We receive many emails enquiring about progress. As answering these takes time away from processing submissions, please email only if absolutely necessary. We are working hard to process registration and update requests as quickly as possible.

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been endorsed by the ANZCTR. Before participating in a study, talk to your health care provider and refer to this information for consumers
Trial registered on ANZCTR

Registration number
Ethics application status
Date submitted
Date registered
Date last updated
Date data sharing statement initially provided
Type of registration
Prospectively registered

Titles & IDs
Public title
The Actionable Intime Insights (AI2) Study: Implementing a digital model for timely and needs-based interventions for individuals on anti-psychotic medication in mental health services by applying algorithms to health care data
Scientific title
Using algorithms to dynamically initiate needs-based interventions for individuals on anti-psychotic medication: Implementation of The Actionable Intime Insights (AI2) Application in community mental health services
Secondary ID [1] 298389 0
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
medication error 313074 0
schizophrenia 313075 0
bipolar disorder 313076 0
serious mental illness 313077 0
relapse 313078 0
hospitalisation 313079 0
medication non-adherence 313080 0
Condition category
Condition code
Mental Health 311561 311561 0 0
Mental Health 311562 311562 0 0
Mental Health 311563 311563 0 0
Mental Health 311564 311564 0 0
Other mental health disorders
Mental Health 311565 311565 0 0
Psychosis and personality disorders
Mental Health 311566 311566 0 0
Mental Health 311567 311567 0 0

Study type
Description of intervention(s) / exposure
The AI2 application: Essentially, AI2 is a digital cloud based application service for healthcare professionals and patients that is centralised around the use of My Health Record data. This digital health system contains information such as: medicines (prescription and dispense records, PBS), and Medicare Benefit Schedule (MBS) claims. The methodological premise behind the AI2 study intervention is to use this data as a way to monitor and manage patients, providing real-time visualisation algorithms that identify patients at-risk of relapse and re-hospitalisation. The AI2application is designed so that each MBS and PBS claim made by the patient is visually displayed as an event in a vertical axis, and placed against a time scale (with the day/month and year) on a horizontal axis. In addition to the event (e.g. GP visit), an accompanying simple notes entry box is also included which allows for clinicians to annotate certain insights or predicted outcomes. The additional benefit of adding the notes initiative into the AI2 application is that it adds a contextual backdrop to the documented events, which, in turn, could facilitate the clinicians to identify and develop effective strategies and post-treatment plans.

When a clinician logs into the AI2 system, they will have access to the following: 1. their individual profile page; 2. patient list (which when clicked on, will take the clinician to a separate page for each individual patient showing their historic timeline of MBS and PBS events on a horizontal platform); 3. an insights alerts tab, which will show particular events flagged by the clinician as potential actionable insights requiring follow-up, whether such insights were followed-up or ignored, and whether the patient has received a phone call or SMS; 4. a list of patients in dashboard view who are at high risk, which will be updated on a weekly basis by the clinician Monitor; and 5. An SMS message tab, which will host a bank of SMS created by the two psychiatrists on the AI2 team, to send out to patients at sensitively appropriate time points. Clinicians will have access to the Ai2 system for the duration of the study (12 months), however, the involved local health networks and Flinders university may discuss an agreement for continual access, depending on the success of the study, and utility of the system.

Healthcare professionals who consent to participation in the study will have their patients enrolled in AI2 in the same method that patients are entered in to their community based information systems by the clinicians who have consented to use the dashboard in clinical care. That is, this information the tool accesses for clinicians is the information that clinicians already have access to through other means, and patients have already provided consent for them to access as part of usual care. As such, the use of this tool will not replace or change usual care. Clinician participants will be asked to meet with the research team to undergo training on how to use the AI2 system, how to correctly identify and interpret the algorithms, and specific appropriate intervention pathways when patients are identified at risk of relapse and hospitalisation. The appropriate intervention pathways that clinicians choose will be based on individual patient-trajectories. For example, if the system triggers a high risk alert of a patient who has missed concurrent prescription refills, the clinician will be advised to immediately make contact with the patient. To help train for this, in-house psychiatrists and the research team will present a series of case studies of individuals with different diagnoses, care plans, and prescription/appointment types to workshop with clinicians involved in the study. These training sessions will be a mix of group and individual face-to-face sessions that will happen during the month before, and the two weeks after the initial start date. The group training sessions will be for an hour weekly, and the individual ones will run for half an hour per clinician once a fortnight. Additionally, a member of the research team will be stations at the trial site for the first two weeks of the intervention in order to provide as-needed training for clinicians.
These data patterns will be presented as explained in the patient signup use case. Patients coded as red, identified as high risk, will be communicated to their case managers by the clinician Monitor who will then make contact with their patient. After the case manager has communicated with their patient, they will record an alert in the AI2 system of the outcome. These system analytics will be used to assess the clinician up-take and adherence to use of the Ai2 system in routine everyday patient monitoring. The nurse responsible for monitoring of the AI2 dashboard will create a list of all at risk patients (sorted form high risk to moderate risk) that will be presented to all healthcare professional staff during at least one team meeting per week where they will delineate how to contact and treat patients. The AI2 dashboard will also be used when reviewing patients ready for potential discharge from community services to aid as a decision making tool. At the end of the study, healthcare professionals will be asked to fill out an exit questionnaire about the usefulness of the AI2 dashboard in their daily practice.
Intervention code [1] 314631 0
Intervention code [2] 314632 0
Intervention code [3] 314633 0
Treatment: Other
Comparator / control treatment
Pre-intervention Medicare (MBS) and PBS data records may be utilised to come to the MBS and PBS data after the completion of the study intervention
Control group

Primary outcome [1] 320271 0
Composite outcome: Evaluate the usefulness, usability and acceptability of the AI2 application for healthcare professionals

A short qualitative structured interview, in the way of predetermined questions, will be administered to clinicians as a way to provide an avenue for further discussion relating to their experience with the AI2 application. These questions will assess clinicians’ experience of using the application (efficacy, simplicity, usefulness). These questions will be designed specifically for the study

Additionally, all healthcare professionals involved in the intervention will be asked to fill in The Unified Theory of Acceptance and Use of Technology scale, that assesses their acceptance of AI2 on dimensions including: performance expectancy, attitude towards using technology, social influence, facilitating conditions, and Self-Efficacy, Anxiety and Behavioural intention to use the system.

Through passive data collection methodology, we will also obtain a basic measurement of usability and feasibility of the AI2 application and web dashboard through uptake and usage parameters, which are being collected automatically by the AI2 system.

Timepoint [1] 320271 0
End of intervention (12 months)
Primary outcome [2] 320272 0
To validate the AI2 algorithms (composite outcome).

The one-year pre-intervention MBS and PBS data will be used for 10-fold cross validation of algorithms to produce actionable indicators that reflect valid changes in functioning and deterioration. These metrics will be compared with real-world patient outcomes, as follows:
Sensitivity: Measures the proportion of patient relapse and hospitalisation that are correctly identified as people at high or moderate risk,
Specificity: Measures the proportion of patients that are correctly identified as not at risk.

These will be assessed by data linkage to MBS and PBS (Medicare) data and SA Health patient journey data, sourced by CBIS or CCC
Timepoint [2] 320272 0
end of intervention (12 months)
Secondary outcome [1] 371042 0
To evaluate the impact of integrating AI2 application in clinical decision making care processes by community mental health case managers, a series of interrupted time series analyses will be conducted.

1. Differences in percentage of patients with green flags discharged between intervention period and control period (ai2 algorithm generated data)
Timepoint [1] 371042 0
end of intervention (12 months)
Secondary outcome [2] 371043 0

2. Difference in percentage of patients with red/orange flags followed up within 7 days instead of fixed time period scheduled appointments (rather than having a contact on a scheduled basis, contact is on demand, triggered by algorithms. Addition visits overlaid on a routine of fortnightly/monthly/3 monthly follow up) (ai2 algorithm generated data)
Timepoint [2] 371043 0
Timepoint: Baseline, 3, 6, 9, and 12 month intervention periods with equivalent pre-intervention historical periods.
Secondary outcome [3] 371044 0

3. Differences in percentage of patients who have a objective information on medication adherence phenotype in the previous 2 years included in their case formulation and problem list (ai2 algorithm generated data)
Timepoint [3] 371044 0
end of intervention (12 months)

Key inclusion criteria
Work in one of the following settings: community mental health setting; an inpatient setting; a (primary care) GP setting; a hospital setting; or, in a pharmacy as the primary pharmacist providing prescriptions to people with serious mental illness
Minimum age
18 Years
Maximum age
No limit
Both males and females
Can healthy volunteers participate?
Key exclusion criteria

Study design
Purpose of the study
Allocation to intervention
Non-randomised trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Masking / blinding
Who is / are masked / blinded?

Intervention assignment
Other design features
Not Applicable
Type of endpoint(s)
Statistical methods / analysis

Recruitment status
Not yet recruiting
Date of first participant enrolment
Date of last participant enrolment
Date of last data collection
Sample size
Accrual to date
Recruitment in Australia
Recruitment state(s)

Funding & Sponsors
Funding source category [1] 302929 0
Government body
Name [1] 302929 0
NHMRC Medical Research Future Fund
Address [1] 302929 0
National Health and Medical Research Council
GPO Box 1421
Canberra ACT 2016
Country [1] 302929 0
Primary sponsor type
Flinders University
Personal Health Informatics
College of Medicine and Public Health
Sturt Rd, Bedford Park SA 5042
Secondary sponsor category [1] 302890 0
Name [1] 302890 0
Address [1] 302890 0
Country [1] 302890 0

Ethics approval
Ethics application status
Ethics committee name [1] 303493 0
The Southern Adelaide Clinical Human Research Ethics Committee
Ethics committee address [1] 303493 0
Flinders Medical Centre
L6 / 6C room 219
8204 7433
Flinders Drive
Bedford Park
Ethics committee country [1] 303493 0
Date submitted for ethics approval [1] 303493 0
Approval date [1] 303493 0
Ethics approval number [1] 303493 0

Brief summary
We are recruiting healthcare professionals aged over 18 years old to participate in a 1-year internet based study, called the Actionable Intime Insights (AI2) Study . The study aims to explore whether the use of patients Medicare Benefit Scheme (MBS) and Pharmaceutical Benefit Scheme (PBS) claims data held in My Health Record, can unveil more sufficient ways for clinicians and other healthcare professionals to provide optimal health care to patients with a chronic mental illness. Specifically, when people go to visit their doctors, they typically lodge a MBS or PBS claim to subsidise some of the payment costs. However, to date, there has not been a successful way of monitoring and managing the MBS and PBS data. We hope that we can be the first to provide a long-term sustainable solution to this problem, with the implementation of our novel digital application called the Actionable Intime Insights (AI2) application. We believe the AI2 application will help transform the way clinicians and patients interact with each other and ultimately improve patient’s well-being and outcomes.
Trial website
Trial related presentations / publications
Public notes

Principal investigator
Name 93850 0
A/Prof Niranjan Bidargaddi
Address 93850 0
Personal Health Informatics, College of Medicine and Public Health, Flinders University, Tonsley (Level 2), GPO Box 2100, Adelaide, SA 5001
Country 93850 0
Phone 93850 0
+61 8 7221 8840
Fax 93850 0
Email 93850 0
Contact person for public queries
Name 93851 0
Ms Lydia Oakey-Neate
Address 93851 0
Personal Health Informatics, College of Medicine and Public Health, Flinders University, Tonsley (Level 2), GPO Box 2100, Adelaide, SA 5001
Country 93851 0
Phone 93851 0
+61 8 7221 8264
Fax 93851 0
Email 93851 0
Contact person for scientific queries
Name 93852 0
A/Prof Niranjan Bidargaddi
Address 93852 0
Personal Health Informatics, College of Medicine and Public Health, Flinders University, Tonsley (Level 2), GPO Box 2100, Adelaide, SA 5001
Country 93852 0
Phone 93852 0
+61 8 7221 8840
Fax 93852 0
Email 93852 0

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No/undecided IPD sharing reason/comment
IPD is sensitive health information, and as per ethics, IPD will not be shared with third parties.
What supporting documents are/will be available?
No other documents available
Summary results
No Results