Introduction
Telemonitoring (TM) has been widely used in the United States, Europe, and parts of Asia to improve monitoring and care for a wide variety of medical conditions (Meystre, Reference Lin, Chien, Willis, O’connell, Rennie, Bottella and Ferris2005; Pare et al., Reference Meystre2007; Bashshur et al., Reference Bashshur, Shannon, Smith, Alverson, Antoniotti, Barsan, Bashshur, Brown, Coye, Doarn, Ferguson, Grigsby, Krupinski, Kvedar, Linkous, Merrell, Nesbitt, Poropatich, Rheuban, Sanders, Watson, Weinstein and Yellowlees2014), in particular cardiopulmonary diseases, such as congestive heart failure and chronic obstructive pulmonary disease (Meystre, Reference Lin, Chien, Willis, O’connell, Rennie, Bottella and Ferris2005) and other chronic diseases, such as diabetes (Bashshur et al., Reference Bashshur, Shannon, Smith, Alverson, Antoniotti, Barsan, Bashshur, Brown, Coye, Doarn, Ferguson, Grigsby, Krupinski, Kvedar, Linkous, Merrell, Nesbitt, Poropatich, Rheuban, Sanders, Watson, Weinstein and Yellowlees2014). In addition to studying the clinical effectiveness of TM for improving disease-specific indicators, TM has also been examined from an economic standpoint as a cost-effective initiative to improve access and quality of care for patients with chronic disease (Bashshur et al., Reference Bashshur, Shannon, Smith, Alverson, Antoniotti, Barsan, Bashshur, Brown, Coye, Doarn, Ferguson, Grigsby, Krupinski, Kvedar, Linkous, Merrell, Nesbitt, Poropatich, Rheuban, Sanders, Watson, Weinstein and Yellowlees2014) and help prevent hospitalizations among high-risk patients (Cherry et al., Reference Chase, Pearson, Wightman, Roberts, Oderberg and Garg2002; Jaana and Pare, Reference Greisinger, Balkrishnan, Shenolikar, Wehmanen, Muhammad and Champion2007; Pare et al., Reference Meystre2007; Polisena et al., Reference Polisena, Coyle, Coyle and Mcgill2009b; Bashshur et al., Reference Bashshur, Shannon, Smith, Alverson, Antoniotti, Barsan, Bashshur, Brown, Coye, Doarn, Ferguson, Grigsby, Krupinski, Kvedar, Linkous, Merrell, Nesbitt, Poropatich, Rheuban, Sanders, Watson, Weinstein and Yellowlees2014). A recent review cited several studies, conducted across a wide range of international settings, demonstrating the effectiveness of TM interventions for improving chronic disease management and reducing service use, mostly within the context of highly controlled randomized controlled trials (Bashshur et al., Reference Bashshur, Shannon, Smith, Alverson, Antoniotti, Barsan, Bashshur, Brown, Coye, Doarn, Ferguson, Grigsby, Krupinski, Kvedar, Linkous, Merrell, Nesbitt, Poropatich, Rheuban, Sanders, Watson, Weinstein and Yellowlees2014).
Diabetes is a global health problem, affecting millions of patients in countries around the world. In the United States, diabetes affects nearly 26 million people and is responsible for $174 billion in healthcare costs each year (C.F.D.C.A Prevention (Centers for Disease Control and Prevention), Reference Pols2011). A total of 511 407 diabetes-related preventable hospitalizations were reported in 2006, an increase of 18% since 1998 (Wang et al., Reference Wang, Imai, Engelgau, Geiss, Wen and Zhang2009). Of these hospitalizations, 36% were caused by short-term complications and uncontrolled diabetes (Wang et al., Reference Wang, Imai, Engelgau, Geiss, Wen and Zhang2009). Better outpatient management of diabetes in primary care settings could help prevent many of these hospitalizations (Dagogo-Jack, Reference Costa, Fitzgerald, Jones and Dunning2002; Greisinger et al., Reference Gomez, Hernando, Garcia, Del Pozo, Cermeno, Corcoy, Brugues and De Leiva2004).
Research on TM effectiveness specifically for diabetes has demonstrated mixed results (Farmer et al., Reference Fairbrother, Pinnock, Hanley, Mccloughan, Sheikh, Pagliari, Mckinstry and Team2005; Pare et al., Reference Meystre2007; Costa et al., Reference Cherry, Moffatt, Rodriguez and Dryden2009). Some studies report little to no effect on glycemic control or hospitalization rates (Farmer et al., Reference Fairbrother, Pinnock, Hanley, Mccloughan, Sheikh, Pagliari, Mckinstry and Team2005; Costa et al., Reference Cherry, Moffatt, Rodriguez and Dryden2009; Lin et al., Reference Koopman, Wakefield, Johanning, Keplinger, Kruse, Bomar, Bernt, Wakefield and Mehr2012; Takahashi et al., Reference Takahashi, Pecina, Upatising, Chaudhry, Shah, Van Houten, Cha, Croghan, Naessens and Hanson2012; Wakefield et al., Reference Wakefield, Koopman, Keplinger, Bomar, Bernt, Johanning, Kruse, Davis, Wakefield and Mehr2014). Others report that tele-health interventions are effective for improving glycemic control, patient education, reducing hospitalization rates among diabetic patients (Cherry et al., Reference Chase, Pearson, Wightman, Roberts, Oderberg and Garg2002; Gomez et al., Reference Farmer, Gibson, Tarassenko and Neil2002; Chase et al., 2003; Jaana and Pare, Reference Greisinger, Balkrishnan, Shenolikar, Wehmanen, Muhammad and Champion2007; Pare et al., Reference Meystre2007; Polisena et al., Reference Polisena, Coyle, Coyle and Mcgill2009b; Weinstock et al., Reference Weinstock, Teresi, Goland, Izquierdo, Palmas, Eimicke, Ebner, Shea and Consortium2011; Stone et al., Reference Stone, Sevick, Rao, Macpherson, Cheng, Kim, Hough and Derubertis2012) and potentially reducing cost (Cherry et al., Reference Chase, Pearson, Wightman, Roberts, Oderberg and Garg2002; Chase et al., 2003).
TM has been proposed as a method to enhance primary care provision by filling patient information gaps between clinic visits and alerting clinicians to potential problems (Davis et al., Reference Dagogo-Jack2014; Koopman et al., Reference Kahn, Fox, Carrington, Desai, Bartlett, Lyle and Kowalski2014; Wakefield et al., Reference Wakefield, Koopman, Keplinger, Bomar, Bernt, Johanning, Kruse, Davis, Wakefield and Mehr2014). Implementing TM into routine primary care (ie, outside of the context of a controlled experimental trial) must be carefully considered, due to increased practice staff workload, concerns over data integration and clinical relevance, and, in the US context, limited reimbursement from insurance companies for care provided between office visits (Davis et al., Reference Dagogo-Jack2014; Koopman et al., Reference Kahn, Fox, Carrington, Desai, Bartlett, Lyle and Kowalski2014; Wakefield et al., Reference Wakefield, Koopman, Keplinger, Bomar, Bernt, Johanning, Kruse, Davis, Wakefield and Mehr2014).
In 2010, Western New York was selected as one of 17 Beacon Communities nationwide by the United States Office of the National Coordinator for Health Information Technology. The Beacon Community Cooperative Agreement Program provided funding for three years to enable these communities to build and expand on regional health information technology (IT) infrastructure and demonstrate how health IT could advance patient-centered care, while achieving better health, better care, at lower cost (Ricciardi et al., Reference Ricciardi, Mostashari, Murphy, Daniel and Siminerio2013; Des Jardins et al., Reference Davis, Freeman, Kaye, Vuckovic and Buckley2015). As part of this regional quality improvement initiative, a community organization implemented a TM pilot program with three homecare agencies to target primary care diabetic patients deemed to be at high-risk of hospitalization. The project was intended to provide monitoring and day-to-day management of high-risk patients in order to identify problems and intervene in the primary care setting, before patients presented to the emergency room or hospital.
The purpose of this qualitative evaluation study was to explore the experience of implementing routine TM in busy primary care settings from the perspective of those delivering the intervention; namely the TM staff, and report on lessons learned that can inform future projects of this type.
Program description
In this community project, the routine TM model was implemented with three homecare agencies and six primary care practices. The overall goal of the TM program was to demonstrate the effectiveness of TM as part of routine primary care for preventing hospitalizations among high-risk patients in real-world settings. In the context of the privatized US healthcare setting, this meant engaging with three different homecare agencies with slightly different equipment and procedures and a variety of practice settings. Each homecare agency provided patients with equipment (differed by agency; see Table 2) and educated patients on how to test themselves daily for weight, blood pressure and glucose. The TM agencies received patient readings on a daily basis; nurses reviewed the patient data and responded to any numbers that were outside of predetermined limits (set in collaboration with the patients’ provider), by calling the patient and/or the provider. In addition to TM, glucometers and test strips were provided for some patients with financial difficulties. The six primary care practices enrolled in the pilot identified diabetic patients (Type I or Type II, regardless of insulin use) meeting the following criteria: (1) between the ages of 18 and 75; (2) have congestive heart failure or chronic kidney disease in addition to diabetes and at risk (as determined by each practice) for hospitalization or re-hospitalization; (3) signed a consent to have data shared electronically; (4) expressed desire to participate in a TM program and follow TM guidelines; and (5) deemed likely that TM will improve care and patient outcomes, based on physician assessment. While practices were asked to enroll patients using these criteria, in this real-world implementation pilot, practices developed their own systems for identifying eligible patients and physician judgment was used to determine whether diabetes was uncontrolled or whether the patient was at risk for hospitalization. Specific parameters in terms of type of diabetes and hemoglobin A1c levels were left flexible, and each practice used different criteria for enrolling patients. Between May 2011 and August 2013, 144 patients were enrolled across the six participating primary care providers (PCPs) and 99 patients remained in the program for at least six months.
Evaluation methods
Qualitative interviews were conducted with staff from the TM vendors (nurses and administrators) to identify key factors affecting the implementation of TM in the primary care practices and the experience of day-to-day TM.
Data collection
Between February and June of 2013, semi-structured qualitative interviews were conducted with nurses and administrators from the three TM agencies participating in the pilot. The same set of questions was used for both nurses and administrators, and in some cases they were interviewed jointly (Table 1). TM staff were asked about their experiences with the Beacon TM pilot including, their daily roles and activities, the nature of their interactions with patients and providers, and their thoughts on using TM to prevent hospitalizations. These participants were chosen to gain insight into the interface between the TM agency and the primary care practices, from the perspective of the agencies providing the intervention. Interviews were conducted by a medical anthropologist (BMV) over the phone or in person and lasted ~30 min. Interviews were audio-taped and transcribed. The study protocol was approved by the Social and Behavioral Sciences Institutional Review Board at the [University at Buffalo]. All participants provided informed consent to participate in the interviews; written consent was used for in-person interviews and verbal consent with the provision of a study information sheet was used for telephone interviews. Participants received a $20 gift card as a thank you for their participation.
Table 1 Telemonitoring (TM) staff interview questions
Data analysis
Two researchers (a medical anthropologist (BMV) and an RN/MPH (VMH)) independently reviewed the transcripts and identified themes. Analysis was conducted by means of immersion-crystallization (Borkan, Reference Borkan1999), an iterative process of data analysis whereby the researchers immerse themselves repeatedly in the data to identify emerging themes. Analysis was content-driven, allowing the data to ‘speak’ for itself without being guided by a theoretical model. Themes were identified based upon repeated ideas that emerged across interviews. After independent review, the researchers met to compare identified themes and resolve any discrepancies. Because the interview questions were the same, the nurse and administrator transcripts were analyzed as a collective, and the themes cut across both participant groups. While a small sample, the eight interviews represent all TM agency staff engaged in this implementation pilot, and therefore, all experiences and perspectives were captured in the analysis. Data analysis was based upon all individuals participating in the pilot, rather than on theoretical saturation. Nonetheless, analysis of the transcripts revealed remarkably consistent responses, perceptions and experiences across all participants, indicating probable saturation.
Results
Interviews were conducted with eight staff from the TM agencies; five TM nurses and three agency administrators. One administrator and at least one nurse from each of the three agencies were interviewed. TM nurses were responsible for working with the patients each day. The administrator from each agency was responsible for overseeing all TM, including the set-up and coordination of the Beacon TM program within their agency. All of the administrators had previously worked as nurses in direct patient care and in TM before shifting into an administrative position at their respective agencies. All of the nurses had some experience working in the TM field (range 2–12 years) and had worked several years in other areas of healthcare before beginning work with TM. These TM nurses were not dedicated solely to the Beacon project, and were simultaneously assigned to other patients within their organization. Each agency reported ~25–50 patients enrolled in the Beacon TM program, but were monitoring hundreds of patients across multiple programs daily. From the perspective of the TM staff, they conducted the same daily procedures and activities related to the Beacon patients as for other TM patients (see description below). TM staff described the main difference between the Beacon TM and other TM programs as the emphasis on diabetes, and the length of time patients were on the TM program (up to two years as compared with 60–90 days of TM that would normally be covered under other programs).
The interviews revealed variation in the TM intervention provided by each of the agencies. The TM agencies varied in their approach to providing TM services, such as: the equipment used with patients, the amount of patient education provided by the TM device, the process for setting the patient up with TM, and the protocols for when and how to contact patients and providers (Table 2).
Table 2 Variation across telemonitoring agencies (information based on interview responses from participants)
Although considerable variation existed in the TM modality used by the three agencies, there was an overarching consistency among the TM agencies in how they conducted their daily TM responsibilities. Patients tested blood sugar, weight, and blood pressure daily, and sent these results to the agency. If any of these measures were outside of predetermined parameters established by the patients’ doctors, the first course of action by all nurses was to contact the patient to assess the situation. The patients’ physicians were notified after this point, though the mechanisms and timing of the notification varied depending on TM agency policy, practice preferences, and the patient situation. Nurses would generally fax an update to physicians if the problem was resolved, or call if a more immediate response was needed.
Analysis of the interview transcripts identified four themes: (1) the nurse–patient relationship is central to a successful TM experience, (2) TM is a useful tool for understanding and addressing patient context, (3) TM staff anecdotally report important potential impacts of TM on patient health, and (4) integrating TM into primary care practices needs to be planned carefully. Each theme is described in the text; exemplary quotations and additional details are provided in Table 3.
Table 3 Telemonitoring (TM) Staff identified lessons learned and example quotations
a Participant numbers refer to the TM Agency (T1, T2, or T3) and the Participant no. (1, 2, or 3) from that agency.
The nurse–patient relationship is central to a successful TM experience
The TM nurse–patient relationship was a predominant theme, highlighted as important to three areas of the TM program; (1) patient education, (2) generating trust, and (3) providing patients with a sense of security.
Patient education
TM staff cited patient education as a significant portion of their role. This education was provided in several different formats. Two of the three agencies used TM systems that delivered disease-specific education through the device itself, in the form of generalized daily ‘quiz’ questions about diabetes self-management. All of the agencies provided patients with educational materials. TM staff reported providing verbal education individualized to each patient’s needs throughout each encounter and indicated that the form of education evolved over the course of the project, from more intense education at the beginning, to ‘refreshers’ as the project progressed and patients became more familiar with their disease and its management.
Generating trust
TM staff described excellent rapport with patients over the phone, as they built trust and learned about their patients. The continuity in the relationship was important in finding out what was really happening in patients’ lives. Trust enabled the TM staff to uncover social and economic factors affecting patients’ test results.
Providing a sense of security
TM staff explained that patients appreciated the relationship; the sense that someone cares and ‘has their back.’ Compliance was facilitated by the reassuring feeling that someone cared about them and their health.
TM is a useful tool for understanding and addressing patient context
TM staff noted that they were involved in the full context of patients’ lives, assisting with a wide range of social, financial, and non-diabetes-related health concerns affecting patients’ ability to manage their diabetes. For example, contact prompted by reviewing patients’ TM data led to TM staff learning about contextual factors that impacted patients’ ability to manage their diabetes, such as difficulties with insurance coverage for certain medications or testing supplies and financial difficulties related to low-income status, such as eating poorer quality foods at the end of the month when resources were low. As one staff member said, TM ‘opens the door’ to a whole range of other issues that impact patients’ health.
TM staff anecdotally report important potential impacts of TM on patient health
TM staff reported improvements they observed in patients who adhered to TM, including: losing or maintaining their weight, decreases in HbA1c, and increased discipline. TM enabled staff to comprehensively address patients’ health needs beyond diabetes, such as discovering undiagnosed hypertension and providing wound care.
In addition, they were able to expedite care for urgent health needs while avoiding unnecessary office visits. Although they had no quantitative data to substantiate this, TM staff reported an impression that TM was effective for preventing hospitalizations and emergency department visits, because they were able to identify and arrange for treatment of health issues, such as those mentioned above.
Integrating TM into primary care practices needs to be planned carefully
TM staff reported a variety of elements affecting the integration of TM into primary care. Communication varied based on practice preferences. The frequency of reporting, the content of reports, and transmittal of reports (phone, fax, etc.) varied by practice. In most cases, the practice had identified a care coordinator or certified diabetes educator as the primary contact for the TM agencies. TM staff reported positive relationships with these individuals, and noted that communication was more difficult in practices without an identified point person. Project intensity for the practices varied over the course of the pilot. TM staff identified several factors at the practice level which they felt impacted the practice experience with TM, including: difficulties with designating staff to coordinate the program, implementing office workflows designed to optimize the use of TM while limiting extra work, and the level of physician buy-in and engagement – which they felt impacted patient engagement. TM staff stressed the importance of selecting appropriate patients for a TM program; based on their experience they reported that patients with uncontrolled diabetes and who were willing to use TM made the best candidates for the program. They also suggested that there should be physician incentives to use the TM data, and ease of use, such as a one-click, visual and easy to read format. Finally, in terms of communication, TM staff would have appreciated more information from the providers regarding care the patient received outside of the TM, such as lab results, medication changes, and appointment results.
Lessons learned
A TM program is one method by which PCPs can more closely monitor high-risk patients and collaborate with TM staff to identify key factors affecting patient health outcomes. Even though we had a small sample, our study uncovered many findings that confirm and add to previous studies, conducted both in the United States and internationally, on this topic (Davis et al., Reference Dagogo-Jack2014; Koopman et al., Reference Kahn, Fox, Carrington, Desai, Bartlett, Lyle and Kowalski2014; Wakefield et al., Reference Wakefield, Koopman, Keplinger, Bomar, Bernt, Johanning, Kruse, Davis, Wakefield and Mehr2014). Our findings strengthen previous work by corroborating their findings from a previously unrepresented perspective, that of the TM staff. Identified themes illustrate important challenges and unexpected benefits of the TM pilot which translate into lessons learned for future projects of this type.
First, TM should focus on the nurse–patient relationship. Our findings underscore the importance of the nurse role in uncovering social determinants of health and acting as a support to patients. Kahn et al. observed this extension of the TM nurse in providing social support and addressing patients’ needs to improve health outcomes (Kahn et al., Reference Johnston and Weatherburn2009). Our study builds on these findings by examining the role of the TM nurse in developing trust with patients and uncovering the social and economic context within which patients manage their diabetes. This was an unexpected benefit of the TM in this pilot, and one that may be critical to improving patient outcomes. Due to their daily involvement and understanding of patients’ situations, TM staff can identify issues crucial to patient health that may be missed in the context of widely spaced office visits. Having a TM nurse who can fulfill this role complements and enhances the care provided in a primary care setting. Providing resources to help TM agencies and practices address the socio-cultural determinants of health uncovered during TM is an important consideration for future projects.
Furthermore, the TM staff in this study repeatedly emphasized additional non-clinical benefits to the patients, such as their appreciation for knowing that someone was monitoring their health. This supports other studies, which have documented the benefits of TM for the patient in terms of mental well-being and security as important, albeit non-quantifiable, positive effects of this type of intervention (Jaana and Pare, Reference Greisinger, Balkrishnan, Shenolikar, Wehmanen, Muhammad and Champion2007; Kahn et al., Reference Johnston and Weatherburn2009; Johnston and Weatherburn, Reference Jaana and Pare2010; Pols, Reference Polisena, Tran, Cimon, Hutton, Mcgill and Palmer2010; Pecina et al., Reference Pare, Jaana and Sicotte2011; Fairbrother et al., Reference Des Jardins, Drone, Hashisaka, Hazzard, Hunt, Massey, Rein, Schachter and Turske2012), which are important considerations in an effort to provide patient-centered care. Further study is needed to determine if TM is the most effective way to provide this type of contact, or if other interventions, such as community health workers and peer models, might provide similar results. One possible advantage of the TM model might be the integration of this support with clinical information.
Similar to what has been reported in other studies (Jaana and Pare, Reference Greisinger, Balkrishnan, Shenolikar, Wehmanen, Muhammad and Champion2007; Fairbrother et al., Reference Des Jardins, Drone, Hashisaka, Hazzard, Hunt, Massey, Rein, Schachter and Turske2012; Davis et al., Reference Dagogo-Jack2014; Koopman et al., Reference Kahn, Fox, Carrington, Desai, Bartlett, Lyle and Kowalski2014), TM staff reported challenges encountered in the integration of TM into the routine workflow of real-world primary care offices, particularly around communication and coordination. From the perspective of the TM staff delivering the intervention, increased communication and integration between the TM agency and the practice are important – especially designating a point person within the office to coordinate TM and address patients’ needs. However, TM staff indicated awareness that providing time for such functions remains a challenge for primary care practices in implementing this model. Some studies have reported that TM programs increase provider time for reviewing and responding to additional patient data (Jaana and Pare, Reference Greisinger, Balkrishnan, Shenolikar, Wehmanen, Muhammad and Champion2007) and may also trigger more office visits (Polisena et al., Reference Pecina, Vickers, Finnie, Hathaway, Hanson and Takahashi2009a). Other studies have reported that TM systems that are not integrated with the electronic medical records create extra work, and that information flows and practice staff workflows need to be carefully thought out and defined (Fairbrother et al., Reference Des Jardins, Drone, Hashisaka, Hazzard, Hunt, Massey, Rein, Schachter and Turske2012; Davis et al., Reference Dagogo-Jack2014; Koopman et al., Reference Kahn, Fox, Carrington, Desai, Bartlett, Lyle and Kowalski2014). Successful office integration of TM was enhanced by designated care coordinators who viewed TM care as part of their role (Koopman et al., Reference Kahn, Fox, Carrington, Desai, Bartlett, Lyle and Kowalski2014). Our results support these findings and indicate that practices and TM agencies should work together to establish workflows that maximize the potential use of TM data while being mindful of provider time and efficiency.
Finally, TM staff in our study emphasized both disease control and patient buy-in and willingness to use the TM technology as key factors to consider in regards to how patients are identified for the program. Koopman et al. come to a similar conclusion, recommending that patients who were changing their regimen, were newly diagnosed or were uncontrolled – yet are motivated to make a change – may experience greater benefit from a TM program (Koopman et al., Reference Kahn, Fox, Carrington, Desai, Bartlett, Lyle and Kowalski2014).
Limitations
The qualitative data presented here were collected from a small sample of respondents participating in one pilot program. However, many of the findings support what has already been reported in the literature, corroborating their importance from the perspective of a different TM user group. Reports of improved patient outcomes as a result of the TM are anecdotal and may be subject to bias. Demonstrating improved outcomes was beyond the scope of this qualitative study. Rather, the goal of the qualitative evaluation was to understand the experiences and lessons learned from implementing TM into primary care practices from the perspective of the TM staff.
Another limitation is the absence of perspectives from the other parties (patients and PCPs) participating in the TM pilot. This may have resulted in a partial understanding of the overall experience of the TM program, providing the perspective of only one set of users. However, the findings from this study complement those conducted in other studies with other TM user groups (Davis et al., Reference Dagogo-Jack2014). We attempted to include staff from the PCP offices but were unable to identify a cohort of individuals across all participating practices that had consistently worked with the TM program for long enough to provide an informed and meaningful perspective and thematic analysis was fragmented. Additionally, consent agreements signed with the patients precluded evaluators from contacting patients enrolled in the pilot for their perspective. Hence we limited this study to the TM staff delivering the service to the patients. While a small sample, the eight individuals interviewed represent 100% of participating TM staff, so we believe that all experiences and perspectives were adequately represented.
The wide variation in TM implementation within the pilot is another limitation. It is not possible to determine the effects of this variation on the experience of the TM or the perceptions of interviewed TM staff. However, this variation is indicative of the real-world primary care landscape and likely circumstances surrounding the implementation of TM, which makes these findings potentially useful for a wide range of settings, both in the United States and internationally.
Conclusion
This qualitative exploration of the implementation of telemonitoring for high-risk diabetic patients in US primary care settings revealed several lessons learned. Telemonitoring may offer benefits for the preventive monitoring of at-risk patients in the primary care setting. However, its implementation into the real-world setting of primary care practices needs to be planned carefully in order to maximize the benefits of TM and minimize additional burden on practices. TM staff are positioned to play a key role in the primary care management of high-risk patients by providing timely information about changes in patient health and by identifying contextual factors that impact patient self-management, contributing to more holistic patient-centered care.
Acknowledgments
This project was funded by the Office of the National Coordinator for Health Information Technology (BEACON Award no. 90BC0003/01).
Conflicts of Interest
None.
