This was a theory-informed, qualitative study seeking to understand determinants of engagement with high frequency virtual care in general and the T1ME trial components in particular; and to map those determinants to feasible implementation strategies. Ethics approval was received from Women’s College Hospital and the Ottawa Health Science Network Research Ethics Board.
Clinical practice guidelines suggest that people with T1D have visits with their diabetes team every 3 months unless their glycemic control is already optimized (11). More frequent visits with certified diabetes educators (CDEs) and other care providers are often needed to help patients to recognize glucose patterns, adjust their insulin doses, and to offer education and technological support on the use of insulin pumps and continuous or flash glucose monitoring. In Ontario, people with T1D may be eligible to receive government funding for insulin pumps and related supplies through Ontario Health’s Assistive Devices Program (ADP). Individuals who are registered in this program are required to receive frequent care from a certified pump team. Within this model of care, patients may need to wait three to six months for appointments with their diabetes team to trouble-shoot issues with diabetes self-management. Prior to the pandemic, most visits were conducted in person, requiring individuals with T1D to take time off work or school to visit their team members. This model of care may not be well-suited to patients who needed additional support or timely enough to enable them to make real-time changes to their diabetes self-management.
The description of the T1D care model described above, featuring mainly in-person care, was applicable up until the COVID-19 pandemic (12). As of March 2020, diabetes clinics in Ontario, Canada were mandated to adopt a virtual care model rapidly and with minimal preparation due to COVID-19 lockdown measures implemented. As of December 2021, most T1D care continues to be delivered virtually. However, despite virtualization, indicators suggest that care is still provided with longer, infrequent appointments every three to six months. Even though second vaccination rates have surpassed 80%, and booster doses have surpassed 30% in Ontario (13), it is unlikely that T1D care will return to the pre-pandemic norm, especially with new variants arising. Instead, diabetes clinics will most likely adopt a “new-normal” model of care that will include virtual options when in-person visits are not feasible or needed, as there will be lingering concerns regarding social distancing for some time, and many clinics have invested in virtual care technologies. Virtualization of diabetes care offers an opportunity to consider shorter, more frequent contacts through more feasible virtual modalities.
The T1ME trial seeks to improve this T1D care model and focus on more patient-centered care. If our high frequency, virtual model is to be successful, we must target key work flow processes and behaviours among diabetes clinic staff. Firstly, many traditionally in-person visits will need to be changed to virtual. This includes understanding and targeting work flow processes relating to the uptake of new telecommunication technology. Secondly, we must understand the behaviour changes needed to accommodate a high frequency care model. Within this model, patients will meet with their CDEs for shorter, but more frequent touch points. This will change the nature of the interaction and affect work flow processes. Additionally, we will also need to evaluate current resource allocation and the potential impact of our high frequency, low touch model on clinic resources. Therefore, in the current study we sought to understand work flow processes, resource allocation, and other factors in implementing a high frequency, low touch care model in diabetes clinics.
Participants and Recruitment
We recruited nurses or dietician CDEs and managers in diabetes education programs at specialized T1D clinics in Southern Ontario. Sites were purposefully selected for variation in factors thought to potentially affect implementation of the intervention including the number of patients, number of patients under age 25, number of health professionals, number of patients with most recent HbA1c above 8%, and rurality. For each site, a recruitment email was sent to the lead physician or clinic manager, inviting them to participate in a 30-45 min telephone interview. We also sent invitation emails to CDEs and managers identified by the investigators’ personal networks. We then recruited additional key-informants at each site using snowball sampling. In particular, we sought a team member of the chosen T1D clinic who provided clinical care or support and/or with knowledge regarding the organization of the clinic processes, including technological processes (e.g. electronic medical records).
Firstly, an electronic survey was sent to the clinic manager at each clinic to obtain descriptive information about the clinic. We collected information such as the number and type of healthcare professionals, types of communication with patients, wait times, and history with implementation of new programming. SD then conducted semi-structured, one-on-one, 30-45 minute telephone interviews during working hours. Interviews were recorded, de-identified and transcribed. Oral informed consent was obtained before beginning the interviews. Field notes were made after each interview.
Interviews followed a semi-structured guide (developed by NI, JP, SD, GB, LL) that aimed to i) explore current processes and procedures for management of T1D patients under routine and semi-urgent scenarios and ii) examine the determinants of uptake and implementation of our high frequency, low touch model of care using the theoretical domains framework (TDF). The TDF is an integrated theoretical framework synthesized from 128 theoretical constructs from 33 theories judged most relevant to implementation questions. Domains of the TDF include items such as knowledge, goals, optimizing, and belief about capabilities (14)
Research team members with a range of disciplinary backgrounds (endocrinology [GB], psychology [JP], family medicine [NI], public health [SD]) reviewed the electronic survey data and transcriptions in depth to understand the current processes in clinic and, importantly, the changes required for the intervention to be implemented as intended. The transcripts were examined to explore how the changes required might vary across clinics (15).
Transcriptions of the interviews were then coded using the TDF domains by two independent researchers (SD, IP) using a word processor. Coding was mainly deductive, involving assigning utterances to the relevant TDF domains; open coding was used if and when important issues were identified that do not seem to fit any existing domain. A codebook was maintained and updated regularly to ensure inter-coder reliability.
When all transcripts were coded, we (NI, JP, SD, GB) identified the most important determinants (domains) to be addressed in the implementation and training plan by 1) frequency (which domains, for which key targeted behaviour, most commonly arise as issues to be addressed in the transcripts); 2) conflict (presence of disagreement across participants on certain domains representing a potential need for tailored strategies); 3) strongly held and strongly emphasized beliefs amongst participants about the targeted behaviour; and 4) most important determinants to be addressed (determinants which have the highest likelihood of impeding or facilitating implementation) (16). Additionally, we grouped domains into higher-level barriers and enablers. Then, we mapped out how each domain interacted with other domains. This allowed us to generate a list of theoretical domains most likely to influence the targeted behaviours for successful implementation of the T1ME trial.
Finally, we (NI, JP, SD) used the Behaviour Change Techniques Taxonomy Version 1 (BCTTv1) developed through an international consensus process, to identify actions that would enable the interventions to become more easily adopted into routine care (17, 18). This taxonomy provides clarity surrounding the specific, active ingredients needed to elicit behaviour change and draws on applied research in behavioural medicine, as well as social and health psychology. BCTs likely to influence key TDF domains have been previously mapped (18). Team members (NI, JP) with training and experience identified the most promising behaviour change techniques (BCTs) thought to be feasible to utilize in the implementation and training strategies for the intervention. We used these BCTs and most relevant theoretical domains to create a comprehensive implementation and training plan, which could be tailored to each site if necessary.
Analysis and data collection occurred concurrently; recruitment ceased once thematic saturation was reached. Our threshold for thematic saturation was twofold. Firstly, our initial analysis sample (minimum sample size) included at least one CDE and one manager from each site. Following that, our stopping criterion was a 0% new information threshold in the key theoretical domains (19, 20).
SD conducted member-checking calls with participants to ensure that our interpretation of the barriers and enablers from the original interviews accurately reflected the context of their specific clinics (21). Since we conducted the member-checking calls during the COVID-19 pandemic, we also took the opportunity to inquire about whether our interview results held true during the context of completely virtual care and understand process clinics initiated to accommodate virtual care.
Member-checking calls were recorded, de-identified, and transcribed. Field notes were made during the member-checking call. Two independent researchers (SD, IP) coded the transcripts using a word processor. We used deductive analysis, assigning quotes to the barriers and enablers from the original interviews. We also used open coding for issues other than the barriers and enablers identified in the interviews. A member-checking codebook was maintained and updated regularly to ensure inter-coder reliability.