HealthTracker will be evaluated using a cluster randomised, controlled trial design. At end of study, HealthTracker will be made available to both the intervention and control arms for a further 12 months free of any licence fees. The study schema including site and patient eligibility criteria are highlighted in .
- Use of Medical Director or Best Practice for EHR management.
- Exclusive use of these systems to record risk factor information, pathology test results and prescribe medications.
- Agreement by all GPs and other designated staff to use HealthTracker.
Services that do not have a compliant software system will be excluded from participation. Services using ‘hybrid’ paper and electronic systems for recording risk factor information, pathology results and medication prescription will also not be eligible to participate.
- Aboriginal and Torres Strait Islander people 35+ years and all others 45+ years (age criteria are based on NVDPA guideline screening recommendations37).
- Attendance at the general practice or ACCHS at least three times in the previous 24-month period AND at least once in the previous 6-month period.
Participating general practices have been recruited from the Sydney region in collaboration with primary healthcare organisations known in Australia as Medicare Locals. Participating ACCHSs have been recruited in partnership with two state representative bodies for ACCHSs, the Aboriginal Health & Medical Research Council (AH&MRC) of NSW and the Queensland and Aboriginal Islander Health Council (QAIHC). A $500AUD reimbursement to participating sites will be made to partially compensate for health service staff time commitment to study-related activities. Sites randomised to the intervention will receive training support in use of the system predominantly via face-to-face visits and webinars. All licence costs and technical support associated with the intervention will be provided free to the intervention sites in the first 12 months and to all sites for the following 12 months after completion of the trial. A newsletter and networking web site will be provided to participating sites. Royal Australian College of General Practitioners Quality Assurance and Continuing Professional Development points will be offered to participating GPs in both arms of the trial.
Sixty services (40 general practices and 20 ACCHSs) will be randomised in a 1:1 allocation to use HealthTracker or ‘usual care’ for 12 months. Clusters will be stratified at three levels:
- ACCHS versus general practices.
- Service size (<500 patients meeting eligibility criteria vs >=500).
- Participation in existing QI programmes (current involvement in one of five national and state programmes involving regular audit and feedback versus past or never involved in these programmes).
A site assessment survey will be administered to all sites to assess for service eligibility and these stratifying variables. Permuted block randomisation will be centrally performed using a web-based form. As this is a pragmatic trial, allocation will be single blinded with outcome analyses conducted blinded to treatment allocation.
The intervention arm will receive the four components of the system described above (point-of-care decision support software, risk communication tools, data extraction tools and access to the QI portal). Clinical staff will be given training in use of the tools and a support service will be available for any technical queries. One initial face-to-face training visit and subsequent site visits and webinars targeting strategies to improve quality of care will be provided. Unless requested by health services the intervention will not be modified or discontinued. Reasons for discontinuation will be outlined and all analyses will be conducted on an intention-to-treat basis (see below).
Sites allocated to this arm will continue usual practice with their current systems without the implementation of HealthTracker. As the George Institute holds exclusive rights to the distribution of the system, there is no possibility of control sites having access to HealthTracker. If these sites already routinely use data extraction tools for assessing their quality of care then this will continue as normal. As with the intervention arm, services participating in any QI initiatives will continue participation as usual. For those sites not routinely using data extraction tools, the automated data extraction tool will be temporarily installed for data collection purposes only and then uninstalled that same day. A feedback report on performance will be provided at study completion only.
Quantitative data collection
Cross-sectional data will be collected in an automated manner for all patients who satisfy the eligibility criteria at each service (). These data will then be sent securely to the George Institute via an export function for the analysis of primary and secondary outcomes.
Prerandomisation: 1 month prior to randomisation, deidentified data will be collected from all sites. These data will be fed back to all sites as a formal report highlighting areas where data quality issues may occur.
Randomisation: Baseline data will be collected and sites will be randomised to intervention or control.
End of intervention period: At the end of 12 months, data will be collected in both study arms.
- Change in the proportion of eligible patients receiving appropriate measurements of their CVD risk in the previous 12 months (measured at randomisation and at 12 months).
- Change in the proportion of eligible patients assessed at high CVD risk receiving appropriate prescriptions for their CVD risk factors in the previous 12 months (measured at randomisation and 12 months).
Appropriate measurement of CVD risk factors is defined as having recorded or updated all the essential risk factors for the measurement of CVD risk (smoking status, blood pressure (BP) in the previous 12 months, total cholesterol and high-density lipoprotein (HDL) cholesterol in the previous 24 months) among those in whom risk assessment is guideline indicated. Unless explicitly recorded, diagnoses of diabetes or left ventricular hypertrophy will be assumed to be absent.
High CVD risk is defined as a calculated 5-year CVD risk of >15%, a history of CVD or the presence of any clinically high-risk conditions (as per NVDPA recommendations). Based on audit data, this is expected to comprise ~30% of the patient population.10
Appropriate prescriptions is defined as a prescription for one or more BP lowering drugs and a statin for people at high risk without CVD; or a prescription for one or more BP lowering drugs and a statin and an antiplatelet agent (unless contraindicated by oral anticoagulant use) for people with established CVD.
- Change in the measurement of individual risk factors separately (smoking status, BP, cholesterol, other non-Framingham risk factors—BMI, chronic kidney disease (CKD) screening with urinary Albumin to Creatinine ratio, estimated Glomerular Filtration Rate);
- Intensification of existing medication regimes among patients at high CVD risk (additional BP and lipid-lowering agents);
- Changes in mean systolic BP, total cholesterol, LDL cholesterol and HDL cholesterol;
- New CVD and CKD diagnoses.
Randomisation of 60 services (30 per arm) will provide 90% power to detect a ≥10% absolute higher occurrence in each primary study outcome among practices receiving HealthTracker. The following assumptions are based on our three audits in ACCHSs and mainstream general practices9–11
and include an assumed improvement of 10% in the two primary outcomes in control practices as a result of study participation.
- Cluster size of eligible population will range from 200 in a small service through to 2000 in a large service. An average cluster size of 750 is assumed.
- Recording rates of essential risk factors needed for risk assessment in the target group (first primary outcome) average 50%.10
- Thirty per cent of the cluster will be either be at high CVD risk or have established CVD (n=250) and prescription of appropriate medicines to high-risk patients (second primary outcome) is 50%. This is based on our published data on drug prescriptions for individuals with and without established CVD.9–11
- An intraclass correlation coefficient of 0.05 for both primary outcomes based on our audit data.9–11
- Two-sided α=0.05
Data analysis will be performed on an intention-to-treat basis using generalised estimating equations.38
Subgroup analyses will be carried out using the three prespecified strata: (1) ACCHS versus general practices, (2) service size (small vs large) and (3) current participation in QI programmes vs past or no involvement in these programmes.
The economic evaluation will have a trial-based component and a modelled evaluation of long-term costs and outcomes. The incremental cost will be based on software, training and other costs incurred with transitioning practices to using HealthTracker. This will help determine the cost barriers experienced by different practices in adopting the system. Data on medications, laboratory tests, consultations and newly recorded diagnoses of CVD events incurred by eligible patients during the trial will be obtained from the data extraction tools. Costs will be calculated from prevailing Medicare rates and standard Australian National Diagnosis Related Groups cost weights for CVD hospitalisations. The incremental cost consequences of the HealthTracker system in achieving each of the primary outcomes will then be estimated, for example, cost per eligible patients assessed at high CVD risk receiving appropriate prescriptions. Trial-based data, however, cannot capture costs and outcomes beyond the trial. To address this, a modelled economic evaluation will enable quality of life and survival to be examined and allow incremental cost-effectiveness ratios to be calculated in terms of cost per Quality Adjusted Life Years gained. Using a Markov model, the eligible patient population in both study arms will be hypothetically tracked over an extended period. Transition between various defined health states, costs and quality of life attached to various health states and the projected long-term intervention effects from that observed in the trial will be based on published evidence. With appropriate discounting, estimates of long-term costs and outcomes will fold out of the model. Sensitivity analyses will be conducted on discount rate, uncertainty in outcome estimates and assumptions made in costing (eg, varying efficiencies with different patient practice ratios to those of the trial setting). This will better inform policy makers as to the resource consequences of rolling out this programme to scale.
The qualitative evaluation of the β-version of HealthTracker suggested that a critical factor affecting the uptake of EDS interventions is whether and how they become embedded in routine healthcare.22
In the TORPEDO trial we will build on this observation through a detailed process evaluation to better appreciate the factors that might influence sustainability beyond the trial setting. Two qualitative methods will be used to explore these factors.
Semistructured interviews with health professionals and staff
A maximum variation sample will be taken to ensure diverse opinions are gained from patients, clinical and managerial health staff and sites with low and high uptake of the intervention.39
Key issues to be explored will include (1) how practitioners use HealthTracker; (2) what effects it has on organisational practices and personnel and (3) what are patients’ experiences of being presented with HealthTracker outputs and what impact does this have on the healthcare encounter. Individual informed consent will be sought and data will be collected towards the end of the intervention period so as not to unduly influence trial outcomes.
A key component of understanding barriers/enablers to use of HealthTracker is a better appreciation of how practitioners and patients use it at the point of care. Data collection using audio/video recording will capture how technological innovations are actually used in practice.40
Ethnographic analysis will greatly augment the interview accounts and will particularly shed important light on (1) how the intervention impacts on the flow of the clinical encounter; (2) how risk information is communicated between health professional and patient and (3) how the patient receives and interprets the information and the role it may play in shared decision-making processes. Although audio/video recorded clinical encounters are commonly used for primary care teaching purposes, such a technique can be potentially sensitive and therefore will be restricted to a small number of sites. Recordings will be conducted toward the end of the intervention period when both health staff and patients are thoroughly familiar with the system. This will occur over a 1-week period at each site. Participants who are approached for an interview will be invited to participate in this component. They will be given the option of having their healthcare encounter audio or video recorded. A follow-up interview will be arranged with these participants (both staff and patients) where the recording is played back for participant interpretation of the data.
These data will be supplemented by project officer field notes to identify any key processes, events, staffing and other resource issues occurring during the intervention period that may be relevant in gaining a better understanding of barriers and facilitators to implementation.
A multidisciplinary research team will guide the analysis process. As is common with qualitative inquiry, data analysis will commence early and be conducted contemporaneously with data collection. This method allows for interview content to be refined for subsequent data collection and to actively pursue emergent themes of interest. Although interviewing will continue until thematic saturation is achieved and therefore the exact number of interviews is unknown, we anticipate from prior experience that around 80 interviews (40 patients and 40 staff) will provide sufficiently rich data to meet our objectives.
Interview data will be digitally recorded, and professionally transcribed. NVivo 9 (QSR International Melbourne, Victoria, Australia) will be used to assist with data organisation and coding for key themes. Video data will be directly analysed and coded for key themes within NVivo. Feedback of findings to participants will be provided by a variety of methods, including workshops, summary reports, newsletters and via the study website.