Digital Transformation of Rural, Regional, and Remote Australian Hospitals: A Pragmatic Strategy for Introducing AI

ABSTRACT

Aims

Rural, regional, and remote hospitals in Australia face barriers to digital transformation, including limited infrastructure, digital literacy, and workforce capacity. This Commentary outlines a pragmatic strategy to build rural digital readiness through the safe implementation of ambient artificial intelligence (AI) scribes as a low‐risk starting point for AI adoption.

Context

AI scribes use generative AI to convert clinical conversations into documentation. They offer potential to reduce administrative burden and workforce strain while preparing rural health services for future AI use. Although the Victorian Department of Health has established minimum standards for AI scribe use, rural hospitals face unique challenges including lower AI literacy, workforce pressure, and limited research infrastructure. Insights from ongoing implementation research highlight the value of simulation methods to examine usability, workflow effects, and ethical considerations before deployment.

Approach

Three key enablers support responsible implementation: (1) clinical simulation for research, (2) harmonisation of evaluation metrics, and (3) shared infrastructure for consent, training, and monitoring.

Conclusion

Implementing AI scribes provides a practical pathway for rural hospitals to strengthen capability, reduce administrative burden, and build readiness for more advanced clinical AI, supporting safe and equitable adoption across rural Australia.

The challenges facing the digital transformation of health are more pronounced in rural, regional, and remote areas (hereafter, rural), and include poorer digital infrastructure, lower levels of digital literacy, and unique workforce challenges, which individually and collectively reduce organisational readiness [1, 2]. Artificial intelligence (AI) is the capacity of computational systems to perform tasks typically associated with human intelligence, such as problem‐solving and decision‐making [3]. AI in healthcare is a transformational technology that has generated significant interest as a solution to the health service challenges facing rural Australia [4, 5]. The impact of AI on a given healthcare organisation depends on where along the spectrum the tool is being used, from inference (i.e., generating or integrating complex information to enable health care professionals to make decisions about care) to automation (i.e., leveraging AI to take over routine clinical or administrative tasks), as well as the technological infrastructure required to implement the tool [6]. Many rural Australian hospitals lack the foundational technological infrastructure, such as electronic medical records (EMRs) or large data storage capacity, that is required to implement inference‐based AI. Rural hospitals have variable EMR adoption and capability, and gaps in digital infrastructure can constrain the safe rollout of AI scribes. Even when the infrastructure exists, there remain workforce and organisational barriers to the implementation of AI, such as trust, AI literacy, and capability, that are more pronounced in rural areas [4].

1. The New Metro‐Rural Gap: AI‐Driven Inequity

Two years ago, AI in hospitals across Australia (outside of digital imaging and pathology) was virtually non‐existent, and Australia was lagging behind other developed countries such as the United States when it came to AI implementation [7]. Researchers argued that the absence of AI in Australian hospitals, particularly higher clinical risk AI, was due in part to a lack of evaluation methods and infrastructure needed to enable safe implementation and Evaluation (see Figure 1). In response, significant efforts led by the University of Queensland have developed both evaluation and infrastructure methods to address this gap [8]. Eighteen months later, the pipeline of AI tools for inference and clinical use cases being developed and tested in metropolitan hospitals in Australia appears to be improving [9], but the same cannot be said for rural settings. Thus in 2025, we are now facing a new potential problem of AI‐driven inequity, where metro hospitals, who have the technological capacity for AI, now also have the blueprint to evaluate and implement it safely, while rural settings have neither.

FIGURE 1.

Conceptual framework illustrating selection of AI use cases in healthcare based on clinical risk and degree of change to existing workflows [3, 7, 8].

The metro‐rural gap between Australia's hospitals in AI capability cannot be understated. AI has the potential to address some of the most serious issues facing the Australian healthcare system, and rural areas of the country have the greatest need [4, 5, 10, 11]. With the speed of technological advancement, rural health services cannot wait until infrastructure is established before starting to address the capability and capacity barriers to digital and AI transformation. To address this AI gap, we argue that rather than wait until the technological infrastructure is in place, the optimal starting point involves lower clinical risk AI that minimally disrupts existing workflows—to both gain immediate benefits of these tools and to prepare for inference and clinical use case AI [4].

2. Scribing as a Quick Win Use Case for Rural Services

A promising example of lower clinical risk is AI scribes, which “listen” to a clinical encounter between a health care professional and a patient and use a type of generative AI known as large‐language model to summarise the encounter and produce a range of outputs such as consultation summaries, referral letters, operation notes, and discharge summaries. AI scribes are not without risk. In fact, there are likely to be significant risks relating to hallucinations (AI‐generated content that is false or not supported by the input data), paradoxical outcomes on workflow (i.e., increase in documentation time to review AI outputs compared to business as usual), and serious ethical considerations, such as privacy and informed consent [12]. Importantly for rural health services, AI scribes can operate independently of an EMR, if required.

AI scribes have significant potential in Australian hospitals. Recent studies estimate that health care professionals spend more than 40% of their time on documentation and compliance‐related tasks, often outside of scheduled hours and without dedicated support [13]. A recent national study found that health care professionals in rural health services also experienced a high administrative burden, with many describing the situation as “unsustainable” and a major source of professional strain [14]. This burden, compounded by workforce shortages and the complex care needs of rural residents, has been directly linked to burnout, reduced clinical capacity, and diminished well‐being [15]. Early international studies suggest that AI scribes may reduce documentation time, improve note completeness, and enhance workforce experience—though results are mixed [16, 17]. A recent commentary on AI scribes in remote healthcare settings in Canada cautions that rural communities are frequently under‐supported in technology rollouts, placing the burden on local providers to implement new tools despite myriad contextual challenges [18].

In Victoria, the Department of Health released an advisory, setting minimum expectations for AI scribe implementation and use [19]. These include defined scope, organisational approval within a governance framework, and privacy and consent processes (including a Privacy Impact Assessment and explicit consent with an opt‐out) [19]. Across Australia, requirements are evolving and may differ by jurisdiction, creating practical challenges for cross‐border and mixed‐jurisdiction networks; services often harmonise to the most restrictive baseline with local governance sign‐off [19, 20, 21]. To support operationalisation in resource‐constrained rural services, recent Australian resources provide pragmatic implementation checklists: [20].

Rural AI Scribe Pilot: Governance checklist [19, 20, 22].

• Data retention and deletion (including vendor‐held copies)

• Model improvement: opt‐in/opt‐out for using local clinical data

• Data residency and local vs. cloud processing

• Encryption (in transit and at rest)

• Third parties and sub‐processors (full disclosure)

• Incident and breach notification (roles, timelines, escalation)

• Offline/downtime mode and safe fallback

• Audit logs (what is recorded and reviewed)

• Role‐based access control

• Contract clauses for rural providers (support/service levels, change control, exit plan, data return + deletion)

Procurement and governance for rural AI scribe pilots should clearly assign responsibilities across the service and vendor, including what data are captured, where it is processed (e.g., local versus cloud), and how data protections and third‐party access are managed. Particular attention is needed for data lifecycle controls, secondary use for model improvement, and operational resilience (incident response and downtime pathways), given workforce constraints and variable infrastructure [19, 20, 21]. This should be interpreted alongside applicable privacy obligations and national digital health guidance [19, 20]. Beyond these minimum controls, rural implementation principles emphasise safe and ethical use that augments locally based clinicians rather than substituting for them, acknowledges variable cybersecurity and governance capacity outside metropolitan services, and, where sensitive narratives may be captured, embeds Aboriginal and Torres Strait Islander perspectives and Indigenous Data Sovereignty considerations, including culturally safe consent processes and appropriate community partnership [21].

3. Research Enablers for AI Scribe Implementation in Rural Settings

After procurement and governance is established, implementing AI scribes safely and effectively in rural hospitals requires attention to context‐specific factors. Insights from the DELIVER program, supported by an MRFF (Medical Research Future Fund), Rapid Applied Research Grant [22], are examining several context‐specific factors relevant to rural implementation, including:

1. Rural consumers have higher levels of trust in their health professionals, which likely impacts their trust and consent for AI scribes.

2. Rural health professionals and consumers have lower levels of digital literacy, which impacts their ability to seek and give consent for AI scribes.

3. Workforce pressures are greater in rural areas.

Clinical Simulation for Research: A Primer.

Clinical simulation for research is an emerging best practice method well suited for pre‐implementation testing of digital health tools, to understand the human factors, workflow changes, and unintended consequences safely and efficiently [1]. Clinical simulation for research, which originates in clinical simulation for education, involves real clinicians, and real patients or patient actors, simulating a relevant scenario such as a consultation while using the digital health tool. Mixed methods data collection pre, peri, and/or post‐simulation, such as observation, ‘think‐aloud’ techniques, quantitative measures, and time taken to complete tasks enable teams to measure a wide range of factors likely to impede successful implementation. The combination of real‐time observation and detailed measurement provides insights into complex human factors that are known to led to digital health failures, such as clinician acceptance, trust, workflow disruption, unintended or paradoxical outcomes, and ethical considerations.

Just as new research enablers have been necessary to ensure the successful development and implementation of clinical AI [8], similarly we must leverage research enablers to ensure successful implementation of AI scribes in rural settings. Pre‐implementation research is critical, requiring methods that can consider local factors, mapping use cases and likely failure points. At the pre‐implementation stage, a useful research enabler is clinical simulation for research. This emerging best‐practice method in digital health [23] can be used by healthcare providers to assess AI scribe output quality, impact on existing workflow, and the technical integration of simulated care delivery scenarios. Moreover, simulation allows direct comparisons between vendor products prior to deployment. Clinical simulation scenarios, tailored specifically to the challenges of rural health services, would generate further valuable evidence about typical local use cases.

Rural health services have long faced underinvestment in research, which has limited growth in research infrastructure, expertise and collaborative networks. The MRFF Rapid Applied Research Translation rural and regional stream helps address rural research inequity by enabling partnerships between research institutions and rural health services. Through this funding, the Centre for Digital Transformation of Health has partnered with rural services via the DELIVER program, supporting locally responsive pilot testing of AI scribes (including scalability and sustainment) before wider rollout.

Rural Simulation Playbook for AI Scribe Pilots.

1. Select scenarios: prioritise common rural encounters (e.g., ED visits, GP clinics, allied health consults).

2. Cast roles: clinician(s) and patient actor(s); nominate an observer.

3. Define key tasks: history/exam, counselling, ordering, and completion of the clinical note.

4. Capture data: audio plus screen capture (where feasible) and simple time stamps for documentation and edits.

5. Core outcome set: documentation time; workload/after‐hours work; cognitive load; clinician satisfaction; patient‐perceived care quality; note quality.

6. Test controls: consent prompts, pause/stop function, offline/low‐bandwidth plan, and redaction (where available).

7. Decision gate: adopt/defer/reject, with governance sign‐off and conditions for rollout.

Illustrative scenario:

Setting: rural ED encounter. Actors: ED clinician + patient actor (observer present). Key tasks: history/exam, admissions planning, and completion of the ED note. Data captured: encounter audio + (where feasible) screen capture + time stamps for documentation and edits.

To assist during implementation, another critical research enabler is harmonisation of evaluation metrics for AI scribes. Although best‐practice guidance is clear that evaluation must occur, there remains a lack of clarity around what evaluation metrics should be selected, with more than two dozen potentially useful metrics to date across process, experience, financial, and quality domains [24]. Rural settings would benefit from clarity around how AI scribes may have a positive impact on clinical outcomes and economic outcomes, such as the return‐on‐investment. Harmonisation of such metrics, with direct input from rural health service providers and patients, would allow for coordination and shared learning between rural settings with limited resources. The final research enabler is shared infrastructure and protocols required to implement and monitor AI scribes. Examples include patient consent protocols, staff training programs, patient communication materials, risk management, and monitoring and evaluation tools.

Despite experiencing the most need, rural Australia is currently lagging the furthest behind in terms of being able to benefit from the transformational potential of AI. We argue that successful implementation of AI scribes could serve as safer starting points to build rural hospitals' levels of organisational readiness, capability, confidence, and digital literacy for more advanced clinical AI, as the necessary technological infrastructure is put in place. At the same time, there are significant, unique risks of shortcuts in AI implementation in rural Australia [5]. This tension can be resolved through research enablers, such as clinical simulation for research at the pre‐implementation stage, harmonisation of metrics to evaluate AI scribes, and shared evaluation infrastructure. Equity should be considered as a design and evaluation dimension, informing site selection, implementation supports, and interpretation of outcomes in underserved rural populations. Vendors should collaborate with research institutions and hospitals to generate the locally needed evidence, and in line with recent Government advisory, rapidly develop monitoring tools for rural health services, supporting responsible and scalable adoption. Finally, we believe that pre‐implementation and real‐world pilot testing in rural and regional hospitals can inform the development of implementation guidelines to support uptake of AI scribes across the country. We failed previously to understand that implementing technologies such as telehealth, which have tremendous potential for rural hospitals, requires a holistic view of the social and technical aspects of an organisation. The steps proposed here may prevent the same mistake with AI scribes.

Author Contributions

Olivia Metcalf: conceptualisation, Investigation, criting – original draft preparation. Joel Fossouo Tagne: conceptualisation, review and editing. Debbie Passey: conceptualisation, review and editing. Wendy Chapman: conceptualisation, review and editing. Anna Wong Shee: regional health perspectives, review and editing. Portia Y. Cornell: conceptualisation, review and editing. Catherine E. Huggins: conceptualisation, review and editing. Peter Steel: conceptualisation, review and editing. M. Field: conceptualisation, review and editing. Rahul Khanna: conceptualisation, review and editing. Lawrence Gray: conceptualisation, review and editing. Kit Huckvale: conceptualisation, supervision, writing – review and editing.

Funding

This work was supported by the Medical Research Future Fund (MRFF), RARUR000072.

Disclosure

This research was funded by the Medical Research Future Fund (MRFF) under the DELIVER project. No part of this research has been previously published.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.