Bagchi, A., Melamed, B., Yeniyurt, S., Holzemer, W. & Reyes, D. (July, 2018). Telemedicine delivery for urban seniors with low computer literacy: A pilot study. Online Journal of Nursing Informatics (OJNI), 22 (2), Available at http://www.himss.org/ojni
The rapid development of health information technologies has been touted for its potential to address health disparities and create a more equitable healthcare system. However, most system designers do not understand the needs and preferences of members of underserved communities and fail to take these factors into consideration when developing applications. Although an increasing number of studies examine consumers’ attitudes towards telehealth, there remains a dearth of information from consumers residing in underserved, urban areas. This pilot study used a mixed methods approach to assess the perspectives of consumers and nurses toward a specific telemedicine application. Consistent with prior studies, the findings suggest a general receptivity toward telehealth but with reservations related to discomfort with computers and the equivalency with face-to-face care. The study suggests the need for larger-scale studies that can help to identify the needs and preferences of members of underserved communities in the design of telehealth applications.
Telehealth has been defined broadly as the remote delivery of health-related services through telecommunications technology and its use has been expanding rapidly in the past two decades (Dorsey & Todd, 2016; Dinesen et al., 2016). Telemedicine (often used synonymously with “telehealth”) has been defined as health-related communication occurring between patients and providers in different geographic locations (George, Hamilton, & Baker, 2009). The World Health Organization (2009) used the term “telehealth” to refer to telemedicine offered through non-physician providers. In the United States, however, nurse practitioners provide most of the same services as physicians in wellness and acute care visits and with equivalent quality of care; therefore, this distinction is largely irrelevant in the U.S. context. This study focused on telemedicine services provided by a nurse practitioner and, therefore, uses the term “telemedicine” to refer to the intervention tested in this pilot study and “telehealth” to refer to more general technology-based health services (including telemedicine), as appropriate.
Although numerous studies and editorials suggest great promise for telehealth to improve health equity and advance the goals of the Triple Aim (i.e., enhanced patient satisfaction, improved population health, and reduced costs), studies examining the efficacy of these models of care have reported inconsistent findings, with the clearest benefits seen for patients living with chronic health conditions (Dinesen et al., 2016; Berwick, Nolan, & Whittington, 2008; Dang et al., 2008; Wootton, 2012). A problem with the current evidence base is the insufficient focus on consumers’ perspectives of telehealth technologies. Dorsey and Topol (2012) citeD Ray Kurzweil’s “law of accelerating returns” to describe how the “exponential evolution” of technology can lead to dramatic declines in cost (Kurzweil, 2001), but if technology is seen as an inadequate substitute for in-person care, or as untrustworthy, consumers may be resistant to its use.
This risk is particularly pronounced among historically underserved groups (i.e., those who have been disadvantaged due to factors such as ethnicity, age, gender, and socioeconomic status, including individuals with limited access to broadband internet service, smart phones, and other developing technologies) that have been excluded consistently from telehealth research and development (Dinesen et al., 2016; Call et al., 2016; Isaacs, Hunt, Ward, Rooshenas, & Edwards, 2016; James, Harville, Sears, Efunbumi, & Bondoc, 2017). As noted in a 2013 report to the Department of Health and Human Services, those who design telehealth services generally do not understand the needs of historically underserved communities and do not seek the input of these consumers when developing health technology applications (NORC at the University of Chicago, 2013). Without a concerted effort to understand the needs and priorities of members of these groups, the health information technology infrastructure risks exacerbating, rather than reducing, health disparities (NORC at the University of Chicago, 2013; McAuley, 2014).
Efforts to address these risks include expansion of the use of health information technology (HIT) in federally qualified health centers under the Health Information Technology for Economic and Clinical Health (HITECH) provisions of the 2009 American Recovery and Reinvestment Act (ARRA), as well as ARRA’s emphasis on the Meaningful Use of HIT (Heisey-Grove, Hufstader, Hollin, Samy, & Shanks, 2012; Heisey-Grove, Hawkins, Jones, Shanks, & Lynch, 2013). In addition, a growing number of studies have begun to examine attitudes toward the acceptability of HIT among rural and urban residents and members of ethnic minority groups (especially those with chronic health conditions), and a recent review examined research on efforts to improve the cultural competency of electronic health services to improve uptake among Latino consumers (Montague & Perchonok, 2012; Grunbaugh, Cain, Elhai, Patrick, & Frueh, 2008; Carter, Nunlee-Bland, & Callender, 2011; Price, Williamson, & McCandless, 2013; Victorson, Banas, & Smith, 2014; Call et al., 2015). In their review of the research on HIT among historically underserved consumers, Montague and Perchonok (2012) highlighted the importance of tailoring technology to the preferences and needs of specific target populations to ensure greater uptake and more meaningful use. In general, although results of research on underserved groups have been mixed, findings suggest a general receptivity to the concept of telehealth services, but with reservations relating to the confidentiality and security of such applications. Many of these studies failed to assess attitudes in the context of any specific application or model of care. Finally, few studies have examined the feasibility of telehealth services for the provision of general preventive or primary care, as opposed to patient monitoring for chronic health conditions.
This study used a mixed methods approach to address the following two research questions: (1) What is the feasibility of implementing a specific telemedicine service among residents of an underserved urban area? and (2) What are the perspectives of these consumers and health care providers on the efficacy and acceptability of this model of care? Specifically, this study was designed to determine whether residents in an impoverished urban community with high healthcare access needs (e.g., transportation barriers and limited access to specialists) would view telemedicine as a viable alternative to in-person primary care. This pilot research was intended to lay the groundwork for further larger-scale studies into more effective means of addressing health service shortages in underserved urban communities.
Setting. Rutgers University’s School of Nursing (SON) operates a federally-qualified health center, the Rutgers Community Health Center (RCHC), in Newark, New Jersey, comprised of a bricks-and-mortar clinic (the Focus Clinic) and a mobile van staffed by nurse practitioners with doctor of nursing practice (DNP) degrees and medical assistants, as well as wellness clinics located in three high-rise, general resident, public housing developments (traditionally described as “projects”; however, due to the pejorative nature of this term we do not use it here), staffed by registered nurses (RNs) and community health workers (CHWs). The majority of residents have incomes well below the federal poverty level and have previously been assessed by health center staff to have low computer and health literacy. Working with the Rutgers Business School (RBS) and a private telehealth care delivery enterprise, (SmartCareDoc), the SON implemented the pilot project at the Pennington Court public housing development managed by the Newark Housing Authority. The intervention consisted of a live videoconferencing encounter accessible through a laptop computer. An RN transported the laptop and other equipment (designed specifically for this telemedicine platform) to the aforesaid housing development to collect and transmit vital signs and other clinical information through a secure system to a nurse practitioner located off-site. The nurse practitioner was able to review clinical data and speak directly with patients and the nurse via live video stream.
Data collection. The SmartCareDoc system (hereafter, to be referred to simply as “the system”) is designed to provide a full array of clinical services, including an electronic health record (EHR), equipment and sensors (Figure 1) for assessing and recording vital signs, live teleconferencing, and an instant messaging function. Vital sign sensors associated with the system include a pulse oximeter, stethoscope, blood pressure cuff, and pen light/camera, all of which are used to upload medical readings and images directly into the clinical documentation, to be archived in the EHR; data can also be entered into the system manually.
Figure 1. Sensors Used with the SmartCareDoc™ System
Data collection was performed over one six-hour period in the wellness center located in the housing development and consisted of (1) basic health information entered into the telemedicine system (i.e., vital signs recorded via the automated sensor equipment and manual entry of height and weight); (2) a brief written satisfaction survey, which included items from the Health Resources and Services Administration’s (HRSA) Health Center Patient Survey (https://bphc.hrsa.gov/datareporting/research/hcpsurvey/index.html) and the Telehealth Usability Questionnaire (Parmanto, Lewis, Graham, & Bertolet, 2016), and (3) written, open-ended responses to a brief questionnaire by patients and the two participating nurses.
The nurse practitioner conducted virtual wellness visits with the patients, reviewing current medications, discussing recent symptoms, and, with the assistance of the RN, performing physical examinations, focused primarily on the audio and visual signs that could be assessed with the available equipment. Although the EHR was not used to its full functionality, the RN recorded health-related information into temporary patient charts using anonymized project identifiers. Patients and nurses responded in writing to open-ended survey questions relating to their satisfaction with the encounter. Among patients, open-ended survey questions focused on the features participants did and did not like about their telemedicine encounter, features that would make the encounters better for patients, and queries on the types of patients that would be best served by such a system. Because most patients responded with brief phrases, the RN conducted short interviews to clarify and expand upon responses. Because these interviews were conducted in an ad hoc manner, they were not audio recorded but the nurse took detailed notes on patients’ responses. Patient encounters (including the wellness visit and interviews) lasted between 20 and 25 minutes. Qualitative input from the nurses included open-ended responses to questions about the benefits and areas of improvement for encounters, other types of resources or equipment that would be useful to include in the system, and suggestions for how a telemedicine system might help increase access to healthcare within the populations served.
Participant Recruitment. Using convenience sampling, CHWs affiliated with RCHC recruited 10 Pennington Court residents who had previously been seen as patients in the Wellness Center and/or on the mobile van. Potential participants were told that they would receive a $10 gift card for attending a telemedicine encounter and answering a set of brief interview questions afterwards. Sample size was predetermined and limited by system availability and resources. The sample was not intended to be representative of all underserved groups in Newark but fairly reflected the background of residents who regularly use the Wellness Center’s services. The study’s lead author served as the RN for system testing and data collection, while the nurse practitioner was a member of the study team who also serves as a clinical administrator at the site and sees patients in a private practice. The lead author explained the purpose of the study prior to starting data collection and participants signed written consent forms for study participation. No personally identifiable information was collected from participants either during recruitment or during the telemedicine visit. The study was approved by the Community Advisory Board of RCHC and by the Rutgers University Institutional Review Board (IRB) under expedited review.
Data Analysis. Given the small sample size, quantitative data were collected from paper questionnaires, with the data recorded and analyzed in an Excel spreadsheet. Based on the descriptive nature of the study and the use of categorical survey responses, analyses were limited to summarizing responses using numbers and percentages. The first and second authors analyzed the qualitative data using a grounded theory approach to identify recurrent themes, using discussions to resolve any differences in interpretation (Glaser & Strauss, 1967).
Demographics. Participants included 10 African American residents of Pennington Court (nine women and one man, with an average age of 59.9 years; see Table 1). Eight out of the 10 participants were single at the time and nine of the 10 had incomes under $25,000 per year. Although a formal health history was not taken for any of the participants, all reported one or more chronic health conditions (primarily diabetes and hypertension) for which they require periodic health care visits.
Satisfaction with Telemedicine Visits. Overall, patient participants gave favorable ratings to the quality of the visits, with the majority giving favorable ratings (i.e., “good” or “great”) to the accessibility of telemedicine (ability and time to be seen) and the provision of care by the nursing staff (Table 2). Similarly, nine out of the 10 respondents said they were “likely” or “very likely” to recommend telemedicine services to their friends and family. However, respondents were more ambivalent on specific questions regarding the utility of telemedicine services and their capacity to improve the quality of care that patients receive. One participant was neutral on the statement that telemedicine is “useful” and two respondents each said that they were either neutral or disagreed with the statements that telemedicine can improve quality of care and is easy to use.
Qualitative Findings. A nurse researcher conducted brief qualitative interviews with patients immediately following the telemedicine visits. To protect confidentiality, interviews were not recorded. Findings related to patients’ perspectives were based on notes taken by the nurse during the interview. The two nurses (encounter facilitator and researcher) involved in the study completed open-ended questionnaires regarding their perspectives on the telemedicine visits, with the nurse researcher conducting follow-up interviews, as needed.
Patients’ perspectives. Features that patients liked about the telemedicine encounter included the efficiency and convenience of the system, including the fact that (1) it would be available to patients who have trouble getting out of the house, (2) patients would not have to sit in a waiting room before being seen, and (3) patients could have the ability to talk to a healthcare provider on short notice. One participant said it “put her mind at ease” to know that she could use the system without having to go out of the house since she does not always feel safe in her neighborhood and experiences transportation issues. Another participant specifically said that she preferred to use technology whenever possible and liked that feature of the system. Finally, one patient said that she liked the fact that the provider could “be here for us” even when she cannot physically be present.
Several participants did not mention anything they did not like about the system. However, three patients said that they preferred face-to-face interactions, two disliked the fact that the system lost connectivity in the middle of the video consultation with the nurse practitioner, and one said she thought the system was faulty because she did not know how to use it. Another participant noted that a problem with telemedicine more generally is that the nurse practitioner is unable to touch the patient and cannot “feel for lumps and bumps.”
With respect to factors that would make the system better for patients, two patients could not think of anything they would recommend. Seven of the 10 respondents mentioned a need for training and/or a more user-friendly system so that patients could interact more directly with the system (the RN entered all of the patients’ clinical information and operated the video functions at the patients’ requests). Three of the 10 respondents said that making the system more user-friendly would enable them to use the system at home; home use would facilitate greater privacy and obviate the need for transportation and its attended cost.
When asked which groups of patients might benefit the most from telemedicine services, one participant said that the system could enhance services for “all patients” since everyone at least occasionally experiences transportation or other barriers to care, and another said that anyone might benefit if they preferred the approach (i.e., “to each his own”). Other groups frequently cited as potential beneficiaries included elderly people, those who have mobility or transportation issues, and those who are generally home-bound. One woman specifically mentioned the fact that she often misses appointments because she cannot afford to take the bus and has to walk everywhere, so the system would be useful for her. Two participants suggested the system would best serve people who do not have major medical issues or do not need acute care services. Other populations identified as potential beneficiaries of telemedicine service delivery included mothers with children (given the ability to easily schedule multiple appointments), those with mental health or substance abuse disorders, and patients needing simple consultation (e.g., for medication refills).
Nurses perspectives. Nurses also cited the efficiency and convenience of telemedicine encounters as positive features of the system. Additional benefits includeD the portability of the system (i.e., the ability to set up anywhere the patient preferred) and the capacity to see the patient in his or her home environment; having this visual information could provide clues related to patients’ environmental needs (e.g., placement of furniture or loose rugs for patients with mobility issues).
From the nurses’ perspectives, ways that the system could be improved included elimination of ambient/background noise and a wider visual field for the video camera (i.e., so that the patient would not have to be sitting directly in front of the camera to be seen by the provider). For the participants in this study, the RN had to sit with the patients to ensure that information was entered correctly and that the equipment was working properly. These technical and computer literacy issues echoed the problems that patients identified with the system. The fact that the nurse had to remain with the patient was less of a problem in a pilot study setting, but was noted as a factor that could lead to workflow inefficiencies in a large-scale implementation. The nurse practitioner also specifically noted the incapacity of telemedicine service delivery to provide hands-on information about patients’ health conditions, noting that “human contact is an important part of the patient-provider relationship.”
The full functionality of the system was not used for this pilot study. The nurse practitioner noted that resources that should be included in the system included (1) data on body mass index; (2) documentation of the patients’ personal and family history, medications and allergies; (3) a list of patient concerns; and (4) subjective data regarding the review of systems (i.e., data reported from the patient’s perspective regarding cardiovascular and respiratory function, gastrointestinal symptoms, etc.). All of these functions are available in the SmartCareDoc system, but were not implemented for the purposes of the pilot study.
Other resources and equipment that the nurses suggested should be included or enhanced in the current system included a high definition camera/otoscope that could provide clearer images, an EKG system that could provide accurate tracings (similar to those used in many primary care settings), and a glucometer to record random glucose readings. An additional feature that the nurse practitioner suggested included webinars and PowerPoint presentations or other written (electronic) patient educational materials that the provider could review with the patient during the videoconference. Although not directly associated with system functioning, the RN noted that it would be essential to have a protocol in place to ensure patient access to advanced medical services in case the telemedicine visits revealed more acute healthcare needs. Side bar consultation services with specialty providers are possible within the system, but there would need to be a protocol in place to transport patients to urgent or emergent care services, if necessary.
Echoing the observations of patient participants, nurses noted similar population groups who could be well served by telemedicine services (e.g., elderly person and those who are homebound and/or have transportation issues). The RN noted that a benefit of the system for people with mental health/substance abuse disorders or other stigmatized health conditions (e.g., HIV) was that patients could maintain greater privacy regarding their healthcare needs. Other specific uses of telemedicine cited by these providers included (1) encounters for reviewing laboratory results, (2) after-hours encounters for routine complaints, (3) directly observable therapy to improve medication adherence, (4) review of medications (whereby patients would have ready access to the medications and other therapies that they use and could show the labels to the providers), and (5) home visits with CHWs or RNs. The nurse practitioner suggested that telemedicine could be an efficient way to re-connect with patients who have missed appointments.
Although designed as a pilot study without the expectation for a representative sample, the use of a small convenience sample is the study’s major limitation. The study’s location was chosen based on the housing development’s affiliation with Rutgers’ School of Nursing; therefore, the residents may not be representative of members of Newark’s low-income/underserved population more broadly.
HRSA defines Medically Underserved Areas and Medically Underserved Populations as those “geographic areas and populations with lack of access to primary care services” (HRSA, 2016). The residents of the public housing development used in this study have access to an RN-led wellness center and a mobile van staffed by DNPs and, in that respect, may have better access to primary care than many other Newark residents. Therefore, the residents of this development may not be representative of the neediest members of the population. However, HRSA has designated the city of Newark as a Health Professional Shortage Area for which telemedicine services may prove beneficial to address ongoing service needs (HRSA 2018).
The sample size was also small and homogeneous, comprised almost exclusively of African-American women. Future research should employ clustered random sampling of housing developments across the city and of residents within those facilities to capture the diversity of Newark’s low-income population, which includes large numbers of Portuguese and Latino immigrants, many of whom face language and other barriers to the uptake of telemedicine services beyond those encountered by the city’s English-speaking, African-American residents. A full-scale study would also expand upon the number and types of covariates collected to identify factors associated with likely uptake of telemedicine services among members of underserved groups.
Although based on a small, convenience sample of patients from one public housing development, the results of this pilot study suggest the utility of telemedicine services for people residing in underserved, low-income urban areas. Similar to prior studies, participants viewed the services favorably and were able to cite conditions under which telemedicine could improve access to healthcare, particularly for specific population groups (e.g., elderly people, those with transportation issues, and people with stigmatized health conditions who might prefer the ability to receive services in the privacy of their own homes), but the particular system tested did not appear to provide sufficient incentive to the majority of study participants to regularly use telemedicine services. Based on the system’s perceived complexity, the “digital divide” appears to remain a barrier to the uptake of telemedicine interventions in underserved communities.
While the most recent Pew Research Center survey on internet usage found a narrowing in disparities in internet uptake, gaps remain. For example, while 90% of American adults reported internet usage in 2016, these rates are 99% among those aged 18 to 29 but only 64% among those 65 and older (Pew Research Center, 2017). Similar gaps exist by income and educational level, with those with household incomes under $30,000 per year and those with less than a high school education reporting the lowest rates of internet usage. Disparities in home broadband service were similarly stark, with older adults, members of ethnic minority groups, those with incomes below $30,000 per year, and those with less than a high school education reporting significantly lower rates of in-home broadband service (e.g., 34% among those with less than a high school degree versus 91% among college graduates) (Pew Research Center, 2017). Perhaps even more telling, overall rates of “smart phone dependency” (i.e., reliance on smart phones for internet access) were around 10% in 2016 but were higher for members of ethnic minority groups (i.e., 9% for whites, 15% for African Americans, and 23% for Latinos) and for those with lower incomes (21% for those with income less than $30,000 per year) and educational completion (i.e., 27% for those without a high school degree) (Victorson et al., 2014). These data suggest both that members of underserved communities face lack of accessibility to telemedicine services and that application designers need to develop systems that work across a variety of platforms to reach a broad market of consumers.
The specific system tested in this pilot faces a number of limitations to widespread adoption within the targeted population. In line with the Pew survey, most of the study participants stated that they lacked broad band access at home and that they rarely used computers. The majority stated that they found the system interface to be insufficiently user friendly. The housing development used in the study had an on-site wellness center available to all residents and the system could be used effectively under the model tested (i.e., with an RN, CHW, or medical assistant available to provide assistance) but would likely need to be adapted to facilitate in-home/after-hours use. Similar models and systems have been tested previously, with positive patient outcomes (Markwick, McConnochie, Wood, 2015); however, what this study highlights is the need to either simplify the user interface and/or implement the system in a 24-hour visiting nurse-type service if the goal is to eliminate access barriers to in-home use. Designers need to consider how systems will be used and under what models of care they will be employed during the process of product development. Systems with more complex interfaces may be acceptable under models of care that incorporate assistive personnel (e.g., CHWs and medical assistants) but fall short of the promise of independence and universal access to services “at the touch of a button.”
Finally, one of the important limitations to the widespread adoption of telemedicine services is the perception that such systems lack the human element available in face-to-face interactions. Several participants in this pilot study expressed a degree of receptivity to telemedicine services but also stated they could not see themselves using such a system regularly due to the “impersonality” of the care delivered. This is consistent with previous studies that have examined patient attitudes toward telemedicine care for those with chronic health conditions. A qualitative study of 17 homebound patients identified resistance to system use based on lack of familiarity with the technology and on patients placing a high value on the social aspects of face-to-face medical appointments (Huang, Tracy, Alizadeh, & Mostaghami, 2016). Similarly, a study of attitudes toward telehealth for the treatment of medical and mental health disorders in rural and urban settings found moderate rates of acceptability of the system of care, but 66% of participants, on average, said the services were “not as effective” as face-to-face encounters (Grubaugh, et al., 2008). These findings suggest potential discrepancy in attitudes toward telemedicine services and actual system uptake.
From a policy perspective, coverage for telehealth services and mechanisms for reimbursement have expanded considerably, with the Center for Connected Health Policy stating that 44 states have introduced more than 200 telehealth-related bills in 2017 alone (Center for Connected Health Policy, 2017). However, as this pilot study shows, there remains the need for larger-scale, well-funded projects to test the feasibility, functionality, and acceptability of telemedicine services for underserved, urban communities. The push for a “patient-centered” health care system should include technologies adapted to the demographic, social, economic, and functional needs of consumers (Montague & Perchonok, 2012; Dinesen et al., 2016). New Jersey’s recently passed telehealth law (Senate and General Assembly of the State of New Jersey, P.L. 2017, chapter.117, 2017) provides policymakers, system designers, and healthcare providers with the opportunity to ensure consumer input into the design of systems and models of care that take into account their needs and preferences.
HRSA has been funding demonstration projects to improve telehealth services to rural populations through the Office for the Advancement of Telehealth and has begun to address the needs of underserved urban communities (e.g., through its recent call for proposals to develop a Telehealth Center for Excellence to study the efficacy of telehealth services in both rural and urban areas) (Dinesen et al., 2016; HRSA, 2017). The studies funded through such a center will need to take into account consumers’ attitudes and preferences as a key component of efficacy and sustainability. Dinesen et al. (2016) described the Model for Assessment of Telemedicine Applications (MAST), which has been widely used in Europe to assess telehealth technologies and which includes patient preferences in the second step of its three-step model (Kidholm et al., 2012). While such a model shows promise for evaluating existing applications, what this pilot study and other research has shown, is that consumers (especially members of underserved communities) need to be consulted in the beginning of the development process to ensure that technological innovation does not leave these individuals behind and exacerbate existing health disparities.
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Ann Bagchi, PhD, DNP, FNP-C, APN, is an instructor in the School of Nursing at Rutgers University. Her work focuses on the social causes of health disparities among underserved populations. She has worked extensively on health care access and stigma among people living with HIV. Dr. Bagchi served as the principal investigator on the telehealth pilot study.
Benjamin Melamed, PhD, is a distinguished professor in Rutgers Business School. His research interests include supply chain management, supply chain financial management, and service chain management. Dr. Melamed served as the project director on the pilot study.
Sengun Yeniyurt, PhD, is a chancellor’s scholar and an associate professor in the marketing department at Rutgers University. Dr. Yeniyurt studies market strategies using econometric models and bridges multiple disciplines: marketing, supply chain management, innovation management, and international business.
William Holzemer, RN, PhD, FAAN, is dean and distinguished professor in Rutgers University’s School of Nursing. He is recognized in the United States and internationally as an expert in academic nursing and a thought leader on key health issues such as HIV/AIDS care. He has provided global leadership to the World Health Organization, the International Council of Nurses, and many universities around the world.
Darcel Reyes, PhD, ANP-BC, is a clinical instructor in the School of Nursing at Rutgers University. In addition to her faculty role, she serves as chief clinical officer at the Rutgers Community Health Center. Her research focuses on health literacy among underserved populations.