Published: 2026-02-05 19:15
AI in Prior Authorisation: Emerging Concerns for Patient Access
Artificial intelligence (AI) is rapidly integrating into various facets of healthcare, promising transformative efficiencies from diagnostics to drug discovery. While much attention focuses on clinical applications, AI’s role in administrative processes, such as prior authorisation, is also expanding.
Prior authorisation (PA) – the process by which healthcare providers seek approval from funders for specific treatments or procedures – is a critical gatekeeper to patient care.
However, emerging concerns, particularly from systems like the US Medicare Advantage, suggest that while AI can streamline these administrative tasks, it may inadvertently create new barriers to patient access. For UK healthcare professionals, understanding these potential pitfalls is crucial as the NHS explores and implements AI solutions across its complex landscape.
This article delves into the promise of AI in prior authorisation, the specific concerns arising, and the implications for patient access and clinical practice within the UK.
Understanding Prior Authorisation in the UK Context
In the UK, the concept of prior authorisation manifests primarily through mechanisms designed to manage resource allocation and ensure evidence-based practice within the NHS. Unlike the US insurance model, where PA is often a direct negotiation between insurer and provider, the NHS system involves commissioning bodies (such as Integrated Care Boards, ICBs) and specialist services determining access to certain treatments.
Key forms of prior authorisation in the UK include:
- Individual Funding Requests (IFRs): These are applications made by clinicians for treatments that fall outside routine commissioning policies. They are typically for rare conditions, complex cases, or innovative therapies not yet widely adopted.
- Commissioning Policies: ICBs and NHS England establish specific criteria for accessing certain procedures, high-cost drugs, or specialist services. Patients must meet these criteria to receive treatment.
- Referral Management: While not strictly PA, some referral pathways require specific information or adherence to guidelines before a specialist appointment is granted, acting as a form of gatekeeping.
The rationale behind these processes is multifaceted: to ensure equitable access, manage finite resources, promote clinical effectiveness, and prevent unnecessary or inappropriate interventions. However, the current manual systems are often criticised for their administrative burden, the delays they can introduce into patient pathways, and the frustration they cause for both clinicians and patients.
The Promise of AI in Administrative Healthcare
The potential benefits of deploying AI in administrative processes like prior authorisation are considerable. Proponents argue that AI can address many of the inefficiencies inherent in current manual systems, leading to faster, more consistent, and potentially more equitable decision-making.
Key advantages often cited include:
- Enhanced Efficiency: AI algorithms can rapidly process large volumes of documentation, identify relevant clinical information, and cross-reference it against commissioning policies or IFR criteria far quicker than human reviewers. This can significantly reduce processing times and administrative backlogs.
- Improved Consistency: By applying predefined rules and criteria uniformly, AI can theoretically reduce variability in decision-making, ensuring that similar cases are treated similarly, irrespective of the individual reviewer.
- Reduced Administrative Burden: Automating parts of the PA process can free up valuable clinical and administrative staff, allowing them to focus on direct patient care or more complex cases requiring human judgment.
- Cost Optimisation: By ensuring adherence to commissioning policies and evidence-based guidelines, AI could help optimise resource allocation, potentially leading to cost savings for the NHS by reducing approvals for non-compliant or less effective treatments.
- Data-Driven Insights: AI systems can analyse patterns in PA requests and outcomes, providing valuable insights into service demand, areas of unmet need, or potential gaps in commissioning policies.
The types of AI typically employed in these scenarios include natural language processing (NLP) to extract information from clinical notes and referral letters, and machine learning algorithms trained on historical approval data to predict outcomes or flag cases for human review. While these capabilities offer compelling reasons for adoption, the practical implementation, particularly when it directly impacts patient access, introduces a complex set of challenges.
Emerging Concerns: Lessons from Abroad and UK Implications
While the UK’s healthcare system differs significantly from the US insurance model, the fundamental concerns regarding AI’s role in prior authorisation are highly transferable. Reports from the US, particularly concerning Medicare Advantage plans, highlight how AI, when primarily focused on cost containment, can lead to increased denials and delays, ultimately hindering patient access to necessary care.
These experiences offer crucial foresight for the NHS.
Reduced Patient Access and Clinical Nuance
Perhaps the most significant concern is the potential for AI algorithms to inadvertently or deliberately reduce patient access to care. Algorithms are designed to follow rules, but healthcare is often nuanced and complex.
A patient’s unique circumstances, comorbidities, social determinants of health, or atypical presentation might not fit neatly into predefined algorithmic parameters.
An AI system, particularly one optimised for efficiency or cost-saving, might flag such cases for denial or require extensive additional documentation, leading to delays. These delays can have serious consequences, especially for time-sensitive conditions or patients requiring urgent interventions.
The “black box” nature of some AI models, where the exact reasoning behind a decision is opaque, further complicates matters, making it difficult for clinicians to understand why a request was denied and how to effectively appeal.
Impact on Clinical Autonomy and Workload
Far from reducing clinician workload, AI-driven denials can paradoxically increase it. Clinicians may find themselves spending more time challenging AI decisions, gathering additional evidence, and navigating complex appeal processes.
This diverts their time away from direct patient care and can lead to significant frustration. If AI systems are perceived as rigid or clinically insensitive, it can erode trust in the technology and create an adversarial relationship between clinicians and the administrative systems designed to support them.
There is also a risk of “deskilling” or an over-reliance on AI. If clinicians become accustomed to AI making initial decisions, their own critical appraisal skills for administrative processes might diminish, making it harder to identify and challenge erroneous AI outputs.
Bias and Equity in Decision-Making
AI models are only as good as the data they are trained on. If historical data reflects existing biases in healthcare provision – for example, disparities in access or treatment based on socioeconomic status, ethnicity, or geographical location – the AI system can learn and perpetuate these biases.
This is a critical ethical concern for the NHS, which is founded on principles of equity and universal access.
An AI system trained on data where certain patient groups historically receive fewer approvals for specific treatments might continue this pattern, exacerbating health inequalities. For instance, if a particular treatment has historically been under-commissioned in a deprived area, an AI might learn to disproportionately deny requests from that area, regardless of individual clinical need.
Ensuring fairness and equity requires careful design, diverse training data, and continuous auditing of AI performance across different demographic groups.
Transparency and Accountability
When an AI system makes a decision that negatively impacts a patient, questions of transparency and accountability become paramount. Who is responsible for an erroneous AI denial – the developer, the commissioning body, the clinician who submitted the request, or the AI itself?
The lack of transparency in how some AI algorithms arrive at their conclusions (the “black box” problem) makes it challenging to pinpoint responsibility and learn from mistakes.
For the NHS, clear governance frameworks are essential. This includes defining roles and responsibilities, establishing robust audit trails for AI decisions, and ensuring that there are clear, accessible, and timely appeal mechanisms for both clinicians and patients. Without these, trust in AI systems will be undermined.
Data Security and Privacy
The deployment of AI in prior authorisation inevitably involves the processing of vast amounts of sensitive patient data. Ensuring the security and privacy of this information is non-negotiable within the NHS.
Compliance with the General Data Protection Regulation (GDPR), the Data Protection Act 2018, and NHS-specific data security standards is paramount.
Any AI system must be designed with privacy by design principles, ensuring data minimisation, robust encryption, and secure access controls. The potential for data breaches or misuse of sensitive health information, even if accidental, poses a significant risk to patient trust and the integrity of the healthcare system.
Regulatory Landscape and Governance in the UK
The UK is actively developing its regulatory framework for AI in healthcare, recognising both its potential and its risks. Several bodies play a role in overseeing the safe and ethical adoption of AI:
- Medicines and Healthcare products Regulatory Agency (MHRA): The MHRA regulates AI as a medical device if it is intended for a medical purpose (e.g., diagnosis, treatment planning). While AI in prior authorisation might not always fall under this strict definition, its impact on patient care means it warrants similar scrutiny.
- Care Quality Commission (CQC): The CQC is responsible for ensuring health and social care services are safe, effective, caring, responsive, and well-led. They will increasingly consider how AI is used within services and its impact on patient safety and quality of care.
- National Institute for Health and Care Excellence (NICE): NICE develops guidance and quality standards for health and social care. While they primarily focus on clinical effectiveness, their frameworks for evaluating new technologies will need to adapt to assess the broader impact of AI, including its administrative applications.
- Information Commissioner’s Office (ICO): The ICO enforces data protection laws, including GDPR, which is critical for any AI system handling patient data.
Despite these existing bodies, there is a clear need for specific guidance and robust governance frameworks tailored to AI in administrative decision-making, particularly where it directly influences patient access to care. This includes establishing clear lines of accountability, mandating human oversight, and ensuring that AI systems are developed and deployed in a way that aligns with NHS values and patient-centred care.
Mitigating Risks and Ensuring Responsible AI Adoption
To harness the benefits of AI in prior authorisation while safeguarding patient access and clinical quality, a proactive and multi-faceted approach is required. This involves careful planning, robust implementation, and continuous monitoring.
Robust Validation and Testing
Before any AI system is deployed in a live NHS environment, it must undergo rigorous validation and testing. This extends beyond technical performance to include real-world scenarios, diverse patient populations, and different clinical contexts.
Pilot programmes with clear evaluation metrics, including patient outcomes and clinician satisfaction, are essential. Testing should specifically look for unintended biases and ensure equitable performance across all demographic groups.
Transparency and Explainability
The “black box” problem must be addressed. Developers should strive to create “explainable AI” (XAI) systems that can articulate the reasoning behind their decisions.
This is crucial for clinicians to understand why a request was approved or denied, enabling them to effectively challenge decisions and learn from the system. Transparency also builds trust among users and patients.
Continuous Monitoring and Auditing
AI systems are not static; they require continuous monitoring and auditing post-deployment. This includes tracking approval and denial rates, identifying any disproportionate impacts on specific patient groups, and assessing the time taken for appeals.
Regular audits should verify that the AI is performing as intended and that its decisions remain aligned with clinical guidelines and ethical principles. Performance metrics should extend beyond efficiency to include patient access and outcomes.
Human Oversight and Appeal Mechanisms
A “human in the loop
Source: Nature
