The promise of AI in healthcare

AI has likely applications across every domain of healthcare

The AI Forum of New Zealand has just published a report on AI and Health in the New Zealand context. The report trumpets some of the potential cost savings and efficiencies that AI will no doubt bring to the sector over the next few years. However, there are other interesting findings in the research report worth highlighting.

Enhanced teamwork and patient safety

AI that employs a combination of voice recognition, and natural language processing could help monitor and interpret healthcare multidisciplinary team interactions and help to ensure that all information relevant to a discussion has been raised or acknowledged.

This is important because we know that there are many failures in healthcare teamwork and communication, which often have their root cause in failures of information sharing and barriers to speaking up in a hierarchical team setting. This can and does impact patient safety. Including an AI assistant in future health teams could help overcome barriers to speaking up and sharing information.

Overcoming fallible human psychology

We also know that a range of psychological biases are at work when healthcare staff make decisions. These biases include a tendency to be influenced by recent experience (rather than statistical likelihood) and the tendency to attend to information that confirms the beliefs already held. Furthermore, doctors and other clinicians do not often get feedback on their diagnoses. This can lead to a tendency to increase confidence with experience without a parallel increase in accuracy.

One key promise of medical AI is that it can fill in the gaps in clinical thinking by providing a list of potential diagnoses or management plans with a statistical likelihood that each is correct. This kind of clinical decision support system could overcome one key failure in diagnosis, which is that the correct diagnosis is often not even considered.

However, in order to embrace these tools clinicians will also need to understand their own fallibility, the psychology of decision making and the very human cognitive processes that underpin these shortcomings. Intelligent digital systems undoubtedly have their own shortcomings, and their intelligence is best suited to particular kinds of problem. Human psychology suffers from complementary blind spots and it is the combination of artificial and biological intelligence that will advance healthcare.

Ensuring safe data

Another issue discussed in the AI Forum’s report is the need to make health data available in a form that AI can consume without worrying breaches of privacy. There is a significant challenge facing developers to find ways to de-identify free text (and other) data and present it in a form that is both machine readable and also unable to be re-identified whether intentionally or accidentally.

There is a risk that identifiable data could be used, for example, to prejudice insurance premiums on the basis of factors that people do not have control over. The process of de-identifying is proving to be very difficult. For example, even with names and addresses and such identifying features removed from clinical records (itself a challenging task given the many ways that such information can be recorded) there is still the possibility that merging datasets such as mobile phone location data held by, say Google, with clinical records that record the day and time of appointments, could intentionally or inadvertently identify individuals. Issues such as this need to be solved as we move forward with AI for healthcare.

We now need cost-utility analyses

The next step is to catalogue the AI tools that we presently have available and begin assessing the potential impact of these systems. Funders and provider institutions need to conduct cost-effectiveness analyses on these new tools and prioritise those that both increase effectiveness and clinical safety while also reducing time and saving costs. These investments might well take priority over investments in expensive new pharmaceuticals that marginally improve outcomes at great additional expense.

There is likely to be a lot of low hanging fruit in routine, repetitive teamwork and diagnostic tasks that AI is suited to assist with and where the public health dollar will go a long way, benefitting many patients not just a few.

AI offers many promising applications in the health setting and all those involved in the sector would be advised to read reports such as the AI Forum of NZ’s report on AI and Health in NZ, and think creatively about how AI might help solve the grand challenges in healthcare in the 21st Century.

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