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 W3 V1 W2 P2 M1 M2 •patients •specimens •phone calls, charts •complaints •$$$

FSC - Process Sequence Chart

O •patients, test results •bill, resolved complaint •Uses resources (capital & labor) •Visit multiple locations •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  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

An Urgent Care Clinic

Vitals/ Assessment Register Complete HHQ Wait Wait Provider Contact Exam Wait Diagnostic/ Intervention Wait Provider Contact/ Results Wait Collections MCHC Pharmacy Wait Outside Pharmacy Leave Discharge Finish Patients visit a series of queueing systems in series

iGrafx Process

Basic Operational Flow Measures Ch 3 of MBPF

R Inputs Processing System Outputs

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  Out > In  Occupancy, queue or inventory grows Occupancy, queue or inventory shrinks Long term

stable process

 Flow In = Flow Out Can combine and split flows R i1 = scheduled clinic patients per day R i2 = clinic walk-in patients per day Process (T=flow time in clinic) R o = total flow of patients out of clinic per day R o = R i1 + R i2

  

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 R 1

20 pats/hr

= type 1 flow in Type 1 Flow Time 10 mins R 1 What is overall average time in dotted box?

R 1 +R 2 Type 1&2 5 mins

5 pats/hr

R 2 = type 2 flow in Type 2 Flow Time 20 mins R 2

Flow Time, Flow Rate, and Inventory Dynamics

R i (t) = instantaneous inflow rate at time t R o (t) = instantaneous outflow rate at time t D R(t) = instantaneous inventory (occupancy) build up rate at t D

R(t) = R

i

(t) - R

o

(t) If R

i

(t) > R

o

(t)



get buildup at rate

D

R(t) > 0 If R

i

(t) = R

o

(t)



get no change in occupancy If R

i

(t) < R

o

(t)



get depletion at rate

D

R(t) < 0

Example: Constant

D

R during (t

1

,t

2

)

In other words, during the time period (t 1 ,t 2 ), occupancy is being depleted or is building up at a constant rate D R. Occupancy change = Buildup Rate x Length of Time Interval

O(t

2

)-O(t

1

) =

D

R(t

2

-t

1

)

Example: If system empty at t 1 , and D 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

Avg # of people arriving Length of time interval

8:40am 8:40-9:10am

225 30

9:10-9:30am 9:30-9:43:20am

300 20 100 13.33

9:43:20-10:10am

200 26.67

Inflow Rate Ri (per min) Outflow Rate Ro (per min) Buildup Rate D R (per min) Ending Occupancy (# people) 0 7.50

7.50

0.00

0

Passengers in Queue at Checkpoint

30 20 10 0 70 60 50 40 D D

R=0/min R=3/min

D

R=-4.5/min

D

R=0/min

8:40 8:50 9:00 9:10 9:20 9:30 9:40 9:50 10:00 10:10 15 12 3 60 Passengers 7.5

12 -4.5

0

Time

8:40 8:50 9:00 9:10 9:20 9:30 9:40 9:50 10:00 10:10 7.5

7.5

0 0

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

70 60 50 40 30 20 10 0 At 10:10 the inventory will start to build again for next flight.

Passengers 8:40 8:50 9:00 9:10 9:20 9:30 9:40 Inventory = 0 from 9:43-10:10 (27 mins) 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 = R

x

     T = I

/

R 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 = T 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

J1 J2 J3 J4 J6 B1 A1 A2 B4 5S 5N 5E 5W 5C 6N Total Occ 23 25 14 29 29 6 24 28 24 30 25 31 31 29 32 380 In 3 8 4 3 5 5 4 5 7 1 7 5 1 3 5 66 Out 5 8 8 8 7 4 8 7 7 3 6 8 1 5 7 92 Lic. Beds 31 30 15 30 34 8 32 34 30 40 30 33 34 30 34 445 34 30 40 28 33 32 30 34 Online 31 30 14 30 34 8 32 440 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%

Step Down - ICU

SICU CICU MICU 6S 6C Total 35 12 9 6 14 76

Maternal-Child

F1 F2 F3Nurs F4Nurs F5 F6 F7 Total

Grand Total

22 14 14 2 9 10 5 76 532 0 2 1 0 4 2 1 10 89 7 2 1 1 2 13 7 3 1 1 3 15 0 1 2 1 6 3 3 16 123 40 16 12 8 16 92 34 26 20 4 16 19 8 127 664 34 26 20 4 16 19 8 127 657 38 16 12 8 16 90 87.5% 75.0% 75.0% 75.0% 87.5% 82.6% 64.7% 53.8% 70.0% 50.0% 56.3% 52.6% 62.5% 59.8% 80.1%

1/14/2002

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% 92.1% 75.0% 75.0% 75.0% 87.5% 84.4% 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?

64.7% 53.8% 70.0% 50.0% 56.3% 52.6% 62.5% 59.8% 81.0% 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 40 35 30 25 20 15 10 5 0 7/ 1/ 19 96 7/ 5/ 19 96 7/ 9/ 19 7/ 96 13 /1 99 7/ 6 17 /1 99 7/ 6 21 /1 99 7/ 6 25 /1 99 7/ 6 29 /1 99 6 8/ 2/ 19 96 8/ 6/ 19 96 8/ 10 /1 99 8/ 6 14 /1 99 8/ 6 18 /1 99 8/ 6 22 /1 99 8/ 6 26 /1 99 8/ 6 30 /1 99 6 9/ 3/ 19 96 9/ 7/ 19 9/ 96 11 /1 99 9/ 6 15 /1 99 9/ 6 19 /1 99 9/ 6 23 /1 99 9/ 6 27 /1 99 6

Date

    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.

Hospital X Group 1 - Medical Group 2 - Cardio-Thoracic Group 3 - Misc. Specialty Group 4 - Neuro Group 5 - Maternal/Child # Beds

676

Tu 3-Nov

571

We 4-Nov

598

Th 5-Nov

583

Week 1 Fr 6-Nov

583

Sa 7-Nov

559

Su 8-Nov

542

Mo 9-Nov

542

Tu We Th Week 2 Fr Sa Su Mo 10-Nov 11-Nov 12-Nov 13-Nov 14-Nov 15-Nov 16-Nov

555 583 576 566 509 492 499 172 152 167 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

Week 3

Raw Data Summary Data

TOD/DOW Avg. and 95%ile Occupancy frequency distribution

Postpartum - Hospital X Occupancy Summary Data based on bed history from July 1992 - September 1992.

Capacity=43

Total Postpartum Occupancy Total Postpartum Discharges

72% of pts on avg are discharged between 10am and 1pm 60 55 50 45 40 35 30 25 20 15 10 5 0 14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

Su n 12 Su am n 06 Su am 12 Su pm 06 M on pm 12 M on am M 06 on am M 12 on pm 06 Tue pm 12 Tue am 06 Tue am 12 Tue pm 06 ed pm W 12 ed am 06 W ed am W 12 ed pm 06 pm Thu 12 am 06 Thu am 12 pm Thu 06 Fr pm i 12 Fr am i 06 Fr am i 12 Fr pm i 06 Sa t 1 2 Sa am t 0 Sa 6 am t 1 2 Sa pm t 0 pm

Time of Week

Postpartum 95th %ile Average Maximum

Table 1. PP Occupancy Distribution # Beds Occupied Pct of Time Cumulative Pct

29 or less 30 31 32 33 34 35 36 37 38 39 40 39.5% 6.3% 6.8% 4.8% 5.3% 5.5% 4.7% 4.1% 3.1% 2.1% 1.9% 2.9% 39.5% 45.8% 52.6% 57.4% 62.7% 68.2% 72.9% 77.0% 80.1% 82.2% 84.1% 87.0% 41 42

43

44 45 46 47 48 49 or greater 2.1% 2.5%

2.2%

1.7% 1.3% 0.7% 0.7% 0.6% 1.3% 89.1% 91.6%

93.8%

95.4% 96.8% 97.4% 98.1% 98.7% 100.0%

Table 2. Average Occupancy by Day of Week Sun Mon Tue Wed Thu Fri Sat Daily Avg Avg # Admits

12.8

14.6

16.6

19.0

16.8

14.7

15.8

15.8

Avg Length of Stay: Avg Occ

28.8

26.8

29.7

32.4

34.8

35.0

32.2

31.4

2.0

Table 3. Discharges by time of day

8.4 % of the time occupancy was > 43 (1-.916).

Time 12AM-8AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9PM-11PM % of Dis.

0% 2% 12% 32% 28% 8% 4% 3% 3% 2% 3% 2% 1% 0% days

Pct Occ

66.9% 62.3% 69.0% 75.5% 81.0% 81.3% 74.9% 73.0%

Cumulative %

0% 2% 14% 46% 74% 83% 87% 90% 92% 94% 97% 98% 99% 100% Discharge timing by hour of week 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

If Arrivals and LOS are Random Variables

Arrivals by Time of Day and Day of Week

6 5 4 3 2 1 0 Su n 12 a Su m n 06 a Su m n 12 p Su m n 06 M p on m 1 2 M am on 0 6 M am on 1 2 M pm on 0 6 pm Tu e 12 am Tu e 06 am Tu e 12 p Tu m e 06 p W m ed 1 2 W am ed 0 6 W am ed 1 2 W pm ed 0 6 pm Th u 12 a Th m u 06 a Th m u 12 p Th m u 06 p Fr m i 1 2 am Fr i 0 6 am Fr i 1 2 pm Fr i 0 6 pm Sa t 1 2 am Sa t 0 6 am Sa t 1 2 pm Sa t 0 6 pm

Time of W e e k

Average 95th %ile

LDR Length of Stay Distribution

400 350 300 250 120.00% 100.00% 80.00% 200 150 60.00% 40.00% 100 50 20.00% 0 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 .5

M or e

LOS (Hours)

.00% Number of Patients Cumulative %

Then,

occupancy

is certainly a random variable that depends on TOD and DOW

LDR

22 20 18 16 14 12 10 8 6 4 2 0 Sun 12 am Sun 0 6 am Sun 12 p m Sun 0 6 p m M o n 12 am M o n 0 6 am M o n 12 p m M o n 0 6 p m Tue 12 am Tue 0 6 am Tue 12 p m Tue 0 6 p m Wed 12 am Wed 0 6 am Wed 12 p m Wed 0 6 p m Thu 12 am Thu 0 6 am Thu 12 p m Thu 0 6 p m

Time of W e e k

Fri 12 am Fri 0 6 am Antepartum Postpartum SPs Other Recovery 95th %ile Fri 12 p m Fri 0 6 p m Sat 12 am Sat 0 6 am Sat 12 p m Sat 0 6 p m 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

Scenario data (in/out/ category) Hillmaker (Access) Arrivals, discharges, occupancy by DateTime-category Arrivals, discharges, occupancy summaries by TOD DOW-category Graphing Templates

Preop/Post-op Space Planning - Option 1 Preop B Simulated Occupancy Preop for Area A and Phase 2 for Area C

11 10 9 8 14 13 12 Capacity=9 7 4 3 6 5 2 1 0 12 :0 0 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 :3 10 0 A M 12 :0 PM 1: 30 P M 3:

Time of W e e k

00 P M 4: 30 P M 6: 00 P M 7: 30 P 9: 00 P M 10 :3 0 PM Avg Phase 2 Avg Preop 95%ile +10% Growth T otal 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.

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.

Redesign the system 2.

3.

Shaping the demand Matching capacity to demand 4. Four key examples 1.

Surgery 2.

3.

4.

Emergency Department Within clinics and physician’s offices Access to care