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
Process Management
Network of
Activities
Inputs
Outputs
A1
O
W1
P1
V1
W2
P2
M1
W3
M2
•patients, test results
•patients
•specimens
•bill, resolved complaint
•phone calls, charts
•complaints
•Uses resources (capital & labor)
•$$$
•Visit multiple locations
O
FSC - Process Sequence Chart
•nursing care, test processing, chart coding
•Value add and non-value (delays)
Information
Flow Units &Attributes
 Flow units – things that
flow through business
processes

Ex: patient, information,
cash, people, supplies, test
results, exams, paper
A1
A3
A3
 Attributes – characteristics
of flow units

Ex: patient type, acuity,
length of stay, admission
origin, discharge status
A2
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
An Urgent Care Clinic
Start/Enter
Start/ntr
Wait
Provider
Contact
Exam
Register
Wait
Collections
MCHC
Pharmacy
Complete HHQ
Diagnostic/
Intervention
Wait
Wait
Vitals/
Assessment
Provider
Contact/
Results
Wait
Wait
Discharge
Outside
Pharmacy
Wait
Leave
Patients visit a series of queueing
systems in series
Finish
iGrafx
Process
Basic Operational Flow Measures
Ch 3 of MBPF
Inputs
Processing
System
Outputs
R Flow Rate or throughput = average number of flow units (entities) that flow
through a certain point in a process per unit time
T Flow time = processing time + wait time (total time in the box)
I
Occupancy or Inventory = number of flow units within the
boundaries of some process
R units/time
I = units of inventory
T = avg flow 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
Ri2 = clinic walk-in patients per day
Process
(T=flow time
in clinic)
Ri1 = scheduled clinic
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
20 pats/hr
R1 = type 1
flow in
5 pats/hr
R2 = type 2
flow in
Type 1 Flow Time
10 mins
R1
Type 1&2
5 mins
Type 2 Flow Time
20 mins
R2
What is overall
average time in
dotted box?
R1+R2
Flow Time, Flow Rate, and Inventory Dynamics
Ri(t) = instantaneous inflow rate at time t
Ro(t) = instantaneous outflow rate at time t
DR(t) = instantaneous inventory (occupancy) build up rate at t
DR(t) = Ri(t) - Ro(t)
If Ri(t) > Ro(t)  get buildup at rate DR(t) > 0
If Ri(t) = Ro(t) get no change in occupancy
If Ri(t) < Ro(t)  get depletion at rate DR(t) < 0
Example: Constant DR during (t1,t2)
In other words, during the time period (t1,t2), occupancy is
being depleted or is building up at a constant rate DR.
Occupancy change = Buildup Rate x Length of Time Interval
O(t2)-O(t1) = DR(t2-t1)
Example: If system empty at t1, and DR=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
Avg # of people arriving
Length of time interval
8:40am
Inflow Rate Ri (per min)
Outflow Rate Ro (per min)
Buildup Rate DR (per min)
Ending Occupancy (# people)
8:40-9:10am
225
30
7.50
7.50
0.00
0
0
9:10-9:30am 9:30-9:43:20am
300
100
20
13.33
15
12
3
60
Passengers in Queue at Checkpoint
70
60
50
DR=3/min
40
DR=-4.5/min
30
20
DR=0/min
DR=0/min
10
0
8:40
8:50
9:00
9:10
9:20
9:30
9:40
9:50
10:00 10:10
Passengers
9:43:20-10:10am
200
26.67
7.5
12
-4.5
0
7.5
7.5
0
0
Time
8:40
8:50
9:00
9:10
9:20
9:30
9:40
9:50
10:00
10:10
Passengers
0
0
0
0
30
60
0
0
0
0
Occupancy & Inventory can be
averaged over time for stable processes
Passengers in Queue at Checkpoint
At 10:10 the inventory
will start to build again
for next flight.
70
60
50
40
Passengers
30
20
Inventory = 0 from 9:43-10:10
(27 mins)
10
0
8:40
8:50
9:00
9:10
9:20
9:30
9:40
9:50
10:00 10:10
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=RT
Average occupancy = Throughput x Avg. Flow Time
Stuff in system = Rate stuff enters x How long it stays
I
T
R
=
=
I
/
R
T
x
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
=
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
RMF/RSF
1/14/2002
Occ
In
Out
J1
J2
J3
J4
J6
B1
A1
A2
B4
5S
5N
5E
5W
5C
6N
23
25
14
29
29
6
24
28
24
30
25
31
31
29
32
3
7
1
7
5
1
3
5
5
4
5
8
4
3
5
5
7
3
6
8
1
5
7
4
8
7
8
8
8
7
Lic. Beds
31
30
15
30
34
8
32
34
30
40
30
33
34
30
34
Online
31
30
14
30
34
8
32
34
30
40
28
33
32
30
34
Total
380
66
92
445
Step Down - ICU
SICU
CICU
MICU
6S
6C
35
12
9
6
14
7
2
1
1
2
7
3
1
1
3
Total
76
13
Maternal-Child
F1
F2
F3Nurs
F4Nurs
F5
F6
F7
22
14
14
2
9
10
5
Total
Grand Total
440
Lic. Occ
74.2%
83.3%
93.3%
96.7%
85.3%
75.0%
75.0%
82.4%
80.0%
75.0%
83.3%
93.9%
91.2%
96.7%
94.1%
85.4%
Online Occ.
74.2%
83.3%
100.0%
96.7%
85.3%
75.0%
75.0%
82.4%
80.0%
75.0%
89.3%
93.9%
96.9%
96.7%
94.1%
86.4%
15
40
16
12
8
16
92
38
16
12
8
16
90
87.5%
75.0%
75.0%
75.0%
87.5%
82.6%
92.1%
75.0%
75.0%
75.0%
87.5%
84.4%
4
2
1
0
2
1
0
6
3
3
0
1
2
1
34
26
20
4
16
19
8
34
26
20
4
16
19
8
76
10
16
127
127
64.7%
53.8%
70.0%
50.0%
56.3%
52.6%
62.5%
59.8%
64.7%
53.8%
70.0%
50.0%
56.3%
52.6%
62.5%
59.8%
532
89
123
664
657
80.1%
81.0%
Little’s Law in action
 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.
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 X
Postpartum Occupancy By Date
July 1996 - September 1996
50
45
Average Daily Occupancy
40
35
30
25
20
15
10
5
 Hard to tell if DOW
effect present
 Impossible to see
TOD effect since
data is daily
 Seasonality?
 At time exceed
capacity?

0
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
9 9 9 9 9 9 99 99 99 99 99 9 9 9 9 99 99 99 99 99 99 9 9 9 9 99 99 99 99 99
/1 5/1 9/1 3/1 7/1 1/1 5/1 9/1 2/1 6/1 0/1 4/1 8/1 2/1 6/1 0/1 3/1 7/1 1/1 5/1 9/1 3/1 7/1
1
7/
7/
7/ 7/1 7/1 7/2 7/2 7/2
8/
8/ 8/1 8/1 8/1 8/2 8/2 8/3
9/
9/ 9/1 9/1 9/1 9/2 9/2
Date


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, 3060-90 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
Tu
We
Th
Fr
Sa
Su
Mo
# Beds
3-Nov
4-Nov
5-Nov
6-Nov
7-Nov
8-Nov
9-Nov
Hospital X
676
571
598
583
583
559
542
542
555
583
576
566
509
492
499
Group 1 - Medical
Group 2 - Cardio-Thoracic
Group 3 - Misc. Specialty
Group 4 - Neuro
Group 5 - Maternal/Child
172
152
167
58
127
149
139
147
49
87
149
143
151
53
102
143
144
152
48
96
152
134
159
51
87
146
126
146
46
95
147
128
143
43
81
152
131
144
42
73
144
131
149
46
85
151
140
153
51
88
145
134
152
50
95
138
131
154
53
90
140
124
127
48
70
139
122
110
42
79
150
124
111
45
69
Summary Data
Tu
We
Th
Fr
Week 3
Sa
Su
Mo
10-Nov 11-Nov 12-Nov 13-Nov 14-Nov 15-Nov 16-Nov
Raw Data
Postpartum - Hospital X
Occupancy Summary
Data based on bed history from July 1992 - September 1992.
Capacity=43
Total Postpartum Discharges
Total Postpartum Occupancy
72% of pts on avg are discharged between 10am and 1pm
60
14.0
50
40
35
30
25
20
15
10
10.0
8.0
6.0
4.0
2.0
95th %ile
Table 1. PP Occupancy Distribution
# Beds
Pct of Cumulative
Occupied
Time
Pct
29 or less
39.5%
39.5%
30
6.3%
45.8%
31
6.8%
52.6%
32
4.8%
57.4%
33
5.3%
62.7%
34
5.5%
68.2%
35
4.7%
72.9%
36
4.1%
77.0%
37
3.1%
80.1%
38
2.1%
82.2%
39
1.9%
84.1%
40
2.9%
87.0%
41
2.1%
89.1%
42
2.5%
91.6%
43
2.2%
93.8%
44
1.7%
95.4%
45
1.3%
96.8%
46
0.7%
97.4%
47
0.7%
98.1%
48
0.6%
98.7%
49 or greater
1.3%
100.0%
Average
Sat 06 am
Sat 04 pm
Fri 10 am
Fri 08 pm
Fri 12 am
Thu 04 am
Thu 02 pm
Wed 06 pm
Tue 10 pm
Wed 08 am
Tue 02 am
Tue 12 pm
Mon 06 am
Mon 04 pm
Sun 10 am
Su
S un 1
S u n 02 a m
S un 1 6 a m
M n 02 p
m
Mon 16 p
m
M on 0 2 a
m
Mon 1 6 a
on 2 m
Tu 0 pm
6
Tue 1 pm
2
Tu e 0 am
6
T e 1 am
Wue 02 p
m
Wed 16 p
W ed 02 am
m
Wed 1 6 a
ed 2 m
Th 0 pm
Thu 16 pm
Th u 02 am
Thu 16 am
u 2
F r 06 pm
Fri 12 pm
F r i 06 a m
Fri 12 am
Sai 06 pm
S a t 12 p m
S a t 06 a m
S a t 12 a m
t0 p
6 m
pm
Time of Week
Postpartum
Sun 12 am
0.0
Sun 08 pm
5
0
Occupancy
frequency
distribution
Discharge
timing by
hour of week
12.0
45
Number of Pts
TOD/DOW
Avg. and
95%ile
Number of Occupied Beds
55
Maximum
Table 2. Average Occupancy by Day of Week
Avg #
Avg
Pct
Admits
Occ
Occ
66.9%
12.8
28.8
Sun
62.3%
14.6
26.8
Mon
69.0%
16.6
29.7
Tue
75.5%
19.0
32.4
Wed
81.0%
16.8
34.8
Thu
81.3%
14.7
35.0
Fri
74.9%
15.8
32.2
Sat
Daily Avg
15.8
31.4
73.0%
Avg Length of Stay:
2.0
days
Table 3. Discharges by time of day
Time
% of Dis.
12AM-8AM
0%
8.4 % of the
9:00 AM
2%
time
10:00 AM
12%
occupancy
11:00 AM
32%
was > 43
12:00 PM
28%
(1-.916).
1:00 PM
8%
2:00 PM
4%
3:00 PM
3%
4:00 PM
3%
5:00 PM
2%
6:00 PM
3%
7:00 PM
2%
8:00 PM
1%
9PM-11PM
0%
Cumulative %
0%
2%
14%
46%
74%
83%
87%
90%
92%
94%
97%
98%
99%
100%
DOW
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
1
Time of We e k
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
10
.5
11
.5
12
.5
13
.5
14
.5
15
.5
16
.5
17
.5
18
.5
19
.5
20
.5
21
.5
22
.5
23
.5
24
.
Mo 5
re
0
Patients
Su
n
Su 12 a
n m
Su 06 a
n m
1
Su 2 p
n m
M 06
on pm
M 12
on am
M 06
on am
M 12
on p m
0
Tu 6 p
e m
1
Tu 2 a
e m
Tu 06 a
e m
Tu 12 p
e m
W 06 p
ed m
W 12
ed am
W 06
ed am
W 12
ed p m
Th 06 p
u m
1
Th 2 a
u m
Th 0 6
u am
1
Th 2 p
u m
06
Fr p m
i1
Fr 2 am
i0
Fr 6 a
i1 m
Fr 2 p m
i0
Sa 6 p m
t1
Sa 2 a
t0 m
Sa 6 am
t1
Sa 2 p
t0 m
6
pm
Occupancy
If Arrivals and LOS are
Random Variables
Arrivals by Time of Day and Day of Week
6
5
4
Average
3
95th %ile
2
LDR Length of Stay Distribution
400
120.00%
350
300
100.00%
250
80.00%
200
150
60.00%
100
40.00%
50
20.00%
0
.00%
LOS (Hours)
Number of Patients
Cumulative %
Then, occupancy is certainly a random
variable that depends on TOD and DOW
LDR
22
20
18
Number of Occupied Beds
16
14
12
10
8
6
4
2
0
Sun
12
am
Sun
06
am
Sun
12
pm
Sun M o n M o n M o n M o n Tue
06
12
06
12
06
12
p m am am p m p m am
Tue
06
am
Tue
12
pm
Tue Wed Wed Wed Wed Thu
06
12
06
12
06
12
p m am am p m p m 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 We e k
Antepartum
Postpartum
Other
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
Graphing
Templates
Arrivals, discharges,
occupancy
summaries by TODDOW-category
Preop/Post-op Space Planning - Option 1
Preop B Simulated Occupancy
Preop for Area A and Phase 2 for Area C
Avg Phase 2
Avg Preop
95%ile +10% Growth
14
T otal 95%ile
13
12
11
10
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
9
Occupancy
8
7
6
5
4
3
2
1
M
M
M
A
A
M
10
:3
0
A
M
12
:0
0
PM
1:
30
PM
3:
00
PM
4:
30
PM
6:
00
PM
7:
30
PM
9:
00
PM
10
:3
0
PM
9:
00
7:
30
M
A
A
A
6:
00
3:
00
4:
30
M
A
A
M
0
1:
30
Arrivals, discharges,
occupancy by
DateTime-category
Hillmaker
(Access)
12
:0
0
Scenario
data
(in/out/
category)
Time of We e k
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 Data
Measuring 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 framework
2. General guidance on delay reduction
3. 27 Change concepts for delay reduction
1.
2.
3.
Redesign the system
Shaping the demand
Matching capacity to demand
4. Four key examples
1.
2.
3.
4.
Surgery
Emergency Department
Within clinics and physician’s offices
Access to care