An Introduction to AI for UK NHS General Practice

Balancing Harm, Benefit, and the Practical Realities of Healthcare Technology

Why Are We Worried in the First Place?

A simple approach

Your specialty is medicine or administration, not technology. Therefore, in this document, we will take a straightforward approach to discussing Artificial Intelligence (AI). The aim here is to provide you—General Practitioners (GPs), practice managers, and other staff in UK NHS general practice—with enough understanding of AI to make informed decisions about adopting or rejecting AI-based tools.

Fundamentally, what are we trying to solve for?

When adopting any new technology, especially AI, there is a fair amount of bureaucracy and process involved. This can sometimes feel cumbersome, but it arises from a critical need to avoid harm. In healthcare, any decision that could impact patient care must be scrutinized carefully—this is akin to the thorough checks we perform when we prescribe pharmaceuticals.

In a broad sense, harm in a GP setting can be grouped into three main types:

• Medical harm – Anything that could adversely affect a patient’s health outcomes.

• Emotional harm – Distress, anxiety, or discomfort caused to a patient.

• Privacy and dignity harm – Breaches of confidentiality, misuse of personal data, or any process that undermines a patient’s trust or autonomy.

Any new technology or process has the potential to introduce one or more of these harms. Equally, if a technology has no potential risks, it often means that it is not meaningfully changing anything. Like effective pharmaceuticals that inevitably carry some risk of side effects, all powerful technologies come with potential downsides. Our job is to balance these potential harms against the benefits.

An Example: Medical Dictation Software

To ground these points in something concrete, let us consider an example AI system: a medical dictation tool. Imagine a device that sits in the consultation room, records the entire patient-doctor conversation, transcribes it, and then codes it medically. The GP would later review the suggested notes/coding and approve or amend them.

Medical harm

• The transcribed notes could be inaccurate or the coding could miss a vital detail (e.g., failing to record a significant symptom or mislabeling a diagnosis).

• If the GP is used to the software working well, they may become over-reliant and fail to catch these inaccuracies, leading to incomplete or incorrect medical records.

Emotional harm

• Patients may feel uneasy knowing that a device is “listening in” on their conversation, affecting their willingness to share sensitive information.

• Some may worry the system will make mistakes or feel their consultation is less personal, reducing trust in their GP.

Privacy and dignity harm

• The audio recordings and transcripts are potentially valuable data. If a bad actor gains access (e.g., through hacking or internal misuse), patient confidentiality is breached.

• Even if no malicious activity occurs, patients have the right to know how their information is being used and may object if it goes beyond their consent.

The Potential Benefits

While it is important to discuss potential harms, new technology is often pursued because of its benefits. When weighing these benefits, you should consider them from the same perspective as the risks: how they affect patient care and well-being.

Just as it would be unethical to justify prescribing a drug purely because it saves the practice time (with no benefit to patients), you must examine whether an AI system meaningfully improves patients’ healthcare experiences.

Benefits in the Medical Dictation Example

• Improved patient interaction By offloading note-taking and data entry to an AI tool, the GP can devote more attention to the patient. This additional focus could enhance the patient’s sense of being heard and potentially improve diagnostic accuracy, as the GP is less distracted.

• Time savings Over the course of a day, automatic dictation and coding could save a significant amount of time. This might allow for more patient-facing appointments or deeper patient engagement in existing appointments.

• Consistent coding quality If the AI has been trained on a large medical dataset and thoroughly tested, it might exceed average human coding accuracy. This can lead to better data quality, which in turn could support improved patient outcomes (e.g., faster referral for potential red flags, better continuity of care).

Knowing Enough About the Underlying Technology to Make an Informed Choice

What is AI, and why does it matter here? AI can be an opaque concept, so let us start with a simple definition by comparison:

• Traditional software: Follows a fixed set of instructions. Once debugged, it generally repeats those steps reliably with minimal errors.

• Human intelligence: Flexible and adaptable, able to respond creatively to new scenarios but prone to variability (fatigue, human error, personal bias, etc.).

AI, in essence, combines aspects of both. It is consistent in its operation (like software) but does not rely on explicit step-by-step instructions for every situation. Instead, it uses models or algorithms trained on data to generate outputs—sometimes incorrectly. Error rates are inevitable in AI systems. The key question is how significant these errors might be and how they affect patients if they happen.

Error Rates and Consequences

Whenever you encounter AI, expect some level of error—sometimes it might be 1% (1 in 100 cases) or 2% (1 in 50). Even if 98% accuracy sounds impressive, consider the implications of those errors and how critical they are. If an AI tool miscodes a serious condition, that could have far more serious repercussions than a typical minor software bug.

Applying This Understanding to the Medical Dictation Example

Using the medical dictation scenario, let us walk through the potential points at which harm and risk could be introduced:

The Device (Hardware)

• It has a microphone, potentially some internal storage, and possibly an internet connection.

• Key questions: How secure is the hardware? Could someone remotely activate the microphone? How do you know when it malfunctions or fails? Have security experts tested it for vulnerabilities?

Processing of the Audio

• The audio may be sent to a remote server (often called “the cloud,” which is just someone else’s data centre).

• Where are these servers located? Are they in the UK, the USA, or elsewhere? Jurisdiction matters for patient data.

• How often does the AI model get updated, and what testing ensures it is still accurate after updates?

Storage of Recordings and Transcripts

• Are the original audio files or transcribed text stored permanently or temporarily?

• Who can access these files—both within the company providing the tool and any partners or subcontractors?

• What evidence is provided that the storage is secure (self-certified vs. externally audited security frameworks)?

• How do they handle requests under GDPR (e.g., a patient’s right to access or delete their data)?

Use of Data to Improve the System

• AI systems often rely on real-world data to refine their performance. Does the vendor store and use patient conversations to improve the tool?

• Do they ask for patient consent to do so, or is it an opt-out model?

• Are third parties involved (e.g., a separate company providing speech-to-text services)? If so, what are their data policies?

Evaluating a Supplier

• You are not expected to be a cybersecurity or AI expert. Instead, you rely on the supplier’s honesty and transparency.

• Look at their track record with the NHS, the clarity of their documentation, and whether their responses to questions are understandable.

• Genuine experts often explain technical matters in simple, meaningful ways. Answers that are overly complex or peppered with vague buzzwords might indicate a lack of clarity or an attempt to obfuscate.

Weighing Risks and Benefits

Using Evidence Appropriately

In healthcare, we typically demand rigorous evidence before introducing pharmaceuticals or new surgical procedures. The same approach should guide us with AI tools:

• Clinical Trials vs. AI Testing Pharmaceuticals undergo clinical trials to demonstrate efficacy and safety. AI developers can provide test results or case studies, but these may not always match the rigour of a randomized controlled trial. Nonetheless, you should look for objective, peer-reviewed evidence—or, at minimum, thorough internal studies accompanied by real-world data from relevant (ideally UK-based) pilots.

• Quantifying Errors A supplier claiming “98% coding accuracy” might sound good, but that also means a 2% error rate—equivalent to 1 in 50 consultations containing a potential error. Is that acceptable if the alternative (human note-taking) has, say, a 5% error rate? Or if the potential harm from those 2% of errors is significant, do the benefits outweigh that risk?

• Holistic Evaluation It is tempting to focus on the “headline” improvement—time saved, cost reduction, or even measured coding accuracy. But good decision-making balances those benefits with the potential downsides for patients and staff. Involve multiple stakeholders (GPs, practice managers, possibly patient representatives) to assess whether the benefits justify the risk in your specific practice environment.

Summary of Key Takeaways

• AI must be approached with caution and clear thinking: Like any powerful tool, it can do great good but also cause harm if poorly managed.

• Think of AI like pharmaceuticals: Every effective intervention has risks (side effects). Your job is to balance those risks against the potential benefits.

• Assess potential harm thoroughly: Look at medical, emotional, and privacy/dignity harms. Consider realistic scenarios where these might occur.

• Benefits must also be tangible for patients: Extra convenience for practice staff or cost savings alone does not justify exposing patients to risk. Ethical decision-making demands that improvements in patient care and outcomes factor heavily in your evaluation.

• Understand the basic technology flow: Where does data go? How is it processed? Who sees it? Is it stored securely? Has the supplier tested its security? How frequently is the system updated and re-validated?

• Demand clarity from vendors: You do not need to become an AI engineer, but you should require sensible explanations and evidence. Watch for signs a vendor either does not understand their own product or is deliberately withholding details.

• Look for evidence that addresses both risks and benefits: A high accuracy rate alone is insufficient. You need to see that the system has been tested in contexts similar to your own, with real-world data, and that it measurably improves outcomes for patients.

Concluding Remarks

AI is already transforming many aspects of healthcare, from diagnostic imaging to patient triage. In UK NHS general practice, the potential advantages—reducing administrative burdens, improving coding accuracy, and freeing GPs to focus on patients—are significant. However, these gains cannot be considered in isolation from the possible risks: medical errors, patient discomfort, and serious privacy concerns.

As a GP or practice manager, your role is not to be an AI expert, but to be an informed decision-maker. Much like prescribing a drug, you will seek assurances from reliable sources, consider robust evidence, review potential side effects, and weigh them against the anticipated improvements in patient care. By doing so, you ensure that any AI tool you introduce is safe, ethical, and genuinely beneficial to your patients and your practice.