Determinants of Behavioral Intent to Adopt the Closed-Loop Artificial Pancreas Among Diabetes Healthcare Providers
Background and Purpose of the Study: Diabetes mellitus for both children and adults are broadly defined as a group of complex diseases characterized by high blood glucose, resulting from a defect in either the production of or action of insulin, or both (National Institutes of Health, 2014). There are 29.1 million people in the US that are estimated to have diagnosed or undiagnosed diabetes (Centers for Disease Control and Prevention (CDC), 2014). Type 1 diabetes accounts for approximately 5-10% of all diabetes cases however, it has serious short term and long-term implications (Daneman, 2006).
Technology for diabetes management is rapidly developing and changing (Markowitz, Harrington, & Laffel, 2013). The results of the Diabetes Complications Control Trial (DCCT) demonstrated the importance of glycemic control and lead to an increased interest in technology to achieve control with minimizing hypoglycemia (DCCT, 1993; Cryer,2016). The Artificial Pancreas (AP), is known as the closed-loop control of blood glucose in diabetes, it is a system that combines a glucose sensor, a computer algorithm, and insulin infusion device (Cobelli, Renard, & Kovatchev, 2011). This innovation has the potential to elevate treatment burden for the patient. Compliance with patients monitoring of glucose, even well-controlled patients is often poor (Clarke & Foster, 2012). The closed-loop system would solve this issue because it requires no patient input (Kudva, Carter, Cobelli, Basu & Basu, 2014). There are currently 18 closed-loop artificial pancreas (CLAP) systems identified as being in clinical phase development, with 5 expected to be available for use at the end 2018 (Trevitt, Simpson, & Wood, 2016)
The role of the healthcare provider puts them in a unique position when it comes to technology acceptance. The healthcare provider–patient relationship is particularly challenging when it involves new treatment technology because the physician must have knowledge of the technology to be able to inform the patient however in many cases, the advancements in technology develop faster than the education required to competently use the devices which leads to a lack of competence and confidence by the practitioner (Caruana, 2012). Normally the end user decides whether to accept or reject the technology or device but in the healthcare environment the healthcare providers play a large part of the decision-making process of whether to use a new medical device such as the closed-loop system (Schonbeck, 2014). The purpose of this study was to create a valid tool entitled “Healthcare Providers Closed-Loop Artificial Pancreas Assessment (HCP-CLAPA)” and then implement this tool in the appropriate populations of healthcare providers who work with patients that have diabetes.
Methods: This study utilized a quantitative methodology with a descriptive, exploratory, cross-sectional and correlations research design to measure the determinants of behavioral intent to adopt the closed-loop artificial pancreas technology. A sample of 207 healthcare providers participated in this study
Results: Reliability for the HCP-CLAPA overall with 10 constructs combined was good (Cronbach’s alpha ɑ= .80). Healthcare providers had a fair understand of the technology with a perceptions of knowledge score of 68%. The binomial regression results were significant, χ2(4) = 35.865, p < .0005. The model explained 24.0%. Of the 9 predictors of behavioral intent to adopt, relative advantage was significant. The odds of adoption were 4.77 times greater when there was a positive relative advantage. In addition, there was no interaction between physicians and non- physicians when it came to the behavioral intent to adopt the closed-loop artificial pancreas by system type. However, the value of the technology for system type was significant for the 24-hour closed-loop artificial pancreas and the hybrid closed-loop artificial pancreas.
Conclusion: The study provides an understanding of factors that influence behavioral intent to use. Intent to use would increase if there is a positive relative advantage above current therapies. Value of a system is based on system attributes. This study did not identify barriers to adoption. However, we know that this technology is not right for everyone considering the complexity of the device. It requires the right practitioner, right technology type, and right patient. The technology is not generalizable to every patient. Multiple themes uncovered the need for advanced technology planning including: healthcare provider education and relevant policies and procedures to ensure appropriate use.