Financing and Organizations in Your Profession

738  |  wileyonlinelibrary.com/journal/jcpt J Clin Pharm Ther. 2017;42:738–749.© 2017 John Wiley & Sons Ltd

Received: 19 September 2016  |  Accepted: 16 May 2017 DOI: 10.1111/jcpt.12574

O R I G I N A L A R T I C L E

Patient preferences for healthcare delivery through community pharmacy settings in the USA: A discrete choice study

M. Feehan PhD1,2  | M. Walsh MS3 | J. Godin MA3 | D. Sundwall MD4 |  M. A. Munger PharmD, FCCP1,5

1Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, USA 2Kantar Millward Brown Inc., New York, NY, USA 3Hall and Partners Inc., New York, NY, USA 4Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT, USA 5Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA

Correspondence M. Feehan, Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, UT, USA. Email: [email protected]

Funding information Skaggs Institute for Research, Salt Lake City, UT, USA, and the Skaggs Foundation for Research, Salt Lake City, UT, USA.

[Correction added on 13 July 2017, after first online publication: The qualification of the author, J. Godin has been changed from “PhD” to “MA”.]

Summary What is known and objective: In order to improve public health, it is necessary to fa- cilitate patients’ easy access to affordable high- quality primary health care, and one enhanced approach to do so may be to provide primary healthcare services in the community pharmacy setting. Discrete choice experiments to evaluate patient de- mand for services in pharmacy are relatively limited and have been hampered by a focus on only a few service alternatives, most focusing on changes in more traditional pharmacy services. The study aim was to gauge patient preferences explicitly for pri- mary healthcare services that could be delivered through community pharmacy set- tings in the USA, using a very large sample to accommodate multiple service delivery options. Methods: An online survey was administered to a total of 9202 adult patients from the general population. A subsequent online survey was administered to 50 payer reim- bursement decision- makers. The patient survey included a discrete choice experiment (DCE) which showed competing scenarios describing primary care service offerings. The respondents chose which scenario would be most likely to induce them to switch from their current pharmacy, and an optimal patient primary care service model was derived. The likelihood this model would be reimbursed was then determined in the payer survey. Results and discussion: The final optimal service configuration that would maximize patient preference included the pharmacy: offering appointments to see a healthcare provider in the pharmacy, having access to their full medical record, provide point- of- care diagnostic testing, offer health preventive screening, provide limited physical ex- aminations such as measuring vital signs, and drug prescribing in the pharmacy. The optimal model had the pharmacist as the provider; however, little change in demand was evident if the provider was a nurse- practitioner or physician’s assistant. The de- mand for this optimal model was 2- fold higher (25.5%; 95% Bayesian precision interval (BPI) 23.5%- 27.0%) than for a base pharmacy offering minimal primary care services (12.6%; 95% BPI 12.2%- 13.2%), and was highest among Hispanic (30.6%; 95% BPI: 25.7%- 34.3%) and African American patients (30.7%; 95% BPI: 27.1%- 35.2%). In the second reimbursement decision- maker survey, the majority (66%) indicated their or- ganization would be likely to reimburse the services described in the optimal patient model if provided in the pharmacy setting.

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1   |   W H AT I S K N O W N A N D O B J E C T I V E

There is a public health necessity to ensure that patients have con- venient, affordable and ready access to quality health care. Shaping the future of health care will be improved by empowering the voice of the patient in asserting their preferences for care. To this end, it is important to understand the level of patient demand for health- care services to develop the most effective models of care delivery. What is the optimal combination of primary healthcare services which would best satisfy patient’s needs at a local level, and thus be more likely to improve the three pillars of patient- centred care—increased access, affordability and better health of communities? One emerging model may be to leverage existing community pharmacy settings to deliver primary care services. Pharmacies are currently the most fre- quently utilized healthcare delivery locations in the United States, as 92% of the population live within 1.6 miles of a pharmacy and there are 67 000 community pharmacies nationwide.1 Convenient walk- in clinics are in a growing number of US pharmacies and are estimated to reach 2700 by 2019.2 This growth is supported by data suggesting that the quality of care is comparable between convenience clinics and physician offices or urgent care clinics.3–5

To inform the development of effective models for the delivery of care in the community pharmacy setting, it is necessary to understand the level of consumer demand for such services. Specifically, what is the optimal combination of services which would best satisfy con- sumer demand and thus be more likely to improve access? An effec- tive approach to determine consumer demand is to use discrete choice experiments (DCEs), whereby consumers are shown competing sce- narios describing potential pharmacy service offerings and then make a choice between the offerings. Rather than using consumers’ stated preferences for features or services, DCEs approximate human experi- ence decision- making, where people make trade- off choices between offerings (taking all attributes into consideration), and the importance of features or attributes are then derived through statistical analysis. DCEs are used widely in healthcare practice and policy research6,7 and are increasingly being used in pharmacy research to inform service de- sign.8,9 These studies have included research into expanding the role of pharmacies in terms of prescribing, medication management and offering more patient- centred services, including point- of- care testing and enhanced counselling.10–14

Much of the DCE research in pharmacy has been conducted out- side the USA, with relatively small sample sizes, which has limited the number of potential attributes describing the range of pharmacy ser- vices that could potentially be offered. These studies have also tended

to focus on attributes that may be perceived to fall in a more tradi- tional scope of pharmacy practice, such as medication management, as opposed to a broader array of primary healthcare services that po- tentially could be offered in the pharmacy setting. The objective of this study was to gauge patient preferences for a range of primary care services that could be delivered through the community pharmacy set- tings in the USA, with a very large sample allowing for multiple service attributes, and model an optimal service configuration that would re- sult in the highest patient demand. The study was designed to include sufficient numbers of minority and socio- economically disadvantaged participants.

In addition, to see whether the optimal patient- centred pharmacy configuration could be sustainable in practice, those responsible for making decisions related to reimbursement in insurance companies were also subsequently surveyed to determine the likelihood that their organizations would reimburse the services desired by patients.

2   |   M E T H O D S

The study protocol and survey instrument were reviewed and consid- ered as exempt based on applicable guidelines involving the ethical treatment of human subjects by the University of Utah Institutional Review Board before the initiation of data collection. The University of Utah Institutional Review Board is fully accredited by the Human Research Protection Programs, Inc.

2.1 | Study design

The design of this study was a cross- sectional survey that included a discrete choice experiment (DCE), conducted across the continental United States.

2.2 | Sample

The patient sample comprised 10 006 adults recruited from an estab- lished nationally representative panel of individuals in the USA, who opted in to be contacted for research purposes (Universal Survey Center, Inc., d/b/a SHC Universal New York, NY). Panellists accessed the survey electronically, through a link in an invitation email, which offered a minimal honorarium for participation. Respondents were prescreened to be an adult aged 18 years or older. To ensure respond- ents would be able to make an informed decision about pharmacy de- sign, respondents had to have a minimal repeat use of a pharmacy for

What is new and conclusion: This United States national study provides empirical sup- port for a model of providing primary care services through community pharmacy set- tings that would increase access, with the potential to improve the public health.

K E Y W O R D S

community pharmacy, discrete choice experiment, primary care

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health care needs—defined as filled at least three or more prescriptions for themselves, at a pharmacy in the past 12 months. Patients with VA, CHAMPUS or TRICARE insurance or who received care through Kaiser, Kaiser Permanente, the Permanente or the Permanente Medical Group were excluded, given the more closed nature of

patient management in these systems. These systems provide their own in- house pharmacy services, with less patient flexibility in making choices between pharmacy scenarios. No other inclusion/exclusion criteria were applied. Patients may have presented at their pharmacy for medications for any health condition. Between 27 August 2015

Characteristic

Total Male Female

N=9202 N=4226 (45.9%) N= 4976 (54.1%)

No. (%) No. (%) No. (%)

Age

Age 65+ 1688 (18.3) 830 (19.6) 858 (17.2)

18- 64 7514 (81.7) 3396 (80.4) 4118 (82.8)

Hispanic origin

Hispanic origin 1088 (11.8) 569 (13.5) 519 (10.4)

Non- hispanic origin 8114 (88.2) 3657 (86.5) 4457 (89.6)

Race

African American 985 (10.7) 453 (10.7) 532 (10.7)

Non- African American 8217 (89.3) 3773 (89.3) 4444 (89.3)

Insurance status

Insured 8701 (94.6) 4008 (94.8) 4693 (94.3)

Non- insured 501 (5.4) 218 (5.2) 283 (5.7)

Poverty levela

Poverty 1346 (14.6) 447 (10.6) 899 (18.1)

Low income 2229 (24.2) 916 (21.7) 1313 (26.4)

Above low income 5627 (61.1) 2863 (67.7) 2764 (55.5)

Income

Very low (Less than $25 000) 2171 (23.6) 817 (19.3) 1354 (27.2)

Low ($25 000- $49 999) 2790 (30.3) 1182 (28.0) 1608 (32.3)

Medium ($50 000- $99 999) 2990 (32.5) 1522 (36.0) 1468 (29.5)

High ($100 000 and above) 1251 (13.6) 705 (16.7) 546 (11.0)

Community residence

Rural 1748 (19.0) 690 (16.3) 1058 (21.3)

small city or town 2879 (31.3) 1311 (31.0) 1568 (31.5)

suburb of a large city 3203 (34.8) 1525 (36.1) 1678 (33.7)

large city 1372 (14.9) 700 (16.6) 672 (13.5)

Region

North- east 1759 (19.1) 838 (19.8) 921 (18.5)

Midwest 2273 (24.7) 1025 (24.3) 1248 (25.1)

South 3546 (38.5) 1560 (36.9) 1986 (39.9)

West 1624 (17.6) 803 (19.0) 821 (16.5)

Pharmacy distance

Very close (Less than 1 mile) 1422 (15.5) 613 (14.5) 809 (16.3)

Close (1- 5 miles) 5847 (63.5) 2777 (65.7) 3070 (61.7)

Far (More than 5 miles) 1933 (21.0) 836 (19.8) 1097 (22.0)

aPoverty threshold was determined by age, number of children in the home and income as defined by the US Census Bureau for 2014 (https://www.census.gov/hhes/www/poverty/data/threshld/). Low income was defined as between the poverty threshold and 200% above the poverty threshold (https://www.census.gov/hhes/www/poverty/methods/definitions.html). All others were considered above low income.

TABLE 1 Demographic characteristics of 9202 adults from the general population surveyed about preferred primary healthcare services that could be offered in community pharmacy settings

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and 21 September 2015, the survey was opened to 15 572 eligible patients. The sampling frame was designed to capture as many pa- tients as possible, but given the limitations of funding the survey was purposely capped, and the survey was closed at 10 006 respondents (64.3% of those eligible). The data set was screened to remove those respondents giving nonsensical data. This included not providing vari- ation in answers within the discrete choice experiment and showing the same likelihood to choose across all scenarios and completing the survey in unrealistically short time (less than half the median), and giv- ing manifestly inconsistent responses, resulting in a final total of 9202 surveys for analysis (92.0% of the 10 006). Quota was established to ensure the final sample approximated the US population in terms of major demographic characteristics and would ensure sufficient sam- ple for future within- demographic analyses. The demographic charac- teristics of the final patient sample are presented in Table 1.

A second 15- minute online survey was conducted with 56 reim- bursement decision- makers from different payer organizations in the United States who opted in to be contacted for research purposes through a market research panel. Panellists accessed the survey elec- tronically, through a link in an invitation email, which offered a minimal honorarium for participation. Respondents were pre- screened to meet an inclusion criterion to be moderately or heavily involved either in an advisory or leadership role in decision- making within a payer orga- nization regarding the “coverage and reimbursement policies and/or protocols” for at least four of nine types of current primary healthcare services (including diagnostic tests, preventive screenings and vacci- nations, emergency care services). This was assessed using a 4- point scale: 1 “not at all involved,” 2 “somewhat involved,” 3 “moderately involved” or 4 “heavily involved.”

They were also required to not be currently a Director of Claims and to have at least 3 years of experience in their current or similar position within an organization that offers commercial plans and/or Medicare part D plans. Between 14 December 2015 and 5 January 2016, the survey was opened to 167 eligible decision- makers. Due to funding limitations and the logistical barriers to interviewing this sam- ple (some plans restrict their personnel from participating in market research), the survey was closed at 56 respondents (33.5% of those el- igible.) Analyses were restricted to 50 reimbursement decision- makers after the data set was cleaned to remove nonsensical responses. The payer questionnaire presented the optimal pharmacy model derived from the patient DCE and asked decision- makers to indicate the likeli- hood their organization would reimburse the range of clinical services offered at this pharmacy on a 1- to- 5 scale where “1” means “Not at all Likely” and “5” means “Extremely Likely.”

2.3 | Questionnaire and discrete choice experiment

The questionnaire was informed by prior qualitative interviews using an online bulletin board with 19 patients, presented elsewhere.15,16 The quantitative survey took approximately 30 minutes to complete. Questions addressed general health and wellness, frequency of pa- tients’ usage of community pharmacies, range of current pharmacy services offerings, attitudes towards pharmacies and demographics.

The DCE utilized a Dual Response None format,17 so for each task, respondents were first asked to indicate which of two pharmacies dis- played they would be most likely to use, and then were asked to rate how likely they would be to switch from their current pharmacy to the one selected on a 5- point scale (1=not at all likely to 5=extremely likely). These responses were utilized simultaneously to provide a probabilistic propensity likelihood intercept of demand for each sce- nario. Shares of preference were allocated according to the logit rule, taking this intercept into account.

To design the DCE, 11 attributes or features of potential pharma- cies were developed and levels were assigned to each attribute, which were systematically varied to generate the potential future pharmacy configurations (see Table 2). Each respondent was provided an intro- duction to the DCE which indicated that a private area would be pro- vided for all point- of- care testing and physical examinations, and all providers would be suitably trained and approved by state and fed- eral laws and regulations to provide services, with physician oversight. With regard to the cost of services, those with insurance were told services described above would be covered by their insurance (drugs not included) with a varying copay amount, cash- paying patients were told the services described (as outlined in Table 2), with drugs not in- cluded, would cost a varying amount (the range shown in Table 2).

The combination of attributes and levels of services resulted in a theoretical maximum of 55 296 potential hypothetical primary care configurations. An experimental design for each choice task was gen- erated using Sawtooth Software’s Choice- Based Conjoint (CBC) de- sign engine within their SSI Web programming framework (Sawtooth Software, Inc., Orem, UT).18A balanced overlap task generation method was used to get more accurate results as an additive compen- satory decision- making expectation does not always or even usually occur in real respondent choice- making.19 Instead, some types of non- compensatory heuristics are often employed, and a design with some overlap can necessitate respondents look deeper across more attri- butes in the design to make their choices.20 This resulted in 40 sep- arate potential design versions, whereby each respondent reviewed 14 scenarios, each showing two pharmacy configurations for which they indicated their preference. A “base- case” pharmacy scenario was shown which offered the minimal level of services to compare prefer- ences for the future optimal care services configuration.

2.4 | DCE statistical analysis

For the patient estimation runs, a total of 1 000 000 iterations were utilized (500 000 burn- in, 500 000 result iterations to ensure full model and parameter convergence), initialized from an aggregate- level multinomial logit estimation (MLE). A total of twelve upper- level covariates (Z variables) were included. Customized Hierarchical Bayes estimation software was used to estimate coefficients for the individual- level utilities of each attribute level. Prior to model estima- tion, the 5- point rating scale for likelihood to switch to the preferred pharmacy in place of each patient’s current pharmacy was recoded into a probabilistic scheme, whereby 5 is recoded to 0.75, 4 to 0.25, 3 to 0.10, 2 to 0.05 and 1 to 0.02. Thereby, a respondent who indicated

742  |     FEEHAN Et Al.

that they would definitely switch (5) to a new pharmacy configuration has a likelihood of 75% for truly switching, versus definitely would not switch (1) with a 2% probability of switching. This conversion is commonly used to provide more realistic and conservative estimates of true switch likelihoods.21 These recoded likelihood to switch re- sponses were utilized simultaneously with the discrete choice of preferred scenario during estimation, to provide a probabilistic pro- pensity likelihood intercept of demand for each scenario. Shares of preference were allocated according to the logit rule, taking this

intercept into account. Individual- level point estimates (means) from the result iterations were imported into a simulator to predict choices patients would make for any of the possible pharmacy configurations. The model estimates “switch rates” (shares of preference) using the standard logit rule, where part- worth utilities for each product con- figuration are summed and exponentiated, then divided by the sum of the exponentiated utilities plus those of the “None” option derived from the overall model intercept; these calculations are made at the individual level and then averaged to produce the final switch rates

TABLE 2 Attributes and levels of attributes comprising potential pharmacy configurations shown to patients

Pharmacy attributes Level of service

Hours of operation • 9 am to 5 pm, closed Sundays (limited weekend hours) • 9 am to 9 pm, restricted hours Sundays (limited weekend hours) • 24 h/7 days a week

Prescription ordering, availability and information

• Telephone or online Internet ordering only • Telephone or online Internet ordering, and two-way discussion with

pharmacist (telephone or online)

Service provider • Pharmacist (with physician oversight) • Nurse-practitioner or physician assistant (with physician oversight)

Medical records • Prescription records only held at the pharmacy and not put into your (the patient’s) medical record

• The pharmacy has access to and can enter prescriptions and health information into your (the patient’s) electronic medical record

Service logistics • (Patients) Walk in and wait for services • (Patients) Walk in and wait for service or make an appointment (via

telephone or online)

Pharmacy provides

Physical examinations

• Not provided • Blood pressure, heart rate and breathing rate • Blood Pressure, heart rate and breathing rate, and physical examina-

tions provided to assess patients’ complaints (e.g pain, allergy, skin or ear/eye examinations)

• Full head-to-toe physical examination (e.g for diagnosis, general physicals, employment or sport physicals)

Diagnostic testing • Not provided • Blood sugar (Diabetes) and cholesterol measurement • Diabetes and lipid/cholesterol measurements plus testing for common

infections including influenza, hepatitis, tuberculosis and HIV • Diabetes and lipid/cholesterol measurements plus testing for common

infections including influenza, hepatitis, tuberculosis and HIV and conducting chemistry, urine, saliva and other blood tests

Preventive services

• Only vaccinations/immunizations • Vaccinations/immunizations and health screening (e.g mental health,

lung function)

Drug prescribing • Drug prescribing at pharmacy not available • Drugs prescribed at the pharmacy by [Insert same provider as above]a

Medication services

• Medication refill reminders (e.g by phone, text or Internet) • Meeting with pharmacist to discuss new prescriptions. Medication

refill reminders (e.g by phone, text or Internet) • Meeting with pharmacist to discuss all your medications, disease and

health. Medication refill reminders (e.g by phone, text or Internet)

Cost of Services • $0 • $15 • $30 • $45 • $60 • $75

aWith physician oversight.

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TABLE 3 Mean part- worth utilities for potential pharmacy attributes and levels based on 100 saved individual- level draws

Pharmacy attributes Level of service Mean part- worth utilities

Standard deviation (SD)

Lower 95% confidence interval (CI)

Upper 95% confidence interval (CI)

Hours of operation 1 9 am to 5 pm, closed Sundays (limited weekend hours)

−0.304 0.314 −0.310 −0.297

2 9 am to 9 pm, restricted hours Sundays (limited weekend hours)

0.052 0.056 0.051 0.053

3 24 h/7 days a week 0.252 0.315 0.246 0.258

Prescription ordering, availability and information

1 Telephone or online Internet ordering only −0.030 0.053 −0.031 −0.029

2 Telephone or online Internet ordering, and two- way discussion with pharmacist (telephone or online)

0.030 0.053 0.029 0.031

Service provider 1 Pharmacist (with physician oversight) 0.013 0.075 0.012 0.015

2 Nurse- practitioner or physician assistant (with physician oversight)

−0.013 0.075 −0.015 −0.012

Medical records 1 Prescription records only held at the pharmacy and not put into your (the patient’s) medical record

−0.059 0.060 −0.060 −0.058

2 The pharmacy has access to and can enter prescriptions and health information into your (the patient’s) electronic medical record

0.059 0.060 0.058 0.060

Service logistics 1 (Patients) Walk in and wait for services −0.008 0.041 −0.009 −0.008

2 (Patients) Walk in and wait for service or make an appointment (via telephone or online)

0.008 0.041 0.008 0.009

Pharmacy provides

Physical examinations 1 Not provided −0.053 0.177 −0.057 −0.050

2 Blood pressure, heart rate and breathing rate 0.164 0.096 0.162 0.166

3 Blood pressure, heart rate and breathing rate and physical examinations provided to assess patients’ complaints (e.g pain, allergy, skin or ear/eye examinations)

−0.036 0.164 −0.039 −0.033

4 Full head- to- toe physical examination (e.g for diagnosis, general physicals, employ- ment or sport physicals)

−0.075 0.106 −0.077 −0.073

Diagnostic testing 1 Not provided −0.387 0.153 −0.390 −0.384

2 Blood sugar (diabetes) and cholesterol measurement

−0.179 0.105 −0.181 −0.177

3 Diabetes and lipid/cholesterol measure- ments plus testing for common infections including influenza, hepatitis, tuberculosis and HIV

0.240 0.111 0.237 0.242

4 Diabetes and lipid/cholesterol measure- ments plus testing for common infections including influenza, hepatitis, tuberculosis and HIV and conducting chemistry, urine, saliva and other blood tests

0.326 0.131 0.323 0.329

Preventive services 1 Only vaccinations/immunizations −0.015 0.043 −0.016 −0.014

2 Vaccinations/immunizations and health screening (e.g mental health, lung function)

0.015 0.043 0.014 0.016

Drug prescribing 1 Drug prescribing at pharmacy not available - 0.101 0.075 - 0.103 - 0.100

2 Drugs prescribed at the pharmacy by [Insert same provider as above]

0.101 0.075 0.100 0.103

(Continues)

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reported herein. Bayesian precision intervals (BPI) around each of the switch rates were computed empirically by running multiple simula- tions for a particular pharmacy configuration, taking random draws from the final saved iterations for each respondent; the results were saved and ranked, and the 95% precision interval was determined by examining the results for the 2.5th and 97.5th percentiles among the set of ranked predictions. Significance was set at a P- value of <.05.

3   |   R E S U LT S

The mean part- worth utilities (estimated from 100 saved individual- level draws) for each level of each attribute associated with the proba- bilistic choice outcome are presented in Table 3. The optimal care services to be delivered at a community pharmacy setting were deter- mined through statistical modelling, giving the combination of attrib- utes that would maximize the probability that patients would switch pharmacies offering their current level of care services to a pharmacy offering a new set of care services (see Table 4). The potential services that could be provided under physician oversight by a pharmacist, nurse- practitioner or physician assistant included vital sign meas- urements, point- of- care laboratory testing (for cholesterol, glucose, common infections, metabolic chemistry panels, haematology and uri- nalysis), immunizations and health screening, and prescribing medica- tions. The average probability that patients would switch from their current pharmacy to the base- case pharmacy offering minimal services was 12.6% (95% BPI: 12.2%- 13.2%). The demand for the optimal ser- vices pharmacy was 2- fold higher than for the base- case pharmacy (25.5%; 95% BPI 23.5%- 27.0%), see Table 5. Across key subgroups, the demand doubled for virtually all groups with the exception of the elderly (aged 65+), the very poor, those in rural areas or for those who lived very close to their current pharmacy. As the BPI intervals were all

overlapping, there were no significant manifest differences between subgroups. Directionally, the demand for these service offerings was highest among disadvantaged groups which may have poorer access to quality primary health care including those of Hispanic origin (switch rate of 30.6%; 95% BPI: 25.7%- 34.3%) and African American patients (switch rate of 30.7%; 95% BPI: 27.1%- 35.2%).

In making choices between pharmacy scenarios, the profession of the actual service provider had minimal impact on patients’ decision- making. The optimal service plan had the highest level of demand when the pharmacist (under physician oversight) was the provider of services. However, after holding all the attributes in the optimal ser- vice model constant in the simulator, changing the provider from a pharmacist to a nurse- practitioner or physician’s assistant (under phy- sician oversight) produced only a slight drop in demand with a switch rate of 25.3% (95% BPI: 23.4%- 26.7%).

A decision rule was included in the simulator that with diagnostic services the cash- paying patient would incur an out- of- pocket minimum cost of $15 per visit. Patients naturally desired their preferred services at this lowest possible cost. However, at double the out- of- pocket cost at $30, the switch rate was still elevated at 17.6% (95% BPI: 16.4%- 18.7%). At $45, the switch rate was the same as for the base services.

The optimal service model (see Table 4) was presented to reim- bursement decision- makers in the second survey. The majority (66%) indicated their organization would be likely or highly likely to reim- burse for these services, 22% were neutral and 12% would be unlikely or very unlikely to reimburse.

4   |   D I S C U S S I O N

This population- based DCE study shows that patients, across every demographic group examined, by the choices they made, would prefer

Pharmacy attributes Level of service Mean part- worth utilities

Standard deviation (SD)

Lower 95% confidence interval (CI)

Upper 95% confidence interval (CI)

Medication services 1 Medication refill reminders (e.g by phone, text or Internet)

−0.035 0.044 −0.036 −0.034

2 Meeting with pharmacist to discuss new prescriptions. Medication refill reminders (e.g by phone, text or Internet)

0.055 0.049 0.054 0.056

3 Meeting with pharmacist to discuss all your medications, disease and health. Medication refill reminders (e.g by phone, text or Internet)

−0.020 0.053 −0.021 −0.019

Cost of services 1 $0 1.261 0.775 1.245 1.277

2 $15 0.939 0.560 0.928 0.951

3 $30 0.356 0.242 0.351 0.361

4 $45 −0.235 0.120 −0.237 −0.232

5 $60 −0.755 0.482 −0.765 −0.745

6 $75 −1.567 1.010 −1.587 −1.546

Intercept=−2.930 (95% CI−2.949,−2.910); SD = 0.946.

TABLE 3  (Continued)

     |  745FEEHAN Et Al.

a greater number of primary care service offerings, delivered through a community pharmacy setting. This is evident across all socio- economic and ethnic minority groups. Patients also prefer access to their full electronic medical records and for any primary care services provided at the community pharmacy to be entered into their record. The major- ity of reimbursement decision- makers are willing to pay for these ser- vices, providing support for the sustainability of this model in practice.

Importantly, the demand for the optimal pharmacy shown is highest among more disadvantaged groups with poorer access to quality primary health care such as those of Hispanic origin and African American pa- tients. The development of a network of pharmacy- centric primary care in close proximity to the patient’s home may be one tool that may help reduce disparities in health outcomes for ethnic minority populations.

Potential improved health care value to patients is exhibited in several ways. Patients value being able to make appointments via tele- phone or online to see a health provider in the pharmacy. They also prefer to use a pharmacy which has the ability to access their full elec- tronic medical records and can enter their pharmacy- derived health information including medication list into that record. This reinforces the model where pharmacies in general, or pharmacy- based primary care clinics, should be operating in partnership with established health- care systems, and be better positioned to deliver preventative and continual chronic disease comprehensive care. In order to meet the access, quality and cost imperatives of patients’ demand for primary care in this setting, several changes will be necessary to the existing structure and logistics of community pharmacies and how they engage with established health systems. The most important will be greater information interconnectivity between the community pharmacy and associated healthcare system to allow health information cross- talk to and from the electronic medical record. Thereby, all care providers will be able to chronicle patient encounters in real time. This will allow instant review of the base electronic record with legend, controlled substances, OTC, vaccinations and nutraceutical histories. Medication adherence patterns will become available and can be used to counsel patients on improved adherence as needed. Connectivity will also allow the physician- pharmacist collaboration to expand. For example, refer- rals from the community pharmacist to the provider or vice versa will be greatly facilitated. Mutual development of clinical decision support systems (CDSS) related to drug therapy can be implemented and evalu- ated, as had been developed for example in cardiology.22 These systems hardwire best practices at the point of care in an easy- to- use manner. Pharmacists’ knowledge of drugs, their application and use, especially potential drug- drug, drug- food, or drug- complementary and alternative medicine interactions or drug use in atypical populations, can be vital in the development of a pharmacy- delivered primary care CDSS.

Consistent with the notion of convenience, patients desire that their preferred pharmacy offer available point- of- care diagnostic test- ing. They also value the role that the pharmacy can play in prevention and would prefer their pharmacy offer health screening. This is consis- tent with an Australian DCE study showing patients have a preference for their pharmacy to offer lung- function tests.22

In terms of physical examinations, patients prefer their pharmacy offer some degree of examinations, such as measuring vital signs.

Higher levels of physical examinations to address patients’ complaints, or head- to- toe examinations, were not favoured, even if offered by a nurse- practitioner or physician’s assistant. This is in line with a DCE study in Australia that showed patients’ willingness to get advice at the pharmacy for minor complaints, but not for more significant symp- toms.13 Even though the choice exercise in the present study indicated there would be private area for diagnostic testing and examinations, and that all providers would be suitably trained and approved to pro- vide services by state and federal regulations, it may be that few pa- tients currently use walk- in clinics, and may be challenged that this is not how they currently see pharmacy services—that is a default to the status quo in terms of preference as noted by other studies.12It would be expected that as a greater proportion of patients experience more pharmacy- centric primary care (e.g through the growth of walk- in clin- ics), then the status quo would shift. If pharmacy primary care clin- ics are presented as a place of care that is open, comfortable, with a modern appearance, this current inherent hesitancy to extend care out from physicians’ offices for chronic care including examinations and point- of- care testing could be assimilated into the primary care para- digm. For pharmacies to become more recognized as providers of pri- mary health care, the physical layout and design of the pharmacy will be important in future pharmacy construction to reinforce perceptions among patients towards convenience, privacy and quality of service.

There are potential limitations to delivering primary care through com- munity pharmacies. These may include possible concerns about pharma- cist prescribing and dispensing potential conflict of interests. Underlying concerns about the combined practice of prescriber- dispensers is the fear they can be susceptible to financial motivations to leverage dis- pensing activity as a salary enhancement mechanism. Application of the American Medical Association Code of Ethics to the pharmacist situation should be considered. It contains specific language about ethical expec- tations involving physician dispensing in their office, when they are also prescribing, which should be considered in future pharmacy practice acts, “§ 8.06—prescribing and dispensing drugs and devices … Physicians may dispense drugs within their office practices provided such dispensing pri- marily benefits the patient.”23 What should be recognized, however, is that the overwhelming majority of states already allow practitioners to dispense medications to their patients.24 Pharmacists could also thus pre- scribe and dispense medications within community pharmacies provided such activities primarily benefit the patient.

A key finding in the present study is the openness of payer or- ganizations to reimburse for primary care services if they were to be delivered in the pharmacy setting (66% indicating their organization would be likely to very likely to reimburse the pharmacy for those pri- mary care services). As payers appear willing to reimburse enhanced services that patients desire, this would remove systemic barriers and facilitate the adoption and sustainability of this model at the commu- nity pharmacy level. Although the majority of payers are very open to convenience clinics and the opportunity they provide for quality care, systems of reimbursement need to be examined for equivalence across the healthcare providers.25,26

Patients want convenience, but the concept of receiving primary care at the pharmacy is new; therefore, there is a need for patients to

746  |     FEEHAN Et Al.

experience positive outcomes from full pharmacy- delivered primary care before endorsing this type of practice. However, adoption of this practice is reinforced by payers’ willingness to reimburse, and extant data showing pharmacists have shown achievement of improved out- comes in the provision of standard of care and patient self- care educa- tion.27–31 It is also encouraging that the European Union is establishing this type of practice and demonstrated lower costs and the provision of effective care.32

Patients prefer access to healthcare delivery that provides primary care services accessible when they need it, delivered by a team of professionals working in a coordinated comprehensive way, and that is convenient. A potential advantage of this model is potential lower costs by expanding the primary care delivery system into existing community pharmacies instead of building more centralized and costly community clinics. This would likely result in lower costs to patients for these services, greater access to healthcare personnel, improved team- based delivery of care and improved continuity of care through incorporation of the pharmacy records into the patient’s medical

record. This model is not in contrast to the patient- centred medical home and could be built factored into that model.33,34

Community pharmacies can contribute to measuring and improv- ing quality of care, and empower patients to become more active par- ticipants in their own care leveraging health information technology. Furthermore, primary care delivery through existing community phar- macies can be part of a system that “supports physicians and hospitals treating patients with help from entire communities that work col- laboratively to improve overall health, so that fewer persons develop illness.”35 Barriers to this model will be building a sustainable, transfer- rable model that all patients, primary care providers and payers recog- nize and support. This will take planning, communication and research to determine a best- fit model, adjusted to the local community that can be tested for quality, outcomes and lowering of costs over time.

A limitation of the study is its cross- sectional and descriptive na- ture. While the DCE experimental approach approximates real- world decision- making thought processes, it cannot equate to the actual ex- perience of a patient receiving primary care services in the pharmacy

Attribute Base- case pharmacy Optimal pharmacy

Hours of operation 9 am to 5 pm, closed Sundays (limited weekend hours)

Same

Prescription ordering, availability and information

Telephone or online Internet ordering, and two- way discussion with pharmacist (telephone or online)

Same

Service provider Pharmacist Pharmacist (with physician oversight)

Medical records Prescription records only held at the pharmacy and not put into your (the patient’s) medical record

The pharmacy has access to, and can enter prescriptions and health information into your (the patient’s) electronic medical record

Service logistics (Patients) Walk in and wait for services

(Patients) Walk in and wait for service or make an appoint- ment (via telephone or online)

Physical examinations

Not provided Blood pressure, heart rate and breathing rate

Diagnostic testing Not provided Diabetes and lipid/cholesterol measurements plus testing for common infections including influenza, hepatitis, tuberculo- sis and HIV and conducting chemistry, urine, saliva and other blood tests

Preventive services Only vaccinations/immunizations Vaccinations/immunizations and health screening (e.g mental health, lung function)

Drug prescribing Not provided Drugs prescribed at the pharmacy by a Pharmacist (with physician oversight)

Medication services Meeting with pharmacist to discuss new prescriptions. Medication refill reminders (e.g by phone, text or Internet)

Same

Cost of services $0 $15

TABLE 4 Base- case pharmacy and optimal pharmacy configurations

     |  747FEEHAN Et Al.

setting or not. Future research will focus on the implementation of pilot service change programmes in community pharmacies and the evaluation of changes in service practices in terms of patient and

provider satisfaction, and differential health outcomes for those re- ceiving more expanded primary healthcare services in these settings. Further, this is a comprehensive evaluation of the US population, and

TABLE 5 Patient demand for the optimal pharmacy, expressed as the change in switch rate from the base- case pharmacy, overall and by demographic subgroups

Subgroup

Switch Rate

Absolute Difference (%)

Relative Percentage Change (%) Change Ratio

Base- Case Pharmacy Optimal Pharmacy

% (95% BPI) % (95% BPI)

All Patients 12.6 (12.2- 13.2) 25.5 (23.5- 27.0) 12.9 102.4 2.0

Gender

Male 12.4 (11.9- 13.1) 23.9 (21.7- 26.0) 11.5 92.7 1.9

Female 12.8 (12.3- 13.5) 26.8 (24.3- 28.5) 14.0 109.4 2.1

Age

Age 65+ 12.0 (11.0- 13.0) 20.7 (17.9- 23.8) 8.7 72.5 1.7

18- 64 12.8 (12.3- 13.4) 26.5 (24.6- 28.4) 13.7 107.0 2.1

Hispanic origin

Hispanic origin 13.7 (12.5- 15.1) 30.6 (25.7- 34.3) 16.9 123.4 2.2

Non- hispanic origin 12.5 (12.1- 13.1) 24.8 (22.9- 26.2) 12.3 98.4 2.0

Race

African American 14.2 (13.0- 15.7) 30.7 (27.1- 35.2) 16.5 116.2 2.2

Non- African American 12.4 (12.0- 13.1) 24.8 (22.9- 26.7) 12.4 100.0 2.0

Insurance Status

Insured 12.6 (12.2- 13.2) 25.4 (23.3- 26.7) 12.8 101.6 2.0

Uninsured 13.1 (11.3- 15.4) 26.5 (19.5- 34.1) 13.4 102.3 2.0

Poverty level

Poverty level 13.4 (12.0- 14.7) 25.0 (20.4- 29.2) 11.6 86.6 1.9

Low income level 13.6 (12.7- 14.6) 25.5 (22.7- 28.1) 11.9 87.5 1.9

Above low income level 12.0 (11.5- 12.8) 25.6 (23.3- 27.4) 13.6 113.3 2.1

Income

Very low (Less than $25 000) 13.8 (12.8- 4.7) 24.4 (22.0- 27.4) 10.6 76.8 1.8

low ($25 000- $49 999) 12.6 (12.0- 13.5) 25.8 (23.9- 27.7) 13.2 104.8 2.0

Medium ($50 000- $99 999) 12.1 (11.6- 12.8) 25.9 ( 23.8- 27.6) 13.8 114.0 2.1

High ($100 000 and above) 11.8 (10.9- 12.3) 25.5 (22.6- 27.4) 13.7 116.1 2.2

Community residence

Rural 13.2 (12.1- 14.3) 25.5 (22.0- 29.2) 12.3 93.2 1.9

Small city or town 12.5 (11.9- 13.2) 24.9 (23.0- 26.5) 12.4 99.2 2.0

Suburb of a large city 12.4 (11.9- 13.1) 25.7 (24.1- 27.0) 13.3 107.3 2.1

Large city 12.7 (11.9- 13.4) 26.0 (24.2- 27.6) 13.3 104.7 2.0

Region

North- east 12.3 (11.4- 13.0) 21.6 (19.4- 25.7) 9.3 75.6 1.8

Midwest 12.3 (11.4- 13.2) 25.6 (22.2- 28.3) 13.3 108.1 2.1

South 12.8 (12.3- 13.5) 27.4 (24.8- 29.2) 14.6 114.1 2.1

West 13.1 (12.0- 14.3) 25.2 (22.1- 28.2) 12.1 92.4 1.9

Pharmacy distance

Very close (Less than 1 mile) 12.4 (11.8- 13.0) 24.1 (22.5- 25.5) 11.7 94.4 1.9

Close (1- 5 miles) 12.6 (12.2- 13.2) 25.5 (23.8- 26.9) 12.9 102.4 2.0

Far (More than 5 miles) 13.0 (12.2- 13.9) 26.4 (23.6- 28.4) 13.4 103.1 2.0

748  |     FEEHAN Et Al.

its application to other countries warrants further study. The reliance on pre- enrolled panellists is not a significant limitation given the deep Internet penetration in the USA and the maintenance of these very large panels to ensure demographic representativeness.

5   |   W H AT I S N E W A N D C O N C L U S I O N

This large DCE study provides empirical support for expanding the role of community pharmacies historically associated with dispensing of medications, to being providers of primary care services through an integrated healthcare system (as evidenced by the demand for linking electronic medical records and physician oversight of advanced services including non- medical practitioner prescribing). Such a model of provid- ing primary care services in the community pharmacy would increase access and potentially lead to an improved public health. Patient’s pre- fer to have their drug information linked to their medical record for a comprehensive health record. This has wide healthcare implications as currently prescribing information is generally not linked to filled pre- scription data, so that healthcare practitioners have little information on the complete medication record or to the patient’s medication adher- ence. Finally, an implication of this research is not to remove the patient from their current physician, but to leverage those physicians to pro- vide oversight for services that add a layer of convenience to the pri- mary care delivery system. The community pharmacy offers a familiar place, close to home, for the delivery of primary and preventative care with monitoring of health outcomes. Development of a primary care pharmacy alternative, electronically linked to the current health system, should be considered as a healthcare delivery model moving forward.

6   |   A U T H O R S H I P S TAT E M E N T

The authors assert the manuscript has been read and approved by all authors and that all authors agree to the submission of the manuscript to the Journal.

C O N F L I C T O F I N T E R E S T

No conflict of interests have been declared.

R E F E R E N C E S

1. Department of Health and Human Services Health Resources and Services Administration Bureau of Health Professions. Adequacy of pharmacist supply: 2004 to 2030. http://bhpr.hrsa.gov/healthwork- force/reports/pharmsupply20042030.pdf. Accessed August 1, 2015.

2. Retail Clinics 2015: Growth of Stores, Consumer Opinion, Leading Competitors, Sales of Products to Clinics (Diagnostic Tests, Pharmaceuticals, Vaccines), Clinic Sales Forecasts and Trends. http:// www.kaloramainformation.com/Retail-Clinics-Growth-8792050/. Accessed August 1, 2015.

3. Reid RO, Ashwood JS, Friedberg MW, Weber ES, Setadji CM, Mehrotra A. Retail clinic visits and receipt of primary care. J Gen Intern Med. 2013;28:504-512.

4. Mehrotra A, Gidengil CA, Setodji CM, Burns RM, Linder JA. Antibiotic prescribing for respiratory infections at retail clinics, phy- sician practices, and emergency departments. Am J Manag Care. 2015;21:294-302.

5. Mehrotra A, Liu H, Adams JL, et al. Comparing costs and quality of care at retail clinics with that other medical settings for 3 common illnesses. Ann Intern Med. 2009;151:321-328.

6. de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012; 21:145-172. https://doi.org/10.1002/hec.1697. Accessed August 1, 2015.

7. Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014;32:883-902. https://doi. org/10.1007/s40273-014-0170-x. Accessed August 1, 2015.

8. Naik-Panvelkar P, Armour C, Saini B. Discrete choice experiments in pharmacy: a review of the literature. Int J Pharm Pract. 2013;21:3-19. https://doi.org/10.1111/ijpp.12002. Epub 2012 Nov 20

9. Vass C, Gray E, Payne K. Discrete choice experiments of pharmacy services: a systematic review. Int J Clin Pharm. 2016;38:620-630.

10. Scott A, Bond C, Inch J, Grant A. Preferences of community pharma- cists for extended roles in primary care: a survey and discrete choice experiment. Pharmacoeconomics. 2007;25:783-792.

11. Tinelli M, Ryan M, Bond C. Patients’ preferences for an increased pharmacist role in the management of drug therapy. Int J Pharm Pract. 2009;17:275-282.

12. Whitty JA, Kendall E, Sav A, et al. Preferences for the delivery of com- munity pharmacy services to help manage chronic conditions. Res Social Adm Pharm. 2015;11:197-215.

13. Naik-Panvelkar P, Armour C, Rose J, Saini B. Patients’ value of asthma services in Australian pharmacies: the way ahead for asthma care. J Asthma. 2012;49:310-316.

14. Rennie L, Porteous T, Ryan M. Preferences for managing symptoms of differing severity: a discrete choice experiment. Value Health. 2012;15:1069-1076.

15. Munger MA, Durante R, Ranker L, Feehan M. Integrating community pharmacies into primary care delivery: a qualitative assessment of pa- tient, pharmacist and payer views. Inside Primary Care. 2016;4:13-20.

16. Feehan M, Durante R, Ruble J, Munger M. Qualitative interviews re- garding pharmacist prescribing in the community setting. Am J Health Sys Pharm 2016;73:1456-1461.

17. Brazell J, Diener C, Severin V, Uldry P. Beyond the Basics: Choice Modelling Methods and Implementation Issues. 2003 ART/Forum Tutorial. Chicago, IL: American Marketing Association; 2003.

18. Sawtooth Software. The CBC/HB System for Hierarchical Bayes Estimation. Version 5.0 Technical Paper. Sequin, WA: Sawtooth Software Inc.; 2009.

19. Gillbride T, Allenby GM. A choice model with conjunctive, disjunctive and compensatory screening rules. Marketing Science. 2004;23:391-406.

20. Orme B. Fine tuning CBC and Adaptive CBC questionnaires. Sequin, WA: Sawtooth Software Inc.; 2009.

21. Jamieson LF, Bass FM. Adjusting stated intention measures to predict trial purchase of new products: a comparison of models and methods. J Mark Res 1989;Vol XXVI:336-345.

22. Njie GJ, Proia KK, Thota AB, et al. Clinical decision support systems and prevention: a community guide cardiovascular disease systematic review. Am J Prev Med. 2015;49:784-795.

23. American Medical Association. Opinion E-8.06: prescribing and dispensing drugs and devices. In: Code of Medical Ethics. Chicago: AMA Press, Issued June 2002. www.ama-assn.org/ama/pub/phy- sician-resources/medical-ethics/code-medical-ethics/opinion806. page. Accessed January 15, 2017.

24. Munger MA, Ruble JH, Nelson SD, et al. National evaluation of pre- scriber drug dispensing. Pharmacotherapy. 2014;34:1012-1021.

25. Iglehart JK. The expansion of retail clinics — Corporate Titans vs. Organized Medicine. N Engl J Med. 2015;373:301-303.

     |  749FEEHAN Et Al.

26. Hwang J, Mehrotra A. Why retail clinics failed to transform health care. Harvard Bus Rev. 2013. [Epub ahead of print].

27. Chen T, Kazerooni R, Vannort EM, et al. Comparison of an inten- sive pharmacist- managed telephone clinic with standard of care for tobacco cessation in a veteran population. Health Promot Pract. 2013;15:512-520.

28. Margolis KL, Asche SE, Bergdall AR, et al. Effect of home blood pres- sure telemonitoring and pharmacist management of blood pressure control: a cluster randomized clinical trial. JAMA. 2013;310:46- 56.

29. Yu J, Shah BM, Ip EJ, Chan J. A Markov model of the cost- effectiveness of pharmacist care for diabetes in prevention of cardiovascular dis- eases: evidence from Kaiser Permanente Northern California. J Manage Care Pharm. 2013;19:102-114.

30. Rubio-Valera M, March Pujol M, Fernández A, et al. Evaluation of a pharmacist intervention on patients initiating pharmacological treat- ment for depression: a randomized controlled superiority trial. Eur Neuropsychopharmacol. 2013;23:1057-1066.

31. Verret L, Courturier J, Rozon A, et al. Impact of a pharmacist- led war- farin self- management program on quality of life and anticoagulation control: a randomized trial. Pharmacother. 2012;32:871-879.

32. Bodenheimer T, Pham HH. Primary care: current problems and pro- posed solutions. Health Aff. 2010;29:799-805.

33. American Academy of Family Physicians. Primary care for the 21st Century: Ensuring a Quality, Physician-led Team for Every Patient. 2012. http://www.aafp.org/dam/AAFP/documents/about_us/ini- tiatives/AAFP-PCMHWhitePaper.pdf?cmpid=npp12_ad_com_na_ van_1. Accessed June 1, 2016.

34. American Academy of Family Physicians. The Patient-Centered Medical Home (PCMH). http://www.aafp.org/online/en/home/mem- bership/initiatives/pcmh/ptexperience.html. Accessed June 1, 2016.

35. Lavizzo-Mourey R. Halfway there? Health reform starts now JAMA. 2016;315:1335-1336.

How to cite this article: Feehan M, Walsh M, Godin J, Sundwall D, Munger MA. Patient preferences for healthcare delivery through community pharmacy settings in the USA: A discrete choice study. J Clin Pharm Ther. 2017;42:738–749. https://doi.org/10.1111/jcpt.12574

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