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Clinical and Case Study Article

Virtual visits and the use of continuous glucose monitoring for diabetes care in the era of COVID-19

Underwood, Patricia PhD, FNP-BC, RN1,2; Hibben, Jennifer Jennifer Hibben AGNP-BC, RN, Jolynn Gibson RN, Monica DiNardo PhD, AGNP-BC, RN (Adult-Gerontology Nurse Practitioner)2; Gibson, Jolynn (Registered Nurse, Certified Diabetes Care and Education Specialist)3; DiNardo, Monica (Adult Nurse Practitioner, Certified Diabetes Care and Education Specialist, Health Science Specialist)3

Author Information
Journal of the American Association of Nurse Practitioners: March 2022 - Volume 34 - Issue 3 - p 586-596
doi: 10.1097/JXX.0000000000000659
  • Open

Abstract

Introduction: Continuous glucose monitoring use, virtual visits, and COVID-19

The coronavirus disease 2019 (COVID-19) pandemic ignited a surge in the use of virtual health care for all patients. To support physical distancing and decrease the spread of COVID-19, the federal government removed barriers to virtual care, allowing practitioners to practice across state lines and waived rural and site limitations for virtual care visits (Gorodeski et al., 2020; Wosik et al., 2021). When the Centers for Disease Control enacted stay at home orders in March 2020, it became necessary for providers and patients to engage in virtual visits for diabetes mellitus (DM) management. Virtual care, also known as telehealth, is defined as clinical activities used to deliver care outside of the traditional face-to-face interaction with the patient (Wosik et al., 2021). Virtual care includes both provider-to-patient and provider-to-provider interactions that occur both synchronously (telephone and video) and asynchronously (secure email communication; Wosik et al., 2021). Previous research demonstrates that patient outcomes associated with virtual DM management are equal to, and in some cases superior to, that of face-to-face care (McDonnell, 2018); however, there is a lack of familiarity and deep understanding among many patients and health care providers with this care model. Further, recent advances in DM technology, particularly continuous glucose monitoring (CGM), allows for remote monitoring of blood glucose patterns that enhances provider decision making, resulting in improved clinical DM outcomes (Beck et al., 2017a; Beck et al., 2017b; Beck et al., 2017c; Fonseca et al., 2016; Oskarsson et al., 2018). Recent policy changes and advances in DM technology have led to an increase in the use of DM virtual visits that use DM technology. Many providers feel overwhelmed by changes in DM technology, managing the large amount of data it generates, and navigating systems to access patient CGM data during virtual visits. The purpose of this clinical case review is to give an overview of CGM technology, present case studies that highlight the benefits of CGM use, and offer a model for managing clinic workflow for all providers, including nurse practitioners (NP), engaged in virtual DM visits where CGM is used.

Background: Continuous glucose monitoring overview

Continuous glucose monitoring is a DM technology used to continuously track glucose levels and trends over 24 hr. Continuous glucose monitoring devices measure interstitial glucose levels through a minimally invasive, adhesive sensor worn on the patient's arm or abdomen, which contains a glucose-sensing electrode inserted subcutaneously under the skin. Depending on the type of CGM, sensors record interstitial glucose levels every 1–5 min and display this data on a dedicated receiver, smartphone, or integrated insulin pump. In addition to displaying current glucose values, CGM also provides insight into glucose trends. For example, CGM depicts glucose trends using trend arrows on the screen that indicate the rate at which glucose levels are increasing, decreasing, or remaining steady over the next 20–30 min. Another key feature of some CGM devices are alarms that alert the patient to current or predicted glucose values outside of their designated range (hypo or hyperglycemia). Using these data, clinicians are equipped to make real-time treatment decisions, evaluate overall treatment efficacy, fine-tune medications, and engage in educational discussions on the effects of nutrition, physical activity, stress, and illness on glycemic control. Further, patients can use the glucose data to learn how their behavior influences glucose levels and adjust their behavior (i.e., take additional insulin, refrain from food and drink high in carbohydrate, and eat to prevent severe hypoglycemia) to improve glycemic control.

Continuous glucose monitoring can be divided into two main categories: professional use and personal use. Professional CGMs are owned, operated, and placed on the patient by the clinic and worn for a short period (usually 7–14 days). Glucose data are either blinded or unblinded to the patient during CGM wear (American Diabetes Association, 2021). Continuous glucose monitoring data provide a much more extensive and comprehensive glucose profile—288 data points in 24 hr—compared with the 2–4 data points obtained using finger-stick blood glucose measurements. Clinicians can then make more informed and accurate treatment recommendations (e.g., insulin dose adjustments). The American Association of Clinical Endocrinologists (AACE) has published algorithms designed to support patient and clinician decisions using CGM data (Fonseca et al., 2016).

Personal CGMs are prescribed for ongoing use, and glucose data are visible to the patient. Optionally, these glucose data can be remotely accessed by friends, family, and caregivers of the patient as well as the patient's providers. Personal CGMs may be further differentiated into flash, or intermittently scanned CGMs (isCGM), and real-time CGMs (rtCGM). Intermittently scanned CGM involves a sensor and reader device only. These sensors detect and record glucose values every minute, although require the patient to physically scan or “swipe” the reader over the sensor to view glucose data. Real-time CGM sensors record glucose values every 5 min and use a transmitter attached to the sensor to wirelessly and continuously send these data to the reader, no manual scan is required. An overview of commonly used CGM devices is presented in Table 1.

Table 1. - Overview of commonly used continuous glucose monitoring
CGM Type rtCGM or isCGM Supplies Accuracy (MARD) Insulin Pump Integration Product Features Cost
Dexcom G6 rtCGM Sensor: 10 days
Transmitter: 3 months
Reader: receiver, smartphone app or pump
9.9% Tandem t:slim X2 basal IQ, control IQ Trend arrows, high and low alarms, predictive low alert in 20 min Sensors (3): $384–410
Transmitter (1): $271–328
Receiver (1): $419–561
FreeStyle Libre 14 day isCGM Sensor: 14 days
No transmitter
Reader: receiver or smartphone app
11.4% None Trend arrows Sensors (1): $65–96
Receiver (1): $84–101
FreeStyle Libre 2 isCGM Sensor: 14 days
No transmitter
Reader: receiver or smartphone app
9.2% None Trend arrows, optional high and low alarms Sensors (1): $65–96
Receiver (1): $84–101
Medtronic Guardian Connect rtCGM Sensor: 7 days
Transmitter: rechargeable
Guardian Connect transmitter used for standalone CGM; Guardian Link 3 transmitter used when paired with pump
Reader: smartphone app or pump
9.4% Medtronic MiniMed pump 630 G, 670 G, 770 G Trend arrows, high and low alarms, predictive high or low alerts in 10–60 min Sensors (5): $496–608
Guardian Connect transmitter kit (1): $775–917
Guardian Link 3 transmitter kit (1): $775–917
Eversense rtCGM Sensor: 90 days, SQ implant
Transmitter: rechargeable
Reader: smartphone app
9.6% None Trend arrows, high and low alarms (including body vibration alerts), rate of change alerts, predictive alerts Sensor (1): $980–1,075
Transmitter (1): $500–680
Note: app = application; CGM = continuous glucose monitor; isCGM = intermittently scanned continuous glucose monitoring; MARD = mean absolute relative difference; rtCGM = real-time continuous glucose monitor; SQ = subcutaneous.

Although CGM has many clinical benefits, it is important to note the limitations of this technology. Continuous glucose monitoring sensors measure glucose in the interstitial fluid, resulting in a physiological lag time of glucose transport from the vascular space to the interstitial fluid. This lag time was found to be between 6.8 and 9.8 min in patients with type 1 DM (DM1) during a steady, fasted state (Basu et al., 2015). Patients may notice that this lag time, manifesting as a discrepancy between fingerstick blood glucose and CGM sensor readings, is especially exaggerated during periods of rapid blood glucose rise and decline. For example, intensive exercise is known to reduce the accuracy of CGM, as blood glucose levels are rapidly changing (Fokkert et al., 2020). Although sensor algorithms are frequently upgraded to compensate for this lag time, patients should be educated on the factors affecting this discrepancy and understand the importance of always performing blood glucose finger sticks whenever their symptoms do not match their CGM readings.

Patient characteristics and clinical outcomes with continuous glucose monitoring use

Current practice guidelines recommend CGM use for patients with DM1, type 2 DM (DM2), and gestational DM (American Diabetes Association, 2021; Bailey et al., 2016; Peters et al., 2016) because several randomized control trials indicate CGM use improves clinical outcomes for patients with intensively controlled, insulin-dependent DM. Several major studies have shown that rtCGM use is associated with lower A1c, decreased glucose variability, and decreased frequency of hypoglycemia in both DM1 (Beck et al., 2017a; Beck et al., 2017c) and DM2 (Beck et al., 2017b). For example, in the DIAMOND trial in individuals with DM1 on multiple daily insulin injections, rtCGM use when compared with finger-stick self-monitoring of blood glucose (SMBG) resulted in: (1) improved A1c at 24 weeks, (2) lower frequency of hypoglycemia, and (3) lower glucose variability (Beck et al., 2017a). Similar results were found in trials studying individuals with insulin-dependent DM2, in which rtCGM use was associated with lower A1c and reduced hypoglycemia (Beck 2017b; Park & Le, 2018). Recently, a randomized clinical trial conducted in individuals with DM2 treated with basal insulin, but not prandial insulin, demonstrated rtCGM use was associated with lower A1c (adjusted difference -0.4%, p = .02) and greater time in target glucose range 70–180 mg/dl (adjusted difference 15%, p < .001) compared with using fingerstick glucose monitoring (Martens et al., 2021). Additionally, the HypoDE clinical trial showed that rtCGM use in individuals with DM1 with a history of severe hypoglycemia resulted in fewer episodes of hypoglycemia and sustained A1c reduction over 3 years (Heinemann et al., 2018). Furthermore, clinical trials demonstrate that rtCGM use within closed loop controlled insulin pump systems is associated with improved glycemic control and lowered rates of hypoglycemia in patients with DM1 (Tauschmann et al., 2016,2018).

Intermittently scanned CGM (isCGM) use is associated with increased glucose time in range, lower glucose variability, and improved quality-of-life scores in both DM1 and intensively controlled insulin-dependent DM2 (Gilbert et al., 2021; Kröger et al., 2020; Nana et al., 2019). More recent studies suggest that use of newer models of isCGM is also associated with fewer Diabetic Ketoacidosis (DKA) admissions, a slight reduction in A1c, and decreased rates of hypoglycemia (Gilbert et al., 2021; Tyndall et al., 2019). However, head-to-head trials indicate that rtCGM is associated with significantly decreased rates of severe hypoglycemia when compared with isCGM (Hásková et al., 2020; Reddy et al., 2018a; Reddy et al., 2018b).

It is important to note that current studies do not show a statistically significant association between CGM use and A1c reduction or decreased rates of hypoglycemia in individuals with non–insulin-dependent DM2. However, it is possible that CGM use may provide clinical benefit in these individuals by supporting improved patient engagement and increased DM self-management (Ehrhardt & Al Zaghal, 2020). Further, CGM use can support glucose monitoring in individuals unable to SMBG via fingerstick (e.g., Parkinson disease, amputations, cognitive impairment; Vigersky et al., 2012).

Several clinical trials demonstrate the clinical utility of both rtCGM and isCGM use in individuals with intensively controlled, insulin-dependent DM. Many clinicians confirm this association in their daily interactions with patients. To demonstrate the clinical utility of CGM use in practice, we offer 3 case studies that highlight the benefit of CGM use in the virtual setting. The following cases depict patients with elevated A1c and early morning hypoglycemia (case 1), asymptomatic hypoglycemia (case 2), and disability (case 3). The standardized ambulatory glucose profile (AGP) report provides an overall summary of glycemic control: average glucose, coefficient of variation, SD, percentage Time CGM Active, Glucose Management Indicator, time in range (70–180), time below range <70, time below range <54, time above range >180, and time above range >250 (American Diabetes Association, 2021). The AGP report also provides an overall glucose profile graph and daily glucose profile graphs and is used in the case studies depicted below.

Case study 1: Elevated A1c with early morning hypoglycemia

History of present illness: 68-year-old Caucasian man with insulin-dependent DM2, obesity (body mass index [BMI] 42 kg/m2), hypertension (HTN), chronic kidney disease 3a (eGFR 45), and coronary artery disease status post coronary artery bypass grafting. He is unable to consistently perform SMBG due to hand tremors. He joins the appointment using a virtual platform established by his endocrine clinic.

Nutrition: He eats two meals per day (10 a.m. and 6 p.m.).

Sleep: Awakes at 9 a.m. and goes to bed at 11 p.m.

DM medications:

  1. Glargine 65 units (un) subcutaneous (SC) injection once a day (qday)
  2. Semaglutide 1.0 mg SC weekly
  3. Empagliflozin 25 mg by mouth (PO) qday
  4. Metformin 500 mg PO twice a day (BID)

Fasting laboratory values: A1c = 9.2%, creatinine (Cr) = 1.6, glucose = 56 mg/dl.

CGM report (Figure 1A): Personal CGM sensor was worn for 14 days with 97.5% use (average glucose = 223 ± 63 mg/dl and glucose variability = 28.2%).

F1
Figure 1.:
Virtual case 1: elevated A1c and early morning hypoglycemia. Continuous glucose monitoring data: A, Baseline: average glucose = 223 ± 63 mg/dl, glucose variability = 28.2%, and A1c = 9.5%. Glycemic pattern indicates early morning hypoglycemia (6–10 a.m.) and postprandial hyperglycemia (2–10 p.m.). B, Three-month posttreatment change: average glucose = 170 ± 45 mg/dl, glucose variability = 26.4%, and A1c = 7.8%. Early morning hypoglycemia resolved and postprandial hyperglycemia improved.

Time in glucose range:

  • <54 mg/dl: 0.0%
  • 54–70 mg/dl: 0.0%
  • 70–180 mg/dl: 27.7%
  • 181–250 mg/dl: 72.3%
  • >250 mg/dl: 33.7%

Patterns of hypoglycemia: Glucose readings drop overnight with lowest readings between 6 and 10 a.m.

Patterns of hyperglycemia: Glucose readings elevated in the afternoon and evenings (Figure 1 pretreatment).

Assessment and plan: A1c goal <8% given age, comorbidities, and high risk of hypoglycemia. Continuous glucose monitoring data suggest that despite an elevated A1c, he is experiencing a decrease in glucose overnight with low glucose readings in the morning (6–10 a.m.). This suggests his basal insulin dose is too high. Further, his elevated A1c is likely elevated due to postprandial (PPD) hyperglycemia. Obtaining an extensive diet history and decreasing his calorie and carbohydrate intake may be helpful in addition to increasing his physical activity. Further, he may benefit from starting prandial insulin. Ideally, clinicians should aim to achieve an even split of 50% basal and 50% prandial insulin toward the total daily dose (TDD) of insulin.

  1. Lower basal insulin (glargine) by 20%, 52 un daily and continue to lower insulin until hypoglycemia is eliminated.
  2. Start aspart 10 un SC before meals. Continue to increase by 20% every 4–5 days until PPD glucose <180 mg/dl. Attempt to achieve 50/50 split between glargine and aspart doses toward TDD insulin.
  3. Nutrition and exercise counseling provided: Patient will moderate portion sizes and limit carbohydrates to 45–60 g or less at meals, focusing on nutritionally dense foods like nonstarchy vegetables, lean proteins, and healthy fats. Encouraged 150 min of cardiovascular exercise per week, which may help offset postprandial glycemic spikes.
  4. Continue on metformin, empagliflozin, and semaglutide.
  5. Continue CGM use.
  6. Refer to obesity clinic.

Three-month follow-up: CGM report (Figure 1B): Personal CGM sensor data were reviewed for past 14 days with 92.5% use (average glucose = 137 ± 36 mg/dl and glucose variability = 26.4%). Overnight hypoglycemia has improved, glucose variability has improved, and PPD hyperglycemia is improved (Figure 1, posttreatment).

Case study 2: Asymptomatic hypoglycemia

History of present illness: 56-year-old Caucasian woman with insulin-dependent DM secondary to chronic pancreatitis (DM type 3b), HTN, hypothyroidism, and overweight (BMI = 27 kg/m2). She joins the appointment using the virtual platform established by her clinic. The nurse reviews medications with her and assists with CGM download. The NP joins to review the data and discuss medication changes.

DM medications:

  1. Glargine 25 un SC qday
  2. Aspart SC qAC: insulin-to-carb ratio: 1:15, insulin sensitivity factor (ISF): 1:45. Tends to take between 5 and 7 un with each meal.

Fasting laboratory values: A1c 7.8%, Cr 1.1, and glucose 198 mg/dl.

Continuous glucose monitoring report (Figure 2A): Personal CGM data were analyzed for the past 14 days with 95.7% use (average glucose = 198 ± 64 mg/dl and glucose variability = 32.4%).

F2
Figure 2.:
Virtual case 2: Asymptomatic hypoglycemia CGM data: A, Baseline: average glucose = 198 ± 64 mg/dl, glucose variability = 32.4%, and A1c = 8.5%. Glycemic pattern indicates high glucose variability and occasional hypoglycemia 8 a.m.–2 p.m. B, Three-month posttreatment change: average glucose = 137 ± 36 mg/dl, glucose variability = 26.4%, and A1c = 7%. Glucose variability improved and hypoglycemia reduced posttreatment. CGM = continuous glucose monitoring.

Time in glucose range:

  • <54 mg/dl: 0.1%
  • 54–70 mg/dl: 0.8%
  • 70–180 mg/dl: 40.3%
  • 181–250 mg/dl: 58.9%
  • >250 mg/dl: 20.6%

Patterns of hypoglycemia: Glucose readings drop after meals. She is asymptomatic with these events.

Patterns of hyperglycemia: Glucose readings elevated in the late evening and overnight.

Assessment and plan: A1c goal 6–7% given age and few microvascular and macrovascular complications.

Continuous glucose monitoring data suggests that despite an A1c above goal, she is occasionally experiencing hypoglycemia from overcorrection of hyperglycemia and carbohydrate intake at meals. Insulin-to-carbohydrate ratio is adjusted to eliminate postprandial lows. Overnight glucose readings are elevated, suggesting her basal insulin dose should be increased (Figure 2A pretreatment).

  1. Increase glargine by 20%: 30 un SC daily
  2. Change aspart insulin-to-carb ratio from 1:15 to 1:18. Continue ISF 1:45.
  3. Continue CGM use.

Three-month follow-up: CGM report (Figure 2B): Personal CGM sensor worn for 14 days with 97% use (average glucose = 170 ± 45 mg/dl and glucose variability = 26.4%). Overnight hypoglycemia has resolved and PPD hyperglycemia is improved.

Case study 3: Patient disability, poor vision, and hypoglycemia

History of present illness: 78-year-old man with DM2, HTN, hyperlipidemia, chronic obstructive pulmonary disorder (COPD), neuropathy, and low vision secondary to diabetic retinopathy. He joins the virtual appointment with his daughter.

Steroid use: He is frequently prescribed steroids for COPD exacerbations.

Social history: He lives with an older family member. His daughter is supportive but lives 30 min away.

DM medications:

  1. Glargine 25 un SC qday
  2. Aspart 7 un SC before meals (qac), often missed doses
  3. Metformin 500 mg po daily
  4. Semaglutide 1 mg SC weekly

Assessment and plan: The patient was seen in the low vision clinic and was provided with magnifiers. Although he was able to see the result on his blood glucose meter, he had difficulty applying blood to the test strip and would go through 3–4 test strips before a reading was obtained. He was unable to safely perform SMBG to inform safe insulin dosing. His blood glucose readings were extremely elevated due to missed aspart doses. He was prescribed a Freestyle Libre 2 CGM so that he could easily monitor his blood glucose readings and this increased his insulin self-administration compliance. With the help of his daughter, his CGM data were downloaded to her personal laptop and reports were transmitted to the clinic for review. After reviewing the data, his provider lowered his insulin doses to reduce his risk of hypoglycemia.

Overview of virtual visits and reimbursement for continuous glucose monitoring care

There are several formats that support CGM use during virtual visits and includes both synchronous and asynchronous interactions. An overview of the formats used for virtual visits for patients using CGM are presented in Table 2. Effective communication between multiple departments, such as billing, coding, medical records, information technology (IT), and legal, may be necessary to ensure that all work is captured for remote CGM management. Current Procedural Terminology codes 95249, 95250, and 95251 can be used to bill for CGM services and interpretation (AACE, 2021). Codes 95249 and 95250 apply to services provided by office staff, e.g., RN or CDE, “incident to” professional service for patient training and sensor placement, hook-up, calibration of monitor, removal of sensor (profession CGM), and printout of recording. Code 95251 is the professional service code for data analysis and interpretation used by physicians (M.D., DO), physician assistants, NPs, and clinical nurse specialists. Data analysis and interpretation (code 95251) does not need to be performed face-to-face with the patient but requires a modifier to designate that the care was delivered virtually. All codes require a minimum collection of 72 hr of CGM data. For personal CGM, code 95249 can be reported just once during the time the patient owns the specific receiver. Reimbursement for code 95251 is limited to once per month, but individual payers may have their own utilization limits.

Table 2. - Virtual care visits for patients using continuous glucose monitor
Virtual Care Visits Synchronous vs. Asynchronous Patient/Provider Interaction DM Case Example
Patient-initiated message Asynchronous Patient to provider Patient emails DM specialist CGM data with request to review recent hypoglycemic events; a video visit is scheduled for further evaluation
Telephone visit Synchronous Patient to provider Provider downloads CGM data from data platform and discusses results and offers recommendations with patient over the phone
Video visit Synchronous Patient to provider Provider downloads CGM data from data platform and reviews the results with the patient suggesting behavioral changes
E-consult Asynchronous Provider to provider Primary care nurse practitioner receives CGM output from patient and forwards to endocrinologist to discuss insulin changes
Note: CGM = continuous glucose monitor; DM = diabetes mellitus.

Continuous glucose monitoring data platforms

Successful virtual care of patients using CGM requires both effective communication and accessibility to CGM data. Efficient data transfer requires an emerging system of integrated digital tools that facilitate seamless transfer of real-time glucose data to guide treatment decisions between patients and their clinicians (Phillip et al., 2021). The ability for health care providers and patients to adeptly use CGM data requires access to cloud-based CGM platforms. Advanced involvement from IT department specialists is often necessary to set up clinical accounts that will allow full access within existing hospital system firewalls.

The Dexcom CGM devices will upload to the Dexcom Clarity site. The Freestyle Libre devices will upload to the Libreview site. Medtronic Guardian CGM is integrated with the Carelink platform. Universal DM management platforms also exist that upload various devices, including glucose meters, CGMs, and insulin pumps. Glooko and Tidepool are two such platforms. They both sync the data from contracted devices and integrate them into various reports that may be shared with the clinic.

Continuous glucose monitoring use and virtual visit clinic workflow

Expanding CGM use within and beyond specialty settings requires restructured workflow and new protocols to streamline processes for device initiation, data download and interpretation, and patient follow-up (Galindo & Aleppo, 2020). Continuous glucose monitoring initiation and follow-up visits are often interprofessional and require coordination between office staff, DM specialists, and health care providers.

The first step in incorporating CGM technology in a clinic is establishing a workflow with clearly defined clinical roles (Galindo & Aleppo, 2020). A sample workflow scheme is presented in Figure 3. The clinic should identify who will be responsible for training patients, downloading/obtaining CGM data, displaying the data or making it accessible to the provider for data interpretation, and entering data in patient records. All CGM manufacturers provide comprehensive online training/education materials and documents for guiding data interpretation on their websites. Further, staff should be trained to ensure everyone is knowledgeable about the details of each device, including supply renewal, data downloads, and device trouble-shooting. Routine staff in-services should be implemented to provide updates on the individual systems and train new staff. Further, identifying a CGM user “champion” to meet regularly with industry clinical representatives and learn about technology advancements can be helpful to keep the clinic informed as technology progresses.

F3
Figure 3.:
Virtual visit workflow for CGM initiation and data review: schema provides an overview of the provider-type and professional roles involved in CGM initiation during a virtual visit. CGM = continuous glucose monitoring.

Role 1: Continuous glucose monitoring initiation and training

Once the equipment and supplies (receiver, transmitter, and sensors) are received, the patient is scheduled for a video or telephone training appointment. Before the training, office staff can mail (or email) instructions to the patient and provide brief handouts available from the CGM manufacturer. Providing a website of instructional videos may also be helpful for some individuals. Telephone or virtual training can be performed by office or clinic staff or by a trainer provided by the manufacturer. Training usually takes 45–60 min. By the end of the session, the patient should be able to insert the sensor, charge the receiver, and set up alerts. Arranging for a support person (family member or friend) to attend the training is often helpful for patients who may require assistance at home. The nurse should also provide trouble-shooting instructions to the patient for the possibility of the sensor failure or displacement (e.g., is knocked off or will not adhere well to the skin). Arrangements for follow-up and trouble-shooting with the trainer within a few days after CGM training are also made at the training visit.

Role 2: Provider visit with continuous glucose monitoring data review

Before the virtual appointment, the patient should be contacted and asked to upload their CGM data to the appropriate platform via their home computer or smart phone. To ensure an efficient virtual visit, data share should occur before the appointment. For those sharing via smart phone, the nurse or staff member can assist by accessing the report from the CGM website before the appointment to make the report accessible to the provider. During the virtual provider visit, the CGM reports will be reviewed with the patient, including a review of time in range, rates of hyperglycemia/hypoglycemia, and glycemic variability (American Diabetes Association, 2021). Treatment changes can be recommended based on the patient interview and assessment of data.

Role 3: Accessory support and use of continuous glucose monitoring data to improve DM care

In addition to the traditional clinical roles of nurse, NP, and physician, other providers can integrate CGM data during the virtual visit to improve patient care. Medical assistants can be trained to assist patients with data downloads and troubleshooting when issues using the device arise. Pharmacists are often experts on DM medication management and DM self-management education. Pharmacy interactions can be scheduled in-between provider visits to assist with insulin and noninsulin medication titration allowing for faster and more aggressive glycemic regulation. Similarly, nutritionists can use CGM data to improve patient's nutritional choices and carbohydrate counting skills and develop strategies to improve lifestyle behaviors that decrease blood glucose variability.

Addressing barriers to continuous glucose monitoring use in the virtual setting

Lack of internet or computer access in the patient's home can be a barrier to virtual CGM use. Some healthcare systems provide iPads to patients free of charge that could be configured for remote CGM data sharing. Data sharing depends on internet or cellular access and the device's security settings. Alternate data platforms, such as GLOOKO or Tidepool, may be more amenable to use with mobile devices than manufacturer software. A patient's smart phone can also be used to share CGM data precluding the need to upload reports between or before virtual appointments. However, not all phones are compatible with CGM technology. Finally, we recommend referring patients to the manufacturer's support team for technological software or device issues. A family member or support person can also be of assistance. When remote troubleshooting is not successful, it may be necessary for the clinician to assist the patient through CGM data screens via telephone to obtain key data points, such as percent time in range and glucose average over 14-, 30-, and 90-day periods.

Conclusions

In the age of the COVID-19 pandemic, virtual care for DM care has expanded. Use of DM technology in the virtual setting, including CGM use, is hallmarked to revolutionize DM care for many patients. This clinical case review highlights the benefits of using DM technology, specifically personal CGM use, to provide virtual DM care. We presented three case studies that highlight the benefits of CGM use and proposed a clinic workflow to help incorporate CGM technology in the virtual DM visit. The first case reviewed the benefits of CGM use in an individual with elevated A1c and early morning hypoglycemia. In this case, CGM use identified unrecognized hypoglycemia and postprandial hyperglycemia. Clinicians were able to titrate insulin doses to improve both the A1c and reduce episodes of hypoglycemia. The second case demonstrated the benefit of using CGM in an individual with unrecognized hypoglycemia. Continuous glucose monitoring use can be life-saving in individuals with hypoglycemia unawareness and can also decrease glucose variability over time (Beck et al., 2017a; Beck et al., 2017b; Beck et al., 2017c; Danne et al., 2017). The third case acknowledged the benefits of CGM use in those with a disability that impairs an individual's ability to monitor glucose measurements via fingerstick. The ease of CGM use allowed more frequent SMBG data, simplification of the DM medication regimen, and improved glucose management.

We also provided an overview of recent clinical trials demonstrating that CGM use most benefits those on intensive insulin therapy; demonstrating improved A1c and decreased rates of hypoglycemia in individuals on greater than three insulin injections per day. Current research suggests that CGM use does not have a significant clinical impact on lowering A1c or decreasing rates of hypoglycemia in individuals not on insulin; however, there does seem to be benefit on quality of life and perhaps behavior change (Ehrhardt & Al Zaghal, 2020; Kröger et al., 2020). Further studies are necessary to identify the optimal setting for CGM-guided DM care and the effect CGM has on DM self-management in the virtual setting.

Given the work load and technology required to use CGM, it is important that clinics are set up for appropriate data download and storage. We propose a clinical work flow that defines clinical roles and maps out the steps of an effective and efficient virtual clinic visit when seeing a patient using CGM. It is important that clinicians understand the type of patient that will most benefit from CGM use, can download and use the data during virtual visits, and interpret and manage the glucose data for improved clinical decisions and patient outcomes. Nurse practitioners are in an opportune position to use CGM data to improve clinical outcomes for their patients with DM. Through training, NPs can interpret CGM data and make appropriate treatment changes, such as changes to insulin doses or alterations in DM medications. Together, NPs, physicians, and allied health professionals can work collaboratively to use the data and improve treatment decisions. Using CGM technology in the virtual setting in an effective and efficient manner is essential to ensure that clinics can manage the workload created during a DM virtual visit.

Given the success of virtual visits for improving access to DM care during the COVID-19 pandemic, it is likely that DM providers will continue to provide care in this format. Patients will likely demand continued use of virtual care because it is easier and less costly than face-to-face visits. Use of advanced DM technology, including CGM use, supports virtual care and provides timely blood glucose data, which is essential to safe and effective remote management of more complex patients.

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Keywords:

Continuous glucose monitor; COVID-19; diabetes technology; tele-health; virtual care

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