Evidence Based Practice Research Paper Diabetes Mellitus


Problem Description

            Diabetes Mellitus (DM) is a common chronic metabolic condition that manifests majorly as hyperglycemia and results from insulin sensitivity or impaired production of insulin by the pancreatic beta cells. The risk factors for DM can be classified into both modifiable and non-modifiable. American Diabetes Association [ADA] (2019) lists the modifiable risk factors like tobacco, alcohol, physical inactivity, obesity/overweight, impaired fasting glucose/impaired glucose tolerance, and dyslipidemia. Contrarily, the non-modifiable risk factors include a family history of DM, ethnicity, hypertension, age >40 years, and previous gestational diabetes (ADA, 2019). Management of DM encompasses both lifestyle interventions and pharmacological therapy. In some cases, despite the recommended therapy, the blood sugar levels remain elevated, a condition referred to as uncontrolled diabetes. Various randomized controlled trial studies have, in an attempt to define uncontrolled diabetes, used hemoglobin A1C (HbA1C) levels as the rationale. Orozco-Beltran et al. (2017) define uncontrolled diabetes as HbA1C ≥8%. However, in this paper, we will use a cutoff HbA1C of >9% to define uncontrolled diabetes.

Uncontrolled diabetes is a significant cause of worry to the public health due to the high risk of developing complications and increased health needs among the patients. According to the CDC (2020) reports on DM morbidity, 34.2 million Americans have diabetes, approximately 1 in 10. This is not different from the global prevalence where it is estimated that 1 in 11 adults have DM (CDC, 2020). Annually, over 1-million deaths are attributed to DM and have been ranked the 9th leading cause of mortality worldwide (CDC, 2020). Further, significant expenditures are incurred towards the management of DM and its related complications. In the United States, it is estimated that $327 billion is incurred annually to manage DM (CDC, 2020). Studies have not explored the epidemiology of uncontrolled DM; however, it is expected that uncontrolled DM burden surpasses that of well-controlled DM. Following the significant morbidity, mortality, and economic strain, DM is an important public health concern that must be addressed. Further, due to the admissions and increased demands for emergency care of patients with uncontrolled DM, and being conscious of the role of advanced practice nurses in care delivery, the condition increases the burden of advanced practice care.

Population, Intervention, Comparison, Outcome (s) and Time (PICOT)

            The PICOT question is as follows: In primary care patients with uncontrolled diabetes (HbA1c>9%) how does virtual telemedicine outreach compared to a telephonic outreach with a case manager influence diabetes control over the next 6 months? The population (P) consists of primary care patients with uncontrolled diabetes (A1C>9. Such patients have increased risks for complications and comorbidities; therefore, appropriate interventions must be established to reduce morbidity and mortality. The intervention (I) proposed in this paper is the use of telemedicine. Wootton (2012) defines telemedicine as the use of information and communication technologies to provide care to patients in remote areas. It can be synchronous-the use of live videoconferencing or asynchronous where information, for example, vital signs are entered, stored into e-health portals, and transmitted from home to the hospital information system (Wootton, 2012). The primary objectives of telemedicine in supporting the care of chronic conditions include intervention teaching, health education, transfer of health data, and facilitating follow-up of patients (Wootton, 2012).

The comparative © intervention is telephonic outreach. This involves making telephone calls to reach distant patients who are unable to have in-person visits (Orozco-Beltran et al., 2017). HbA1C levels are the primary outcomes (O) in this case. HbA1C is used to determine glycemic control in the past 3 months and the risk for diabetes-related complications. Current assays consider HbA1C less than 5.7% as normal while above 6.4% used as a diagnostic criterion for DM (ADA, 2019). For most patients with Type 2 DM, evidence-based guidelines recommend clinicians maintain the HbA1C levels between 7% and 8% (ADA, 2019). Values above 8% despite the therapeutic interventions herald the diagnosis of uncontrolled diabetes (Orozco-Beltran et al., 2017). Watt et al. (2021) however define uncontrolled diabetes as HbA1C >9% (75mmol/l), the values adopted in this paper. Besides the HbA1C levels, quality of life, rates of admission, number of emergency unit visits, and mortality rates can be used to compare the productiveness of the two interventions. A period of 6 months (T) is set to determine the influence of telemedicine on HbA1C as compared to the telephonic approach. In this paper, data has been extracted and synthesized from 16 articles regarding the use of telemedicine in the management of uncontrolled diabetes.

Literature Search Methods

            Research methodology refers to the process of collecting data and analyzing the data. This process shows how data was collected, the methods used for collecting data, and how data is interpreted to give it meaning. For this particular research, data was obtained from secondary sources. To achieve this, I established the key words in the PICOT question and the supportive research questions, and run them through different databases, among the CINAHL, EBSCO, PubMed, Medline, and Google Scholar. I then filtered the results based on relevance, recency and accuracy. Across all platforms, the cumulative search results gave more than 200 resources, out of which 29 were eventually screened for relevance to this study. The research aims to find out if a virtual telemedicine outreach in comparison to a telephonic outreach with a case manager does influence diabetes control over 6 months period. Thus, the PICOT question is: In primary care patients with uncontrolled diabetes (A1c>9%) how does virtual telemedicine outreach compare to a telephonic outreach with case manager influence diabetes control over the next 6 months?


            For the past 20-years, copious amounts of research have been done surrounding the use of telemedicine in the management of chronic conditions. The process has however been slow due to a group of scholars who think that close interaction with the patient (face-to-face) is superior to remote monitoring. Nevertheless, the evidence available shows that telemedicine contributes massively to the management of chronic conditions such as diabetes, COPD, and heart failure.

Orozco-Beltran et al. (2017) researched to determine the role of telemedicine in the management of primary care patients with chronic conditions (diabetes, COPD, heart failure, and hypertension). The impetus for this was the changing population demographics (increasing aging population), the rise in chronic conditions prevalence, and the increased need for care models that are compatible with home care. Commonly used interventions compatible with home care are telemedicine and telephonic support; however, their effectiveness is not equal. Before the intervention, essential telemedicine equipment was distributed to the participants for self-monitoring. Among them include glucometers, blood pressure monitors, pulse oximeters, and weight scales. In addition to videos that instructed them on the use of the instruments, the patients were fully equipped. The spectrum of telemedicine as regards the research included entering vital signs into the e-health portals, and an alarm system that detects any alteration in the data entered. The alarm would therefore prompt the care team’s reaction and in return, intervention and health education would ensue. For a seamless transfer of data and interpretation, each patient had a unique e-health record and an identifier. Following the receipt of the information by the care team, a nurse would decide whether to make a call, go to the patient’s house, scheduled for a face-to-face visit or consult their seniors.

During the 1-year intervention, 521 participants completed the study. The majority of them were elderly, averagely 70.4 years; this proves that chronic conditions have a predilection for the aged population. The post-program analysis found that the intervention had a significant impact on weight loss, blood pressures, heart rate, and HbA1C levels. At the end of the study, while leveraging telemedicine technologies, the number of people who had ≥8% HbA1C levels had reduced by 44% (Orozco-Beltran et al., 2017). A similar study by Watt et al. (2021) underpins the reduction in patients’ HbA1C levels after a virtual diabetic program. The study by Watt et al. (2021) explores the significance of a sustainability transformation program (STP) in promoting foot care and reducing risks of amputation among diabetic patients. The STP consists of primary care, face-to-face multidisciplinary foot (MDFT) care, a virtual MDFT, and community podiatry. At the beginning of the study which took 6-months, the weight and HbA1C ranges were as follows: 99.4 ± 25 Kg and 59.3 ± 16 mmol/l respectively.  At the end of the study, the weight and HbA1C ranges measured as follows: 95.5 ±24.2 Kg and 54.8 ±12.9 Mmol/l respectively. A different study by Meneghini et al. (1998) has almost the same findings; HbA1C decreased by 0.8% and 0.9% after a 6-month and a 12-month intervention respectively. From the study by Watt et al. (2021), it is evident that both weight and HbA1C levels decreased moderately after the 6-month duration of an STP program to diabetic patients. Cahn et al. (2018) underpin that virtual diabetic programs only cause a modest reduction in HbA1C but with increased patient satisfaction. Besides the virtual MDFT care, the community had a website and a Facebook page that facilitated interaction between the care team and the diabetic patients. Well-controlled diabetes, marked by a reduction in HbA1C levels decreases complications such as diabetic foot and therefore reduces the risks for amputation (Thomson et al., 2021; Watt et al., 2021). Even though specific statistics regarding the readmission rate and the mortality rates post the telemedicine projects are unavailable, Orozco-Beltran et al. (2017) estimate that Tele-monitoring has reduced readmission by 28% and mortality by 24 % among patients with chronic conditions. Contrarily, telephonic outreach has not caused a significant impact on the management of chronic conditions; however, studies report that it reduces mortality due to relapses in patients with heart failure and diabetes (Kelley et al., 2020; Orozco-Beltran et al., 2017).

Despite the significance of technology in the management of chronic conditions, its use is affected by the patients’ geographical location, access to the internet, education level, and patient knowledge regarding information communication technology (ICT). According to Itamura et al. (2020) research on the uptake of virtual visits in the otolaryngology department during the COVID-19 pandemic, patients report difficulty in communication with the care providers while using the telecommunication devices; others mentioned the audio-video lag and the server speed. To minimize the digital divide attributed to lack of knowledge on ICT, the project team educated the participants regarding the use of software applications and Tele-monitoring devices (Emerson et al., 2015; Orozco-Beltran et al., 2017; Watt et al., 2021; Wootton, 2012). Further, the patients received contact details from the companies where the hardware and the software were procured. Even though telemedicine benefits outweigh that of telephonic outreach in terms of HbA1C control, readmission rates and mortality, the Luddites and the majority of the elderly would prefer telephone calls due to ease of use and lack of complex technologies required (Huygens et al., 2016; Orozco-Beltran et al., 2017; Watt et al., 2021; Wootton, 2012).

The current increased use of telemedicine devices is attributed to the COVID-19 pandemic. Despite the guidelines established by the World Health Organization and various governments such as the restriction of movement, social distancing, and the stay at home, care must continue. This is therefore a timely opportunity to leverage technology in the care of vulnerable populations. Telemedicine interventions ensure care continuity even with the stay-at-home and social distancing initiatives. Horrell et al. (2020) conducted a study to determine the magnitude of Telemedicine use among patients with chronic diseases during the COVID-19 pandemic. Further, Horrell et al. (2020) examined the causal relationship between socio-demographic characteristics and telemedicine use. Participants of the study were patients with chronic conditions such as hypertension, COPD, asthma, hyperlipidemia, Type 2 DM, heart failure, HIV, and Alzheimer’s disease. The measure of the Telemedicine engagement was determined by asking the patients ‘yes’ or ‘no’ questions. For instance, have you received any virtual care from your doctor for the last 4-months? Further, the participants were asked how they obtain information regarding COVID-19, their main concerns during the pandemic, and the sources/platforms they use to learn more about the COVID-19 pandemic. The findings revealed that 49% of the 2210 participants had participated in virtual visits with their health care provider; 45% rescheduled or canceled their regular clinic visits and 37% rescheduled or postponed their routine medical check-up. The high number of people (49%) participating in virtual visits can be attributed to the COVID-19 pandemic restrictions which limit movement and encourages people to stay at home. Communication with the care providers occurred via phone (73%) and e-portals (43%); the statistics depict significant embracement of telemedicine.

The socio-demographics affected the telemedicine use in the following ways: more women participated in telemedicine than men; those with higher incomes >$100000/year engaged more than those with <$30000/year; the higher the level of education, the greater the telemedicine use; telemedicine use was more among those <55 years of age and decreased dramatically among people <56 years of age. From the study, there was reported improved quality of life among the patients who continued care via virtual visits as opposed to those who canceled or postponed care to later dates. Among the diabetic patients, improved quality of life was defined by better glycemic control which is determined by the HbA1C levels, and reduced symptoms such as polyuria, polydipsia, and acute complications, for instance, non-ketotic hyperglycemic coma. A similar study by Iyer et al. (2021) underpin that telemedicine improved quality of life, satisfaction, and reduced exposure to COVID-19 among geriatric patients who engaged in virtual visits during the pandemic. Embracement of technology is more among younger patients as compared to the elderly population (Horrell et al., 2020; Iyer et al., 2021; Jain et al., 2020). There is ambivalence towards the use of telemedicine technologies which is attributed to patients’ knowledge of ICT and personal qualities (Dugdale et al., 2020; Jain et al., 2020; Norden et al., 2020; Orozco-Beltran et al., 2017). All the studies, however, in one way or the other, provide enough evidence that telemedicine use in the management of chronic conditions is superior to telephonic outreach. Further, the studies underpin the increased implementation of telemedicine in the management of diabetes especially during the COVID-19 pandemic as this would decrease HbA1VC levels as well as reduce exposure to COVID-19.

Strengths and Limitations

            Following an intensive literature search, the review provides adequate information regarding the influence of telemedicine on the management of diabetes. It differs from the majority of the reviews which explore the effect of telemedicine on chronic diseases in general without distinctiveness. Part of the limitation of the study is that each of the articles was regarded as having equal values. Further, it was difficult to synthesize data from several studies.


            The use of technology in the management of chronic diseases has been intensively explored by varied researchers. According to Orozco-Beltran et al. (2017), after a 1-year telemedicine program, the number of people with HbA1C levels above 8% reduced significantly by 44%. A similar study conducted by Watt et al. (2021) underpins that a 6-month implementation of a virtual diabetic program moderately reduced HbA1C levels from a range of 59.3±16 to54.8±12.9. Telemedicine acceptance has even increased during the COVID-19 pandemic due to the guidelines on movement restrictions and stay-at-home initiatives (Horrell et al., 2020; Itamura et al., 2020; Iyer et al., 2021). Besides HbA1C levels reduction, other outcomes reported while using telemedicine devices include improved quality of life, patient satisfaction; readmission rates, and reduced mortality (Cahn et al., 2018; Dugdale et al., 2020; Emerson et al., 2015; Horrell et al., 2020). According to Iyer et al. (2021), telemedicine use is limited by a lack of basic ICT knowledge and the elderly population which recommends easier methods to communicate with the care providers. To increase the acceptability of telemedicine, patients and care providers are taught how to operate the telecommunication devices before the commencement of the project (Itamura et al., 2020; Jain et al., 2020; Kelley et al., 2020; Norden et al., 2020; Smart et al., 2021).

Based on the body of evidence extracted from the articles, it is recommended that hospitals should embrace telemedicine in care for patients with chronic conditions such as diabetes. Further, it is recommended that telemedicine use should continue even after the COVID-19 pandemic to minimize health disparities caused by geographical locations (proximity to care centers). Since fewer elderly people embraced telemedicine more than the younger population, a recommendation to the project implementation team includes intensive training on the use of the ICT devices and possible use of simpler technologies.


            20 years ago, even though the technology was available, its maximal effects in healthcare had not been realized. For the past two decades, the healthcare sector has seen immense advancement in technology and increased implementation of healthcare informatics projects. The projects involving the use of telemedicine in the management of chronic diseases have been increasingly reported. Multiple studies have since then explored the influence telemedicine has in the management of chronic diseases such as diabetes hypertension, heart failure, and COPD. With regards to uncontrolled DM (HbA1C>9%), the studies have found a moderate reduction in the levels. Therefore, diabetic patients who engage in telemedicine interventions show improved glycemic control, reduced readmission rates, decreased mortality, and improved quality of life. The COVID-19 pandemic has been an impetus for patients to change their face-to-face appointments to virtual visits and a few express their willingness to continue with the telemedicine interventions even after the COVID-19 predicament cease.


  • American Diabetes Association. (2019). 1. Improving care and promoting health in populations: Standards of Medical Care in diabetes-2019. Diabetes Care42(Suppl 1), S7–S12. https://doi.org/10.2337/dc19-S001
  • Cahn, A., Akirov, A., & Raz, I. (2018). Digital health technology and diabetes management: Journal of Diabetes10(1), 10–17. https://doi.org/10.1111/1753-0407.12606
  • Centers for Disease Control and Prevention. (2020). National Diabetes Statistics Report, 2020. Cdc.Gov. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf
  • Dugdale, D., Khor, S., Lee, E. S., Marcotte, L., & Flum, D. (2020). Transforming population‐based hypertension management—an efficient and effective approach. Health Services Research55(S1), 81–81. https://doi.org/10.1111/1475-6773.13442
  • Emerson, J. F., Welch, M., Rossman, W. E., Carek, S., Ludden, T., Templin, M., Moore, C. G., Tapp, H., Dulin, M., & McWilliams, A. (2015). A multidisciplinary intervention utilizing virtual communication tools to reduce health disparities: A pilot randomized controlled trial. International Journal of Environmental Research and Public Health13(1), ijerph13010031. https://doi.org/10.3390/ijerph13010031
  • Horrell, L. N., Hayes, S., Herbert, L. B., MacTurk, K., Lawhon, L., Valle, C. G., & Bhowmick, A. (2020). Telemedicine use and health-related concerns of patients with chronic conditions during COVID-19: Survey of members of online health communities (preprint). In JMIR Preprints. https://doi.org/10.2196/preprints.23795
  • Huygens, M. W. J., Vermeulen, J., Swinkels, I. C. S., Friele, R. D., van Schayck, O. C. P., & de Witte, L. P. (2016). Expectations and needs of patients with a chronic disease toward self-management and eHealth for self-management purposes. BMC Health Services Research16(1). https://doi.org/10.1186/s12913-016-1484-5
  • Itamura, K., Rimell, F. L., Illing, E. A., Higgins, T. S., Ting, J. Y., Lee, M. K., & Wu, A. W. (2020). Assessment of patient experiences in otolaryngology virtual visits during the COVID-19 pandemic. OTO Open4(2), 2473974X20933573. https://doi.org/10.1177/2473974X20933573
  • Iyer, S., Mehta, P., Weith, J., Hoang-Gia, D., Moore, J., Carlson, C., Choe, P., Sakai, E., & Gould, C. (2021). Converting a geriatrics clinic to virtual visits during COVID-19: A case study. Journal of Primary Care & Community Health12, 21501327211000236. https://doi.org/10.1177/21501327211000235
  • Jain, S. R., Sui, Y., Ng, C. H., Chen, Z. X., Goh, L. H., & Shorey, S. (2020). Patients’ and healthcare professionals’ perspectives towards technology-assisted diabetes self-management education. A qualitative systematic review. PloS One15(8), e0237647. https://doi.org/10.1371/journal.pone.0237647
  • Kelley, L. T., Phung, M., Stamenova, V., Fujioka, J., Agarwal, P., Onabajo, N., Wong, I., Nguyen, M., Bhatia, R. S., & Bhattacharyya, O. (2020). Exploring how virtual primary care visits affect patient burden of treatment. International Journal of Medical Informatics141(104228), 104228. https://doi.org/10.1016/j.ijmedinf.2020.104228
  • Meneghini, L. F., Albisser, A. M., Goldberg, R. B., & Mintz, D. H. (1998). An electronic case manager for diabetes control. Diabetes Care21(4), 591–596. https://doi.org/10.2337/diacare.21.4.591
  • Norden, J. G., Wang, J. X., Desai, S. A., & Cheung, L. (2020). Utilizing a novel unified healthcare model to compare practice patterns between telemedicine and in-person visits. Digital Health6, 2055207620958528. https://doi.org/10.1177/2055207620958528
  • Orozco-Beltran, D., Sánchez-Molla, M., Sanchez, J. J., Mira, J. J., & ValCrònic Research Group. (2017). Telemedicine in primary care for patients with chronic conditions: The ValCrònic quasi-experimental study. Journal of Medical Internet Research19(12), e400. https://doi.org/10.2196/jmir.7677
  • Smart, M. H., Mandava, M. R., Lee, T. A., & Pickard, A. S. (2021). Feasibility and acceptability of virtual academic detailing on opioid prescribing. International Journal of Medical Informatics147(104365), 104365. https://doi.org/10.1016/j.ijmedinf.2020.104365
  • Thomson, A. J., Chapman, C. B., Lang, H., Sosin, A. N., & Curtis, K. M. (2021). Outpatient virtual visits and the “right” amount of telehealth going forward. Telemedicine Journal and E-Health: The Official Journal of the American Telemedicine Association. https://doi.org/10.1089/tmj.2020.0468
  • Watt, A., Beacham, A., Palmer-Mann, L., Williams, A., White, J., Brown, R., Williams, E., Richards, G., White, L., Budge, P., Darvall, K., Bond, E., & Paisey, R. (2021). Service user and community clinician design of a partially virtual diabetic service improves access to care and education and reduces amputation incidence. BMJ Open Diabetes Research & Care9(1), e001657. https://doi.org/10.1136/bmjdrc-2020-001657
  • Wootton, R. (2012). Twenty years of telemedicine in chronic disease management–an evidence synthesis. Journal of Telemedicine and Telecare18(4), 211–220. https://doi.org/10.1258/jtt.2012.120219


THT- Telehealth Technology

HIPAA-The Health Insurance Portability and Accountability Act

Synthesis Statement

The studies focused largely on determining the efficacy of telehealth use in patient monitoring, particularly from the patient perspective. The results show that with effective and appropriate implementation, leveraging telehealth in patient monitoring not only improves patient interaction, communication, and experience with the caregiver, but also results in better patient health outcomes.