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HCM 540 – Healthcare Operations Management
Process Flow Basics
(Chapter 3 in MBPF)
General 4-stage framework for managing healthcare resources (staff and physical
capacity)
1) Demand/workload characterization and forecasting
2) Translation from demand to capacity
3) Scheduling
4) Short-term allocation
The details of these 4 stages all vary depending on the specific healthcare context.
1. Demand/workload characterization Basic process flow physics
How the work flows Occupancy/census/inventory/work in process analysis
TOD/DOW nature of workload Healthcare operational data
Getting data about workload Patient/work classification systems
Different types of work require different levels of resources Forecasting
Predicting future workload from past and other causal factors Work measurement and productivity monitoring
Understanding the inputs and outputs relationship Important component of staffing analysis
2. Demand Capacity
Labor and physical capacity costs dominate in healthcare
Queueing and simulation models might be useful for helping to set capacity levels when tradeoffs between capacity cost and patient delay
and/or access is important hospital bed allocation, ancillary staffing surgical block allocation, clinic capacity
Staffing analysis standards, nurse-patient ratios, variable vs. constant
tasks, benefit allowances, benchmarking
Good Resources for healthcare operations info and ideas
Institute for Healthcare Improvement - http://www.ihi.org/ Family practice web site - http://www.aafp.org/
Journal has nice toolbox - http://www.aafp.org/x7502.xml Healthcare management engineering mailing list – HME
group in Yahoo groups Very active practitioner forum about process improvement, operations
management, industrial engineering, etc. in the healthcare industry
Knoxville ED Study See course website for PPT, report and xls file for this nice study
which was done by a professor at Univ. of Tennessee and a management engineering group
I. Business Process Perspective on Healthcare Delivery
Inputs Outputs
•patients, test results•bill, resolved complaint
•patients•specimens•phone calls, charts•complaints•$$$
O W1 P1
V1 W2 P2 W3
A1
M1 M2
O
FSC - Process Sequence Chart
•Uses resources (capital & labor)•Visit multiple locations•nursing care, test processing, chart coding•Value add and non-value (delays)
Process Management
Network ofActivities
Information
Flow Units &Attributes Flow units – things that
flow through business processes Ex: patient, information,
cash, people, supplies, test results, exams, paper
Attributes – characteristics of flow units Ex: patient type, acuity,
length of stay, admission origin, discharge status
A1
A3
A2
A3
Each attribute like index card in a pocket
HW1 examples of Processes, Flow Units, Attributes?
As Entities Flow… Generated (enter system)
ED, walk-in, call for appointment, specimen arrives at lab, charts to medical records and billing, patient admitted
Attributes checked and/or set time of arrival, preliminary diagnosis, urgency status noted, surgical
case type, IP or OP, DRG Resources gotten and released
registration clerks, nurse, physician, bed, imaging equipment, transporters, biller, customer service rep
Locations visited inpatient units, ED cubicle, waiting room, radiology, lab, waiting areas
Get processed and/or transformed care delivered, procedure done, bill generated, chart filed, diagnosis
made May be delayed, combined, split, rejoined, and eventually exit the
system
Wait Register Complete HHQ WaitStart/ntrVitals/
AssessmentWait
ProviderContactExam
WaitDiagnostic/Intervention
WaitProviderContact/Results
Wait Discharge
CollectionsMCHC
PharmacyWait
Leave
OutsidePharmacy
Wait
Start/Enter
Finish
An Urgent Care Clinic
Patients visit a series of queueing systems in series
iGrafx Process
Basic Operational Flow MeasuresCh 3 of MBPF
Inputs OutputsProcessing
System
Flow Rate or throughput = average number of flow units (entities) that flow through a certain point in a process per unit time
Occupancy or Inventory = number of flow units within the boundaries of some process
Flow time = processing time + wait time (total time in the box)
R
T
I
I = units of inventoryT = avg flow time
R units/time R units/time
Throughput (Flow Rate) Concepts Throughput rates are the number of flow units per unit time
admits/day, tests/hour, phone calls/hour, $/month Flow is conserved – what flows in, must flow out Inflow and outflow fluctuate over short term
In > Out Occupancy, queue or inventory grows Out > In Occupancy, queue or inventory shrinks
Long term stable process Flow In = Flow Out
Can combine and split flows
Process(T=flow time
in clinic)Ri1 = scheduled clinic
patients per day
Ri2 = clinic walk-in patients per day
Ro= total flow of patients out of clinic per day
Ro= Ri1 + Ri2
Flow Time Concepts Flow time is amount of time spent in some process
May include both waiting and processing It’s a duration and measured in units of time
length of stay, exam length, processing time for a test, procedure length, time to register, recovery time
Service rate = 1/avg flow time Example: avg flow time = 0.5 hours service rate of 2/hr
Flow time varies for individuals and/or different types of flow units consider average flow time for now
Type 1 Flow Time10 mins
R1 = type 1 flow in
R2 = type 2 flow in
Type 2 Flow Time20 mins
Type 1&25 mins
R1+R2
R2
R1
What is overall average time in
dotted box?20 pats/hr
5 pats/hr
Flow Time, Flow Rate, and Inventory DynamicsRi(t) = instantaneous inflow rate at time tRo(t) = instantaneous outflow rate at time tR(t) = instantaneous inventory (occupancy) build up rate at t
R(t) = Ri(t) - Ro(t)
If Ri(t) > Ro(t) get buildup at rate R(t) > 0
If Ri(t) = Ro(t) get no change in occupancy
If Ri(t) < Ro(t) get depletion at rate R(t) < 0
Example: Constant R during (t1,t2)In other words, during the time period (t1,t2), occupancy is being depleted or is building up at a constant rate R.
Occupancy change = Buildup Rate x Length of Time Interval
O(t2)-O(t1) = R(t2-t1)
Example: If system empty at t1, and R=3 people/minute, how many people are in the system after 10 minutes?
TABLE 3.2 Buidling Rates and Ending Inventory Data: Vancouver Airport Security Checkpoint of Example 3.1
Time 8:40am 8:40-9:10am 9:10-9:30am 9:30-9:43:20am 9:43:20-10:10amAvg # of people arriving 225 300 100 200Length of time interval 30 20 13.33 26.67
Inflow Rate Ri (per min) 7.50 15 7.5 7.5Outflow Rate Ro (per min) 7.50 12 12 7.5Buildup Rate R (per min) 0.00 3 -4.5 0Ending Occupancy (# people) 0 0 60 0 0
Time Passengers8:40 08:50 09:00 09:10 09:20 309:30 609:40 09:50 0
10:00 010:10 0
Passengers in Queue at Checkpoint
0
10
20
30
40
50
60
70
8:40 8:50 9:00 9:10 9:20 9:30 9:40 9:50 10:00 10:10
Passengers
R=0/min R=0/min
R=3/min R=-4.5/min
Occupancy & Inventory can be averaged over time for stable processes
Passengers in Queue at Checkpoint
0
10
20
30
40
50
60
70
8:40 8:50 9:00 9:10 9:20 9:30 9:40 9:50 10:00 10:10
Passengers
At 10:10 the inventory will start to build again
for next flight.
Inventory = 0 from 9:43-10:10(27 mins)
So, what’s the average inventory in here (from 9:10-9:43)?Hint: How can we interpret the AREA of this triangle?
Avg inventory = (33(30) + 27(0))/60 minutes = 16.5 people
Little’s Law: I=RTAverage occupancy = Throughput x Avg. Flow Time
Stuff in system = Rate stuff enters x How long it stays
I TR x=
T = I / R
If you know any two, you can calculate the third You choose what to manage and how Relationship between some important averages Can be applied to many different types of business
processes Put “Little’s Law” into Google and you’ll see the wide
variety of applications of this basic law of systems
R = I / T
Simple Applications of Little’s Law
Avg # Customers in Line = Customer arrival rate * Avg Time in line
Length of billing cycle = $ in Accounts Rcv / Avg Sales per Month
Avg Hospital Daily Census = Admission Rate * Avg Length of Stay
Avg # customers at web site = Hit Rate * Avg Time Spent at Site
Work in process = work input rate * Avg Processing Time
In class flow analysis (handout)
Patient Flow Model 01 one patient type, one unit, infinite capacity average arrival rate and length of stay given
Patient Flow Model 02 two patient types with different average length of
stay
Exercise 3.10 in MBPF A little Hotel Occupancy problem (we can
always learn from other industries)
Hospital X - Daily Census Report 1/ 14/ 2002
RMF/ RSF Occ In Out Lic. Beds Online Lic. Occ Online Occ.J1 23 3 5 31 31 74.2% 74.2%J2 25 7 7 30 30 83.3% 83.3%J3 14 1 3 15 14 93.3% 100.0%J4 29 7 6 30 30 96.7% 96.7%J6 29 5 8 34 34 85.3% 85.3%B1 6 1 1 8 8 75.0% 75.0%A1 24 3 5 32 32 75.0% 75.0%A2 28 5 7 34 34 82.4% 82.4%B4 24 5 4 30 30 80.0% 80.0%5S 30 4 8 40 40 75.0% 75.0%5N 25 5 7 30 28 83.3% 89.3%5E 31 8 8 33 33 93.9% 93.9%5W 31 4 8 34 32 91.2% 96.9%5C 29 3 8 30 30 96.7% 96.7%6N 32 5 7 34 34 94.1% 94.1%
Total 380 66 92 445 440 85.4% 86.4%
Step Down - ICU
SICU 35 7 7 40 38 87.5% 92.1%CICU 12 2 3 16 16 75.0% 75.0%MICU 9 1 1 12 12 75.0% 75.0%6S 6 1 1 8 8 75.0% 75.0%6C 14 2 3 16 16 87.5% 87.5%
Total 76 13 15 92 90 82.6% 84.4%
Maternal-Child
F1 22 4 6 34 34 64.7% 64.7%F2 14 2 3 26 26 53.8% 53.8%F3Nurs 14 1 3 20 20 70.0% 70.0%F4Nurs 2 0 0 4 4 50.0% 50.0%F5 9 2 1 16 16 56.3% 56.3%F6 10 1 2 19 19 52.6% 52.6%F7 5 0 1 8 8 62.5% 62.5%
Total 76 10 16 127 127 59.8% 59.8%
Grand Total 532 89 123 664 657 80.1% 81.0%
Typical daily census report
Monthly summary similar – may include comparison to previous month or same month last year
What does this show?
How created? What doesn’t this
show?
The numbers reported in the Free Press a few years
ago.
Little’s Law in action
Beyond Averages Little’s Law is about averages Average may be meaningless
Example: bimodal distribution from pooling long and short procedure times, extreme DOW volume swings
Upper percentiles 90% of calls answered in less than 1 minute 95% of the time we have <= 200 patients in house
Time of day and/or day of week (TOD/DOW) effects may be significant Seasonal effects may be significant Range
be careful with minimums and maximums Example from ED consulting report
Hands on – let’s create some histograms of real healthcare data We’ll do this with some real length of stay data momentarily
Hospital Census Data
Hospital XPostpartum Occupancy By Date
July 1996 - September 1996
0
5
10
15
20
25
30
35
40
45
50
7/1/
1996
7/5/
1996
7/9/
1996
7/13
/199
6
7/17
/199
6
7/21
/199
6
7/25
/199
6
7/29
/199
6
8/2/
1996
8/6/
1996
8/10
/199
6
8/14
/199
6
8/18
/199
6
8/22
/199
6
8/26
/199
6
8/30
/199
6
9/3/
1996
9/7/
1996
9/11
/199
6
9/15
/199
6
9/19
/199
6
9/23
/199
6
9/27
/199
6
Date
Ave
rag
e D
aily
Occ
up
ancy
Hard to tell if DOW effect present
Impossible to see TOD effect since data is daily
Seasonality? At time exceed
capacity? data quality? is capacity correct? census reflects
patient type
Enhanced Census Reporting Examples
Bed Allocation Committee Monthly Report Used @ monthly meeting of stakeholders to assess
occupancy issues Daily, weekly census, Overall & M-Thu summaries, 30-
60-90 day trends, unit group summaries, validity checks Obstetrical Occupancy Reports
Used as part of planning for OB expansion
Note: Data values and sources have been modified to preserve confidentiality.
Week 1 Week 2 Week 3Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo
# Beds 3-Nov 4-Nov 5-Nov 6-Nov 7-Nov 8-Nov 9-Nov 10-Nov 11-Nov 12-Nov 13-Nov 14-Nov 15-Nov 16-Nov
Hospital X 676 571 598 583 583 559 542 542 555 583 576 566 509 492 499
Group 1 - Medical 172 149 149 143 152 146 147 152 144 151 145 138 140 139 150Group 2 - Cardio-Thoracic 152 139 143 144 134 126 128 131 131 140 134 131 124 122 124Group 3 - Misc. Specialty 167 147 151 152 159 146 143 144 149 153 152 154 127 110 111Group 4 - Neuro 58 49 53 48 51 46 43 42 46 51 50 53 48 42 45Group 5 - Maternal/Child 127 87 102 96 87 95 81 73 85 88 95 90 70 79 69
Raw Data
Summary Data
Postpartum - Hospital XOccupancy Summary
Data based on bed history from July 1992 - September 1992.
Table 1. PP Occupancy Distribution Table 2. Average Occupancy by Day of Week# Beds Pct of Cumulative Avg # Avg Pct
Occupied Time Pct Admits Occ Occ29 or less 39.5% 39.5% Sun 12.8 28.8 66.9%
30 6.3% 45.8% Mon 14.6 26.8 62.3%31 6.8% 52.6% Tue 16.6 29.7 69.0%32 4.8% 57.4% Wed 19.0 32.4 75.5%33 5.3% 62.7% Thu 16.8 34.8 81.0%34 5.5% 68.2% Fri 14.7 35.0 81.3%35 4.7% 72.9% Sat 15.8 32.2 74.9%36 4.1% 77.0% Daily Avg 15.8 31.4 73.0%37 3.1% 80.1% Avg Length of Stay: 2.0 days38 2.1% 82.2%39 1.9% 84.1% Table 3. Discharges by time of day40 2.9% 87.0% Time % of Dis. Cumulative %41 2.1% 89.1% 12AM-8AM 0% 0%42 2.5% 91.6% 9:00 AM 2% 2%43 2.2% 93.8% 10:00 AM 12% 14%44 1.7% 95.4% 11:00 AM 32% 46%45 1.3% 96.8% 12:00 PM 28% 74%46 0.7% 97.4% 1:00 PM 8% 83%47 0.7% 98.1% 2:00 PM 4% 87%48 0.6% 98.7% 3:00 PM 3% 90%
49 or greater 1.3% 100.0% 4:00 PM 3% 92%5:00 PM 2% 94%6:00 PM 3% 97%7:00 PM 2% 98%8:00 PM 1% 99%
9PM-11PM 0% 100%
Total Postpartum Discharges
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
Sun
12
am
Sun
10
am
Sun
08
pm
Mon
06
am
Mon
04
pm
Tue
02
am
Tue
12
pm
Tue
10
pm
Wed
08
am
Wed
06
pm
Thu
04
am
Thu
02
pm
Fri
12
am
Fri
10
am
Fri
08
pm
Sat
06
am
Sat
04
pm
Nu
mb
er o
f P
ts
Average Maximum
8.4 % of the time occupancy was > 43(1-.916).
Total Postpartum Occupancy
0
5
10
15
20
25
30
35
40
45
50
55
60
Sun 12
am
Sun 06
am
Sun 12
pm
Sun 06
pm
Mon
12 am
Mon
06 am
Mon
12 pm
Mon
06 pm
Tue 12
am
Tue 06
am
Tue 12
pm
Tue 06
pm
Wed
12 am
Wed
06 am
Wed
12 pm
Wed
06 pm
Thu 12
am
Thu 06
am
Thu 12
pm
Thu 06
pm
Fri 12
am
Fri 06
am
Fri 12
pm
Fri 06
pm
Sat 12
am
Sat 06
am
Sat 12
pm
Sat 06
pm
Time of WeekN
um
ber
of
Occ
up
ied
Bed
s
Postpartum 95th %ile
Capacity=43
72% of pts on avg are discharged between 10am and 1pm
TOD/DOW Avg. and 95%ile
Occupancyfrequency
distribution
DOW
Discharge timing by hour of week
Discharge timing by
hour of day summary
Analysis of Time of Day Dependant Data
Many processes in healthcare have important TOD/DOW effects high variability and uncertainty in timing of arrivals and
length of stay (or duration of process) overall averages simply not that useful timing of arrivals, occupancy and discharges drives staffing
and capacity planning Examples: recovery & holding areas, emergency, IP OB,
walk-in clinics, call centers, short-stay units Applies to any units of flow such as tests, phone calls,
patients, nursing requirements
If Arrivals and LOS are Random Variables
LDR Length of Stay Distribution
0
50
100
150
200
250
300
350
400
LOS (Hours)
Patie
nts
.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
Number of Patients Cumulative %
Arrivals by Time of Day and Day of Week
0
1
2
3
4
5
6
Sun 12 am
Sun 06 am
Sun 12 p
m
Sun 06 p
m
Mon 1
2 am
Mon 0
6 am
Mon 1
2 pm
Mon 0
6 pm
Tue 12 am
Tue 06 am
Tue 12 p
m
Tue 06 p
m
Wed
12 am
Wed
06 am
Wed
12 p
m
Wed
06 p
m
Thu 12 am
Thu 06 am
Thu 12 p
m
Thu 06 p
m
Fri 12 am
Fri 06 am
Fri 12 p
m
Fri 06 p
m
Sat 12 am
Sat 06 am
Sat 12 p
m
Sat 06 p
m
Time of Week
Occ
upan
cy
Average
95th %ile
Then, occupancy is certainly a random variable that depends on TOD and DOW
LDR
0
2
4
6
8
10
12
14
16
18
20
22
Sun12am
Sun06am
Sun12pm
Sun06pm
Mon12am
Mon06am
Mon12pm
Mon06pm
Tue12am
Tue06am
Tue12pm
Tue06pm
Wed12am
Wed06am
Wed12pm
Wed06pm
Thu12am
Thu06am
Thu12pm
Thu06pm
Fri12am
Fri06am
Fri12pm
Fri06pm
Sat12am
Sat06am
Sat12pm
Sat06pm
Time of Week
Num
ber
of O
ccup
ied
Bed
s
Antepartum Other
Postpartum Recovery
SPs 95th %ile
Question: See p34 in IHI Guide. What exactly is Figure 3.1 showing?
Hillmaker – A Tool for Empirical Occupancy Analysis
Data has in/out date-timestamp admit/discharge, start/stop, enter/exit, etc. Example: entry and exit times from a surgical holding areas was available in surgical
scheduling system
Interested in arrival, discharge, occupancy statistics by time of day and day of week mean, min, max, and percentiles Time bins: ½ hr, hr, 2hr, 4hr, 6hr, 8hr Example: mean and 95%ile of occupancy with ½ hr time bins
Want statistics by some category or classification of interest as well as overall Example: category created was combination of location (which holding area) and phase
of care (preop, phase I, phase II)
Freely available from http://hillmaker.sourceforge.net/
Why Hillmaker needed? Many processes in healthcare have important TOD/DOW effects
high variability and uncertainty in timing of arrivals and length of stay overall averages simply not that useful timing of arrivals, occupancy and discharges drives capacity planning Examples: recovery & holding areas, emergency, IP OB, walk-in clinics, call
centers, short-stay units Applies to any units of flow such as tests, phone calls, patients, nursing
requirements, dollars, specimens, staff, etc. Provides important first step in applying stochastic patient flow
models such as simulation or queueing Estimation of arrival rate parameters
Standard hospital information systems usually are very weak in area of TOD/DOW metric reporting
Consider the traditional inpatient census report “Can you explain ‘percentile’ again to me?” said the manager.
Obsession with averages and uncomfortable with distributions Yes, I’m amazed that such tools aren’t standard fare in a healthcare
manager’s arsenal
What Hillmaker Does
Arrivals, discharges, occupancy by
DateTime-category
Hillmaker (Access)
Scenario data
(in/out/ category)
Arrivals, discharges, occupancy
summaries by TOD-DOW-category
GraphingTemplates
Preop/Post-op Space Planning - Option 1Preop B Simulated Occupancy
Preop for Area A and Phase 2 for Area C
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
12:00 A
M
1:30 A
M
3:00 A
M
4:30 A
M
6:00 A
M
7:30 A
M
9:00 A
M
10:30 A
M
12:00 P
M
1:30 P
M
3:00 P
M
4:30 P
M
6:00 P
M
7:30 P
M
9:00 P
M
10:30 P
M
Time of Week
Occ
upan
cy
Avg Phase 2
Avg Preop
95%ile +10% Growth
Total 95%ile
Simulated preop occupancy based on average preop time of 90 minutes. Though capacity exceeded by 95%ile under 10% growth scenario, results for Preop D suggest 90 minute preop time too long.
Capacity=9
In/Out Data
Hillmaker Interface
Data source inputs
Date/time related inputs
Algorithmic options
Output products
A portion of Excel graphing engine
Day of week graphs
Getting Hillmaker http://hillmaker.sourceforge.net/ Isken, M. W., Hillmaker: An open source
occupancy analysis tool. Clinical and Investigative Medicine, 28, 6 (2005) 342-43.
Ceglowski, R. (2006) Could a DSS do this? Analysis of coping with overcrowding in a hospital emergency department, Nosokinetics News (http://www2.wmin.ac.uk/coiec/Nosokinetics32.pdf), 3(2) 3-4.
Sources of Internal Workload DataMeasuring Flow Time & Rate
Departmental information systems lab, radiology, surgical scheduling, nursing, ED patient tracking,
patient transport Hospital information systems
Reg ADT, billing, appointment scheduling, finance Data warehouses and data marts
Management engineering, finance, planning, marketing Clinical data repositories
Log books, tally sheets, hard copy reports (yuck!) Will devote a session to “business intelligence” technology
data warehousing, OLAP, data mining Getting data out of information systems Tips for data collection
See p38 in IHI Guide I’ll show you some techniques for Excel based data collection tools
Patient Classification
What are our products and services? What types of workload drives demand?
classifying workload into a manageable number of different classes facilitates forecasting and capacity planning models that are robust to changes in workload mix
A myriad of classification schemes exist for both patient types, procedures, tests
We’ll look in detail at productivity monitoring schemes and nursing classification schemes when we discuss staffing in a few weeks
Guiding Principles for Classification Schemes
Similar bundle of goods and services in diagnosis and treatment of patients similar resource use intensity
Based on “readily available” data administrative data, clinical data
Manageable number of classes Similar clinical characteristics within a class
medically meaningful
Sampling of Patient Classification Systems
MDC, DRG – the basic for PPS CCS – Clinical Classification Software
AHRQ developed for health service research CSI, Disease Staging, MedisGroups, RDRG, APR-DRG,
SRDRG – severity based systems APG, APC – outpatient version of DRGs Service – a simple proxy often used internally (e.g. based on
attending physician, surgeon, etc.) Nursing Unit / Unit Type - another simple proxy
ignores effect of overflows
Why is classification hard? Not all diseases well understood Treatments for same disease differ Coding illnesses is difficult
some classes too narrow, some too broad Tradeoff between manageable number of classes and within
class homogeneity Severity matters Administrative easily available but other data in chart more
expensive to obtain Different classification schemes needed for different
purposes resource allocation, financial reimbursement, outcomes analysis
DRGs Originally intended as production definition for hospitals
(dev’d @ Yale by Fetter et al 70’s & early 80’s) To serve as basis for budgeting, cost control and quality
control Adopted by Medicare in 1983 for PPS Based on MDC (medical and surgical), ICD9-CM codes,
age, some comorbidities & complications Statistical clustering along with expert medical opinion See Fetter article in Interfaces for very nice description of
DRG development
Diagnosis Related Groups: Understanding Hospital Performance
Fetter, Robert B.. Interfaces. Linthicum: Jan/Feb 1991. Vol. 21, Iss. 1; p. 6 (21 pages)
Refinements to DRG’s
DRG’s questioned on ability to describe resource use Limited account of severity
Numerous severity based refinements to DRG’s proposed Computerized Severity Index Fetter et al developed Refined DRGs which better reflect severity and
resource use will be phased in by HCFA (now CMS)
Bottom line – no one perfect classification system for resource management
become familiar with many and use each as needed important to use SOMETHING as gross aggregate measures are not
extremely useful for detailed resource management
IHI: Reducing Delays and Waiting Times
1. IHI’s process improvement framework2. General guidance on delay reduction3. 27 Change concepts for delay reduction
1. Redesign the system2. Shaping the demand3. Matching capacity to demand
4. Four key examples1. Surgery2. Emergency Department3. Within clinics and physician’s offices4. Access to care