Trial Review

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Trial registered on ANZCTR


Registration number
ACTRN12625000469415
Ethics application status
Approved
Date submitted
12/12/2024
Date registered
16/05/2025
Date last updated
16/05/2025
Date data sharing statement initially provided
16/05/2025
Type of registration
Prospectively registered

Titles & IDs
Public title
Diabetes Management Errors in Australia: A Factorial Randomised Controlled Trial of a Health Workforce Educational Platform (The WDEP.AI RCT) for Rehabilitation Wards
Scientific title
Diabetes Management Errors in Australia: A Factorial Randomised Controlled Trial of a Health Workforce Educational Platform (The WDEP.AI RCT) for Rehabilitation Wards
Secondary ID [1] 312652 0
Nil known
Universal Trial Number (UTN)
Trial acronym
The WDEP. AI RCT
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Diabetes Management 334615 0
Condition category
Condition code
Diet and Nutrition 332948 332948 0 0
Other diet and nutrition disorders
Public Health 331201 331201 0 0
Health service research

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
A 6-month Pilot 2x1 Factorial cluster RCT will be conducted in six rehabilitation wards within Sydney metropolitan area. Health professionals in all six wards will be invited to participate in the study. Each Phase will span 3 months.

Phase 1: Baseline Data Collection (3 months)
1. All eligible staff members from the first two randomly selected wards will be invited to join the trial.
2. Baseline data on diabetes management knowledge of consenting staff will be assessed using a diabetes management knowledge quiz (Pre-Intervention DMKQ for wards 1 and 2).

Phase 2: Intervention 1 – Standard version of the WDEP.AI platform (3 months)
1. Intervention 1 (I1): the first two wards will be randomised in a 1:1 ratio to either receive access to the standard version of the WDEP.AI platform (WDEP.AI-Standard) to complete the online topic on Diabetes and Aged Care over a 10-week period, or, to serve as the Control group, which will continue with standard care without additional education.
2. Following topic completion, staff will be invited to provide feedback through an online topic evaluation (TE) form.
3. 3-month post-intervention DMKQ (I1) will be performed to measure learning outcomes from Intervention 1 (WDEP.AI-Standard) in wards 1 and 2.

Phase 3: Post Intervention 1 Analysis, Machine Learning Enhancement and Baseline data collection for Intervention 2 (3 months)

3.1 Post Intervention 1 analysis:
1. After staff data collection is complete, staff from the control ward will have access to WDEP.AI-Standard.
2. Data from the initial intervention with WDEP.AI-Standard will be analysed using AI and Machine Learning (ML) algorithms to enhance the platform's content. Learning outcomes will be measured by comparing baseline DMKQ results pre and post Intervention 1, offering insights into the platform's effectiveness and guiding further improvements.
3. 6-month post-intervention DMKQ (I1) will be performed to measure the retainment of knowledge from Intervention 1 (WDEP.AI-Standard) in wards 1 and 2.
4. Focus group/ one-on-one interviews will be conducted for participants randomised to WDEP.AI Standard during Intervention 1.

3.2 Machine Learning Enhancement and Baseline Data Collection for Intervention 2:
1. All eligible staff members from the remaining four wards (3-6) will be invited to join the trial.
2. Baseline data on diabetes management knowledge of consenting staff will be assessed using a diabetes management knowledge quiz (Pre-Intervention 2 DMKQ for wards 3, 4, 5 and 6).

Phase 4: Intervention 2 – Enhanced Version of the WDEP.AI Platform (3 months)

4.1 Intervention 2 (I2):
1. Intervention 2: The remaining four wards will be randomly assigned 1:1 to either receive access to the enhanced version of the WDEP.AI platform (WDEP.AI-Enhanced) to complete the online topic on Diabetes and Aged Care over a 10-week period, or, to serve as the Control group, which will continue with standard care without additional education.
2. Following topic completion, staff will be invited to provide feedback through an online topic evaluation (TE).
3. 3-month post-intervention DMKQ (I2) will be performed to measure learning outcomes from Intervention 2 (WDEP.AI-Enhanced) in wards 3, 4, 5 and 6.

Phase 5: Post-intervention 2 Analysis (3 months):

5.1 6-month post-intervention DMKQ (I2):
1. 6-month post-intervention DMKQ (I2) will be performed to measure the retainment of knowledge from Intervention 2 (WDEP.AI-Enhanced) in wards 3, 4, 5 and 6.

5.2 Post-intervention 2 Analysis:
1. Access to WDEP.AI-Enhanced platform to control and randomised groups in wards 1 and 2, and to control group in wards 3, 4, 5 and 6 will be provided, ensuring all participants benefit from the enhanced educational resources.
2. Qualitative data will be gathered through focus groups/one-on-one interviews for wards 3, 4, 5 and 6, exploring the usability, acceptability, and feasibility of the WDEP.AI-Enhanced platform from the perspective of health professionals.
3. Data collected pre and post WDEP.AI-Enhanced will be analysed.
4. Analysis of TE metrics
5. A health economic analyses will be conducted to evaluate the cost-effectiveness of the intervention, providing valuable information on its potential for wider implementation.

**Ward inpatient data collection - inpatient data will be collected retrospectively (before during and after the intervention) for the duration of the study for all wards**

Phase 6: Wrap-up Phase (3 months):

6.1 Comprehensive analysis:
1. Comprehensive analysis of all trial metrics (Quantitative).
2. Analysis of Focus Group Transcripts (Qualitative)
3. Preparation of the final report.

Building on the work completed on the foundation WDEP platform and utilising a specific focus in the aged care population, WDEPAI will focus on improving care, education and reducing the impact of diabetes related complications. The four themes we have focused on revolve around factors affecting aged care, reducing diabetes complications, supporting staff through national and local policies and early recognition of deterioration.

Through the use of artificial intelligence (AI) and machine learning (ML) the platform will evaluate learning styles, learner engagement and how by improving diabetes clinical knowledge will lead to improved patient outcomes, reduced medication errors and harm minimisation. Resources have been developed utilising available best practice guidelines, patient information and diabetes advocacy organisations such as the National Diabetes Service Scheme. Diabetes Australia and the Aged Care Quality and Safety Commission frameworks.


WDEP.AI is structured so that the learners can work at their own pace in small sessions bite-sized chunks at a time. The advantage is that they can log into the platform and devote whatever time they have available, while saving their progress and continuing the package promptly.
Through the use of introductory videos, web links appropriate to the topic and easily accessible resources, learners can build their knowledge and confidence in delivering evidence-based based safe diabetes care across all populations of those living with diabetes.
Learners have the opportunity to reattempt incorrect answers, with resources relevant to the incorrect answers provided between each attempt. This approach allows them to improve their knowledge and likelihood of success in each competency. The competencies support policies and patient-centred strategies, such as language matters, medication safety, nutrition, timing of meals, supporting a holistic approach thereby improving quality of life.



Adherence will be assessed using analytics on time spent, number of attempts, and abandonment rates for onboarding, each theme, and each competency. Completion metrics will include % logging in, % completing onboarding, % completing all content, % completing each theme, and % completing each competency. Qualitative feedback will cover appearance, navigation, and overall user experience



Artificial Intelligence (AI) personalises learning by tailoring educational content to individual learners' styles, paces, and needs, enabling more effective engagement and outcomes. It analyses large datasets in real-time to track learner interactions, predict areas needing extra support, and dynamically adjust content difficulty through adaptive learning. AI also provides personalized feedback, recommends relevant resources, and identifies at-risk learners, allowing for timely interventions to enhance motivation and success.

References:
-Onesi-Ozigagun, O., Ololade, Y. J., Eyo-Udo, N. L., & Ogundipe, D. O. (2024). Revolutionizing education through AI: a comprehensive review of enhancing learning experiences. International Journal of Applied Research in Social Sciences, 6(4), 589-607.
-Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793-803.
-Kump, B. (2010). Evaluating the domain model of adaptive work-integrated learning systems.
Sharma, N., Doherty, I., & Dong, C. (2017). Adaptive learning in medical education: the final piece of technology enhanced learning?. The Ulster medical journal, 86(3), 198.
-Wade, S. W., Moscova, M., Tedla, N., Moses, D. A., Young, N., Kyaw, M., & Velan, G. M. (2020). Adaptive tutorials versus web-based resources in radiology: a mixed methods analysis in junior doctors of efficacy and engagement. BMC Medical Education, 20, 1-12.



Participants who consent to participate in the focus groups will be invited to take part. Each focus group will include approximately 20 participants, made up of endocrinology consultants, nurses, allied health professionals, researchers, patients or their carers, pharmacists, and general practitioners.

We are seeking feedback on the ease of use of the website and program, any issues encountered, the impact of WDEP-AI on participants’ diabetes knowledge and practice, the relevance of the questions and resources to their work, desired content areas to strengthen their knowledge, facilitators and barriers to accessing or completing WDEP-AI, reasons for potentially giving up on the module, and participants’ likes, dislikes, and suggestions for improvement.



Retrospective Data Collection for the Study:

This study involves the use of retrospective inpatient data to evaluate patient outcomes before the implementation of intervention strategies. The data will be sourced from the electronic medical records of patients admitted to the relevant hospital wards participating in the study.

Pre-Intervention Retrospective Data:
To establish baseline characteristics and outcomes, retrospective data will be collected for the three months preceding the start of each intervention stage.

For Intervention Stage 1 (expected to commence in May 2025), retrospective data will cover the period from February 2025 to April 2025.

For Intervention Stage 2 (expected to commence in November 2025), retrospective data will cover the period from August 2025 to October 2025.

These retrospective data will assist in comparing pre- and post-intervention outcomes. All data will be extracted from patient medical records maintained by hospital clinical information systems.

Note: While additional data will be collected during and after each intervention stage for evaluation purposes, only the time periods mentioned above constitute the retrospective component of data collection.
Intervention code [1] 329177 0
Behaviour
Comparator / control treatment
Control: Diabetes education as usual as per ward settings. Involves educational activities scheduled on the ward, organised and delivered by credentialed diabetes educators.
Control group
Active

Outcomes
Primary outcome [1] 339033 0
The primary outcome measure for this study is good diabetes days (GDD).
Timepoint [1] 339033 0
Electronic records will be examined baseline (Phase 1) and through all phases of the trial including post intervention (Phase 5).
Secondary outcome [1] 446660 0
Diabetes management and knowledge
Timepoint [1] 446660 0
Baseline, Post intervention (after 3 months and 6 months)
Secondary outcome [2] 439888 0
Rates of hypoglycaemia and hyperglycaemia detection
Timepoint [2] 439888 0
Electronic records will be examined baseline (Phase 1) and through all phases of the trial including post intervention (Phase 5).
Secondary outcome [3] 438314 0
Self-reported confidence in the importance of review and early escalation and decreasing failure to rescue rates
Timepoint [3] 438314 0
Post completion of Phase 4 for intervention stage1 and stage 2
Secondary outcome [4] 439889 0
Length of hospital stay
Timepoint [4] 439889 0
Electronic records will be examined baseline (Phase 1) and through all phases of the trial including post intervention (Phase 5).
Secondary outcome [5] 439887 0
Uptake of WDEP.AI
Timepoint [5] 439887 0
Post completion of Phase 1 and Phase 2
Secondary outcome [6] 439890 0
Appropriate hypoglycaemia management
Timepoint [6] 439890 0
Electronic records will be examined baseline (Phase 1) and through all phases of the trial including post intervention (Phase 5).

Eligibility
Key inclusion criteria
1. Group 1: Health professionals working within the six rehabilitation wards
2. Group 2: Health professionals associated with the six rehabilitation wards.
3. Group 3: Rehab ward inpatients within the six rehabilitation wards
4. Participants must be proficient in English, to be able to use the WDEP.AI platform.
Minimum age
18 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
Individuals who are not healthcare professionals or who do not work directly in the rehabilitation wards within the specified local health districts/facilities will be excluded from the study. Due to the need for a stable clinical workforce for the trial temporary workforce was excluded e.g. agency nurses, medical and nursing students.

Study design
Purpose of the study
Educational / counselling / training
Allocation to intervention
Randomised controlled trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
Allocation will be concealed from the researcher determining participant eligibility and collecting baseline data as allocation occurs after questionnaires are completed and after baseline data is collected.
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Randomisation numbers will be allocated by the database randomiser using a block randomisation approach. The wards which are randomised for the WDEP.AI intervention will be added to the REDcap database. REDcap will onboard all participants via emails and SMS notifications which will contain details on how to access the WDEP.AI intervention.
Masking / blinding
Blinded (masking used)
Who is / are masked / blinded?


The people assessing the outcomes
The people analysing the results/data
Intervention assignment
Other
Other design features
This study is a single randomised controlled trial (RCT) employing a 2x1 factorial cluster design with six hospital sites (clusters). The study evaluates the effectiveness of an artificial intelligence (AI)-enabled intervention for improving inpatient diabetes management.

The trial is conducted in two sequential strata:
• Stratum 1 (Intervention Stage 1) - Two hospital sites begin the intervention in the first stage (). These clusters serve as the initial test of the intervention, during which data are collected at two time points.
• Stratum 2 (Intervention Stage 1) - Four additional hospital sites begin the intervention at a later time point. By this stage, the AI-enabled intervention is more advanced, benefiting from learnings in Stratum 1.

All six sites contribute to a single RCT with continuous data collection across both strata. The design enables evaluation of both early and later versions of the intervention, allowing analysis of its evolving impact as staff gain more experience and the AI improves.

This is not a sequential series of RCTs, but rather a single RCT with two predefined strata included a priori in the analysis. The factorial structure allows assessment of intervention effects across both stages, accounting for differences in intervention maturity and implementation timing.
Phase
Not Applicable
Type of endpoint/s
Efficacy
Statistical methods / analysis
Data Analysis:

- Quantitative Data Analysis:
Blinded statisticians will conduct an intention-to-treat (ITT) analysis, utilizing ANOVA for baseline differences and Poisson/binary logistic regression for primary and secondary outcomes including good diabetes days, rates of detection of hypos and hyperglycaemia, diabetes medication (e.g., insulin) errors and appropriate hypoglycaemia management adjusting for clustering and baseline differences. STATA MP15.1 will facilitate all analyses, with 2-tailed tests and P<0.05 indicating significance.

- Qualitative Data Analysis:
Thematic analysis will be performed on the focus group/one-on-one interview transcripts using Braun and Clarke's six-step process. Transcripts will undergo systematic initial coding, validated by a second coder for 10% of the data, with discrepancies resolved through consensus. A coding framework will be established using Quirkos 2.5.2 software. to provide detailed results from the RE-AIM data collection and analysis approach.

- Health Economic Analysis:
Health economic analysis will include cost effectiveness analysis of the training program. This will comprise of costs of using the WDEP-AI and its different components by time spent and average wages. Changes in the knowledge from the quantitative data will be used to assess any presenteeism and absenteeism. On the benefits, assessment of differential health services costs between the intervention and control group will be undertaken. Redacted clinical coding data routinely collected includes admission pathway, the ambulance report, treatments given, episodes of care, length of stay, discharge summaries between units, transferred to episode, unit type of admission i.e, emergency department, direct transfers, hours in ICU, discharge destination, allied health services provision during stay, financial liability, private health insurance and diagnostic coding. To further evaluate the health economic impacts/benefits of the RCT.

Recruitment
Recruitment status
Not yet recruiting
Date of first participant enrolment
Anticipated
30/05/2025
Actual
Date of last participant enrolment
Anticipated
15/05/2026
Actual
Date of last data collection
Anticipated
15/08/2026
Actual
Sample size
Target
150
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
NSW
Recruitment hospital [1] 27348 0
Camden Hospital - Camden
Recruitment hospital [2] 27350 0
Prince of Wales Private Hospital - Randwick
Recruitment hospital [3] 27351 0
Braeside Hospital - Prairiewood
Recruitment hospital [4] 27349 0
Fairfield Hospital - Prairiewood
Recruitment hospital [5] 27781 0
Kareena Private Hospital - Caringbah
Recruitment hospital [6] 27782 0
The Sutherland Hospital - Caringbah
Recruitment postcode(s) [1] 43439 0
2570 - Camden
Recruitment postcode(s) [2] 43441 0
2031 - Randwick
Recruitment postcode(s) [3] 43969 0
2229 - Caringbah
Recruitment postcode(s) [4] 43440 0
2176 - Prairiewood

Funding & Sponsors
Funding source category [1] 317083 0
Other Collaborative groups
Name [1] 317083 0
Digital Health CRC Grant
Country [1] 317083 0
Australia
Funding source category [2] 318307 0
Other Collaborative groups
Name [2] 318307 0
Maridulu Budyari Gumal [Sydney Partnership for Health, Education, Research and Enterprise (SPHERE)]
Country [2] 318307 0
Australia
Primary sponsor type
University
Name
Western Sydney University
Address
Country
Australia
Secondary sponsor category [1] 320692 0
None
Name [1] 320692 0
Address [1] 320692 0
Country [1] 320692 0

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 315835 0
South Western Sydney Local Health District Human Research Ethics Committee
Ethics committee address [1] 315835 0
Ethics committee country [1] 315835 0
Australia
Date submitted for ethics approval [1] 315835 0
20/11/2024
Approval date [1] 315835 0
03/12/2024
Ethics approval number [1] 315835 0
2024/ETH01412

Summary
Brief summary
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 135950 0
Prof David Simmons
Address 135950 0
Macarthur Clinical School, Western Sydney University Parkside Cres, Campbelltown, NSW 2560
Country 135950 0
Australia
Phone 135950 0
+61 437961795
Fax 135950 0
Email 135950 0
[email protected]
Contact person for public queries
Name 135951 0
David Simmons
Address 135951 0
Macarthur Clinical School, Western Sydney University Parkside Cres, Campbelltown, NSW 2560
Country 135951 0
Australia
Phone 135951 0
+61 437961795
Fax 135951 0
Email 135951 0
[email protected]
Contact person for scientific queries
Name 135952 0
David Simmons
Address 135952 0
Macarthur Clinical School, Western Sydney University Parkside Cres, Campbelltown, NSW 2560
Country 135952 0
Australia
Phone 135952 0
+61 437961795
Fax 135952 0
Email 135952 0
[email protected]

Data sharing statement
Will the study consider sharing individual participant data?
Yes
Will there be any conditions when requesting access to individual participant data?
Persons/groups eligible to request access:
• The data will be available to genuine researchers identified through the WDEP.AI Investigator Group Review

Conditions for requesting access:
• Yes, conditions apply:
• Requires a data sharing agreement between data requester and trial custodian or sponsor
What individual participant data might be shared?
• Data and data dictionary will be shared on reasonable request.

What types of analyses could be done with individual participant data?
• Statistical

When can requests for individual participant data be made (start and end dates)?
From:
31/12/2027
To:
31/12/2028
Where can requests to access individual participant data be made, or data be obtained directly?
• Data may be requested by contacting Professor David Simmons. [email protected]

Are there extra considerations when requesting access to individual participant data?
No


What supporting documents are/will be available?

No Supporting Document Provided


Results publications and other study-related documents

Documents added manually
No documents have been uploaded by study researchers.

Documents added automatically
No additional documents have been identified.