MARCIA A. TESTA, SERGIO SALDIVAR-SALAZAR, MAXWELL SU, LINDA G. MARC, DONALD C. SIMONSON, Boston, MA, Wellesley Hills, MA

While there are many online courses explaining how clinical factors influence glycemic control, translating knowledge into practice poses a significant challenge, especially when the focus is on patient-centered outcomes (PCO). We developed an online, case-based, interactive training toolkit as part of a social media learning collaborative feasibility study to facilitate translating PCO knowledge into clinical practice. The toolkit’s learning objective was to improve clinical competency adhering to the 2016 ADA and AACE guidelines for individualizing HbA1c targets based on patient-centered demographic, clinical, psychological and socio-economic differences and disparities. We first modeled the probability of achieving HbA1c < 8% and < 7% in 2,927 T1D and T2D patients from 8 pooled clinical trials in 413 clinics using 12 regimens of insulin and oral agents (metformin, SU, TZD) alone or in combination during 24-52 wks. Subjects were 22.6% T1D (53% male, age 32 ± 14 yrs, HbA1c 8.0 ± 1.0%) and 77.4% T2D (58% male, age 56 ± 10 yrs, HbA1c 9.2 ± 1.2%, BMI 31 ± 5 kg/m²). Endpoint HbA1c was 7.7 ± 1.2% with interquartile range of 6.9-8.3%, (p = ns for T1D vs. T2D). Socio-demographics, treatment satisfaction (71 items) and quality of life (154 items) questionnaires were completed longitudinally. Outcomes of HbA1c < 8% and < 7% were modeled with logistic regression, and resulting estimators used to develop benchmarking calculators using WebOS, Android, iOS and Windows compatible WordPress software. Calculators were field tested and optimized within case-based learning exercises allowing the user to simultaneously modify patient characteristics to explore and visualize how individual patient profiles might influence the probability of reaching target glycemic goals. Conclusion: Social media interactive learning collaboratives may be used to help translate diabetes PCO research findings into clinical practice, while providing a novel approach to competency-based training on ADA and AACE guidelines.

Press Conference and News Releases: A Social Media Learning Collaborative Approach to Competency-Based Training in Diabetes
Diabetes Education, June 12, 2017

The following article has been excerpted from the 2017 American Diabetes Association Press Release that can be accessed by going to http://www.diabetes.org/newsroom/press-releases/2017/health-disparities-scientific-sessions-2017.html .  The article was entitled:

Health Disparities Among Patients with Diabetes Can Be Improved by New Approaches and Insights

Contact

Michelle Kirkwood
press@diabetes.org
703-299-2053

San Diego, California
June 12, 2017

Under the section entitled “Patient- and clinician-focused mobile technology improves outcomes; patient support programs utilizing community health workers had positive impact on care; and new insights indicate racial/ethnic differences that impact the development of type 1 diabetes” the news reporter wrote the following synopsis covering the oral presentation “A Social Media Learning Collaborative Approach to Competency-Based Training in Diabetes (cited above)”.

A Social Media Learning Collaborative Approach to Competency-Based Training in Diabetes

Teaching clinicians how best to assist patients with diabetes and their caregivers is an important aspect of continuing medical education. While many research studies and courses explain how clinical factors influence glycemic control, translating that knowledge into a patient care setting is often challenging. This study, “A Social Media Learning Collaborative Approach to Competency-Based Training in Diabetes” (368-OR), emphasized personalizing therapeutic options to fit the individual needs of patients by developing an online, case-based, interactive training toolkit. The study aimed to facilitate the interpretation of research results and to determine how patient-centered factors such as age, gender, socio-economic status, education, race and ethnicity, body weight and current glycemic control can impact the effectiveness of various diabetes treatments.

The study investigators pooled data from 19 clinical trials with a total of 6,954 patients on 38 diabetes regimens from 1,002 clinics, in addition to using Electronic Health Records from 233,627 diabetes patients, to estimate the odds that a particular patient would achieve good glycemic control with different treatment regimens, based upon individual personal characteristics.

Subsequently, eight of the 19 randomized clinical trials contained full quality-of-life and patient satisfaction data from 2,927 patients from 413 clinics. Researchers modeled the probability of achieving HbA1c levels of less than 8 percent and less than 7 percent using 12 regimens of insulina hormone that helps the body use glucose for energy. The beta cells of the pancreas make insulin. When the body cannot make enough insulin, it is taken by injection or through use of an insulin pump.X and oral agents alone or in combination during a 24 to 52 week period. Of the 2,927 patients analyzed, 22.6 percent had type 1 diabetesa condition characterized by high blood glucose levels caused by a total lack of insulin. Occurs when the body’s immune system attacks the insulin-producing beta cells in the pancreas and destroys them. The pancreas then produces little or no insulin. Type 1 diabetes develops most often in young people but can appear in adults.X and an average HbA1c level of 8.0; and 77.4 percent of the patients had type 2 diabetes and an average HbA1C level of 9.2 percent.

The primary endpoint at 52 weeks (one year) was HbA1c levels of 7.7 percent. Patients’ socio-demographic information was assessed, and treatment satisfaction questionnaires and quality of life assessments were completed throughout the study. Outcomes of HbA1c levels of less than 8 percent and less than 7 percent were modeled with logistic regression, and resulting estimators were used to develop benchmarking calculators using WebOS, Android, iOS and Windows compatible WordPress software. Calculators were then tested and optimized within case-based learning exercises. During the exercises, the clinician could simultaneously modify patient characteristics to explore and visualize how individual patient profiles might influence the probability of reaching target glycemic goals.

The study determined that the interactive learning collaboratives tested could be beneficial in translating diabetes research findings into clinical practice, while providing a novel approach to competency-based training that meets both the American Diabetes Association’s and the American Association of Clinical Endocrinologists’ clinical care guidelines.

“Relying on the published literature and more passive online courses to translate research findings into concepts that can be applied in practice is not sufficient, and often does not result in knowledge retention or a change in behavior,” said study author Donald C. Simonson, MD, MPH, ScD of the Division of Endocrinology, Diabetes and Hypertensiona condition present when blood flows through the blood vessels with a force greater than normal. Also called high blood pressure. Hypertension can strain the heart, damage blood vessels, and increase the risk of heart attack, stroke, kidney problems and death.X at Brigham and Women’s Hospital and Harvard Medical School in Boston. “Additionally, data on the effectiveness of various diabetes treatments are typically based upon the average effect estimated for a specific group of individuals in randomized clinical trials. However, there is large variability in treatment response that is not well quantified. Some patients respond very well to particular therapies, while others patient do not; and much of this variability can be explained by the personal characteristics of the patients. Our research emphasizes personalizing therapeutic options to fit the individual needs of patients so that clinicians can be made aware of how patients differ in their response to the same treatment based on various patient-centered demographic, socio-economic, behavioral and quality-of-life characteristics.”

The study group plans to continue refining the predictive models and intends to help communicate, disseminate and implement their findings and toolkit into practice by extending the social media learning collaborative to additional practitioners.