How artificial intelligence could transform taking a medical history

Medical students the world over have been told some variant of the adage “80% of clinical diagnosis is in the history”. This wisdom has been reiterated since at least 1975, when Hampton and colleagues published that “a diagnosis that agreed with the one finally accepted was made [by physicians] after reading the referral letter and taking the history in 66 out of 80 new patients” (here). Tiny sample size aside, a good history remains the cornerstone of patient assessment and diagnosis. However, although much of medicine has advanced unrecognisably since 1975, the process of history taking has remained a stoic bastion of traditional (perhaps even paternalistic) care – formulaic, clinician-directed and, for the most part, face-to-face.

Since 1975, the realities of health care have changed. In 2022–2023, 35% of patients attending emergency departments in Australia’s public hospitals were waiting too long for their treatment to start, when measured against the standard for their triage category, entailing hours spent in a waiting room. This is mirrored in general practice, where in 2021–2022, 39% of people who saw a general practitioner for urgent medical care waited for 24 hours or more.

What if we were to repurpose those hours and days into productive time, where the patient provides their history and thus begins to make steps towards their diagnosis before they ever meet the clinician? From the moment that face-to-face interaction arrives, the clinician then has a wealth of information at their disposal.

The role of artificial intelligence (AI) in health care is already beginning to crystalise, as we see it applied to medical image analysis, radiology reporting, personalisation of treatment options, and early disease detection. History taking should not be seen as too basic, too fundamental, or indeed too human to be included in AI’s reimagining of health care.

As doctors, we should disavow ourselves of the notion that history taking is an art form predicated on years of experience. Instead, we should embrace the potential for AI to enhance history taking and, in particular, the empowerment of patients to provide their own history asynchronously from a clinician. AI has the potential to ask questions of patients that are personalised and refined according to their previous answers, gathering the relevant information to reach the diagnosis, all before they see a clinician.

For example, while “John” sits in an emergency department waiting room, the clock marking his third hour waiting with chest pain, he could begin the process of contributing to that fabled 80% of his diagnosis. He could be asked a series of questions via his smartphone that prompt him, through an intelligent and adaptive set of questions, to describe the nature of his pain, his cardiac risk factors, previous treatments, comorbid conditions, and lifestyle factors. In the same way, as the experienced clinician knows not to immediately accept John’s answer “no” to the question “do you have any past medical history?”, such an AI-assisted history system would probe a little deeper and directly question if John is known to a cardiologist, is on any cardiac medications, or has ever had a previous electrocardiogram, echocardiogram, or angiogram. When John reports he gets short of breath, AI could help him characterise if it is dyspnoea during rest or exertion, and question if he also gets short of breath lying flat.

In the 2021 census, almost 25% of Australian households spoke a language other than English; if English was not John’s first language, the system could instead use a more appropriate language to improve the accuracy and speed of history taking.

If John were to present to a regional or remote hospital, there is potential for such a system to assist junior clinicians in asking the right questions to gather all the essential information, especially if there is a scarcity of on-site senior support.

What’s next? AI could then determine from John’s history the differential diagnosis list – prioritising the most likely diagnosis while reminding the clinician to consider others to avoid anchoring bias. Ultimately, it may then be able to integrate the history with John’s triage vital signs and basic demographic data and feed it into a validated clinical scoring system, to give the likely severity of that diagnosis.

It could then refine it all into a succinct, relevant and digestible summary for the clinical decision maker and for the electronic records.

In resource-constrained health care systems, imagine if we could use this AI-assisted history to more accurately triage John’s need for urgent clinical review. It may even be able to predict, in the first minutes of presentation, the likelihood of John needing admission and thus help to navigate bed pressures and access block.

All this could occur before John has had a single blood test, scan, or indeed has even left his waiting room chair.

Despite the opportunities, it’s important to discuss the risks and limitations. There will always be times when the nuanced communication skills of a doctor–patient interaction are required to elicit the key details of a patient’s presentation. There will also always be a need for human empathy in the patient assessment and a need for a clinician to overlay the lens of hard-learned experience to an AI-generated history. And, of course, nothing replaces a doctor’s instinct.

In addition, a patient-initiated AI-assisted history system should never substitute clinical review and should never go unchecked. The development of any AI-assisted history-taking tools will need to be met with rigorous testing and regulation and conform to data privacy standards. Nonetheless, in an under-resourced health care system where so much of a patient’s time is spent waiting, we as clinicians and diagnosticians should recognise the power of AI to transform history taking and allow the patient to take the lead – even if we have always considered it one of our most fundamental and human skills as doctors.

Dr Jack Marjot is an advanced trainee in emergency medicine at the Prince of Wales Hospital in Sydney.