The 2011‐12 Australian Health Survey revealed that 10% of Australian adults had biochemical signs of chronic kidney disease (CKD) [1]. CKD is a significant risk factor for cardiovascular disease [2], which is Australia’s leading cause of death [3]. Early intervention can slow the deterioration in kidney function and reduce the risk of cardiovascular complications [4]. Australian CKD guidelines recommend the use of angiotensin-converting enzyme inhibitors (ACEis) or angiotensin-receptor blockers (ARBs) in the presence of albuminuria or hypertension (defined as a blood pressure above 130/80 mm Hg), and statins are recommended for everyone with CKD over the age of 50 years or for younger individuals with CKD in the presence of comorbidities [5].

CKD is underrecognized and undertreated in Australian general practice. One study found that 76.8% of people with biochemical results consistent with CKD did not have the diagnosis recorded in their medical file [6], and another found that only 65% of people with a formal diagnosis of CKD were prescribed an ACEi/ARB while 56% were prescribed a statin [7]. A known barrier to achieving quality improvement in chronic disease is the lack of timely and straightforward access to clinical guidelines [8,9]. Electronic clinical decision support tools aim to address this barrier by integrating with clinical records to automatically provide key information from appropriate clinical guidelines.

The Future Health Today (FHT) program is a general practice quality improvement technology platform developed by the University of Melbourne and Western Health. FHT integrates with the electronic medical record to provide 2 components: a clinical decision support tool active at the point of care (PoC) and a web-based dashboard that facilitates practice-wide audit. Screenshots of FHT are available in Multimedia Appendix 1. FHT was developed using a service design approach, whereby clinicians were involved throughout an iterative technical development process [10]. It underwent optimization at 12 general practice clinics, using guidelines for CKD, cardiovascular disease, and cancer prevention. A qualitative optimization study for the cancer recommendations has been described elsewhere [11]. A cluster randomized controlled trial was completed in 2022 (trial registration: ACTRN12620000993998) with the aim of exploring whether the FHT platform increased the prescription of ACEis/ARBs and statins in people with CKD at high cardiovascular disease risk [12]. This paper reports on barriers and facilitators of clinical performance improvement as reported by clinicians themselves.

General practice provides primary care to more than 8 in 10 Australians each year [13] and operates on a fee-for-service model, with rebates available through Medicare, the health care insurance scheme funded by the Australian Government Department of Health and Aged Care. General practices may bulk bill (payment covered in full by Medicare), privately bill (patients pay a fee in excess of the Medicare rebate), or mixed bill (variably bulk bill or privately bill patients).

The FHT trial was conducted in 39 general practices in Victoria and 1 general practice in Tasmania, Australia. In summary, clinics were randomized into 2 arms: cardiovascular risk reduction in people with CKD or identification of cancer risk, with each group acting as the control for the other [12]. Twenty-one general practices were allocated to the CKD arm, with 19 practices completing the trial. The trial ran from October 2021 to September 2022. The CKD algorithm was deactivated in December 2021 after errors in algorithm deployment were noted and was reactivated in February 2022. The interviews analyzed in this paper occurred between May and July 2022. The state of Victoria was subject to a COVID-19 pandemic declaration throughout the duration of the trial.

This qualitative implementation study focuses on the clinical experience and use of FHT for medication management to reduce cardiovascular risk in people with CKD. Semistructured interviews were conducted with general practitioners (GPs) and practice nurses (PNs) participating in the CKD intervention arm of the FHT trial.

Participating clinics were recruited via VicREN (Victorian Primary Care Practice-Based Research and Education Network) at the Department of General Practice and Primary Care, The University of Melbourne, and The University of Tasmania’s Northern Tasmania General Practice-based research network. Each practice was asked to nominate a practice champion at the beginning of the trial; this tended to be a PN or practice manager. The practice champion was requested to assist with the recruitment of GPs and PNs to participate in one-on-one interviews held between 8 and 10 months into the trial. Recruitment was also facilitated by the FHT trial coordinator. Potential participants were emailed a plain-language statement and consent form to complete if they were interested in participating. Participants were reimbursed with a AUD $50 (US $33.69) debit card voucher.

Interviews occurred over the phone and were recorded for transcription. There were 3 interviewers: CM, NL, and KS. CM is an academic GP registrar, NL is a research fellow, and KS is a research assistant. All are female and are affiliated with the University of Melbourne. NL acted in a liaison role for practices during the trial. An interview guide (Multimedia Appendix 2) was developed to focus on questions regarding the clinical usefulness of the recommendations, impact on clinical workflows, and perceived change in clinical performance.

Interview data were analyzed using Clinical Performance Feedback Intervention Theory (CP-FIT), a theory for designing, implementing, and evaluating feedback specifically in the health care context [14]. The theory proposes 42 variables that influence a feedback cycle via 7 mechanisms. Each step in the cycle is vital for successful feedback to occur. Each interview was independently coded by CM and BH (a qualitative researcher) using a combination of NVivo (Lumivero) and Microsoft Excel. Any discrepancies in coding were discussed and a consensus was reached.

The overall FHT trial protocol was approved by the Department of General Practice University of Melbourne Human Ethics Advisory Group and the Faculty of Medicine, Dentistry, and Health Sciences Human Ethics Sub-Committee. The ethics ID is 2056564. These ethics committees use the National Statement on Ethical Conduct in Human Research published by the Australian Government National Health and Medical Research Council.

A total of 13 interviews were held, with staff from 10 clinics. The demographic and clinic details of the 7 GP and 6 PN participants are summarized in Table 1. Participants were assigned a code based on their role (GP or PN) and their clinic’s allocated number within the trial. All 10 clinics were located in the state of Victoria and varied in their location (regional Victoria or metropolitan Melbourne) and billing policy (mixed or bulk billing). The sample size aimed to gather perspectives from as many staff at the 19 intervention practices as possible but was limited by the response rate. A total of 5 practices did not respond to the request for interview, 1 canceled citing personal reasons, and 1 canceled citing lack of use of FHT. Duration of interviews ranged from 14 to 39 minutes (average 21 min).

All interviewees had used the FHT PoC tool (N=13), with only 7 interviewees having experience using the FHT dashboard. The 5 GPs not using the dashboard were not aware of it, were not able to access it due to technological issues, or had not had time to use it. Five of the PNs had used the dashboard, but none had used it to recall patients.

Results were mapped to the CP-FIT feedback cycle. An adapted version of CP-FIT is shown in Figure 1, with variables and mechanisms relevant to this study included in the diagram. In total, 10 explanatory variables and 5 mechanisms were identified as influencing how clinicians used FHT.

Results for the PoC tool are presented in sequential order following the CP-FIT feedback cycle as shown in Figure 2, with key quotes to illustrate themes. Some steps of the feedback cycle are presented concurrently, reflecting that participants described these processes occurring simultaneously.

Results for the dashboard tool are shown in Figure 3. Feedback cycle steps 1 and 2 (goal setting and data collection) operated in the same manner as the PoC tool. However, participants became “stuck” at either steps 3 or 4.

In this qualitative study exploring the implementation of FHT using CP-FIT, we found that the FHT PoC tool was well received by clinicians and facilitated guideline-informed care. When mapped to the CP-FIT feedback cycle in Figure 2, we could see that they were able to move rapidly through all steps of the process, facilitated by the variables identified. GPs found that the tool was compatible with their quality improvement goals and clinical workflows; was simple to use; and provided credible, clinically useful information. This was supported by their pre-existing confidence in managing CKD, so the tool was seen as a quick reminder system that prompted further personalized review and discussion. Clinicians in our study worked across a range of location and billing styles, but all described similar workflows and clinical knowledge that supported uptake of the tool. This concept of a clinical decision support tool working most effectively as an “aid-memoire” is supported by existing literature [16]. PNs had a slightly different experience given their self-reported lesser knowledge of CKD and limited role in medication prescribing, but nonetheless, they described the PoC tool as useful and simple to use.

Use of the FHT dashboard tool was limited. Most GPs did not get to the “feedback” stage of accessing the dashboard, and most PNs did not progress past initial interactions with the software. During the co-design phase, clinicians had expressed a desire for FHT to facilitate both planned and spontaneous interactions [10], but in practice, most interactions occurred spontaneously. The functions available in the dashboard did align with the goals described by clinicians in both the co-design phase and this implementation study, but this was insufficient to overcome competing clinical priorities, particularly the lack of time and staff resources in the context of the COVID-19 pandemic.

The barriers to the use of the dashboard tool stood in clear contrast to the facilitators for the PoC tool. Whereas the PoC tool was simple to access by virtue of being a “pop-up,” the dashboard required clicking away from the patient file. The visual design of the PoC tool and wording of the recommendations were designed with simplicity and conciseness as a priority. By contrast, the dashboard tool has multiple aspects of functionality, allowing for extensive customization (Multimedia Appendix 1). The few clinicians who reached the tool described being overwhelmed or not having enough time to learn how to use the tool.

The nature of Australian general practice funding tends to incentivize opportunistic rather than systematic preventative health activities [17]. Medicare uses a fee-for-service model, whereby patients receive a rebate for attendance either in person or via telehealth/telephone. Alternative payment mechanisms have been proposed that allow for more of a focus on proactive, coordinated care that includes funding non–face-to-face care, such as that required to interact with tools like the FHT dashboard in any meaningful way [17].

All clinicians we interviewed had used FHT in their workplace; by nature of agreeing to be interviewed, they were able to describe the enablers that led them to progress along the CP-FIT feedback pathway. Respondent bias is a limitation of our study, as those who feel most strongly are most likely to speak with us. We also acknowledge that the sample size may not encompass the experiences within the diverse general practice context. However, given the impact of the COVID-19 pandemic on primary care, we are grateful to the 13 participants who took the time to speak with us. The barriers remain unknown for clinicians who did not use FHT, although they can be hypothesized, as discussed below.

While this study focused on the implementation of FHT from the clinical perspective, there are of course other important considerations. In particular, other studies have identified concerns related to the financial costs of staff time and the software itself [18].

Many electronic quality improvement programs in current use in Australian general practice have not been evaluated in a research context, and as such, there is a lack of data exploring implementation factors for these programs. The Australian Government Department of Health has recognized the need for clearer policy around the governance of general practice data and the development of clinical decision support tools. In particular, concerns have been raised about the variable quality of existing clinical decision support tools and lack of implementation considerations, which may create general distrust in electronic clinical decision support by GPs [19].

A commonly reported barrier to the use of clinical decision support systems is that of “too much information,” either in the content of a prompt or in the total volume of prompts [20,21]. This notification fatigue is an important consideration for any clinical decision support tool that activates automatically. There is a delicate balance between “active delivery” to facilitate easy interaction with a tool, and unobtrusiveness to the extent that clinicians are not aware of a tool, which is what occurred with the FHT dashboard. Clinicians in our study praised the physical size and placement of the PoC pop-up on the screen, as well as the simplicity of wording of the recommendations. Only 1 GP brought up concerns about notification fatigue, although did not report it as a barrier for their own use of the PoC tool. As more FHT modules are developed in the future, it will be vital to account for potential alert fatigue in how the pop-ups are deployed and displayed.

Lack of time has similarly been cited as a widespread barrier to clinical care for both GPs and nurses [9,18]; however, clinicians in our study did not describe time as a barrier to the use of the PoC tool. This is likely due to the speed at which they interacted with the tool, which itself was due to the usability of the tool and knowledge of the GPs. The marginal time required to interact with the tool, however, will inevitably become more significant as more FHT modules are added. It will be important for the sustainability of the FHT program to take this into account, to reduce the risk that lack of time becomes a barrier to the use of the entire PoC tool.

A study in 2015 in the United States reported barriers to the implementation of a clinical decision support tool specific to CKD, which included limited knowledge of CKD guidelines, lack of interest or confidence in technology, concerns about patient engagement, and time and competing demands [22]. In our study, clinicians’ knowledge of CKD and interest in technology for quality improvement were strong facilitators for FHT, and they were confident in discussing recommendations with their patients. Lack of knowledge in the identification and management of CKD has been cited as a general barrier to care [9]. Overall, it appears that our group of clinicians were particularly knowledgeable and proactive, and this greatly facilitated their engagement with FHT. The PoC tool had been designed with usability and clarity in mind, but these health professional characteristics were vital for uptake.