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Abstract

The COVID-19 pandemic enhanced the use of telehealth as a means of delivering services to patients who required continued and uninterrupted care. This helped to reduce readmission to hospitals where COVID-19 hospitalization was prioritized. Patients with HCV and HIV and other chronic diseases require this type of care. This study evaluated the post-pandemic acceptability of pharmacist-delivered telehealth services among HCV and HIV monoinfected and coinfected patients in Washington DC. This was a cross-sectional study conducted in a community pharmacy setting in Washington DC whose primary outcome was the acceptability of pharmacist-delivered telehealth services through a proposed platform(docsink). A validated questionnaire, borrowed from the literature was used to determine telehealth acceptability, measured as behavioral intention, among patients who receive care from this pharmacy. The study recruited 100 participants. Descriptive statistics were conducted as well as bivariable and multivariable analyses to assess predictors of telehealth acceptability. In the unadjusted model, PU/EM (OR 0.571, 95% confidence interval (0.45-0.73), P < .0001)), PEOU(OR 0.72, 95% confidence interval (0.61-0.85)) and IM(OR 0.733, 95% confidence interval (0.62-0.87), P = .0003)) were significant predictors of behavioral intention. Overall, the study found that lower Perceived Usefulness/Extrinsic Motivation scores decrease the odds of intending to use pharmacist-delivered telehealth (OR = 0.490, 95% confidence interval (0.29-0.83), P = .008). This study determined that the impact of perceived usefulness and extrinsic motivation was critical to the acceptance of pharmacist-delivered telehealth among a predominantly Black/African American study population.

Keywords

Telehealth COVID-19 Pharmacy Telemedicine Human Immunodeficiency Virus Hepatitis C Virus Technology Acceptance Model Motivational Model Perceived Usefulness Perceived Ease of Use

Introduction

Epidemiology and Burden of HCV and HIV in the US and Washington DC

It is estimated that 71 million people globally and 4.1 million people in the United States (US) are infected with Hepatitis C Virus (HCV).¹ Chronic hepatitis is associated with significant morbidity and mortality which, based on data from the National Center for Health Statistics, has now surpassed Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) as a cause of death in the US.² HCV could result in a projected increase in total healthcare costs from $6.5 billion to $9.1 billion by 2024 if patient identification and treatment are not optimized.³ The National Health and Nutrition Survey (NHANES) estimates that 12,400 (2.3%) of the adult population in Washington DC were HCV-positive in 2016. However, this prevalence does not include individuals who were incarcerated, homeless, living in nursing homes, persons on active military duty, and immigrants.⁴

The burden of HIV is also significantly high in the United States. An estimated 1.2 million people in the US aged 13 and older are living with HIV, 3% of whom have undiagnosed HIV infection.⁴ Washington DC has epidemic levels of HIV in the nation with a reported prevalence that is 6 times the national rate (1.8% vs 0.3%).⁵

HCV-HIV co-infection is also a significant problem. Approximately 5% of adults with chronic HCV have coinfection with HIV. Fifteen to thirty percent of people living with HIV(PLWH) in the US have coinfection with HCV on account of shared modes of transmission.⁶ About 75% of people with HIV who inject drugs also are infected with HCV. Individuals with HIV-HCV coinfection have accelerated liver disease progression with faster rates of hepatic fibrosis and a more aggressive course of liver disease^7
-9 which has been shown to occur 12 to 16 years earlier in co-infected patients as compared to those with HCV mono-infection. About 80% to 90% of liver-related deaths occur due to HCV infection.^10,11 All the aforementioned statistics point to a significant problem both nationally in the US, and in the district of Columbia in this patient population.

The availability of Direct Acting Antivirals (DAA) for HCV treatment has greatly improved outcomes for monoinfected and co-infected patients. Previously, treatment with interferon-based regimens was sub-optimal with limited uptake due to low response to treatment, high rate of adverse effects, concerns for interaction with antiretroviral medication (ARV), and a high prevalence of comorbidities.^12
-14 Similarly, the availability of Highly Active Antiretroviral Therapy(HAART) for HIV-infected patients has improved patient outcomes. Appropriate management of patients requires close attention to potential drug interactions between HCV medications and HAART.¹ It also requires monitoring of behavioral health issues such as substance abuse and sexual risk behavior during the entire course of treatment as these may predispose patients to reinfection. Pharmacists have been at the forefront of providing medication therapy management, chronic disease management, and adherence coaching through patient education in community pharmacy settings. Provision of these services at the pharmacy level alongside providers increases benefit to the patient and provides uninterrupted access to care, especially during the COVID-19 pandemic.

HCV/HIV Management–Challenges Experienced During the COVID-19 Pandemic

The COVID-19 pandemic led to an unexpected shift in the practice of medicine. Patients’ ongoing healthcare needs have been pushed to telehealth modalities in the ambulatory and community clinic settings. As a consequence, the use of telehealth in the form of telephone or videoconferencing can be encouraged to ensure that patients with chronic illnesses have access to health care in unprecedented times. HCV and HIV patients in the District of Columbia were especially vulnerable during the pandemic. These patients were seen among the first who would be able to take advantage of expanded access to telehealth by the District of Columbia Healthcare Finance (DCHF).¹⁵

The sudden transition to telehealth during the COVID-19 pandemic provided immense opportunities for the introduction and advancement of telehealth in healthcare delivery for HCV and HIV patients, especially in the pharmacy setting where specialty services can be provided. These patients, in particular, needed continuity of care during the pandemic to ensure that (1) treatment goals were met (2) they were not hospitalized in care settings already resource-strained and burdened from COVID-19 care, and (3) they were not placed at risk of contracting COVID-19 themselves on account of their immunocompromised state. It is therefore relevant that patient acceptance and satisfaction be continually monitored to ensure that pharmacist-delivered telehealth services are effectively delivered and tailored to meet patient needs.

The Rationale of the Study

This study sought to fill gaps in the literature about the feasibility of telehealth implementation via an established online platform—docsink—in the community pharmacy setting. A review of the literature found pilot studies on the feasibility of pharmacist-delivered telehealth studies for diabetes and hypertension as chronic conditions; however, none had looked at similar models for HCV and HIV patients.^16
-20 Also not well explored is the acceptability of pharmacist-led telehealth programs from a patient’s perspective in terms of acceptability. When effective, telehealth potentially eliminates the challenges of time and distance constantly experienced by hard-to-reach patients. At the time of conducting this study, there were no studies that focused on pharmacist-led telehealth acceptability by HCV and HIV patients. More importantly, the timing of this project is unique and will fill an important gap in the post-pandemic nuances of acceptability and feasibility, for Black/African American patients’ whose’ attitudes may have shifted. This study is based on the integrated model of the Technology Acceptance Model (TAM) and the Motivational Model. This model will be used to determine whether model constructs determine behavioral intention (BI) to accept and use telehealth among a population of HCV/HIV monoinfected, and co-infected patients. The TAM was originally proposed by Davis in 1986 and derived from the Theory of Reasoned action. It explains how various variables influence belief, attitude, and intention to use technology. The motivational model was developed in 1992 by Davis, Bagozzi, and Warshaw. It proposes that intrinsic motivation (IM) and extrinsic motivation (EM) are important in determining BI to use telehealth. The integration of these 2 models takes into account key similarities and differences. Both models include a construct that emphasizes personal gain; Extrinsic motivation and perceived usefulness are measured using the same items, therefore, prompting the integration of these 2 constructs into one.²¹

Materials and Methods

Study Design and Population

The study followed a cross-sectional design and used an interviewer-administered telephone survey as the primary method of data collection. This study was conducted at an independent community pharmacy in Washington DC, Grubbs pharmacy which has 2 locations: one in Southeast DC and another in Northwest DC. Inclusion criteria for study participation were: (1) 18 years and older, (2) Living in Washington DC, (3) At the time of study execution, receiving either HCV or HIV medication, or both for coinfected patients, from Grubbs pharmacy in Washington DC. The study was submitted to and approved as expedited review by the Howard University Institutional Review Board (HU-IRB).

Survey Creation

The study used a validated questionnaire adapted from a study by Wilson and Lankton which evaluated the relationship between the TAM, the Motivational model, and BI to use e-health.²¹ The face validity of the content in the questionnaire was determined in consultation with pharmacists offering care at Grubbs pharmacy and 2 faculty members from the college of pharmacy at Howard University.

Participant Recruitment and Consenting

Participants were recruited from Grubb’s pharmacy HCV and HIV database, via telephone with assistance from pharmacists providing care to these patients. Addresses on file were used to determine if patients were eligible to participate that is, living in Washington DC. Participants had a date of birth on file as well which was used to determine their age. It was determined that all the patients in the database were adult patients hence all were eligible for the study based on their age. Given that this was an exploratory study, the number of participants needed for this study was determined based on the regression rule of thumb. This rule states that there should be at least 10 observations per variable. This study intended to assess 10 key independent variables. Based on the “n=10k” rule, where n is the sample size and k is the number of variables, the minimum sample size would be 100. Recruitment was stopped once 100 participants were successfully recruited to participate in the study.

A consent form provided by the Howard University IRB was prepared and administered over the phone and voluntary acceptance was recorded once patients agreed to take part in the study. A copy of the informed consent form was delivered to the participants either by mail or email based on their preference. The researcher, with the help of the attending pharmacist, described the aim of the study to the participants before administering it to ensure clarity of the study goals. Participants received a $35 gift card for participating in the study. The gift card was either mailed to the participants or picked up from the pharmacy depending on participant preference.

Study Variables

The primary outcome of the study was telehealth acceptability, measured as behavioral intention to use a pharmacist-led telehealth videoconferencing platform, using the integrated model (Technology Acceptance Model and the Motivational Model). The main predictor variables included Perceived Usefulness/Extrinsic Motivation (PU/EM), Intrinsic Motivation (IM), Perceived Ease of Use (PEOU), and 5 additional antecedent factors which included patient satisfaction, perceived knowledge, information-seeking preference, healthcare need, and internet dependence. Perceived usefulness in this study is described as the degree to which a person believes that using telehealth would be beneficial to their health. Perceived ease of use is the degree to which a person believes that using telehealth would be free of effort. Extrinsic motivation is when an individual is motivated to perform an activity to earn a reward or avoid punishment. Intrinsic motivation is when an individual is motivated to perform an activity for its own sake and personal reward. Behavioral intention is the likelihood of the person using telehealth.

The study also included sociodemographic factors such as age, gender, comorbidities, race, marital status, level of education, whether patients have had a virtual doctor visit in the last 12 months, and whether patients were reluctant to attend ambulatory visits because of fear of contracting COVID-19.

The study instrument used a 5-point Likert scale to measure model constructs(Strongly disagree to Strongly agree). The factors were further recoded to determine proportion of participants who agreed or disagreed with model constructs. “Strongly disagree,” “Disagree” and “Neither agree nor disagree” were defined as “NO” while “agree” and “strongly agree” were defined as “YES.” For linear modeling of the variables, higher scores for all the constructs indicated unlikelihood while lower scores indicated likelihood for the variable as answered on the questionnaire. Health care need was assessed as a categorical variable for the presence of comorbidities and as a continuous variable for the number of remote telephone visits that the patient had been on in the past 12 months (Figure 1).

Figure 1.

Integrated TAM and Motivational model.

Statistical Analysis Plan

Descriptive statistics were conducted on socio-demographic and clinical factors to obtain the mean and median for continuous variables and proportions for categorical variables. Simple and multiple logistic regression models were used to determine factors predicting telehealth acceptability in the patient population by examining odds ratios and 95% confidence intervals. Covariates were included in the regression analysis using the enter method, as independent variables (P < .25). All statistical analysis was conducted using SAS v9.4 at an alpha level of 0.05.

Results

A total of 100 patients were recruited for this study (figure 2). Responses were collected after a description of the study aim, expectations and questionnaire was given to the participants. Table 1 illustrates the sociodemographic characteristics of the patient population included in the study. The mean age of participants in this study was 55. The majority were male (64%), of Black race (88%), single (78%), unemployed (72%), and had more than a high school education (84%). About 85% of patients had a remote appointment in the past 12 months and 73% avoided doctors’ appointments for fear of contracting the COVID-19 virus. A higher proportion of the participants agreed with the antecedent factors measured in the study based on the Integrated TAM/Motivational model. About 98% were satisfied with pharmacy services, 98% were knowledgeable about their health, 90% depended on the internet for various purposes, and 99% preferred to be given information on their treatment. A higher proportion of participants also agreed (score = yes) with model constructs; 87%-PU/EM, 80%-PEOU, 84%-IM. Overall, 80% of the participants accepted that they would use/intend to use the telehealth platform (Table 1).

Figure 2.

Recruitment flow diagram. Total recruited participants = 100.

Table 1.

Participant Characteristics.

Characteristic	N	(%)
Age
18-56	46	46
57-75	52	52
>76	2	2
Gender
Male	64	64
Female	36	36
Race
Black	88	88
White or Other	12	12
Comorbidities
Yes	60	60
No	40	40
Marital Status
Single	78	78
Married	8	8
Divorced/Widowed/Separated	14	14
Employment Status
Yes	27	27.7
No	72	72.3
Level of education
Less than High school	16	16.6
Highschool or more	83	84.3
Remote Appt in the last 12 months
Yes	85	85
No	15	15
Missed appt in the last 12 months to avoid contracting COVID
Yes	27	27
No	73	73
Satisfaction with Pharmacy services
Yes	98	98
No	2	2
Health knowledge
Yes	98	98
No	2	2
Internet dependence
Yes	90	90
No	10	10
Information-Seeking Preference
Yes	99	99
No	1	1
PU/EM
Yes	87	87
No	13	13
PEOU
Yes	80	80
No	20	20
IM
Yes	84	84
No	16	16
BI
Yes	80	80
No	20	20

The model fit from the logistic regression fits the data well with a non-significant P-value for the Hosmer Lemeshow test(P = .4223). The independent variables explain 37% of the variability in the dependent variable as given by the R² value.

In the Unadjusted model, PU/EM (OR 0.571, 95% confidence interval (0.45-0.73), P < .0001)), PEOU(OR 0.720, 95% confidence interval (0.61-0.85)) and IM(OR 0.733, 95% confidence interval (0.62-0.87), P = .0003)) were significant predictors of behavioral intention. This means that patients with lower PU/EM, PEOU, and IM had significantly lower odds of telehealth acceptability.

In the adjusted model, PU/EM was the only significant predictor of telehealth acceptability (OR 0.490, 95% confidence interval (0.29-0.83), P = .008)) Patients with lower PU/EM had lower odds of telehealth acceptability. Table 2 gives a summary of the results from the logistic regression analysis.

Table 2.

Predictors of Behavioral Intention/Telehealth Acceptability.

Characteristic	Unadjusted OR (95% CI)	P-Value	Adjusted OR (95% CI)	P-value
Gender
Male	Reference
Female	1.89 (0.63-5.74)	.26	0.68 (0.12-3.87)	.66
Age	1.01 (0.96-1.06)	.68	1.03 (0.94-1.14)	.5
Race
Black	Reference
White/ Other	0.718 (0.18-2.94)	.65	0.20 (0.02-2.61)	.22
Comorbidities
Yes	Reference
No	0.465 (0.17-1.25)	.13	0.95 (0.16-5.59)	.95
Marital Status
Single	Reference
Married	2.1 (0.242-18.21)	.95	1.22 (0.06-23.21)	.9
Div/Sep/Wid	3.9 (0.477-31.88)	.4	0.99 (0.07-13.28)	1
Employment Status
Yes	Reference
No	1.59 (0.56-4.54)	.39	0.61 (0.11-3.45)	.57
Level of education
High school or more	Reference
Less than High school	0.48 (0.15-1.61)	.24	0.14 (0.01-1.31)	.08
Remote Appt in the last 12 months
Yes (ref)	Reference
No	1.00 (0.25-3.94)	1	1.90 (0.19-18.92)	.59
Missed appt in the last 12 months
Yes (ref)	Reference
No	0.62 (0.19-2.05)	.43	0.71 (0.10-5.23)	.74
Satisfaction with Pharmacy services	0.80 (0.54-1.20)	.29	0.56 (0.27-1.17)	.12
Health knowledge	0.93 (0.57-1.53)	.8	2.15 (0.87-5.29)	.09
Internet dependence	0.93 (0.84-1.04)	.2	1.00 (0.82-1.23)	.97
Information-Seeking Preference	0.88 (0.65-1.09)	.23	1.26 (0.78-2.03)	.35
Health Care Need	1.10 (0.97-1.24)	.13	1.24 (0.98-1.56)	.08
PUEM	0.57 (0.45-0.73)	<.0001	0.49 (0.29-0.83)	.008
PEOU	0.72 (0.61-0.85)	.0001	0.78 (0.53-1.13)	.19
IM	0.73 (0.62-0.87)	.0003	1.02 (0.68-1.52)	.93

Discussion

Pharmacists play a key role in the continuum of care. They can establish medication therapy management (MTM) services and other complex patient care services. During the pandemic, pharmacists had to rely on telehealth to deliver services. Changes to federal law and regulations during the pandemic created opportunities for pharmacists to provide and be reimbursed for services delivered remotely hence referred to as telepharmacy.²² Services provided under telepharmacy include MTM, chronic disease management, transitions of care, pharmacogenomics, remote dispensing, and ambulatory care. Pharmacists who reported using telehealth in the literature indicated that the no-show rate to appointments decreased compared to the in-patient percentage and it was also possible to reach patients that had previously been difficult to reach.²³ This study determined the factors associated with the acceptance of a pharmacist-led telehealth platform. Studies in the literature have shown that BI has a positive significant effect on actual usage.²⁴ These studies suggest that actual use can be replaced by intention to use when the technology is still undergoing development as was the case in this study.

Significant predictors of BI intention/telehealth acceptability in this study, from the adjusted model include PU/EM. IM and PEOU did not significantly predict BI/telehealth acceptability in the adjusted model. Several studies in the literature show that PU and PEOU are significant predictors of BI. A study by Rho et al showed that PU and PEOU directly impacted BI to accept a telemedicine service by physicians. There was a positive significant effect (PU, β = .236, P < .01), (PEOU, β = .537, P < .001).²² A similar study by Kissi et al also determined that PU had a positive and significant influence on BI (β = 2.268, P < .05).²⁴ These results show that overall, the relationship between PU/EM and BI is significant. Emphasis on these constructs should be encouraged when strategies for remote healthcare delivery. In modeling patients’ acceptance of provider-delivered E-health, Wilson and Lankton found that all these factors except IM predicted significant relationships with BI.²¹

Between 2015 and 2019, about 92% of households in DC had a computer and 83% had broadband internet subscriptions. The average broadband adoption rate for wards 1, 2, 3, 4, and 6 is 85% while that in wards 5, 7, and 8 is 65%.^25,26 Based on these numbers, internet dependence was expected to be a significant predictor of BI in this study. However, with a smaller sample size, this hypothesis was not proven in this study. Limited access to smart hand-held devices such as smartphones and tablets that make it easier to communicate could possibly have an effect on the use of telehealth in this patient population.

Pharmacists are highly skilled and valued community care providers who are easily accessible to patients in the community. However, unlike physicians and nurse practitioners, they do not possess the legal capacity to assess, prescribe and administer biological products. As of October 2020. Only 37 states allowed pharmacists to qualify as medical providers under Medicare part B. In Washington DC, pharmacists can only prescribe contraceptives by law. Additionally, all 50 states and DC allow pharmacists to dispense naloxone for opioid overdose, either under a standing order or collaborative practice agreement or without a prescription. Pharmacists are the only healthcare professionals who do not have full provider status at the federal level²⁷ and face administrative barriers when seeking reimbursement for clinical services.²⁷ Designing and delivering telehealth models delivered by pharmacists will eliminate barriers such as limited access to primary care providers. Pharmacists have the potential to help streamline care for various conditions and chronic diseases that require uninterrupted care for hard-to-reach patients. In addition to this, the ability of pharmacists to bill for such services to meet administrative and logistical costs will enhance the efficiency and effectiveness of these models and help to fill any gaps in health care delivery. This study had a telehealth acceptance rate of 80%, meaning that patients are willing to receive telehealth services offered by pharmacists.

Successful pharmacist-delivered telehealth programs can be used as a means for plans to meet Medicare star ratings for Medicare Advantage plans, Medicare Prescription Drug Plans, and Medicare Cost Plans. Pharmacists being last in contact and most easily accessible to the patient, act as the rate-limiting step by determining the rate of retention to care, ensuring patient adherence, and conducting therapeutic drug monitoring. Enhanced patient-centered pharmacy services can improve care, decrease emergency room visits and hospital admissions, limit abuse/misuse of medications, and reduce the risk of drug therapy problems.²⁸

In response to the COVID-19 pandemic, the use of telemedicine/telehealth has notably grown. Critical underlying issues, antecedent factors, and underlying needs that have temporal precedence to the use of IT must be resolved to ensure proper setup and sustenance post-pandemic. This can only be achieved by conducting extensive needs assessments of specific patient groups and tailoring service delivery to their needs. This research presents interesting directions for future research. Users anchor their perceptions of telehealth to antecedent factors. These factors have temporal precedence to Integrated model constructs and should therefore be used to determine opportunities for training to enhance user perceptions of usefulness, ease of use, and intrinsic and extrinsic motivation.

Additional patient specific concerns that were expressed in this study included a lack of continuous and uninterrupted supply of internet, lack of infrastructure such as smartphones, computers, or tablets, concerns about special accommodations for special needs patients such as blind patients, and challenges associated with navigating telehealth user interphases. The majority of participants who took part in this study were aged 57 to 75 (baby boomers, 52%) and Black race (88%). These patients disproportionately and severely bear the burden of HCV and HIV and experience barriers to internet access and challenges in using technology. It is hoped that the results from this study will be used to influence future studies that will help shape the use of telehealth and increase healthcare access in this patient population.

Study Limitations

Overall, this study found that there is limited published evidence on pharmacist-delivered telehealth in the management of HIV and HCV. Further studies should be conducted to identify specific patient needs and the effect of antecedent factors that determine the intention and usage of telehealth in the pharmacy setting. This study used a validated questionnaire borrowed from the literature. Given the small sample size, no prior testing was done to further validate the questionnaire. Pilot testing the questionnaire and potentially pre-exposing the patient to the intended telehealth platform before the needs assessment may enable capturing patient perceptions that are free from bias. Future studies should also incorporate other models such as the diffusion innovation theory of communication which explains how over time an idea gains momentum and spreads or diffuses through a specific population or social system. The goal of diffusion is to determine whether people as part of a social system adopt a new idea, behavior, or product and accept to do something differently.²⁹ Such models will enable healthcare providers to determine the sustainability of telehealth, especially in disaster settings where the demand for telehealth as experienced during the COVID-19 pandemic surpassed the ability of healthcare providers to supply it.³⁰ Self-reported data has the risk of response bias and recall bias. However, the questions were delivered after an extensive explanation of the study goals to ensure the accuracy of responses. Participants were also required to request further explanation if they did not understand any of the questions, voluntarily give responses, and also gave feedback outside of the items measured in the survey questionnaire. The proportion of HIV, HCV, and coinfected patients included in the study may not be enough to extrapolate the results to larger settings. Future studies with larger sample sizes and power analysis should therefore be conducted to increase the external validity of findings.

Conclusion

This cross-sectional study determined that the impact of perceived usefulness and extrinsic motivation was critical to the acceptance of pharmacist-delivered telehealth among the predominantly Black/African American study population. From a care coordination perspective, this study can form the basis of a needs assessment for telehealth services for HCV/HIV patients in the District of Columbia at the level of the pharmacy.

Supplemental Material

sj-pdf-1-inq-10.1177_00469580231171338 – Supplemental material for A Cross-Sectional Survey on the Post-Pandemic Acceptability of Pharmacist-Delivered Telehealth Services Among HCV-HIV Patients

Supplemental material, sj-pdf-1-inq-10.1177_00469580231171338 for A Cross-Sectional Survey on the Post-Pandemic Acceptability of Pharmacist-Delivered Telehealth Services Among HCV-HIV Patients by Rachel K. Mayaka, Mary K. Awuonda, Earl Ettienne, Monika Daftary, La’Marcus Wingate and Jacqueise Unonu in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

This study was part of a dissertation thesis by the corresponding author and is available on ProQuest.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

IRB Approval

This study was approved by the Howard University Institutional Review Board ( Reference number IRB-21-Pharm-03).

ORCID iD

Rachel K. Mayaka

Supplemental Material

Supplemental material for this article is available online.

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