The Association of Diabetes and Hyperglycemia on Inpatient Readmissions.

OBJECTIVE: To evaluate the association between inpatient glycemic control and readmission in individuals with diabetes and hyperglycemia (DM/HG). METHODS: Two data sets were analyzed from fiscal years 2011 to 2013: hospital data using the International Classification of Diseases, Ninth Revision (ICD-9) codes for DM/HG and point of care (POC) glucose monitoring. The variables analyzed included gender, age, mean, minimum and maximum glucose, along with 4 measures of glycemic variability (GV), standard deviation, coefficient of variation, mean amplitude of glucose excursions, and average daily risk range. RESULTS: Of 66 518 discharges in FY 2011-2013, 28.4% had DM/HG based on ICD-9 codes and 53% received POC monitoring. The overall readmission rate was 13.9%, although the rates for individuals with DM/HG were higher at 18.9% and 20.6% using ICD-9 codes and POC data, respectively. The readmitted group had higher mean glucose (169 ± 47 mg/dL vs 158 ± 46 mg/dL, P < .001). Individuals with severe hypoglycemia and hyperglycemia had the highest readmission rates. All 4 GV measures were consistent and higher in the readmitted group. CONCLUSION: Individuals with DM/HG have higher 30-day readmission rates than those without. Those readmitted had higher mean glucose, more extreme glucose values, and higher GV. To our knowledge, this is the first report of multiple metrics of inpatient glycemic control, including GV, and their associations with readmission.

Diabetes Management Through Remote Patient Monitoring: The Importance of Patient Activation and Engagement with the Technology.

Background: The documented efficacy and promise of telemedicine in diabetes management does not necessarily mean that it can be easily translated into clinical practice. An important barrier concerns patient activation and engagement with telemedicine technology. Objective: To assess the importance of patient activation and engagement with remote patient monitoring technology in diabetes management among patients with type 2 diabetes. Methods: Ordinary least squares and logistic regression analyses were used to examine how patient activation and engagement with remote patient monitoring technology were related to changes in hemoglobin A1c (HbA1c) for 1,354 patients with type 2 diabetes monitored remotely for 3 months between 2015 and 2017. Results: Patients with more frequent and regular participation in remote monitoring had lower HbA1c levels at the end of the program. Compared to patients who uploaded their biometric data every 2 days or less frequently, patients who maintained an average frequency of one upload per day were less likely to have a postmonitoring HbA1c > 9% after adjusting for selected covariates on baseline demographics and health conditions. Conclusions: Higher levels of patient activation and engagement with remote patient monitoring technology were associated with better glycemic control outcomes. Developing targeted interventions for different groups of patients to promote their activation and engagement levels would be important to improve the effectiveness of remote patient monitoring in diabetes management.

Rural Telehealth Visits in the Management of Type 1 Diabetes.

INTRODUCTION: Despite advances in and increased adoption of technology, glycemic outcomes for individuals with type 1 diabetes (T1D) have not improved. Access to care is limited for many, in part due to a shortage of endocrinologists and their concentration in urban areas. Managing T1D via telehealth has potential to improve glycemic outcomes, as the barriers of travel-related time and cost are mitigated. METHODS: Our endocrine telehealth program started in 2013 and currently provides care to nine rural community hospitals in Nebraska and Iowa. A retrospective cohort study was performed to evaluate glycemic outcomes in people with T1D who received care at these telehealth clinics from 2013-2019. Data were collected on age, race, gender, prior diabetes provider, use of diabetes technology, and A1c values over time. RESULTS: One hundred thirty-nine individuals were followed for an average duration of 32 months (range 4-69 months). Sixty-six percent of people were previously under the care of an endocrinologist. The most common therapeutic action, in addition to insulin adjustment, was addition of a CGM (52%). Each year in telemedicine care was associated with a decline of 0.13% in A1c (95% CI: -0.20, -0.06). There was no association between A1c and age or gender. When stratifying by previous diabetes provider, all groups had a statistically significant decline in A1c, even those with a previous endocrine provider. There was no statistically significant decline in A1c based on addition of technology. CONCLUSION: We have shown that traditional telehealth visits are an effective way to provide care for people with T1D long-term and may provide distinct advantages to home telehealth visits.

Telehealth Fatigue: Is It Real? What Should Be Done?

This commentary article discusses the recent trends and changes in popularity of telehealth usage as well as the most recent efforts to redefine telehealth value and usability. Six strategies to improve the patient experience and increase telehealth acceptance by overcoming simultaneous barriers are presented, which include (1) creating a new healthcare paradigm using telehealth, (2) scheduling the telehealth visit, (3) preparing for the telehealth visit, (4) conducting the telehealth visit, (5) using data and biomarkers, and (6) providing digital equity. With the application of these strategies, we believe that the recent decline in the popularity of telehealth can be reversed.

Appropriate Use of Telehealth Visits in Endocrinology: Policy Perspective of the Endocrine Society.

OBJECTIVE: This work aims to guide clinicians practicing endocrinology in the use of telehealth (synchronous patient-clinician visits conducted over video or telephone) for outpatient care. PARTICIPANTS: The Endocrine Society convened a 9-member panel of US endocrinologists with expertise in telehealth clinical care, telehealth operations, patient-centered care, health care delivery research, and/or evidence-based medicine. EVIDENCE: The panel conducted a literature search to identify studies published since 2000 about telehealth in endocrinology. One member extracted a list of factors affecting the quality of endocrine care via telehealth from the extant literature. The panel grouped these factors into 5 domains: clinical, patient, patient-clinician relationship, clinician, and health care setting and technology. CONSENSUS PROCESS: For each domain, 2 or 3 members drew on existing literature and their expert opinions to draft a section examining the effect of the domain's component factors on the appropriateness of telehealth use within endocrine practice. Appropriateness was evaluated in the context of the 6 Institute of Medicine aims for health care quality: patient-centeredness, equity, safety, effectiveness, timeliness, and efficiency. The panel held monthly virtual meetings to discuss and revise each domain. Two members wrote the remaining sections and integrated them with the domains to create the full policy perspective, which was reviewed and revised by all members. CONCLUSIONS: Telehealth has become a common care modality within endocrinology. This policy perspective summarizes the factors determining telehealth appropriateness in various patient care scenarios. Strategies to increase the quality of telehealth care are offered. More research is needed to develop a robust evidence base for future guideline development.

Remote Patient Monitoring and Clinical Outcomes for Postdischarge Patients with Type 2 Diabetes.

The objective of this study was to evaluate changes in clinical outcomes for patients with type 2 diabetes (T2D) after a 3-month remote patient monitoring (RPM) program, and examine the relationship between hemoglobin A1c (HbA1c) outcomes and participant characteristics. The study sample included 955 patients with T2D who were admitted to an urban Midwestern medical center for any reason from 2014 to 2017, and used RPM for 3 months after discharge. Clinical outcomes included HbA1c, weight, body mass index (BMI), and patient activation scores. Logistic regression was used to estimate the likelihood of having a postintervention HbA1c <9% by patient characteristics, among those who had baseline HbA1c >9%. Most patients experienced decreases in HbA1c (67%) and BMI (58%), and increases in patient activation scores (67%) (P < 0.001 in all 3 cases) at the end of RPM. Logistic regression analyses revealed that among patients who had HbA1c >9% at baseline, men (odds ratio [OR] = 3.72; 95% confidence interval [CI], 1.43-9.64), those who had increased patient activation scores after intervention (OR = 1.05; 95% CI, 1.01-1.09), those who had higher baseline patient activation scores, and those who had a greater number of biometric data uploads during the intervention (OR = 1.02; 95% CI, 1.00-1.04) were more likely to have reduced their HbA1c to <9% at the end of RPM. RPM for postdischarge patients with T2D might be a promising approach for HbA1c control with increased patient engagement. Future studies with study designs that include a control group should provide more robust evidence.