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