
physicians’ cooperation
 Smoothing elective case volume requires
administrative leadership
 There might be a need for financial
incentives
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Capping Admissions:
Luther Midelfort Mayo Health System Study
 300 Beds community hospital (March-Dec ‘01)
 Increased patient throughput through better
utilization of hospital capacities (the opportunity
that was previously lost) resulted in the increased
revenue of about $200,000/month.
 Increased percent of patients put into bed within 1
hour from 23% to 40%
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Capping Admissions:
Luther Midelfort Mayo Health System Study
 Emergency Department diversions have
been reduced from 12% to 1-2%
 Overall number of open nursing positions
decreased from about 10% to 1%
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Conclusions
 Separation of “scheduled” and “unscheduled”
beds will not affect the overall scheduled
surgical case volume, and would allow to reduce
diversion hours and to calculate the necessary
additional beds to satisfy the demand
 Neither ED diversion, nor nursing retention or
medical errors problems will be satisfactorily
resolved unless artificial flow variability is
smoothed
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Proposed New Standard
(Domestic US)
 LD.3.4 (NEW – as of August 25, 2003)
 The leaders develop and implement plans
to identify and mitigate impediments to
efficient patient flow through hospital
processes.
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Elements of Performance
1. Leadership assesses the scope of patient flow
issues within the organization, including the ED,
the impact of those issues on patient safety, and
engages in planning to mitigate that impact.
2. Planning encompasses the delivery of
appropriate and adequate care to admitted
patients who must be held in temporary bed
locations, e.g. PACU and ED areas.
No longer includes: “These temporary locations must be outside of the Emergency Department
and in an appropriate patient care area.”
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Elements of Performance
3. Planning includes the delivery of adequate care and
services to those patients in the ED who are placed
in overflow locations, such as hallways.
4. Specific critical performance indicators are
identified and measured that enable leadership to
monitor the efficiency and safety of support services
and patient care and treatment areas that are part of
the patient flow processes for ED patients.
5. Performance indicators are reported to leadership on
a regular basis and are available to those individuals
who are accountable for processes that support
patient flow.
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Elements of Performance
6. The organization improves those processes identified
by leadership as essential in the efficient movement
of patients through the organization.
7. Planning includes collaboration with the Medical
Staff to assess and develop processes that support
efficient patient flow.
8. Criteria are written and defined for diversion
decisions.
9. The organization defines criteria for clarification of
negative outcomes as sentinel event classification in
ED patient.
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What Should We Do?
(Practical Steps)
 Identify, classify, and measure types of
variability.
 Distinguish and eliminate artificial
variability.
 Separate remaining natural variability
into homogeneous sub-groups and
optimally manage.
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And Create a Tiered Triggered
Response Plan
 ED Staffing & Equipping
 Ancillary Support Turn Around Times
Laboratory
Radiology
Pharmacy
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And Create a Tiered Triggered
Response Plan
 In-Patient Bed Availability
Critical Care
Step-down
General Medical Surgical
Pediatric
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Thank You
[email protected]
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