Faculty Advisor: Prof. Greg DobsonInstitutional Liaison: Susan Powell

Thursday, December 5, 2013

Table of Contents

1. Executive Summary 22. Key Definitions and Abbreviations 33. Objective 5

1

Recommendations to Improve New Patient Visit Wait Times for the NeuroMedicine Pain Management Program

Project Team 1:Addie Bardin

Christopher GallatiRaquel Martinez-Calleri

Melitta MendoncaHolly Smock

4. Background 55. Project Scope 56. Market Assessment 77. Primary Data Source 118. New Patient Visits (NPVs) 11

a. Wait Times 12b. Demand 13c. Capacity 15d. Demand vs. Capacity 18e. Queuing Model 19

9. Follow Up Appointments (FUAs) 21a. Utilization of FUAs 21b. Length of Time Spent in Practice (LOS) 24

10. Relationship of FUAs and NPVs 2711. Financial Analysis 29

a. Revenue 29b. Expenses 32c. Profit and Loss Statements 34

12. Recommendations 3413. Appendices Attached

a. Current Queue Modelb. Forecasted Queue Model for adding an MD performing proceduresc. Forecasted Queue Model for NP, PA or MD Non-Proceduralistd. Reimbursement Rates by CPT and Payer Mixe. 2013 MD Performing Procedures Reimbursement by CPTf. 2013 MD Non-Proceduralist Reimbursement by CPTg. 2013 APP Reimbursement by CPTh. Profit and Loss Statement – MD Performing Proceduresi. Profit and Loss Statement – MD Non-Proceduralistj. Profit and Loss Statement – NPk. Profit and Loss Statement – PAl. Procedures Referred by Additional APPm. Procedures Referred by Additional MD Non-Proceduralist

1. Executive Summary

The University of Rochester Medical Center’s (URMC) NeuroMedicine Pain Management Program

(NMPMP) must reduce new patient wait times, 80% of new patient visits (NPV) scheduled within

2

14 days of initial request, to meet the institutional standard. The NMPMP is a healthcare clinic

within the Department of Neurosurgery that provides comprehensive pain care. NMPMP is

currently experiencing wait times of 30 days. They suspect that demand exceeds their capacity and

are considering hiring an additional provider in order to meet this wait time standard.

The group assessed the regional market to determine geographic location, services offered, and

wait times. We analyzed the clinic scheduling and billing data to determine capacity and utilization.

The group forecasted the financial impact of adding a provider to the clinic.

The regional market assessment revealed 40 competing pain providers in Monroe County and the

15 surrounding counties. The services offered varied by clinic with few offering the same

comprehensive care as the NMPMP. Only seven out of forty competing clinics had a wait time of 14

days or less.

Analyzing the scheduling data, NMPMP had 51% of NPVs with wait times of ≤14 days, which is

significantly below the desired 80% URMFG standard. There are 40 NPV requests per week and

only 29.9 NPVs seen. A queuing model confirmed that demand exceeds current capacity and

showed which provider type yielded the greatest decrease in NPV wait times.

Billing data shows the length of stay for patients and how much clinic time they use is not the

driving factor for new patient wait times. NMPMP is currently not filling their follow-up

appointment (FUA) capacity utilizing the current 20% NPV / 80% FUA scheduling model.

Financial projections of profit and loss statements were generated for a Nurse Practitioner, a

Physician Assistant, MD non-proceduralist, and MD performing procedures. We determined that

the best option to support their mission, decrease NPV wait times, while still being financially

viable is to add capacity by hiring a Nurse Practitioner.

Additional recommendations include: block scheduling, not allowing new patients to request a

specific provider, multiple provider / single queue model, charging a cancellation fee, adjusting the

20% NPV / 80%FUA ratio for appointments, recording actual time spent with patients, tracking

patients who choose not to accept an appointment due to wait time, recording when patients are

discharged from the practice.

2. Key definitions and abbreviations:

APP Advanced Practice Provider – includes NP and PA

3

Arrival rate Rate of patients calling and booking a NPV appointment

Capacity Amount of resource available

CPT code Current Procedural Terminology codes used to report medical procedures and services under public and private health insurance programs

Data Set Data from January 1, 2011 through June 30, 2013

FUA Follow Up Appointment

I STOP – legislation Legislation passed by NYS requiring the provider to record any controlled substance prescriptions on a centralized database by patient

Length of stay (LOS) Time between patient’s first and last appointment

Market Monroe County and the 15 surrounding counties

MD Non-Proceduralist MD who is a pain specialist but does not perform interventional procedures, typically a neurologist or anesthesiologist

MD performing procedures MD who is a pain specialist and performs interventional procedures, typically a neurologist or anesthesiologist

NCQA An independent, non-profit organization that certifies physician organizations, and accredits managed care organizations and preferred provider organizations

NMPMP NeuroMedicine Pain Management Program

NMPMP mission “The URMC NeuroMedicine Pain Management Center was established with the goal to provide the most comprehensive and optimal care in the region by bringing interventional, medical, rehabilitative and psychological approaches to pain management under one roof.” From URMC Website

NP Nurse Practitioner-midlevel provider limited in NPV, 85% reimbursement rate

NPV New Patient Visit

NPV queue Number of patients who have requested an NPV but have not had their first visit

NPV wait times Difference between the date of first requesting a NPV and the date of the appointment booked

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PA Physician Assistant-midlevel provider limited in NPV, 85% reimbursement rate, requires close monitoring by MD

Procedures Interventional treatments for pain

Requests Phone calls received by office from new patients seeking a new patient visit

Scheduled Patients booked by office staff for an appointment with a specific date, time and duration

Seen Patients arriving for their scheduled appointment

URMC University of Rochester Medical Center

URMFG University of Rochester Medical Faculty Group

Utilization Actual use of an available resource

3. Objective

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Reduce new patient wait times, the difference between the date of first requesting a NPV and the

date of the appointment booked, in the URMC NMPMP and determine the effect of adding an

additional provider on financial performance and wait times of new patients.

4. Background

The URMC NMPMP is a healthcare clinic within the Department of Neurosurgery that provides

comprehensive pain care. The services include interventional, medical, rehabilitative and

psychological approaches to pain management. NMPMP provides a unique and comprehensive

approach to pain management that is superior to its competitors. NMPMP was named a 2013

Clinical Center of Excellence for pain management by the American Pain Society, one of only two

centers nationwide. NMPMP was founded in 2008 and has grown substantially. As the clinic has

grown it has not kept up with demand. The University of Rochester Medical Faculty Group

(URMFG) has set standards for wait times for new patients. This group is responsible for

credentialing physicians, negotiating payment rates with third-party payers and is certified by the

National Committee for Quality Assurance (NCQA). URMFG has adopted the NCQA’s standard wait

time for new patients: 80% of NPV scheduled within 14 days of initial request. The NMPMP has

recently experiencing wait times of 20.1 days and is currently quoting 30 days. The NMPMP is

seeking to reduce its wait time to meet this standard. It is assumed that the NMPMP is rapidly

growing and experiencing high demand, they suspect that demand exceeds their capacity and are

considering hiring an additional provider in order to meet this wait time standard.

5. Project Scope

In order to analyze the NPV wait time problem we assessed the operations and productivity of the

clinic, the regional market, and value-added of a new provider. We began by assessing the market to

determine who the competitors are, their locations, services and associated wait times. We then

obtained scheduling and billing data from the NMPMP. Next, we attempted to analyze the

operational efficiency and productivity of the NMPMP. Because there is no standard wait time for

follow-up appointments (FUAs), we reduced this focus to only include the NPV queue. Additionally,

as the clinic visits are scheduled separately from procedures we have assumed procedures have no

direct impact on the NPV queue. Our analysis is limited to the scheduling model and does not

include the flow of patients through the clinic. We will discuss the ratios and relationships of NPVs

to FUAs and procedures. I-STOP legislation was anticipated to increase the demand for NPVs and

6

LOS. However, I-STOP has only been in effect for one month at the time of this analysis. Finally, we

will determine the financial viability of adding a provider and their impact upon NPV wait times.

6. Market Assessment

Patients choose a physician each time they seek medical care. There are many contributing factors

to how the patients make this choice. Marketing, referrals, word of mouth, reputation of provider,

insurance coverage, services offered, appointment availability, and geographic location all play

significant roles in this decision process. A market assessment will show who the competition is,

where they are located, the services they offer and the wait times for a NPV.

The new patients choosing the NMPMP are primarily referred by their PCP, so for the patient, the

significance of direct patient marketing, word of mouth and reputation of provider are diminished.

The marketing efforts of the department are focused on the PCP; however, the URMC employs a

marketing initiative “medicine of the highest order” which helps build the reputation of the clinic by

its association with the medical center.

The clinic accepts all insurances so local and regional patients are covered by in-network co-pay

and co-insurance rates. It is important to note that the implementation of the Affordable Care Act

and the establishment of Accountable Care Networks may change the in-network availability of the

clinic in the near future.

It is believed that if the wait times for NPVs are too long, the patient will be referred to a competing

practice. Patients will also consider the services offered and how far they are willing to travel. The

focus of the market analysis, therefore, will be on services offered, appointment availability, and

geographic location.

The region has been defined as Monroe County and the 15 surrounding counties, as determined by

the URMC’s director’s office. A search of this region has shown that there are 40 other pain

treatment centers or physician offices the patient, by PCP referral, can choose. The search was

confined to practitioners that offer medical management and at least one additional qualifying

service to be considered a competitor. The criteria eliminated the holistic practitioners,

chiropractors, acupuncturists, physical therapists, massage therapists and other non-traditional

practitioners from the comparison as they were not considered direct competition but substitutes

to the services offered by NMPMP. A list of clinics and providers which met the search criteria were

selected from web pages, marketing material publically available, and the list provided by the

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NMPMP administration. For those practices without web pages or available marketing material, a

phone survey of the competitors was conducted regarding their available services.

The highest concentration of competing clinics is in the Rochester area with the second highest in

the Buffalo area. These findings are consistent with the region surrounding these two metropolitan

areas being highly rural with less dense populations. Competitors and their services offered, and

wait times for NPVs are shown in the following chart (Chart 1):

Chart 1

Cen

ter

Med

ical

Man

agem

ent

Epi

dura

l

Ner

ve B

lock

Spi

nal D

ecom

pres

sion

- S

urgi

cal

Spi

nal C

ord

Stim

ulat

or im

plan

t

Phy

sica

l The

rapy

Acu

punc

ture

Cou

nsel

ing

- Psy

chol

ogis

t

Mas

sage

Chi

ropr

actic

Car

e

Hyp

noth

erap

y

Life

styl

e / N

utrit

ion

Cou

nsel

ing

Bio

feed

back

Wai

t Tim

e in

Day

s

Interventional Pain Mgmt x x x x x x *Finger Lakes Pain Mgmt x x 14AMS Pain Management x x x x x x 37Upstate Pain Clinic x x x x x *

Highland Pain Mgmt Center x x x x x x x x 21

URMC Spine Center x x x x x x 83URMC Pain Treatment Cntr x x x x x x x x x x 21Rochester Brain and Spine x x x x x x x x x x 7Genesee Valley Pain CenterNeuromedicine Pain Mgmt Ctr x x x x x 30

Pain and Symptom Mgmt Ctr x x x r r r r 70Private Practice x 7Center for Pain Mgmt x x x r r r r Maxwell Boev Clinic x x x x x 28Unity Spine Center x x x x x xPain Interventions x x x x x 21Rochester Pain Management x x x x 7Unity Spine Center x x x x x xFinger lakes Spine Center x x x x x 21Pain Treatment Medicine x x x x x x x 21

Schuyler Pain Management x x *Guthrie Interventional Pain Mgmt x x x 34Dansville Anestesia and Pain Cntr x x x x x *Unity Spine Center x x x x x x *Jones Memorial Hospital Pain Mgmt Center x x x x *Chautauqua Pain Medicine x x x x x 10Olean General Hospital x x x x 60Omni Pain & Wellness Centers LLC x x x x x x x 60Erie County Medical Center x x x x x x x x x x *Pain Rehab Center of Western New York x x x x x x 180Gosy and Associates Pain and Neurology Center x x x x x 60H. Koritz Pain Management x x x x 90United Memorial Pain Center x x x x x 30Private Practice x x 1Advanced Pain & Wellness Institute x x x x x x x x x * Pain Management and Headache x x * Mount St. Mary's Hospital- Pain Management x x *Spine and Sports Medicine x x x 2Mount St. Mary's Hospital *Buffalo General Medical Center- Pain Mgmt Centerx x x *

Legend: X= service offered, R=service by referral, *=not available, blank = no service

8

Wait times were collected in October, utilizing a mystery shopper method. The mystery

shopper presented as a woman with lower back pain lasting 8 weeks, having Excellus

insurance, seeking an appointment with the first available physician in the practice. The

caller asked if a referral from their PCP was required and the date of the first appointment

available. The wait times between the initial call for an appointment and the date of the

actual appointment are plotted in the graph below (Graph 1).

Graph 1

14 Days o

r Less

15-30 Days

Greater

Than

30 Days

Sched

uling D

ifficu

lties

Clinic D

iscontinued

0

2

4

6

8

107

89 9

7

NPV Wait Times from Initial Call to Actual Visit

Clinic Wait times

Num

ber o

f Clin

ics

Only 7 of the clinics surveyed report a wait time of 14 days or less, they have been

categorized ‘blue’ in the comparison chart (Chart 1). Clinics not meeting the URMFG

standard of ≤14 days are categorized ‘red’ in the comparison chart. This category includes

eight clinics, including the NMPMP, between 15 and 30 days and nine clinics greater than

30 days with the highest being 180 days.

There are 9 clinics which are categorized as having scheduling difficulties and they have

been categorized ‘orange’ in the comparison chart. Four of these clinics operate without

telephone or reception staff. The clinics required the patient to leave a message on their

voice mail with a promise of a return call for scheduling the appointment. The remaining 6

clinics had rigorous pre-screening making it difficult to enter their practice. These

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screenings ranged from a telephone assessment, which the doctor would review and then

return the call if the patient was a candidate for their clinic, to full medical history including

MRI, CT, X-Ray and written assessment by the PCP, before an appointment could be

scheduled.

There are 7 remaining clinics in the comparison chart were discontinued and categorized

‘black’. These clinics are either out of business or have been consolidated with another

practice. One of these clinics reported that their doctor left the practice greater than six

months prior, and they are having difficulty finding a pain specialist to take his place.

A map has been constructed showing the geographic location of the competing providers.

The pins are color coded, following the coding pattern above, based on the reported wait

times.

The map shows that the clinics with shorter wait times are all in metropolitan areas with

the rural areas requiring longer waits. It is reasonable to assume that patients living in

rural areas would be willing to travel to the metropolitan areas to be seen more quickly.

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Due to data limitations, we cannot determine how many patients are refusing an

appointment due to extended wait times. This data is not currently collected in the

NMPMP; however, it is the suggestion of this team that the clinic consider the importance

of this data in estimating demand, and in their considerations to hire an additional

provider. The NMPMP assumes their marketing personnel can generate sufficient demand

to meet additional capacity created by adding a provider, by steering PCP referrals back to

this practice.

In this analysis of the competition, we found that there are only 7 practices in the market

that have a wait time of 14 days or less. Of these, only 3 offer the comprehensive range of

services that the NMPMP offers. It is reasonable to assume the practice can gain some

market share by improving their NPV wait times.

7. Primary Data Source

We obtained raw de-identified scheduling and CPT billing data from the NMPMP’s manger

of data integrity and analysis. Data was provided from January 1, 2008 through June 30,

2013. Prior to 2011 the clinic was staffed with one MD and one NP. In January 2011 the

NMPMP added a second MD, and in March 2011 it added a second NP. The second MD left

in August and was replaced in the same month by another MD. The current clinic provider

complement, 2 MDs and 2 NPs, began in 2011. Additionally, the clinic did not perform

procedures before August 2009, and the clinic moved to its current location in October

2009. Due to these changes before 2011, we analyzed data from January 1, 2011 through

June 30, 2013 as this represents the clinic in its current format.

8. New Patient Visits (NPVs)

NPV wait times, demand, and capacity of the NMPMP will be determined based on clinic scheduling

data. The relationship of demand to capacity was also analyzed. Finally, a queuing model was

created and current and forecasted queue lengths and wait times were determined.

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a. Wait TimesHistorical and current wait times will be determined in this section.

NMPMP’s main concern and goal is to improve NPV wait times; therefore, we began with calculating

NPV current and historical wait times. NPV wait times were defined as the difference between the

date of first requesting a NPV and the date of the appointment booked. The average wait time has

been steadily increasing since 2011 from 14.5 to 20.1 days currently (Chart 2).

Chart 2

CY 2011 CY 2012 CY 201310

12

14

16

18

20

22

Avgerage NPV Wait Time

Year

Aver

age

wai

t tim

e (d

ays)

We subsequently determined the percentage of NPV wait times of ≤14 days over 3 years (Chart 3).

We found that from 2011-2013 the percent of NPVs with wait times ≤7 days decreased from 49% to

35% and wait times ≤14 days decreased from and 66% to 51%.1 The 51% of NPVs with wait times

of ≤14 days is significantly below the desired 80% URMFG standard. (Note: 2013 is only a half-year,

but the trend is similar even when comparing the 1st two quarters of each year). Also of note, the

51% of NPV wait times ≤14 days we report does match the URMFG’s report, thus our methods of

calculation appear to be consistent.

1 Although the URMFG measures their wait time standards by percent of patients with ≤14 days wait, they also report the percent of patients with ≤7 days wait. As this seems to be important to the URMFG and possibly a future standard compliance measure, we have reported this value as well.

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Wait times have been increasing and quarters 1 and 2 for CY 2013 show an average wait time of

20.1 days. Additionally, only 51% of NPVs are seen within 14 days.

Chart 3

0%

10%

20%

30%

40%

50%

60%

70%% NPV wait times ≤ 14 days

CY 2011 CY 2012 CY 2013

% o

f NPV

requ

ests

*note 2012 and 2011 are full CY, 2013 is a half year

b. DemandHistorical and current demand will be determined in this section.

In order to determine demand for NPVs, we used the arrival rate, defined as patients calling the

NMPMP and booking a NPV appointment. This arrival rate of NPV requests is not the true demand

as the clinic does not currently keep track of patients who request an appointment but decide not to

schedule an appointment, i.e., customers lost. Including these lost patients and those scheduling

appointments would yield the true demand. The demand does not truly match that of the arrival

rate, yet it appears to approximate it closely, at least with the current wait times. Based on this

assumption and limited data, we used the booking of NPVs as the arrival rate of NPVs and

calculated the average demand for years 2011, 2012 and 2013. This was calculated as follows:

The number of NPV appointments scheduled was determined for each week and averaged

for each year.

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We found that weekly arrival rates have steadily increased from 31 NPV requests in 2011 to 40 in

2013 (Chart 4).

Chart 4

CY 2011 CY 2012 CY 201305

1015202530354045

Average NPV requests per week

Year

NPV

requ

ests

per

wee

k

We also examined the daily arrival rate of NPV requests for quarter 1 and 2 of 2013. The arrival

rate ranged from 1 to 29 per day with a standard deviation of 4.4 (Chart 5).

14

Chart 5

January-13 February-13 March-13 April-13 May-13 June-130

5

10

15

20

25

30 NPV appointment requests per day, Q1-2 2013

Date

NPV

requ

ests

Chart 4 shows that the demand for NPVs is growing over time and Chart 5 shows that there is

significant variation from day to day.

In the future, we recommend the clinic record all patients requesting an appointment, not just those

booking appointments. The data will provide the true demand for NPVs which will enable a more

accurate projection using the queuing model discussed later.

Demand has been increasing and currently stands at 40 NPV requests per week. There is significant

variation in NPV requests from day to day.

c. CapacityCapacity of the clinic was measured to determine whether it could meet the demand for NPVs.

We first began with defining the schedule of the clinic. We consulted with the NMPMP office

manager who provided us with the hours that each provider is scheduled to see patients for office

visits and procedures. Table 1 was constructed and shows each provider and the number of hours

they are scheduled to see patients each day of the week and whether they were seeing patients for

office visits or procedures.

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Table 1

Clinic schedule, office visits (hours)M T W T F Total

MD 1 6.25 6.25 2.5 15.0MD 2 6.25 6.25 12.5

MD totals 6.25 6.25 6.25 6.25 2.5 27.5MD average 13.75

NP 1 4.0 6.25 4.75 4.25 5.5 24.75NP 2 6.25 6.25 6.25 3.75 22.5

NP totals 10.25 12.5 4.75 10.5 9.25 47.25NP average 23.63

All providers 74.75

Procedure schedule, patient procedures (hours)M T W T F Total

MD 1 7.25 7.5 14.75MD 2 7.5 5.5 13.0

MD 7.25 7.5 5.5 7.5 27.75*MD = Medical Doctor, NP = Nurse Practitioner

**Time represented in the above table represents only time scheduled to see patients, i.e. all breaks, such as lunch are accounted for and not included.

In the clinic each NP works with a dedicated MD, i.e., they work as dedicated pairs or teams. The

two MD providers have separate clinic and procedure schedules while the two NPs have only clinic

schedules. One MD/NP pair shares a common clinic schedule and has separate schedules at times.

The other MD/NP pair never share a common clinic schedule. As the focus of the project was to

examine and improve wait times for new patients we focused on the clinic portion rather than the

procedure portion of scheduling. This is reasonable as these office time and procedure time for

providers are “blocked” separately.

There are two types of clinic visits, NPVs and FUAs. All NPVs are booked for 30 minutes and all

FUAs are booked for 15 minutes. The office does not keep a record of the actual time spent with

patients. However, this information should be recorded. Knowing the real time spent with a patient

allows for more accurate scheduling of appointments and would decrease scheduling variability

and in-clinic wait and processing times. We first needed to calculate the actual volumes of NPVs and

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FUAs and the proportion of NPVs to FUAs to determine the clinic capacity for NPVs assuming a

stable ratio. Chart 6 shows the volumes and their relative ratios of NPVs and FUAs actually seen in

the clinic for 2011 and 2012.

Chart 6

CY 2011 CY 20120%

10%20%30%40%50%60%70%80%90%

100%

1121 1195

4129 4540

Volume of Visits by Type

FUANPV

% o

f tot

al cl

inic

visit

s

This same approximate ratio of 20 NPVs : 80 FUAs existed for the first two quarters of 2013 as well

(Chart 7). Going forward we will assume this same 20:80 ratio of NPVs to FUAs for capacity and

utilization calculations.

Chart 7

CY 2011 CY 2012 CY 20130%

10%20%30%40%50%60%70%80%90%

100%

539 592 667

1908 2048 2721

Volume of Visits by Type, Q1-2

FUANPV

% o

f tot

al cl

inic

visit

s

*Note that the volumes of both NPVs and FUAs are steadily increasing, thus confirming the NMPMP belief of increased demand.

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Using our 20:80 ratio, and the hours From Table 1, we estimated the capacity of each provider

(Table 2) and the subsequent utilization rates based on the actual number (average) of NPVs each

day of the week in 2013 determined from scheduling data.

Table 2

NPV capacity and utilization for 2013M T W T F Total

% NPV 20% 20% 20% 20% 20%

Actual NPVs/day (avg) 7.1 6.9 3.8 6.6 3.7 28.1

Total Clinic Capacity (hours) 16.5 18.8 11.0 16.8 11.8

Total Clinic Capacity (# visits/day) 6.6 7.5 4.4 6.7 4.7 29.9Utilization 108% 92% 86% 99% 78% 93%

The findings in general demonstrated that the utilization rates of providers for NPVs were very

high, ranging from 78%-108% per day, with Monday being the highest and Friday the lowest. The

weekly average utilization was 93%.

The weekly clinic capacity is 29.9 NPVs per week given a historically stable 20% NPV / 80% FUA

ratio. The average utilization rate is 93%.

d. Demand vs. Capacity The demand and capacity calculated are compared.

Given that the demand is 40 NPV appointment requests per week and the clinic has the capacity to

see 29.9 NPVs per week given the current ratio of NPVs to FUAs, it is clear that demand far exceeds

capacity. This could certainly lead to prolonged wait times for NPVs to be seen and adding a

provider would certainly add capacity to help better meet the demand.

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e. Queuing Model A queuing model is described and simulations for current and forecasted models were performed to determine the queue length and wait times for NPV.

Model description

We assumed a Poisson distribution for arrival rate of NPVs. We assumed the

probabilities that patients would accept a NPV appointment based on the quoted

wait times and constructed a probability table using this assumption. We assumed a

NPV processing rate to clear the queue. We determined the length of the queue

(number of patients) that have booked a NPV and have not yet been seen. We then

simulated each day as follows:

1) The total number of days to process the NPV queue is determined by dividing

the length of the queue by the average number of NPVs seen each day.

2) The number of days is then rounded to the nearest whole number and the

probability that a patient will take the offered appointment based on quoted

wait time is determined from the probability table.

3) The number of arrivals of patients requesting a NPV is then randomly generated

from the probability distribution.

4) The number of patients who actually book a NPV can then be simulated and a

resulting new queue length and wait times can be estimated using the assumed

NPV processing rate.

Current model

We assumed a mean arrival rate of 8 NPV requests per day calculated as described

in the ‘Demand’ section. We assumed the probabilities that patients would accept

the appointment based on the quoted wait times to be nearly 100%. We assumed

that 29.9 NPVs are processed per week as determined in the ‘Capacity’ section. We

used the length of the queue (number of patients) that have booked a NPV and have

not yet been seen. The length of queue for 2013 was calculated in the following

manner:

I. The difference between the first day of clinic that year (1/2/2013) and

the date of all NPV requests was calculated for each scheduled visit.

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These were then added sequentially to determine the number of

departures. The same was done for the date the NPV was requested to

determine the number of arrivals. The “VLOOKUP” function was then

utilized in excel to calculate the number of arrivals and departures for

each day. The difference between these two calculated the length of the

queue, which was then plotted in Chart 8.

Chart 8

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 10210811412080

90

100

110

120

130

140

150

Length of Queue, Q1-2 2013

Day of the year

Patie

nts i

n qu

eue

II. The length of the queue ranged from 96-141 patients and averaged

about 120. The tight clustering with little variation suggests demand

exceeds current capacity. If the NMPMP’s demand wasn’t met or was less

than their appointments available, one would see greater variability in

the length of the queue. We previously discussed that given the demand

exceeds capacity by 10 NPVs, we would expect to see an exponential

growth in the NPV queue. However, as seen here, it is stable. This may

be due to cancellation or no-shows. This is feasible as there is a 17%

combined late cancellation and no-show rate (discussed further below).

Currently, the NMPMP has a wait time of approximately 4 weeks and simulations

typically projected wait times of 5-7 weeks at 2 months into the future. (See

Appendix A)

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Forecasted model

We assumed the same Poisson distribution and mean arrival rate of 8 NPV requests

as in the current model. We used the same probabilities that patients would accept

appointments from the current model. We used assumed NPVs processing rates

based on the addition of another provider. For a MD performing procedures, we

assumed 5.5 additional NPVs per week in additional capacity. For a NP, PA, and

non-procedural MD, we assumed an additional 9.5 NPVs per week in in added

capacity. We used the average length of the queue (120 patients) as described

previously as our starting point.

Finally, running we ran the simulation the same as we did for the current model we

found that a MD performing procedures would decrease the forecasted wait time to

2-5 weeks at 2 months into the future and did not significantly decrease after that

time. (Appendix B) However, the NP, PA or non-proceduralist MD decreased the

forecasted wait time to 1-3 weeks at 2 months into the future and it continued to

decrease significantly after that time. (Appendix C)

A queuing model is useful to project wait times and queue length in the future. The current

model confirms increasing wait times. The forecast model shows that a NP, PA, or non-

procedural MD is preferred over the MD performing procedures in terms of reducing wait

times.

9. Follow Up Appointment (FUA)

a. Utilization of FUAsCapacity and utilization of FUAs was examined.

The utilization of providers for FUAs was low, ranging from 39%-53% per day, averaging 45%

(Table 3).

Table 3

FUA capacity and utilization for 2013

21

M T W T F Total% FUA 80% 80% 80% 80% 80%

Actual FUAs/day (avg) 27.8 31.2 13.8 21.0 16.2 110.0

Total Clinic Capacity (hours) 16.5 18.8 11.0 16.8 11.8

Total Clinic Capacity (# visits/day) 52.8 60.0 35.2 53.6 37.6 239.2Utilization 53% 52% 39% 39% 43% 45%

The overall utilization rates of provider hours in the clinic for all patient types ranged from 49-64%

per day and averaged 55%. These utilization rates may be considered reasonable as the focus of the

NMPMP is quality and they spend significant amounts of time working with patients. If we assumed

the excess unused capacity of FUAs from Table 3 could be converted to NPVs, the clinic might be

able to reduce the wait times and length of the queue. The excess capacity appears to be nearly 130

FUAs or 65 NPVs. Running the queue simulation this would eliminate the queue in under 3 weeks

time.

It is unlikely that low utilization is due to slow process clinic time, because even if patient visits ran

longer than scheduled they would all be seen by the end of the day, i.e. if the clinic is scheduled for 6

hours and it takes 7 hours to see all of the patients this would not affect the utilization as we

calculated it. There is variability due to cancellations <48 hours and no-shows which generate

unused capacity as there is no time to fill these appointments as their patient population has

trouble obtaining transportation on short notice (Table 4).

Table 4

CY 2011 CY 2012 CY 2013

22

% FUAs keeping their original appointment 56% 57% 59%% NPVs keeping their original appointment 64% 65% 61%

% FUAs NOS 5% 6% 6%% NPV NOS 6% 6% 7%

% FUAs CAN 36% 34% 30%% NPVs CAN 30% 29% 29%

% FUAs NOS and CAN <48hrs 17% 18% 17%% NPVs NOS and CAN <48hrs 17% 16% 17%

*NOS = no-shows, CAN = cancellations

One suggestion to discourage no-shows and late cancellations is to charge patients a fee for doing

so. NMPMP believed that NPVs were more likely to “no-show” and FUAs were more likely to cancel.

This is not the case, as the rates are nearly identical. Since FUA utilization is low, more of their

capacity could be shifted to NPVs to match the NPV demand that is exceeding capacity and

therefore decreased NPV wait times. However, if trying to match the demand for NPVs in this

manner one could potentially significantly increase wait times for FUAs and cause patient

dissatisfaction. This represents the ability to game the system by defining a standard for NPV wait

times and not for FUAs. FUAs are different from NPVs and difficult to standardize as they have

variable treatment plans.

NMPNP believes there is a shift to more FUAs as a result of extended time patients spend as a

member of the practice. One would expect a larger rate of growth of FUAs than NPVs and this is

supported by Chart 9.

Chart 9

23

CY 2011-2012 CY 2012-20130.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

9.8% 12.7%

7.3%

32.9%

Growth by Visit Type

NPV rate of increaseFUA rate of increase

% g

row

th

*Data represents Quarters 1 and 2 of each calendar year. Full 2013 CY data is not available

FUA utilization is generally low which may be explained by high cancellation and no-show rates.

FUAs are increasing at a faster rate than NPVs.

b. Length of Stay (LOS)Historical and current LOS was calculated based on billing data to determine the FUA demand of the clinic.

Our initial hypothesis was that as NPVs are added to the clinic, and stay in the practice longer, the

availability of appointment slots decreases and the wait time for a NPV increases. To test this

hypothesis the LOS was calculated, using only data from 2011-2013 for the average number of

visits patient had per year, and the average time in weeks they spend in the practice.

We calculated the number of patient visits per year as follows:

The average number of billed visits per patient for each year and total average visits per

patient over their length of stay was obtained from billing data and calculated (Chart 10).

Chart 10

24

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5 Average LOS

fiscal year '11 '12 '13 total visits

aver

age

num

ber o

f vis

its p

er

patie

nt

The average number of visits for patients appears to rise slightly from 2011-2013 at 2.63, 2.74, and

2.74, with standard deviations of 1.91, 2.04, and 2.11, respectively. These findings are not

conclusive as some patients have a LOS of more than one year; so calculating the average visits per

year is not representative of how many visits patients have. Thus the average number of visits per

patient was calculated (represented as “total”) to be 3.81. Due to large variations in the number of

visits per patient, the average number of visits does not actually tell us how much FUA demand is

on the clinic or space/time patients are taking up in the clinic, as the average is skewed. Therefore,

the amount of scheduled time for each NPV and FUA was calculated and plotted to determine FUA

demand. The distribution of time was calculated by counting the number of days between each

patient’s first and last appointment, dividing by 7 days to show LOS in weeks (Chart 11).

Chart 11

25

0 7 14 21 28 35 42 49 56 63 70 77 84 91 98 1051121191261331401471540

20

40

60

80

100

120

140

160

180

200Distribution of LOS in weeks

Number of weeks in practice

Num

ber o

f pati

ents

*Note that the vertical scale has been adjusted. The number of patients at ‘0’ weeks in the practice is 1349.

The range of LOS in weeks is 0-155, 0 represents one NPV then exiting practice this distribution

shows the variation in LOS. While interesting to observe that many patients only stay for one visit,

and the majority stay less than 30 weeks, this chart also does not translate to the amount of

space/time patients are taking up in the clinic, again not leading us to the FUA demand.

Our next attempt to determine the actual amount of space/time patients are taking up in the clinic,

each billed patient visit was converted into the amount of scheduled time, 15 minutes for FUA and

30 minutes for NPV, then sorted by patient ID, summed for each patient and plotted as a

distribution (Chart 12).

26

Chart 12

0 30 60 90120

150180

210240

270300

330360

390420

4800

200

400

600

800

1000

1200

1400

Total time patients spend in clinic

Time in mins

Num

ber o

f pati

ents

The chart shows the amount of time, in minutes, that patients spend in the clinic is quite variable.

Most patients take up 90 minutes or less of time in the clinic schedule, indicating that the amount of

time FUAs are taking up in the system, or rather the FUA demand is not currently the main driving

factor for NPV wait time. The impact of I-STOP, may later affect this distribution, if patients start

spending more time in the clinic to manage their controlled substance prescriptions.

The average LOS is 3.8 visits and the distribution is quite variable. The amount of time FUAs are

taking up in the system, or rather the FUA demand is not currently the main driving factor for NPV

wait time.

10. Relationship of FUAs and NPVs

Having analyzed NPVs and FUAs demand independently we then examined the relationship

between the two. Knowing that the clinic schedules approximately 80 FUAs to every 20 NPVs we

needed to determine how many FUAs per week were required to sustain the given NPV rate. To

determine this calculation we looked at a sample of weeks from January 1, 2011 to June 14, 2013,

where patients already in the practice (had their NPV previous to X week, and their last visit on or

after X week) then calculated the percentage of these patients who actually had an appointment in

the given weeks (Chart 13).

27

Chart 13

1/3/11-1/7/1

1

3/14/1

1-3/18/1

1

5/9/11-5/13/11

7/11/1

1-7/15/1

1

9/5/11-9/9/1

1

11/7/11-11/11/11

1/9/12-1/13/12

3/5/12-3/9/1

2

4/9/12-4/13/12

6/4/12-6/8/1

2

8/13/1

2-8/17/1

2

10/15/12-10/19/12

12/10/12-12/14/12

2/4/13-2/8/1

3

4/8/13-4/13/13-0.50%

0.00%0.50%1.00%1.50%2.00%2.50%3.00%3.50%

% of follow up patients that had an ap-pointment

Chart 13 displays the follow up patients, or patients currently in the practice typically take up

approximately two percent of the appointment schedule. We excluded data after April 13, 2013 as

the volumes of patients appear to significantly decrease as we approach the end of the data set and

this cause the percentage of FUAs to be skewed. We plotted the actual count of FUAs against the

total number of existing patients in the practice.

28

Chart 14

1/3/11-1/7/1

1

3/14/1

1-3/18/1

1

5/9/11-5/13/11

7/11/1

1-7/15/1

1

9/5/11-9/9/1

1

11/7/11-11/11/11

1/9/12-1/13/12

3/5/12-3/9/1

2

4/9/12-4/13/12

6/4/12-6/8/1

2

8/13/1

2-8/17/1

2

10/15/12-10/19/12

12/10/12-12/14/12

2/4/13-2/8/1

3

4/8/13-4/13/13

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Number of patients in practice

number of patients that had an appt

existing patients in sys-tem

Num

ber o

f pati

ents

*Note that the volumes are increasing in 2011, suggesting growth in the clinic.

The number patients having an appointment seems to remain fairly constant, again indicating that

the number of or demand of FUAs does not appear to be the main contributing factor for length of

NPV wait times. It is important to consider this percentage when adding new patients to the clinic,

as they will need to increase the clinic time by 2% to account for the needed FUAs. This could come

from some unused capacity, but would eventually require adding overall capacity.

11. Financial Analysis

a. Revenue An analysis of the revenue generated by each physician type will show expected gain from hiring an additional provider. The revenue is a compilation of reimbursement from office visits and procedures, gain sharing and contracted income.

The NMPMP utilizes a blended billing style. The reimbursement rates are different for physicians

and NPs. The NPs are only reimbursed 85% of the reimbursement rate for services billed under

their license. Since they work directly with the physicians, they are able to bill 75% of their visits

under the physician code, yielding a 100% reimbursement rate for those visits. Some of the payers

will not reimburse any amount for a NP to see the patient for a NPV. These visits are either booked

to the physician, or billed under the physician.

29

Reimbursement rates are set by contract with the individual payers and the providing institution

on an annual basis. The URMC rates are on the SharePoint site

http://sharepoint.mc.rochester.edu//sites/MFGBO/referencesandresources/FeeSchedule/

default.aspx

Under direction of the clinic financial director, the Master Fee Schedules (ie. Office) was used to

determine reimbursement rates. Each individual Current Procedural Terminology (CPT) code was

looked-up and the rates for each payer recorded on a spreadsheet. The rates from 2012 and 2013

were compared for percent change to forecast the future reimbursements. The data set is limited to

two points, as available on the site, and thus any change greater than 10% was considered to be an

outlier and adjusted to the average percent change for each individual payer. There are seven main

payers that were considered (Table 5):

Table 5

Payer Payer Mix

Medicare 14%

Medicaid 27%

MVP 10%

Aetna 2%

Blue Shield 17%

Blue Choice 5%

WCMVA 24%

Self-Pay 1%

100%

MVP is a commercial insurance with a moderate reimbursement rate. The payer mix assigned to

MVP is inclusive of all other miscellaneous commercial payers. Self-Pay is uninsured patients.

There is no expectation of reimbursement for any care provided to self-pay patients, they are

considered charity care. WCMVA is worker’s comp and motor vehicle insurance combined into one

category. They are very good payers for procedures, but pay very little for office visits and consults.

The reimbursement rate for these insurances is set by RVU units. Each CPT code is assigned an

RVU value. This RVU is then multiplied by the rate for office visits, $8.84, or procedures, $184.12, to

determine the reimbursement for that CPT. See appendix D for breakdown.

30

These reimbursement rates were then multiplied by the volume billed for each CPT code by

provider type. This gave us the anticipated income for each new provider: MD performing

procedures, non-proceduralist, NP/PA. (Appendices E-G)

A profit and loss statement has been prepared for each provider type, to compare revenue and

expense. The goal of the NMPMP is to at least break even on the added provider within 3 years.

The profit and loss statement has been constructed utilizing the calculated revenue by provider

type and CPT code volume. It is assumed that if operating at capacity, the volume will remain

constant provided the demand is maintained. New providers do not start billing immediately. The

proceduralist and non-proceduralist start billing in their third month and the NP/PA level

providers start billing in the fifth month. The revenues for the first year were adjusted at 83%

revenue for physician and 67% revenue for the NP/PA to account for this delayed billing.

The pain provider participates in a contracted gain sharing which yields additional $200,000

revenue per year for the pain provider. The clinic also has a contract with a provider in Auburn, NY

and they receive a monthly Professional Service Fee in the amount of $10,000. This is for one

proceduralist and one nurse practitioner holding clinic in Auburn two days a month. The revenue

is split $8200 additional revenue for the physician and $1800 additional revenue for the NP. A

similar contract may be attempted with an additional provider.

The non-proceduralist and NP/PA will indirectly generate additional revenue for procedures.

(Appendices H-I) They are unable to perform procedures, but the office visits will generate more

procedure volume. There is excess capacity in the procedure booking blocks and the additional

volume can be absorbed by the pain providers by adding additional hours on Wednesdays and

Fridays (see Table 1). This will yield a higher volume of procedures which reimburse at a higher

rate. In 2013 29% of the CPT codes billed are procedures. Applying this to the additional visits

created by adding a NP or PA, the procedure volume will increase by 40%. The additional visits

created by the non-proceduralist will increase the procedure volume to 51% of all CPT codes.

Revenue - Average Year

MD Proc MD Non-Proc NP PAProvider Revenue 421,570.13$ 248,483.41$ 156,442.44$ 156,442.44$ Increase in Procedure Revenue by % of CPT -$ 157,366.85$ 123,424.98$ 123,424.98$ Professional Service Fees 98,400.00$ -$ 21,600.00$ 21,600.00$ Gain Sharing 200,000.00$ -$ -$ -$ Total Revenue 719,970.13$ 405,850.26$ 301,467.42$ 301,467.42$

31

The MD performing Procedures yields the highest total revenue. This is due to the procedures

performed. The reimbursement rates for procedures are much higher than office visits and the

proceduralist is the only provider eligible for the gain sharing.

b. Expenses Expenses were calculated using data received from the NMPMP and independent research. The expenses will show salary and benefits for each provider type, startup costs, licensing fees and assessments.

The salary for each provider was obtained from Salary.com and verified with the department. The

average national salary was selected for each provider. This was accurate to the department

estimates with the exception of the Pain Provider with Procedures. The NERVES (Neurosurgery

Executives’ Resource Value and Education Society a society conducting surveys and analysis for

neurosurgery practice managers and administrators) survey was used to verify the salary for the

pain provider and the lower 25% salary was determined to be too high. The salary from Salary.com

and NERVES was averaged to reach the $380,310.00 estimate. It is assumed that the providers will

each receive a 2% salary increase per year.

The URMC assigns a sliding scale to the cost of benefits for the providers. The benefit rate for the

Pain Provider – with procedures is 18.25% of base salary. The benefit rate for the Non-

Proceduralist is 27.77% of salary and the benefit rate for the NP or PA are 37.3% of salary.

Startup costs are comprised of recruiter fees, travel expenses for interviews, moving expenses and

office set up including furniture and computers. The recruiter fee for a physician is between $25K

and $50K, a mean value of $37.5K has been assumed. For the NP / PA the recruiting fee is between

$8K and $10K, a mean value of $9K has been assumed. The interview travel expenses for a

physician has been calculated at $20K, allowing for 2 trips each for 5 candidates. The interview

travel expenses for NP / PA $250.00, allowing for 5 candidates one trip each. The NP /PA

candidates are recruited from the local region so there is limited expense involved in the interview

travel. Moving expenses are allotted to physicians depending on the distance of the move. The

range is $8K to $30K, a mean value of $19K was assumed. No moving expenses are allowed for NP /

PA providers.

32

Start Up CostsMD APP

Recruitment 57,500.00$ 9,250.00$ Offi ce Set Up 20,250.00$ 10,250.00$ Moving Expense 19,000.00$ -$ Total Start Up Costs 96,750.00$ 19,500.00$

Malpractice insurance rates have been estimated by taking the current year rate for one of the

procedural MDs. URMC malpractice is self-insured with four peer academic medical centers paying

into a resource pool for the payment of claims. There is an expected 5% increase in the rate per

year. This rate has been applied to the MD performing procedures and MD non-proceduralist. The

URMC purchases a bulk malpractice policy for the NP and PA staff. There is no cost at the

department level for this coverage.

Dues were assumed at one professional organization per provider. The fees vary by organization;

however, an average fee has been suggested by the clinic financial director. Licensing fees assessed

every three years and set by the license agency and are provider level specific. Travel expenses are

provider for professional conferences. The rate was determined assuming 3 trips per year for a

physician and 1 trip per year for the NP / PA at a rate of $2,500 per trip.

The department has set a rate of $5,000 per provider for phones, transcription services and

miscellaneous expenses. The assessments and fees are set by the URMC and are based on a percent

of revenue.

Expenses - Average YearMD Proc MD Non-Proc NP PA

Salary & Benefits 467,885.12$ 285,779.86$ 139,775.71$ 131,552.01$ Malpractice Insurance 8,699.83$ 8,699.83$ -$ -$ Liscences / Dues / Travel 9,545.00$ 9,545.00$ 3,545.00$ 3,545.00$ Fees & Assessments 64,324.03$ 48,630.30$ 55,096.27$ 55,096.27$ Total Expenses 550,453.98$ 352,654.98$ 198,416.98$ 190,193.28$

The MD performing Procedures incurs the highest expenses. The PA incurs the lowest expenses.

These must be compared against revenue to determine the most desirable provider.

33

c. Profit and Loss StatementsThe revenues and expenses have been applied to a profit and loss statement, by provider type (Appendices J-M).

The analysis shows that the MD – Non-proceduralist is the least profitable provider option, not

showing a positive account balance until year 5 showing a total gain of $55,026.39. The MD

performing procedures is the most profitable showing a positive account balance in year 1 and a

total gain of $624,031.21 at the end of year 5. The NP and PA both show positive account balance in

year 1 and a total gain of $435,491.83 and $476,626.44 respectively.

Gain Year 5MD Proc MD Non-Proc NP PA

Revenue 3,534,102.08$ 1,890,690.88$ 1,431,001.80$ 1,431,001.80$ Expenses 2,910,070.88$ 1,835,664.49$ 995,509.97$ 954,375.36$ Gain (Revenue over Expenses) 624,031.21$ 55,026.39$ 435,491.83$ 476,626.44$

Although the MD performing procedures yields the higher gain, hiring a provider at this level is

riskier. The time to recruit and hire the provider is longer, the upfront costs are higher and if an

MD is hired, additional administrative staff will be required. The NP does not require additional

administrative staff and the upfront costs are $77,250 less than the MD.

12. Recommendations

The NMPMP is currently exceeding capacity in requests for NPVs. This is causing a queue to form

and the clinic to exceed the goal of 80% seen in ≤ 14 days. It is clear that clinic capacity must be

increased to meet the demand and the goal. The following recommendations are made based on

the analyses described in this paper.

Immediate changes that are possible based on evidence

FUA utilization is low and more of capacity could be shifted to NPVs, therefore decreasing

NPV wait times. However, if trying to match the demand for NPVs in this manner could

significantly increase wait times for FUAs and cause patient dissatisfaction. This represents

the ability to game the system by defining a standard for NPV wait times and not for FUAs.

The clinic does not block time for NPVs or FUAs; they schedule them on a “first-come” basis.

Scheduling FUAs 15 minutes apart takes away slots for 30 minute NPVs, thus NPVs are

more difficult to fit in. By setting aside a specific amount of time for each type of

appointment, we can reduce variability. This is commonly referred to as block scheduling.

34

Given the average 20% NPV / 80% FUA we would recommend using this to block ratio for

the clinic

Attempting to schedule NPV on a first provider available basis would reduce wait times and

allow better predictability of the queuing from the simulation rather than allowing patients

to request specific providers. By doing this, patients’ wait times will be shorter as there is a

single queue with multiple providers. This can be done at the time of booking the

appointment and again when they arrive to the clinic for both NPVs and FUAs, by seeing the

next available provider. This could dissatisfy follow-up patients who prefer their existing

provider.

Discourage no-shows and cancellations with <48 hour notice by charging the patient a no-

show fee, a widely accepted practice in the medical field.

Data to collect for future decisions

Record the actual time spent with patients. Knowing the real time spent with a patient

allows for more accurate scheduling of appointments and would decrease scheduling

variability and in clinic wait and processing times.

Record the number of patients who call to request an NPV and decline based on the wait

time. This will give the true demand of the arrival rate and show the expected increase in

booked appointments if the wait time issue is resolved.

Record when the patient is discharged from the practice. NMPMP believes patients are

staying longer and suspect that I-STOP will increase LOS. Keeping this data will enable

them to monitor LOS and demand for FUAs.

Long Term Solution

Expand capacity in the clinic by hiring an APP. I-STOP legislation is changing the nature of

the visit. It is reported by one of the NP providers that there is an increase in the number of

patients who believe they need medical management. The I-STOP database is giving the

providers more information about the regional treatment of the patient and is enabling

them to avoid prescribing unneeded drugs. The NP is spending extended visit times with

these patients to offer counseling and direction for their treatment. It is better use of clinic

resources for an APP to filter out these drug seeking patients.

35

The clinic model yields a high volume of medical management and drug monitoring. The

volume of procedures is reasonable, according to the clinic director. This model is not the

best financial model for a pain treatment clinic; however, it meets the mission of the URMC.

Adding an APP to the practice allows for referrals to the MD performing procedures for the

patients requiring interventional treatment. This adds medical management cases to the

clinic and increases the procedures, which yield a higher reimbursement rate an estimated

$99K in the first year, without the higher expenses associated with an MD. The current

staffing model is paired teams of one MD and one NP working as a team. This will be

unbalanced by the addition of an APP, however the clinic has structured their schedule so

that an MD is in clinic at all times. The added APP will work under both MD’s and the

attending will be the MD in clinic on the given day. Since an NP yields similar gain to the

practice and requires less direct supervision than a PA, and it is recommended that an NP

be added to the clinic.

36