Precision public-health intervention for care coordination: a real-world study

Context and setting

In Australia, the GP is the cornerstone of primary care coordination. Approximately 84% of Australians see a GP every year, and 77% of patients have a preferred GP.¹⁸ The goal of the proposed intervention was to help GPs identify their patients who were vulnerable and to promote follow-up appointments during the period of restrictions. The intervention material was delivered digitally or via the postal service. Some particularities of the Australian health system determined the technology choice, including:

• geographical location — veterans are distributed across Australian states and territories. Although there are a few GPs specialised in veteran care, most GPs have fewer than four veterans under their care. Patients are free to choose their GP, irrespective of geographical location, which may increase patient satisfaction and access, but the lack of patient registration makes it harder for practices to define their population, which has the potential to reduce continuity of care;¹⁹

• technological readiness — Australian GPs have had near-universal use of EHRs for more than 10 years,²⁰ and there is a large penetration of secure-messaging infrastructure for receiving laboratory test results; and

• public-funded, privately operated model of care — primary care in Australia is provided by trained GPs and a universal healthcare scheme, called Medicare, provides basic cover. The Department of Veterans’ Affairs (DVA) provides additional cover for eligible veterans. GPs and primary health clinics are independent providers, so any intervention focused on GPs must be highly collaborative and involve practitioners from the start. There is a high degree of agency during GP appointments, and payers (that is, those funding the care, rather than patients) have limited influence on practice.

The Veterans’ MATES programme

The initiative reported here was developed as part of the Veterans’ Medicines Advice and Therapeutics Education Services (Veterans’ MATES) programme. Veterans’ MATES is funded by the DVA and aims to improve medicine and health services use and health outcomes for all persons in the veteran community across Australia. The programme drives professional behaviour change via a multifaceted intervention, composed of an educational component and an audit and feedback component delivered to GPs; these are supported by educational components delivered to pharmacists, other relevant health professionals, and veterans. Veterans’ MATES is informed by social cognitive theory,²¹ the transtheoretical model,²² and the health-promotion PRECEDE-PROCEED model.²³ As such, it has a strong focus on education, increasing veterans’ participation in their therapeutic choice, providing repeated interventions over time, and reinforcements and tailored decision support based on available data.

Interventions are created in three sequential steps:

• Step one — an epidemiological inquiry is conducted to identify trends and potential issues in healthcare access and use. Examples include the long-term prescription of medicines recommended for acute issues, doses above guideline recommendations, and lack of screening tests for an eligible population. The programme has access to the DVA’s health claims database, which is updated monthly and includes all dispensed medicines requiring prescription, along with claimed healthcare services and laboratory services, home care, and aged care;

• Step two — educational material, along with audit and feedback documents, are developed. This is a collaborative process with heavy stakeholder involvement, including multiple health professionals and behaviour-change specialists;

• Step three — the identification and delivery of the intervention to veterans and their main healthcare provider. This step requires the use of patient-level information contained in the database to print personalised audit and feedback documents at scale, reaching tens of thousands of veterans and GPs per intervention.

The programme has been extensively described elsewhere,²⁴ and has focused on increasing the use of underused medicines, reducing adverse medicine events, reducing use of unnecessary medicines, and improving the utilisation of health services. It has been shown to be effective for changing professional behaviour in different domains,²⁴ including promoting medicine review²⁵ and osteoporosis screening,²⁶ improving uptake of health services,²⁷ reducing inappropriate proton pump inhibitor use,²⁶ and reducing hypnotics use for insomnia.²⁸

Veterans’ MATES ran the first intervention in 2004 and, since then, has delivered four interventions each year. In 2019, a digital precision public-health initiative was implemented, which used digital technology infrastructure available at healthcare provider practices. The goal of this initiative was to use the large longitudinal claims database to create risk profiles, and use data to tailor interventions for GPs. Each digital document sent to GPs is dynamic, and may contain different elements (prompts, goal setting, charts²⁹) and recommendations based on identified patient risk. For the intervention to improve care coordination during the COVID- 19 pandemic, the aim was to improve intervention efficacy by increasing GP engagement and reducing the lag between the detection of an issue and the GP being notified about it.

The digital solution developed takes advantage of Australia’s secure-messaging infrastructure, which is commonly used to communicate electronically laboratory results and referral letters from the source directly to the GP’s desktop computer. Electronic documents are visualised in the clinical software, and a GP can request actions from the clinic nurse or reception on the same screen. The paper-based intervention documents were developed as HTML pages, converted to portable document format (PDF), encrypted, and embedded in a Health Level 7 (version 2) file using internally developed software.

Study design and sample

A non-randomised experimental study was conducted to evaluate the effectiveness of a digital precision public-health intervention at promoting care coordination during national emergencies compared with usual delivery via the postal system.

Patients were allocated to a postal or digital group in two sequential steps. First, eligible patients were identified based on information contained in the administrative health claims database. Patients were eligible if they were identified as being at highest risk of poor outcomes from COVID-19 — that is, aged >70 years with a comorbidity of hypertension,^30–37 chronic heart disease,^30–37 diabetes,^30–38 chronic airways disease,^31–37 cerebrovascular disease,^30,31,34,39 chronic liver disease,³⁶ chronic renal failure,^{31,33,35–37} malignancy,^{30,31,34,35,37,40} or were immunocompromised.³⁶ Identification algorithms were composed of clinical rules with varying levels of complexity, looking for past diagnostic codes (International Classification of Diseases, 10^th edition) during hospitalisations, use of medicines indicating treatment for one of the target comorbidities (for example, carvedilol, a medicine that can only be newly prescribed for patients with moderate or severe heart failure in Australia), and combinations of services and medicines used.

After patient eligibility was checked, the primary GP was identified using a proprietary algorithm based on the frequency and recency of appointments. GPs with at least one eligible patient were eligible for inclusion in the intervention. The digital group comprised all GPs with the capability to receive the digital intervention (access to EHR and secure-message delivery); the postal group comprised the remaining GPs.

Intervention development

The main goal of this intervention was to promote care coordination during lockdown and while social restrictions were in place. It was conducted using a collaborative, pragmatic approach, influenced by Greenhalgh et al’s diffusion of innovations model,⁴¹ in order to develop a solution that could be implemented at national scale. The model summarises a collection of theoretical and empirical findings, and highlights the interplay between an innovation, the adopter, the context in which the innovation takes place, and the implementation and the diffusion processes. The model suggests innovation developers consider nine dimensions during intervention creation:

• innovation;

• adopter;

• assimilation;

• communication and influence;

• system antecedents for innovation;

• system readiness for innovation;

• outer context;

• implementation process; and

• linkage.

The processes involved in intervention development complemented the three steps used by Veterans’ MATES interventions, suited for rapid care coordination (Figure 1). The development of all content and interventions was based on the best evidence available at the time and supported by repeated reviews by panels of health professionals.

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Figure 1.

Schematic representation of the digital intervention delivery.

The patient-specific feedback document (Figure 2) was developed and submitted to a stakeholder review group, including health professionals (pharmacists and GPs, among others). The behaviour-change techniques (BCTs) used included, as described using the BCT taxonomy:⁴²

• goal setting — for example, schedule appointments to ensure vulnerable patients are still receiving necessary care;

• prompts — for example, demonstrate why patient is vulnerable, such as medicine dispensing suggesting respiratory disease; and

• information about health consequences — that is, rationale for suggested actions.

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Figure 2.

Sample of the electronic document sent to GPs as part of the intervention.

The underlying theory was that the provision of personalised recommendations and BCTs in the form of a document delivered directly to a GP’s clinical software would change that GP’s behaviour, influence patient recall, and trigger an early appointment. The BCTs aim to increase motivation and trigger active patient search by the GP.

In addition to the intervention material delivered to the healthcare practitioner, all veterans living in the community setting (68 872 individuals) received educational material via the postal service. The material included information about COVID-19 infection prevention, prompting them to maintain regular contact with care providers and to continue to adhere to health plans, as well as how to access care during the pandemic given the expansion of video and telehealth appointments, along with free medicine delivery services for eligible persons. The intervention aimed to promote patient activation and the veteran requesting a GP appointment. The online version of the printed materials is available at the Veterans’ MATES website (www.veteransmates.net.au).

Outcomes and statistical analysis

To evaluate the intervention, the total number of visits post-intervention and the time to first appointment with the primary GP were compared between the digitally delivered and postal delivery groups. It was hypothesised that the time to first appointment would be shorter in the electronically delivered material given improved workflow fit (document is downloaded directly to the clinical software, and appears next to other clinical documents) and the ease of requesting actions in the software. The time to first appointment post-intervention was measured using a time-to-event analysis, in which the index date was the intervention delivery date and the event was first visit with the primary GP. Events were censored at 3 months from the index date.

At the time of the intervention, Australia had in place restrictions on visitors to aged care; as such, veterans living in aged care were excluded from the time-to-event analysis. Cox regression was used to determine whether the time to first GP appointment differed between the intervention delivery modalities after adjusting for gender and patient age at the time of intervention. To account for the difference in GP attendances between the two groups prior to the intervention being delivered, adjustment was made for the number of visits in the previous year. This was done to account for the possibility that those veterans attending clinic with a GP whose practice had electronic clinical management systems had more regular visits to their GP.

Given the large sample and the purposeful sampling, a 99% confidence interval (CI) (P<0.01) for all hypothesis tests was considered. All analyses were performed in Python (version 3.7). The main statistical library used for time-to-event was lifelines (version 0.25).