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. Author manuscript; available in PMC: 2021 May 22.
Published in final edited form as: West J Nurs Res. 2020 May 22;43(1):5–12. doi: 10.1177/0193945920923082

Primary Care Clinic Nurse Activities with a Telehealth Monitoring System

Chelsea Howland 1, Laurel Despins 1, Jeri Sindt 1, Bonnie Wakefield 1, David R Mehr 2
PMCID: PMC7680344  NIHMSID: NIHMS1591813  PMID: 32443961

Abstract

The purpose of this study was to evaluate differences in the types of nursing activities and communication processes reported in a primary care clinic between patients who used a home-based monitoring system to electronically communicate self-monitored blood glucose and blood pressure values and those who assumed usual care. Data were extracted from electronic medical records from individuals who participated in a randomized controlled trial comparing in-home monitoring and usual care in patients with Type 2 diabetes and hypertension being treated in a primary care clinic. Data about nursing activities initiated by primary care clinic nurses were compared between groups using descriptive statistics and independent t-tests. Significant differences between groups were identified for the direct care nursing activities of providing lifestyle and health education, medication adjustments, and patient follow-up. This study provides evidence of greater nursing activity reported in a primary care clinic in patients who utilized a home-based monitoring system.

Keywords: telemedicine, telehealth, diabetes, hypertension, nursing activity

Type 2 diabetes (T2DM) and hypertension are common co-morbid chronic disease processes (American Diabetes Association [ADA], 2019a). The co-existence of T2DM and hypertension result in an increased risk for macro and microvascular complications, including cardiovascular disease, cerebrovascular accident, retinopathy, and nephropathy (Khangura et al., 2018). An estimated 21 million adults in the United States have been diagnosed with T2DM, representing 8.6% of the population (Bullard et al., 2018). Hypertension is estimated to affect 85.7 million adults aged 20 years or older, with 73.6% of adults diagnosed with diabetes having co-morbid hypertension (Benjamin et al., 2018; Centers for Disease Control and Prevention [CDC], 2017). The management of both T2DM and hypertension are costly. Estimated total costs of the management of T2DM in the United States are $245 billion and $53.2 billion for hypertension (Benjamin et al., 2018; CDC, 2017). Both the direct costs incurred through medical management and indirect costs associated with loss of work and wages result in a significant economic burden (World Health Organization, 2016).

Nursing Activity with Home-Based Technology

Both T2DM and hypertension require interaction with healthcare providers to develop a patient-centered plan of care and the performance of self-management strategies to prevent and/or delay the onset of chronic complications (ADA, 2019a; ADA, 2019b). Patient self-management support via home-based technologies can provide additional data to primary care team members to increase between-visit monitoring, make more timely changes to the care, and direct collaborative goal setting with patients (ADA, 2019c). When incorporating home-based technologies into primary care clinics, it is important to recognize the role of registered nurses in interpreting data, communicating with primary care providers, and implementing changes to patient care. Nurses are integral to the management and analysis of home-based technologies and patient follow-up (Greenwood et al., 2014; Hu et al., 2018; Lee et al., 2018).

As nurses take on the role of analyzing and interpreting home-monitored data, recognizing the role of signal detection to identify pertinent data, and workload and workflow implications based on newly created and identified tasks is critical (Despins et al., 2010; Wickens, 2002). In signal detection theory, an individual confronted with a stimulus must decide whether the stimulus represents a signal or not (Wickens, 2002). When translating the theory to a primary care setting, the nurse reviewing home-monitored blood glucose and blood pressure data must discern the relevance of the data points (Despins et al., 2010; Wickens, 2002). A correctly identified signal, or hit, is a data point that requires follow-up or intervention (Wickens, 2002). To identify this relevant data, the nurse must sift through irrelevant data and avoid misidentifying relevant data (signals) as irrelevant, resulting in a miss, that is, failure to follow-up or intervene, or identifying irrelevant data as relevant resulting in a false alarm, that is, following-up or intervening unnecessarily (Wickens, 2002). The nurses’ discernment of signals is influenced by levels of training, fatigue, how distinct the signal is from irrelevant data, and responder bias (Despins et al., 2010; Wickens, 2002). Responder bias, the willingness to identify data as relevant, is related to the nurses’ priorities regarding signal detection, which can lead to over- or under-identification and correct interpretation of signals (Despins et al., 2010; Wickens, 2002). Levels of training, fatigue, how distinct the signal is from irrelevant data, and responder bias have implications related to nursing workload and workflow. Missed signals may lead to patient deterioration, while responding to irrelevant data will unnecessarily increase nursing workload.

Nursing workload has been defined by Alghamdi (2016) as the amount of time the nurse can devote to patient care, workplace activities, and professional development, meaning that both non-nursing and nursing activities must be accounted for when measuring total workload. Non-nursing activities include any work related or professional development activities that do not encompass patient care, such as attending meetings, mentoring new nurses, and participating in educational and professional development activities (Alghamdi, 2016). Implementing new technology, such as a home-based monitoring system, incorporates nursing education activities in order to optimize signal detection. Nursing activities encompass patient care activities, which can be indirect or direct (Alghamdi, 2016). Typical direct patient care nursing activities in a primary care setting include care management, patient education, medication counseling, and collaborative work with primary care providers (Anderson et al., 2012). Direct patient care nursing activities may be influenced by more frequent treatment plan changes when a home-based monitoring system is integrated into patient care. Indirect patient care activities in a primary care setting include following-up with patients, entering information into the electronic medical record and communicating with outside agencies (Anderson et al., 2012). The requirement for data relevance determination, data imputation, and communication will influence the time spent performing indirect patient care activities when a home-based monitoring system is integrated into clinical care. An integral component of direct patient care is nursing intensity, which is made up of factors from the nurse and patient, including patient acuity, the amount of care needed by the patient from the nurse, and the time needed to carry out nursing activities (Alghamdi, 2016; Morris et al., 2007). Requisite nursing intensity varies as patient acuity varies. In relation to telehealth strategies, it is important to recognize that their use in higher risk patients has implications for the level of nursing intensity required for each patient.

An additional component to acknowledge when exploring nursing workload, is nursing workflow. Workflow refers to a group of tasks and people or resources required to accomplish a process outcome (Cain & Haque, 2008). Nursing workflow will be impacted when integrating home-based technologies into a primary care clinic due to the time spent analyzing data and detecting signals, process changes to incorporate new tasks, and increases in communication to manage patient care. By exploring the workflow processes of nursing activities and communication, valuable information can be gained to improve workflow which leads to more consistent, reliable, and safe patient outcomes (Cain & Haque, 2008). Identifying workflow processes will provide evidence to support nursing workload, by improving the efficiency of time spent performing nursing activities.

Through the work of data interpretation and identification of signals, interprofessional communication, treatment plan changes, and patient communication, nurse workload and workflow will be impacted. There is increasing interest in the role of the nurse in primary care (Norful et al., 2017; Smolowitz et al., 2015). It is important to explore the nursing role when technology interventions are used, since the nurse has an integral role in data management and communication. However, there have been few published studies examining the role of nursing in the delivery of home-monitored blood glucose and blood pressure interventions within the context of a primary care clinic. Wakefield et al. (2013) examined nursing workload implications in the context of a blood glucose and blood pressure home-based monitoring program using data collected from a randomized controlled trial which utilized study nurses. The study found the most frequent nursing activities to be communicating with primary care providers, providing education about T2DM and hypertension to patients, and making social contact with the patient (Wakefield et al., 2013).

The purpose of this secondary analysis of data is to evaluate differences in the types of nursing activities and communication processes reported in a primary care clinic between patients who used a home-based monitoring system to electronically communicate self-monitored blood glucose and blood pressure values and those who assumed usual care. Data were drawn from a study evaluating the effect of a home-based monitoring system in patients with co-existing T2DM and hypertension, implemented in a primary care clinic setting (Wakefield et al., 2014). This study reports an analysis of nursing activities and communication processes reported in the electronic medical record.

Methods

The primary study findings have been previously published (Wakefield et al., 2014). The primary study was a randomized controlled trial which evaluated the effectiveness of data transmission from in-home devices (blood glucose meter and blood pressure machine) to a primary care clinic on treatment outcomes in patients with co-morbid T2DM and hypertension. Primary outcome measures were glycated hemoglobin (A1C) and systolic blood pressure (SBP). The University of Missouri Institutional Review Board approved the study prior to data collection (Approval Number 1095618). Data were collected from patients receiving care from six Family Medicine and General Internal Medicine clinics at a midwestern academic medical center.

Sample

Patients were identified for study inclusion based on a history of established T2DM, with an A1C > 8% and/or SBP > 130 mmHg. In addition to A1C and SBP, inclusion criteria included being 18 years or older, taking either oral antihyperglycemic or injectable insulin, a diagnosis of T2DM for 1 year prior to study enrollment, currently using or owning a glucometer compatible with study equipment, having an in-home analog phone line or computer with internet connection, and receiving care from Family Medicine or General Internal Medicine clinics from an attending physician and anticipating to continue to receive care for 12 months. Patients were excluded if they had been diagnosed with T2DM within the previous 12 months, had type 1 diabetes, were legally blind, resided in a long-term care facility, were severely cognitively impaired, or had someone else from their immediate household enrolled in the study. After enrollment patients were randomized to intervention and control groups using sequentially numbered sealed opaque envelopes prepared in advance by the study data manager. It was not possible to blind patients or physicians to group assignment.

Procedure

The study intervention utilized a telemonitoring system from Numera (formerly IMetrikus, Mountain View, CA) to transmit self-monitored blood glucose and blood pressure data. Self-monitored blood glucose and blood pressure data were transmitted using the Numera Net Connectivity Hub® to a password-protected secure website using the company’s Food and Drug Administration cleared gateway. Intervention group patients were instructed to test their blood glucose and blood pressure a minimum of once daily for the 12-week study duration and to upload their readings at least every other day. Readings could be transmitted individually, or several readings could be stored on the blood glucose meter or blood pressure machine and transmitted in bulk. Transmitted data were reviewed by a clinic nurse a minimum of twice weekly. Any issues identified by the clinic nurse were printed, verbally reported, or electronically communicated to the provider for review, who could then make treatment changes on an individual basis. Changes in treatment plans were communicated to patients per the clinic protocol, typically via a telephone call, which was documented in the electronic medical record.

Control group patients were asked to measure their blood glucose and blood pressure daily, then record both readings and bring them to clinic visits. After 12-weeks, all patients had an A1C and blood pressure measurement obtained at a follow-up clinic visit. During the follow-up clinic visit, all patients received a $20 gift card for study participation.

At the conclusion of the main study, there were no significant differences in A1C or SBP after the 12-week intervention between the in-home monitoring and usual care group patients. The main study concluded that the addition of self-monitoring technology alone, without additional supportive interventions, is not likely to result in improved outcomes. Further, the main study demonstrated the importance of incorporating processes to more effectively utilize data obtained from in-home monitoring in a primary care clinic setting.

Measures

For this secondary analysis of data, data codes were created for electronic medical record extraction based on communication processes and nursing activities. Data were extracted from patient electronic medical records by a nursing doctoral student who was experienced in reviewing records. The primary investigator reviewed the categories on the chart abstract form with the student; both independently reviewed 10 records, discussed their ratings, and came to consensus on the coding. Data were then entered into a spreadsheet for analysis.

Extracted data were grouped by communication processes and nursing activities for analysis. Communication processes refer to any form of communication which results in the information being shared and results in a communication outcome. The communication process incorporates a communication initiator and communication initiation directed towards a communication recipient leading to a communication outcome.

Nursing activities encompass direct care nursing activities, indirect care nursing activities, and non-patient care nursing activities. In this study, direct care nursing activities are defined as activities which had a direct influence on patient care, such as patient education, making changes to the treatment plan, and addressing clinical issues. Indirect care nursing activities are defined as adjunct activities which could influence direct care nursing activities, including entering home monitored data into the electronic medical record, requesting primary care provider input, and acknowledging blood glucose and blood pressure measures. Non-patient care nursing activities were defined as activities that were not specific to the treatment plan, such as enrolling the patient in the study, troubleshooting device issues, and reminding the patient to transmit data for the study.

Analysis

Data from 74 patient electronic medical records were analyzed using SPSS version 26, using a pre-determined alpha level of 0.05. Each communication process and nursing activity were analyzed using descriptive statistics by group (in-home monitoring and usual care). Nursing activities and communication processes were compared between groups using independent t-tests.

Results

Nursing Activities

In total, 786 nursing activities were coded for 36 in-home monitoring group patients (64.6%) and 38 usual care group patients (35.4%). On average, there were 14.1 (±8.8) nursing activities performed for in-home monitoring group patients and 7.3 (±4.5) nursing activities for usual care group patients over the 12-week study duration (in-home monitoring group median were 13 activities and usual care group median were 6 activities). The number of nursing activities per patient ranged from 1 to 38 in the in-home monitoring group and 2 to 19 in the usual care group.

In the in-home monitoring group, the most frequently reported individually coded nursing activities were adding blood glucose data to the electronic medical record (11.6%), making medication adjustments (10.6%), noting blood glucose out of range (10.4%), reminding patients to self-monitor and transmit blood glucose and blood pressure (7.1%), and noting blood pressure out of range (6.1%). The usual care group most frequently reported individual nursing activities were noting blood glucose out of range (11.5%), communicating test results to patients (9.7%), noting blood glucose in range (9.4%), making medication adjustments (9.4%), and ordering labs (8.6%). When data were categorized by nursing activity type, 54.1% of nursing activities were indirect care activities (Table 1), 37.6% were direct care activities (Table 2), and 8.3% were non-patient care related activities (Table 3), for the in-home monitoring group. For the usual care group, 50.7% of reported nursing activities were indirect care activities (Table 1), 42.4% were direct activities (Table 2), and 6.8% were non-patient care related activities (Table 3).

Table 1.

Indirect Care Nursing Activities (n = 416)

Intervention (N=275) Control (N=141)
Problems/Interventions Number Percentage Number Percentage
Home monitoring measures added to EMR 84 30.55 8 5.67
 BP 25 29.76 2 25
 BG 59 70.24 6 75
Appointment scheduled 1 0.36 0 0
Communication of test results/patient information 22 8 27 19.15
PCP input requested* 15 5.45 1 0.71
BP Measurements 55 20 36 25.53
 Noted 8 14.55 9 25
 Noted in-range 13 23.64 12 33.33
 Noted out of range* 31 56.3 15 41.67
 Home monitoring log reviewed 3 5.45 0 0
BG Measurement 90 32.73 67 47.52
 Noted 8 8.89 7 10.45
 Noted in-range 27 30 26 38.81
 Noted out of range 53 58.89 32 47.76
 Home monitoring log reviewed 2 2.22 2 2.98
Systems issues 8 2.91 2 1.42
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Note. BG: Blood glucose; BP: Blood pressure; EMR: Electronic medical record; PCP: Primary care provider.

*

p˂0.05

Table 2.

Direct Care Nursing Activities (n = 309)

Intervention (N=191) Control (N=118)
Problems/Interventions Number Percentage Number Percentage
Provided lifestyle or health information or education (DM or HTN related)* 63  32.98 29 24.58
 Diet * 28 44.44 13 44.83
 Smoking 2 3.17 1 3.45
 Foot Care 4 6.35 3 10.34
 Exercise* 29 46.03 12 41.38
Addressed clinical issue 20 10.47 22 18.64
 BG 11 55 10 45.45
 BP 6 30 3 13.64
Not related to BG/BP 3 15 9 40.91
Medication adjustment* 54 28.27 26 22.03
New Medication ordered 8 4.19 6 5.08
Labs ordered 26 13.61 24 20.34
No changes to treatment plan 6 3.14 8 6.78
RN followed up with patient* 14 7.33 3 2.54
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Note. BG: Blood glucose; BP: Blood pressure; DM: Diabetes mellitus; HTN: Hypertension; RN: Registered nurse.

*

p˂0.05

Table 3.

Non-Patient Care Nursing Activities (n = 61)

Intervention (N=42) Control (N=19)
Problems/Interventions Number Percentage Number Percentage
Numera device related 3 7.14 0 0
Reminder to self-monitor/transmit data 36 85.71 17 89.47
Enrollment in study 3 7.14 2 10.53
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Comparisons between groups were made for each nursing activity using independent t-tests. Within the nursing activities categorized as direct care, significant differences were identified between groups for the overall provision of diabetes or hypertension health information or education (t(52.9)=2.497, p=0.016), with in-home monitoring group patients receiving more health information or education. Within subgroups of health information or education significant differences were identified for diet (t(62.2)=2.407, p=0.019) and exercise (t(52.4)=2.397, p=0.020) health information or education. Medication adjustments were made more frequently in the in-home monitoring group than in the usual care group (t(72)=2.560, p=0.013). In-home monitoring group patients received significantly more follow-up from the clinic nurses than the usual care group (t(45.3)=2.522, p=0.015). For the indirect care nursing activities, in-home monitoring group patients received significantly more requests for primary care provider input (t(37.2)=2.200, p=0.034) and had more blood pressure measurements noted out of range (t(72)=2.208, p=0.030). No other significant differences were identified for nursing activities within the direct care or indirect care categories or for non-patient care activities.

Communication Processes

In total, 264 communications were coded for 36 in-home monitoring group patients (62.9%) and 38 usual care group patients (37.1%). On average, there were 4.6 (±2.4) communications per in-home monitoring group patient and 2.6 (±1.3) communications per usual care group patient over the 12-week study duration (in-home monitoring group median were 4.5 communications and usual care group median were 2 communications). The number of communications ranged from 1 to 11 in in-home monitoring group patients and 1 to 5 in the usual care group.

Nurse-initiated communications accounted for 42.2% of all communications in the in-home monitoring group and 8.2% of communications in the usual care group, which was statistically significant (t(39.7)=5.682, p˂0.000). The most frequent communication recipient was in the category of “other”, which included clinical notes, routine clinic visits, and consultations in both groups, with the in-home monitoring group patients having significantly greater reports of “other” as a communication recipient (t(45.7)=4.757, p˂0.000). No other significant differences were identified for communication recipients.

In the in-home monitoring group, the most frequently reported methods of communication initiation were by clinic notes (42.2%), face-to-face encounters (27.1%), and telephone messages (19.3%). For the usual care group, face-to-face encounters (43.9%), letters (23.5%), and telephone messages (20.4%) were the most frequent methods of communication initiation. Significant differences were identified between groups, with in-home monitoring group patients having significantly more clinic notes as a communication initiation method (t(40.5)=5.991, p˂0.000). No other significant differences were identified for communication initiation methods.

Communication outcomes in the in-home monitoring group, most frequently occurred as a telephone call (47.6%) or “other” category, such as ordering labs or faxing to a pharmacy (37.9%). In the usual care group, the most frequently occurring communication outcomes were “other” (41.1%) and telephone calls (30.4%). Significant differences were identified between groups for communication outcomes occurring as a telephone call (t(42.4)=4.601, p˂0.000) and “other” (t(51.9)=4.506, p˂0.000).

Discussion

An evaluation of nursing activities between in-home monitoring and usual care group patients with co-existing T2DM and hypertension using a home-based monitoring system revealed that patients in the in-home monitoring group on average received almost twice as many nursing activities (14.1 ±8.8) than patients who received usual care (7.3 ±4.5). Most in-home monitoring group nursing activities included adding blood glucose data to the electronic medical record, making medication adjustments, noting blood glucose levels as out of range, reminding patients to self-monitor and transmit blood glucose and blood pressure data, and noting blood pressure levels as out of range. When data were grouped by nursing activity type, most nursing activities were indirect care activities for both groups. Significant differences between groups were identified for the overall provision of diabetes or hypertension health information or education, diet and exercise health information or education, medication adjustments made, follow-up with nurses, request for primary care provider input, and blood pressure noted as out of range.

Communication process evaluations revealed more communications on average for the in-home monitoring group patients (4.6 ±2.4) than in patients who received usual care (2.6 ±1.3). Nurse-initiated communications accounted for the greatest amount of communications in the in-home monitoring group; which occurred significantly more than in the usual care group. Communication recipients occurred most frequently in the “other” category, with clinic notes being the most commonly reported, in the in-home monitoring group. Additionally, clinic notes for provider communications were the most common form of communication initiation method for in-home monitoring group patients, with face-to-face encounters being more common in usual care group patients. Communication outcomes in the in-home monitoring group most frequently occurred as a telephone call to the patient, with significant differences identified between groups.

While the body of literature around the use of home-based monitoring programs in patients with T2DM and hypertension continues to grow, the focus is rarely on the role of nursing in care delivery or communication with patients. The timeliness of nurse responses to alerts automatically triggered by a telehealth measurement system have been explored; however, the nursing activities occurring in relation to the response were not detailed (Murphy, 2018a, 2018b). More frequently, studies focus reporting on the telehealth intervention, outcomes relevant to blood glucose and blood pressure management, and telehealth system design and usage than describing nursing activities (Baron et al., 2012; Lim et al., 2016; Zhou et al., 2014). This study describes different types of nursing activities and communication processes in relation to an in-home monitoring system.

When published studies have included nurses within the intervention component, few have occurred with nurses in a real-world primary care clinic setting. Wakefield et al. (2013) described nursing activities of study nurses in a randomized controlled trial using a telemonitoring program. The current study differs by describing nursing activities and communication processes of nurses within a primary care clinic. In a review of literature, no other studies were identified which evaluated nursing activities within a home telemonitoring program within the context of a real-world setting. It is integral to examine nursing activities and communication processes of nurses within a primary care clinic setting, to begin to develop a framework for understanding implications for nursing workload and workflow when in-home monitoring systems are incorporated into patient care. Additional implications to recognize are the need to educate clinic nurses to improve telehealth integration and ensure appropriate detection of signals (e.g., out of range blood glucose and blood pressure). To further guide telehealth integration, additional policies and procedures require development, to guide nurses and primary care providers. Exploring legal guidelines is necessary to guide policy and procedure development. Finally, recognizing the necessity of collaborative relationships between nurses and primary care providers, due to the reliance on nursing to detect and communicate relevant patient information.

In studies using a telehealth intervention, few have defined the amount of time required of nurses to deliver patient care or explored implications for nursing workload or workflow. In a study of diabetes self-management using a telehealth intervention, study nurses developed action plans with patients during a biweekly 30-minute videoconference (Carter et al., 2011). In a previously described study by Wakefield et al. (2013), 41% of reported nursing activities were spent providing direct patient care. This study identified 54.1% of nursing activities were indirect patient care activities, 37.6% were direct patient care activities, and 8.3% were non-patient care activities. Differences in nursing activity allocations between this study and the results identified by Wakefield et al. (2013) could be due to additional items being included in the indirect care nursing activity category for data analysis. While this study did not measure the quantity of time spent performing nursing activities, it identified that nurses allocated increased amounts of their workload using signal detection to analyze and enter home-monitored data into the electronic medical record, identify relevant information requiring communication, enter clinic notes as a communication method, initiate communication with patients, and initiate telephone calls as a communication outcome. Implications for workflow have been identified due to the process changes required when integrating a home-based monitoring system and increases in interprofessional collaboration and communication. Koopman et al. (2014) found that although new work was created through communication, clinic nurses thought the overall impact on workflow was positive due to increased time to focus on other important tasks during clinic visits. In the study, clinic nurses managed no more than 10 patients at a time and denied feeling overwhelmed by the amount of new information received (Koopman et al., 2014). Understanding the changes to workload and workflow generated when a home-based monitoring system is integrated into a primary care clinic has significant implications for nursing practice, as nurses will be expected to integrate new nursing activities and communication processes into their existing workload and workflow. Additional studies are needed to further identify time spent performing nursing activities, to more clearly understand the influence of integrating a telemonitoring system on nursing workload and workflow, and better understand the number of patients a nurse can appropriately manage.

Due to the nature of secondary data analysis, a limitation of this study is the use of existing data from a study which focused on measuring outcomes related to intervention efficacy and patient outcomes. While data were available to evaluate nursing activities and communication processes, no data were captured to quantify time spent performing nursing activities. Additionally, the performance of nursing activities was not evaluated in direct relation to improvement in patient outcomes. The small sample size limits generalizability of study findings to a larger population. Future, larger studies need to be conducted in additional clinic settings to more fully describe nursing activities, communication processes, and implications for nursing workload.

This study adds to the body of literature by describing nursing activities and communication processes of nurses when an in-home monitoring system is used with patients who have co-morbid T2DM and hypertension in a primary care clinic setting. By describing nursing activities and communication processes, implications for patient care can begin to be revealed, including increased health education provision, increased recognition of abnormal blood pressure, more frequent medication adjustments, increased recognition of the need to communicate with a primary care provider, and an overall increased communication between patients and healthcare providers. Further, evaluating nursing activities and communication processes can provide insights into nursing workload and workflow. While further evidence is needed to quantify the amount of time spent by nurses with an in-home monitoring system, this study provides evidence that can be applied to future studies in other real-world settings. It would be beneficial for future studies to explore additional data visualization methods to aid in effective signal detection, which could influence nursing workload related to time spent analyzing and interpreting transmitted data.

Acknowledgements

Authors’ are thankful to Jennifer O’Connor, a graduate student, Sinclair School of Nursing, University of Missouri, who aided in cleaning and data entry.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work for the main study was supported in part by the Agency for Healthcare Research and Quality [Grant Number R18HS017035]. This work was supported in part by the National Institute for Health [Grant Number T32NR015426] awarded to the Sinclair School of Nursing, University of Missouri.

Footnotes

Conflict of Interest

The authors declared no potential conflicts of interest with respect to research, authorship, and/or publication of this article.

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