Secondary Logo

Journal Logo


Evaluation of the implementation of a best practice gestational diabetes model of care in two Australian metropolitan services

Wilkinson, Shelley A. PhD, AdvAPD1; Palmer, Michelle PhD, AdvAPD1,2,3; Smith, Shelley MasterNut&Diet, APD4; Porteous, Helen GradDipNut&Diet, APD2; McCray, Sally GradDipNut&Diet, APD5,6

Author Information
JBI Evidence Implementation: March 2022 - Volume 20 - Issue 1 - p 10-20
doi: 10.1097/XEB.0000000000000295


What is known about this topic?

  • Adoption of best practice models of care relies on more than simply dissemination of guidelines. Practice change requires a targeted, theory-driven approach to elicit change in health professionals, teams and systems.
  • Beyond the initial change in a successfully implemented model of care, further work is required for spread and sustainability of learnings for wider patient and health service benefits.
  • Understanding and documenting the facilitators and negotiations required in these dissemination and local adaptation processes is essential for future adoption and service integration improvements.

What does this article add?

  • Despite using a previously successful facilitated implementation of a best practice GDM dietetic model of care, this project experienced incomplete adoption and model of care integration process with minimal impact on medication reduction previously recorded.
  • In addition to determining innovative ways to overcome insufficient resourcing to deliver best practice models of care, acknowledgement of the wider system issues are required.
  • Addressing and streamlining processes around governance requirements for health service redesign, methods of engaging stakeholders beyond ‘symbolic’ involvement, and the agility of project sites to adapt to organisational changes while continuing to deliver projects is required.


Dissemination and local adaptation of best practice models of care are often poorly achieved in knowledge translation processes.1 Following initial implementation projects, momentum is slowed because of many reasons including not planning for sustainability, staff turnover and organisational change.1 This can result in reduced clinical effectiveness, missed opportunities for providing quality care and wasted resources when time is not spent in proven, effective ways, with loss of potential patient benefits. Understanding and documenting the iterative cycles of improvement can elucidate barriers, enablers and benefits of the process for future adoption of best practice service integration improvements.2

A body of work focussing on addressing evidence-practice gaps in the delivery of best practice nutrition care for women with gestational diabetes mellitus (GDM) in Queensland, Australia attempted to address and overcome knowledge translation barriers through an iterative improvement process using implementation science methodology.3–6 This work identified that no Australian GDM nutrition practice guidelines (NPGs) existed and systematic delivery of dietetic care to women with GDM did not occur in many Australian centres.7,8 However, Australian guidelines recommend a dietitian as an important member of the multidisciplinary team caring for a woman with GDM,9 and medical nutrition therapy (MNT) is a cornerstone intervention strategy for managing blood glucose levels (BGLs) in women diagnosed with GDM.10 Further, validation of American GDM NPGs demonstrate that care delivered to a woman diagnosed with GDM between 24 and 28 weeks of pregnancy in an evidence-based schedule of visits with a minimum of a 1 h initial counselling session and two review appointments with a dietitian, plus a postnatal follow-up session results in better blood glucose control and less requirement for insulin.10 This appointment schedule has been incorporated into the Queensland GDM Guidelines and requires a referral to a dietitian within 48 h of diagnosis.10,11

To date, an implementation science approach has been applied to develop and evaluate an MNT model of care by a lead site (stage 1, 2013),3,5 with subsequent local adaptation at two regional sites (stage 2, 2015–2016).4,6 In stage 1, the lead site identified barriers and enablers to the model of care being followed,5 addressing them with the systematic application of evidence-based strategies.3 Examples included staff training regarding guideline content, incorporation of the dietitians into the clinical pathway, sufficient dietetic resourcing in clinic, the identification and use of profession-specific clinical champions, and audit & feedback of clinic activity aligned with guideline recommendations.3 Stage 2 used a hub-spoke model of facilitated implementation with each regional site.4,6 Outcomes at all sites (stages 1 and 2) included a greater proportion of women seen according to NPGs, fewer women requiring medication to manage GDM, improvements in self-reported lifestyle measures, and increased patient and staff satisfaction.3,6 Learnings from stage 2 dissemination showed that key elements of facilitation involve building confidence and capacity in local implementers through regular contact, encouraging local networking, linking to higher management support and assessing and influencing workplace culture.4


This third stage of iterative work aimed to refine the process of local adaptation for future scalability of the model of care collaborating with two additional Queensland sites delivering GDM dietetic care. The aims were to evaluate clinical outcomes and the process of the model of care implementation further to previous knowledge translation iterations from stages 1 and 2, at two additional sites. Of specific interest was the identification and refinement of processes required to successfully embed the model of care locally and to determine whether the facilitated implementation of the model of care influenced: the elements of best practice (time from diagnosis to first appointment; time from first to second visit; number of dietitian visits per woman; proportion of women receiving best practice care for each of these elements); and the need for pharmacotherapy to manage GDM.


Following the same methodology as the previous stage 2 local adaption process,6 the project used a hub (research team)-spoke (sites) model12 in two centres: site 1 and site 2. Sites were selected following an expression of interest process distributed via a professional Queensland dietetic managers network. Site 1 was 26 km south of Brisbane with ∼3700 births/year and ∼12% GDM prevalence. Site 2 was 39 km west of Brisbane with ∼2800 births/year and a GDM prevalence of 18.6%. Both sites serviced areas of lower socioeconomic profiles and high health service needs. This study was approved by the Metro South Health Service District Human Research and Ethics Committee (HREC/18/QPAH/241). It conformed to the provisions of the Declaration of Helsinki (as revised in Tokyo, 2004). Consent was provided by all dietitians who delivered care within this project.

Sites were selected based on preproject stakeholder engagement (number of key stakeholder signatures of support obtained), hospital size and population type (preference for those with a large proportion of women with GDM in their service). Sites were required to obtain support in writing from key stakeholders to participate in the study. In addition to being a regulatory step, this process was used to provide a formal mechanism to ensure key members of the multidisciplinary team were aware of the project to enhance future engagement. Sites selected demonstrated strong GDM team culture and strong medical, dietetic and nursing management research project support. An implementation science approach was used to embed and facilitate the evidence-based model of care into practice at each site13–15 systematically applying the findings from stage 1's systematic barrier analysis using the Theoretical Domains Framework and a tailored strategy selection approach using the Behaviour Change Wheel.13,14,16

The project phases were: Consultation, Baseline, Transition, Implementation, and Evaluation (Table 1). The Consultation phase involved site project team formation, role negotiation and explanation, project planning and communication plan refining, participant consent, and site's familiarization with project resources. During the Baseline phase, sites assessed their local barriers to the GDM model of care implementation using a best-practice decision tree tool.6 This tool summarised effective strategies from stage 1's project and allowed each site to assess their own barriers and select the most appropriate evidence-based strategies to overcome them.3,5 The tool facilitated team decision-making around reallocation, realigning and planning of resources and allowed identification of and links to evidence-based nutrition training and resources in preparation for the implementation. A reporting sheet to summarize local barriers and suggested actions of overcoming identified barriers was also provided (Appendix I, The project research team (hub) supported the sites (April 2018 to December 2020) via two (site 1) and four (site 2) visits, quarterly group teleconferences and communication and clarification of issues between teleconferences via telephone, E-mail and/or face-to-face visits, as suited sites. Communications included six newsletters (collated by the ‘hub’ team, using information prepared by sites) describing progress and distributed at key project milestones through Statewide Clinical Networks and to project sites. Newsletters included translating research into practice tips, site's baseline data, as well as team member reflections on project progress.

Table 1 - Schematic diagram outlining project phases with corresponding activities
Project stage Consultation and engagement Baseline Transition Implementation Evaluation
Purpose and processes at sites Site team formationRole negotiationRefining site communication planLaunch at sites with research team members’ visitSites’ resource familiarization and refinementEthics and governance Site's monitoring of current practiceEach sites’ stakeholder assessment of GDM service using the best practice decision tree flowchart, facilitating decision-making around space and human resources, recommended education materials developed from the findings of the initial projectFeedback identified needs/service gaps in project meetingsStart to plan service changes required (identified through flowchart activity) Embedding the new model of care, informed by the best practice decision tree assessment from the previous phase Delivering the evidence-based model of careContinuing to evaluate process and clinical outcomes Data analysis and dissemination of results
Measures1. Clinical outcomes2. Process outcomes Feedback to refine resources Minimum data setIdentified service gaps/site Feedback regarding transition; time to embed Minimum data set Analysis

A local minimum data set was collated in an Excel spreadsheet, informed by the GDM best practice guidelines.17 This included: date of GDM diagnosis, week of pregnancy GDM diagnosed, date of first and subsequent dietetic appointments, format of appointment (group or individual), number of review appointments, and the use of pharmacotherapy (insulin or metformin). Despite the guidelines also recommending referral to the dietitian within 48 h of diagnosis, in the original study this proved too difficult to routinely collect, thus we used time from referral to first appointment as a proxy of this process. Only women who were diagnosed with GDM between 24 and 28 weeks were included. Women with ‘early’ GDM (<24 weeks) or who entered the service after 28 weeks (’late’) were excluded from data analysis as the guidelines had not been validated in these patient groups. Sites were to collect data for 6 months per phase (Baseline; Implementation) or until 120 women's data were collected allowing 2 months for the Transition phase between baseline and implementation.

Quantitative data were analysed using SPSS for Windows version 25 (SPSS, Chicago, Illinois, USA). Means and standard deviations were calculated for continuous data, and medians and interquartile ranges were reported for skewed data. Frequencies and percentages were calculated for categorical data. Difference from time 1 (Baseline) to time 2 (Implementation) was calculated for variables. Differences were examined with Mann–Whitney U-tests and independent group χ2 tests (or Fisher's exact test if cell counts were <5). Three ‘best practice’ outcome variables were constructed based on the guideline recommendations; one for ‘time to first appointment’ (i.e. the proportion of women who had their first visit with the dietitian within a week of referral, one for ‘schedule of visits’ (i.e. the proportion of women who had at least three individual visits with a dietitian), and a combination of these (i.e. women who were seen within a week of referral and received at least three individual appointments with the dietitian).

Although medication initiation was not the primary outcome measure of the study, the sample size required to detect a 7% difference between groups in medication initiation [based on Reader et al.'s validation of the (former) American Dietetic Association's NPG study demonstrating significant difference between usual care (31.7%) versus intervention (24.6%)] was 120 women in each phase of the project. This was required to provide 80% power and two-sided alpha set at 0.05.10


The project commenced in August 2018. Role clarification and site member appointments (self-selected; lead, champion, clinician and researcher), management engagement (diabetes education and dietetic), site visits, service assessment decision-tree tool and database review and update were all completed within the first 3 months for site 1. Site 2 had a 13-month delay in commencement until November 2019 because of extensive local ethics and governance requirements associated with contracts and agreements. However, appointment of site members occurred at the same time as site 1. All roles in site 1 came from within the Department of Dietetics. At site 2, the project lead and clinician came from the Department of Dietetics, with the research role being fulfilled by a senior allied health manager/research co-ordinator and the champion role was filled by a diabetes educator. All of site 1's team members attended all meetings; only site 2's lead and clinician (with additional GDM dietitians) attended the meetings. Project meeting minutes were sent to site 2's researcher and champion, with limited other communication or input from them. The results of the decision tree review are shown in Table 2.

Table 2 - Barriers and planned actions identified by sites during the baseline phase of the project after working through the decision tree
Consider if your clinic's GDM medical nutrition therapy (MNT) is aligned with best practice principles Consider if you have an effective clinic and how you monitor this?
Site People involved Do dietitians use standardized, best practice resources appropriate to your patient group? Do dietitians deliver evidence-based MNT? Is the dietetic service integrated with the multidisciplinary GDM clinic? Do women have a (1 : 1) appointment with a dietitian within a week of diagnosis? Do women have a (1 : 1) review appointment with a dietitian 1 week after their first appointment? Do women receive tailored, individualized (i.e. 1 : 1) MNT? Are data routinely collected and used to monitor processes and resultant clinical outcomes?
Site 1 Project lead, on behalf of department Yes. NEMO GDM booklet is too large and costly to use. We have developed our own based on evidence. Understanding GDM, GI and food record. Gestational Diabetes guide of Maori and Pasifika Women; Carbohydrate counting for traditional Indian and Pakistani foodsACTION: nil required Yes. Have watched and promoted the e-learning webinar Yes. Parent clinic structure appointments are aligned with doctor's appointments No. Not 1 : 1, first contact in a group but is within a week. Women who need an interpreter receive 1 : 1 appointment. ACTION: revisit model of care for first contact Yes (almost) – seen at 1.5 weeks in line with doctor's appointmentsHave 0.6FTE (and require ∼0.5FTE) Partially. As group care is initial contact. ACTION: we recognize we need to do things differently and reorient our services so that a time can be allocated to high risk patients No. However, there is central reporting for proportion of women on diet versus medication for GDM management.ACTION: nil required
Site 2 Dietetic team Yes. Use Baker IDI healthy eating GDM; NEMO GDM booklet; NEMO carb awarenessNEMO 15 g carb meal guideACTION: standardize GDM resource folders; investigate availability resources for women who are culturally and linguistically different Yes but need to standardize practice. Dietetics already integrated into antenatal clinic with MDT. ANC doctors, mid wives, diabetes educators, social workACTION: team to watch the Dietitians Australia ‘DINER’ GDM roadshow web presentation on MNT Yes. Appointments align with ANC Dr appointments, same clinic space, standardized diet assessment forms based on RBWH diabetes in pregnancy forms No: within 1 week but group.ACTION: revisit model of care for first appointment; clinic organisation, and revisit staffing levels No, within 2 weeks to align with ANC. Currently we use ∼15 h, 0.4FTERequired: 25–30 h, 0.45–0.7 FTEACTION: revisit model of care for first appointment; clinic organisation, and revisit staffing levels Partially, as initial contact as a group. However, patients attend two face to face reviews post initial education in group.ACTION: revisit model of care for first appointment; clinic organisation, and revisit staffing levels No.ACTION: Develop data collection tool
ANC, antenatal clinic; DINER, Dietetic Information and Nutrition Education Resources; FTE, Full Time Equivalent; GDM, gestational diabetes mellitus; GI, glycemic index; MDT, multidisciplinary team; MNT, medical nutrition therapy; NEMO, nutrition and education materials online; RBWH, Royal Brisbane and Women's Hospital.

Numerous local hospital and health service specific factors impacted project delivery and recruitment timelines during the study, which was planned to run for a maximum of 6 months for each phase (Baseline and Implementation) and 2 months for Transition. These included: introduction of an electronic medical record; staff turnover; large increase in women with GDM diagnosed in early pregnancy; and global factors (COVID-19). As indicated in Table 3, site 1's Baseline phase ran for 5 months, Transition for 10 months, and Implementation for 6 months. Site 2's Baseline ran for 8 months, Transition period for 8 months, with the Implementation lasting for 9 months; the final model of care was impacted by COVID-19 restrictions.

Table 3 - Minimum best-practice data set for sites 1 and 2, Baseline and Implementation
Variable Site 1 Site 2 P values (change from Baseline to Implementation)
Project phase (months) Baseline (August–December 2018) Implementation (November 2019–April 2020) Baseline (February 2019–September 2019) Implementation (May 2020–January 2021)
Number of eligible women (n) 144 138 106 39
Number of ineligible women [’early’ GDM (<24 weeks) or ‘late’ referred >28 weeks] 101 130 179 125
Initial dietetic consult format % (n) Group (F2F) Group (telehealth) Individual Missing 84.0 (121)n/a16.0 (23)- 88.8 (119)n/a11.2 (15)2.9 (4) 93.2 (99)n/a6.6 (7)- n/a92.3 (36)7.7 (3)-
Time from referral to first appointment, weeks (median; interquartile range) 1.1 (1.0) 1.1 (0.6) 1.6 (1.1) 1.1 (1.4) P = 0.43 (site 1) P = 0.06 (site 2)
Proportion of women with review appointments (%) 0 1 only 2+ 0.7 (1)2.1 (3)97.2 (140) 0.0 (0)5.8 (8)94.2 (130) 2.8 (3)40.6 (43)56.6 (60) 0 (0)74.4 (29)25.6 (10) P = 0.13 (site 1) P = 0.001 (site 2)
Time from referral to first appointment = best practice (<1 week), % (n)a 47.9 (69) 44.2 (61) 17.0 (18) 41.0 (16) (n1 missing) P = 0.53 (site 1) P = 0.004 (site 2)
Time from first to second appointment = best practice (<1 week), % (n) 64.3 (92) 43.5 (60) 0.9 (1) 5.1 (2) P < 0.001 (site 1)P = 0.11 (site 2)
Schedule of visits (1 new and ≥2 reviews; all individual) = best practice, % (n) 15.3 (22) 11.2 (15) (n4 missing) 1.9 (2) 2.6 (1) P = 0.32 (site 1)P = 0.8 (site 2)
Best practice care (time and number of visits)b, % (n) 8.3 (12) 4.5 (6) (n4 missing) 0.9 (1) 0 (0) P = 0.19 (site 1)P = 0.5 (site 2)
Requirement for pharmacotherapy, % (n) 72.9 (105) 63.0 (87) 53.8 (57) 51.3 (20) P = 0.08 (site 1)P = 0.71 (site 2)
aGuidelines recommend that women have their first visit with the dietitian within a week of referral and their first review a week after their initial appointment; ∼ guidelines recommend that women have at least three individual visits with a dietitian.
bAppointment within a week and at least one new and two review appointments; F2F = face to face.
Difference investigated between 0, 1, or 2+ appointments. P values in bold indicate statistical significance.

In addressing their barriers, site 1 chose not to introduce 1 : 1 appointments as the project team (lead-dietetic manager, GDM clinician and researcher) decided that incorporating an additional 20 new individual 1 : 1 patient referrals each week was not an ‘efficient use of existing resources’ and it was felt that this would reduce the dietitians’ ability to see other review clients (in their designated slots). Furthermore, this was also not possible because of their large increase in the number of women diagnosed with GDM in early pregnancy also needing to be seen within the service. Changes to the ‘baseline-delivered’ group at site 1 included the introduction of two, simultaneously delivered, smaller groups offered each week. Both groups were serviced by the dietitian and diabetes educator, alternating as first and second presenters. Site 1 also developed a 11 min GDM information video to standardize and simplify messages delivered within the group. Following the video being viewed, the dietitians answered questions from the group. The video's content included recommended food groups for pregnancy; carbohydrate serves; and suggested meal plan to distribute carbohydrates evenly over the day. The diabetes educator covered ‘what is GDM?’ and how to test blood glucose levels. The final 3 months of the Implementation phase occurred during COVID-19 service changes, which resulted in review (individual) appointments delivered as telephone reviews to limit patient contact with the hospital.

Site 2 did not have resources to implement 1 : 1 appointments with all newly diagnosed women because of their large increase in the number of women diagnosed with GDM in early pregnancy and so they chose to continue to offer group education sessions. The creation of a PowerPoint presentation to facilitate ease of delivery of the group education also ensured that all clinicians delivering the session provided the same information to women. The presentation highlighted the key education messages required, while written education material provided more detail.18 This presentation of key education messages also helped keep group sessions to the designated 30 min timeframe for dietetics. Topics included: what is GDM?; how we manage GDM; carbohydrate foods + counting; glycaemic index (GI); medication; physical activity; measuring blood sugar levels; trouble shooting; and ‘Your GDM to-do list’. In March 2020, the impacts of COVID-19 emerged, and the site 2 Nutrition and Foodservice department made the decision, with support from the antenatal team, to move all new (group) and review (individual) appointments to virtual care (telehealth) to limit the patient contact within the hospital. During this change, the dietitian and diabetes educator commenced the group together (rather than one following the other) and as a consequence recognized that the education each provided was similar. As a result, to ensure no future double up of information, the dietitian and the diabetes educators combined their presentations and streamlined the delivery of the session to ensure that patients received all information in a concise, systemised format within a designated 60 min timeframe.

As shown in Table 2, a common barrier emerged in the delivery of the model of care at both sites. Neither site delivered one-to-one tailored, individualized MNT to women for initial appointment. Except for women requiring interpreters, all women attended an initial group appointment delivered in conjunction with the diabetes educator as their first contact with a dietitian (site 1 group = 90 min, including 45 min with the dietitian; site 2 group = 60 min, including 30 min with the dietitian). This was despite site 1 initially appearing to have sufficient resources to provide one-to-one care (0.6 FTE); however, because of their dietitian-led clinic service design, with minimal diabetes educator input, many review appointments were required to be offered by dietitians absorbing much of this FTE. Site 2 did not have sufficient FTE, clinic organization, or a method of collecting clinic attendance numbers to deliver and monitor best practice care. Site 2 reoriented resources from within the department to increase GDM dietitian FTE from 1.51 to 1.64 FTE.

As shown in Table 3, site 1 had between 138 and 144 women and site 2 had 39 and 106 women with GDM diagnosed between 24 and 28 weeks attending their service over each phase of the project. Ineligible women who were also being seen within the service at these times were site 1: time 1 n = 101 (‘early’ (K4–24) n = 71, ‘late’ (K = 29–37) n = 30); time 2 n = 130 (‘early’ n = 98, ‘late’ n = 32); site 2: time 1 n = 179 (‘early’ (K4–24) n = 141, ‘late’ (K = 29–37) (n = 38); time 2: n = 125 (‘early’ n = 94, ‘late’ n = 31).

The time taken for women to have their first appointment with the dietitian after GDM diagnosis was slightly above the recommended 1 week as per guidelines at both sites [median of 1.1 (interquartile range (IQR 1.0) to 1.1 (IQR 0.6) weeks, and 1.6 (IQR 1.1) to 1.1 (IQR 1.4) weeks, sites 1 and 2, respectively]. No change in the proportion of women who received care within the recommended time period occurred (47.9 versus 44.2%, P = 0.53) at site 1 and increased from 17 to 41% (P = 0.004) at site 2. However, site 1's GDM schedule for dietitian's appointments facilitated women to be seen within 1.5 weeks of referral to align with doctors’ appointments. However, there was no change in the proportion of women seen within 1.5 weeks [76.4% (n = 110) versus 77.5% (n = 107), P = 0.82].

The proportion of women seen by a dietitian for a review appointment within the recommended 1 week of their initial dietetic appointment decreased at site 1 (64.3–43.5%, P < 0.001) but did not change at site 2 (0.9–5.1%, P = 0.11). Similarly, for site 1, 82.5 and 58.7% of women were seen within 1.5 weeks, pre/post, respectively (P < 0.001). The median time for women to be seen at their second appointment remained similar at site 1 [2.0 (IQR = 1.0)] and increased from 2 weeks (IQR 0) to 3 weeks (IQR 1.0) at site 2.

The proportion of women who received best practice dietetic care, defined by number of individual appointments (at least one new, and a minimum of two reviews)10 regardless of timing remained stable at site 1 from 15.3 to 11.2% (P = 0.32) and 1.9 to 2.6% at site 2 (P = 0.8) (Table 3). Very few women received one-to-one appointments for their first visit at Baseline or Implementation at either site (site 1: 16%; 11.2%; site 2: 6.6%; 7.7%). Those who did were generally women requiring interpreters and who were not eligible to attend a group session. The proportion of women who received three or more visits remained static at site 1 (97.2 versus 94.2%) whilst this significantly decreased at site 2 (56.6 versus 25.6%, P < 0.001).

Combining the guideline-recommended time to first appointment and number of visits variables into a composite best-practice score, the proportion of women who received best practice care remained low with minimal change at both sites: site 1 (8.3 versus 4.5%, P = 0.19); site 2 (0.9 versus 0%, P = 0.5). Medication use did not change from pre to post at either site [site 1, 9.9% (P = 0.08); site 2, 2.5% (P = 0.71)].


This article describes the planning, delivery, outcomes and challenges of a third stage-facilitated implementation of a best practice GDM dietetic model of care across two Queensland sites. Initial engagement processes involved sites trialling resources and processes developed through stage 1's project.3 This project commenced with a high level of engagement and consultation feedback by both sites resulting in a more efficient method to monitor patient outcomes (previously noted as a barrier).6 The decision-tree flowchart prompted sites to review site-specific barriers and develop relevant solutions. However, the lack of multidisciplinary stakeholder involvement in this process at each site as well as lack of team involvement at site 2 potentially influenced the incomplete adoption and model-of-care integration process. This resulted in minimal changes in the format and timing of care delivery, with minimal impact on reduction of medication use for GDM management.

Barriers to delivery of the best practice model of care were primarily around the organization and timing of initial clinical contact. This was primarily because of inadequacy and organization of dietetic staffing affecting each site's ability to deliver timely, individualized care according to best practice appropriate frequency. In addition to lack of stakeholder engagement (also noted to negatively impact a previous site's model of care adoption),6 the impact of ‘early’ GDM patient numbers (diagnosed before 24 weeks)19 on the capacity of the service to deliver best practice care cannot be understated. Compounding the prevalence of GDM increasing to over 14% of pregnancies in Australia,20 early GDM is reported to make up almost a third of these pregnancies and requires greater and longer involvement of team members, particularly because of greater documented complications.21 Within this project, almost half of the ineligible women at site 1 and three quarters at site 2 were because of early diagnosis (before 24 weeks) of GDM. The recommended dietitian staffing levels for this project's model of care were calculated as sufficient resourcing for best practice care delivery to women being diagnosed with GDM between 24 and 28 weeks of pregnancy. Discounting women with early GDM into service delivery planning did not and would not in future accommodate this workload burden, preventing the individualized 1 : 1 care required for best practice within the clinic.

A major limitation of this study was the delays introduced by external factors. One was the requirement of a full ethics application for both sites, despite two previous iterations of the implementation being deemed exempt from ethics review (stage 1 and 2 projects). In addition, one site required a further collaborative agreement with the ‘hub’ team's site, affecting project momentum and diminished engagement from local stakeholders beyond the core/spoke project team. Standardization in project assessment by research ethics and governance boards for health service research is imperative to expedite implementation projects, particularly in regional and rural settings22,23 to facilitate collaborations across multiple health services and academic centres where different expertise is held.

The other major contributing factor to lack of project success related to the interplay between delays in project delivery, organizational issues and disconnected clinical teams and management. Greater understanding of factors that affect project momentum is required by management and other project participants (i.e. stakeholders, such as clinicians, administration staff, etc.). These include methods to engage and retain stakeholders’ involvement (beyond ‘tokenistic’ to ‘engaged’),6,24 essential health service research requirements (including baseline data collection prior to introduction of the innovation),2 and the need for purposeful, consultative, system-embedded change rather than rapid parallel-to-service project delivery, often completed quickly, with limited consultation.25 Further delays were experienced at one site with the introduction of an electronic medical record across the hospital and health service, resulting in a ceasing of all nonessential (project) work during this time. Despite these issues around loss of momentum, the need for health service redesign projects must take into account these factors more explicitly as changes will always be occurring in a complex system, such as a hospital or health service. Health services, health service researchers, managers/directors and clinicians must become more skilled at predicting, adapting and integrating contingencies into such projects; each player will have their role to facilitate successful delivery. As shown in stage 2 of this work, managers have a key responsibility to bring a strategic awareness of organizational initiatives and levers to create an environment for success to allow the clinicians with local (clinical space) knowledge and influence to deliver the change.4

The emergence of the COVID-19 global pandemic during both sites’ Implementation phase increased ‘fail to attend’ occurrences of review appointments when delivered as telephone reviews. These were anecdotally noted to be regarded by the women as ‘less important’ clinical contacts. Further work is being undertaken by the sites to investigate women's preferences regarding the integration of telephone contact and telehealth (video-calls) in the GDM services.

The strengths of this project include application of a previously used implementation strategy.6 This approach had been updated to incorporate learnings from formative work around earlier iterations of the model of care's implementation regarding communicating and managing project processes, team expectations, potential risks.4 Women's attendance to appointments was used as the basis of comparing performance of maternity services against the guidelines. However, it does not account for appointments that may have been offered to patients within guideline timeframes that were not attended. Given that site 1 and 2 women can have complex social needs (because of the sites being chosen in areas of lower socioeconomic status), this may be an important explanatory factor that was outside of the scope of this study.

Conclusion: implications for research and practice

There are two clear directions for future research in the delivery of best practice nutrition care for GDM; awareness and management of women with early GDM and the potential efficiencies digital health solutions may introduce. Firstly, the design and testing of models of care for women with GDM diagnosed before 24 weeks is required to describe current evidence-practice gaps followed by development of effective solutions co-created with women. Evaluation of these service delivery solutions is essential to ensure they achieve meaningful health behaviour change for optimal blood glucose control and improved pregnancy outcomes (rather than solely knowledge, satisfaction, or time savings).26 Secondly, we must explore novel and effective ways of delivering best practice nutrition care to women with GDM. Services cannot continue to deliver inefficient and ineffective models of care that are time-intensive for women and clinicians and are nontailored as they do not facilitate improvements in patient outcomes. If services continue to deliver groups, they must be guided by evidence-informed frameworks, such as the Academy of Nutrition's process for delivering MNT in a group setting.27 However, research-informed guidelines have shown that individualized advice is required for better management of blood glucose levels and reduced need for insulin,10 with comparisons between individual and group GDM care showing that group attendance is a significant predictor of insulin therapy.28 Digital health solutions have the potential to optimize information sharing and ongoing support to improve workflow, allow efficient and effective use of limited resources and introduce equity of access to services.29–31 These include apps used as communication tools to share blood glucose levels and provide timely and responsive advice in a low cost, broad reach approach.32

In the development of any new GDM service, it is essential that co-creation principles with end-users (women and the health service) are met. This approach aligns with The National Safety and Quality Health Service Standard, ‘Partnering with Consumers’,33 ensuring patients are involved in planning their own care adding value to healthcare decision-making, ensuring alignment with values and needs and results in efficient use of resources.34 Important principles in the development of new GDM models of care should include the exploration of delivery of standardized, introductory advice regarding what is deemed essential by healthcare professionals and women, in a format(s) delivered that is low-intensity, broad-reach and inexpensive. Strategies and timing of how and when dietitians follow-up women should enable more tailored and individualized advice. Essential digital disruption30 may lead to novel models of care that allow timely, spaced, culturally appropriate two-way engagement with women.

This third iteration of the dissemination of the best practice model of nutrition care for GDM in two Queensland hospital and health service districts did not achieve successful clinical or process outcomes. However, valuable learnings and recommendations regarding future clinical and research health service redesign are suggested.


We would like to thank all of the dietitians involved in the delivery of this project and its model of care at the two sites as well as members of the ‘spoke’ project teams, including Lilly Bishop-Campbell, Georgina Foster, Holly Gower, Shannon Huxtable, Christine Josephson, Phillip Juffs, Lisa McGuire, Rachel Phillips, Roisin Rafferty, Penelope Rayner and Jillian Ross.

Ethics approval and consent to participate: this study was approved by the Metro South Human Research and Ethics Committee (HREC/18/QPAH/241). It conformed to the provisions of the Declaration of Helsinki (as revised in Tokyo, 2004). Consent was provided by all dietitians who delivered care within this project.

Consent for publication: authors provide consent.

Availability of data and materials: these can be made available on reasonable request.

Funding: S.W. was supported by a Queensland Health Health Research Fellowship.

Authors contributions: All authors have participated sufficiently in the article to take public responsibility for the content. Dr Shelley Wilkinson (corresponding author) developed the study, with input from Sally McCray. Data collection was completed by Shelley Smith and Helen Porteous and analysis was completed by Drs Shelley Wilkinson and Michelle Palmer. Data interpretation was completed by all authors. Dr Shelley Wilkinson drafted the manuscript. All authors provided significant critical input into the manuscript. All authors are in agreement with the manuscript and declare that the content has not been published elsewhere.

Conflicts of interest

There are no conflicts of interest.


1. Yoong SL, Bolsewicz K, Grady A, et al. Adaptation of public health initiatives: expert views on current guidance and opportunities to advance their application and benefit. Health Educ Res 2020; 35:243–257.
2. Straus S, Tetroe J, Graham I. Knowledge translation in healthcare. Moving from evidence to practice. Oxford: Wiley-Blackwell/BMJ Books; 2009.
3. Wilkinson SA, McCray SJ, Beckmann M, McIntyre HD. Evaluation of a process of implementation of a gestational diabetes nutrition model of care into practice. Nutr Diet 2016; 73:329–335.
4. Wilkinson S, O’Brien M, McCray S, Harvey D. Implementing a best-practice model of gestational diabetes mellitus care in dietetics: a qualitative study. BMC Health Serv Res 2019; 19:122.
5. Wilkinson S, McCray S, Beckmann M, Parry A, McIntyre H. Barriers and enablers to translating gestational diabetes guidelines into practice. Practical Diabetes 2014; 31:67–72.
6. Wilkinson S, McCray S, Kempe A, Sellwood B. Clinically relevant improvements achieved from a facilitated implementation of a gestational diabetes model of care. Nutr Diet 2018; 75:271–282.
7. Wilkinson SA, Tolcher D. Nutrition and maternal health: what women want and can we provide it? Nutr Diet 2010; 67:18–25.
8. Morrison M, Collins C, Lowe J. Dietetic practice in the management of gestational diabetes mellitus: a survey of Australian dietitians. Nutr Diet 2011; 68:189–194.
9. Hoffman L, Nolan C, Wilson J, Oats J, Simmons D. Gestational diabetes mellitus – management guidelines. the Australasian Diabetes in Pregnancy Society. Med J Aust 1998; 169:93–97.
10. Reader D, Splett P, Gunderson EP. Impact of gestational diabetes mellitus nutrition practice guidelines implemented by registered dietitians on pregnancy outcomes. J Am Diet Assoc 2006; 106:1426–1433.
11. Queensland Clinical Guidelines. Gestational diabetes mellitus (GDM). Guideline No. MN21.33-V4-R26. Brisbane: Queensland Health; 2021. Available at: [Accessed 18 March 2021].
12. Wakerman J, Humphreys J, Wells R, et al. Features of effective primary healthcare models in rural and remote Australia: a case-study analysis. Med J Aust 2009; 191:88–91.
13. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci 2011; 6:42.
14. French SD, Green SE, O’Connor DA, et al. Developing theory-informed behaviour change interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework. Implementat Sci 2012; 7:38.
15. Wensing M, Grol R. Knowledge translation in health: how implementation science could contribute more. BMC Med 2019; 17:88.
16. O’Reilly S. Translational research: the ingredients are only the start of the recipe for better dietetic practice. Nutr Diet 2016; 73:307–311.
17. Reader D, Sipe M. Key components of care for women with gestational diabetes. Diabetes Spectr 2001; 14:188–191.
18. Queensland Health. Healthy eating for gestational diabetes mellitus. Queensland: Nutrition Education Materials Online (NEMO); 2009. Available at: [Accessed 18 March 2021].
19. Nankervis A, McIntyre H, Moses R. ADIPS consensus guidelines for the testing and diagnosis of gestational diabetes mellitus in Australia 2014. Available at: [Accessed 18 March 2021].
20. Laurie JG, McIntyre HD. A review of the current status of gestational diabetes mellitus in Australia — the clinical impact of changing population demographics and diagnostic criteria on prevalence. Int J Environ Res Public Health 2020; 17:9387.
21. Sweeting A, Ross G, Hyett J, et al. Gestational diabetes mellitus in early pregnancy: evidence for poor pregnancy outcomes despite treatment. Diabetes Care 2016; 39:75–81.
22. Stevenson F, Gibson W, Pelletier C, Chrysikou V, Park S. Reconsidering ‘ethics’ and ‘quality’ in healthcare research: the case for an iterative ethical paradigm. BMC Med Ethics 2015; 16:21.
23. Greville H, Haynes E, Kagie R, Thompson S. ’It Shouldn’t Be This Hard’: exploring the challenges of rural health research. Int J Environ Res Public Health 2019; 16:4643.
24. Goodman M, Sanders Thompson V. The science of stakeholder engagement in research: classification, implementation, and evaluation. Transl Behav Med 2017; 7:486–491.
25. Greenhalgh T, Papoutsi C. Spreading and scaling up innovation and improvement. BMJ 2019; 365:12068.
26. Murphy A, Guilar A, Donat D. Nutrition education for women with newly diagnosed gestational diabetes mellitus: small-group vs individual counselling. Can J Diabetes 2004; 28:147–151.
27. Academy of Nutrition & Dietetics. MNT Versus Nutrition Education 2006. Available at: [Accessed 18 March 2021].
28. Barnes R, Ross G, Jalaludin B, Flack J. Initial group dietary education compared to individual education in gestational diabetes mellitus management: do outcomes differ? Diabetes Res Clin Pract 2018; 140:88–96.
29. Furness K, Huggins C, Truby H, Croagh D, Haines T. Attitudes of Australian patients undergoing treatment for upper gastrointestinal cancers to different models of nutrition care delivery: qualitative investigation. JMIR Form Res 2021; 5:e23979.
30. Kelly J, Collins P, McCamley J. Digital disruption of dietetics: are we ready? J Hum Nutr Diet 2020; 34:134–146.
31. Willcox J, Wilkinson S, Lappas M, et al. A mobile health intervention promoting healthy gestational weight gain for women entering pregnancy at a high body mass index: the txt4two pilot randomised controlled trial. BJOG 2017; 124:1718–1728.
32. Mater Mothers Hospital. Gestational diabetes app supporting flexible care for parents-to-be 2020. Available at: [Accessed 24 August 2021].
33. Australian Commission on Safety and Quality in Healthcare. Partnering with Consumers Standard 2019. Available at: [Accessed 18 March 2021].
34. Greenhalgh T, Jackson C, Shaw S, Janamian T. Achieving research impact through co-creation in community-based health services: literature review and case study. Milbank Q 2016; 94:392–429.

gestational diabetes mellitus; health services research; implementation; medical nutrition therapy; model of care

Supplemental Digital Content

© 2021 JBI. Unauthorized reproduction of this article is prohibited.

A video commentary on implementation project titled: How do health professionals prioritise clinical areas for implementation of evidence into practice? The commentary is provided by Andrea Rochon RN, MNSc, Research Assistant, Queen's University, Ontario, Canada