Artificial intelligence (AI) has the potential to revolutionise healthcare, as we keep being told. Presently, much is written about the potential much less obvious is how AI currently translates to contemporary patient care. We explore some (not too distant) applications of AI for improving patient touchpoints.
How might AI be used to improve touchpoints between patients and providers in the future?
Personalised care: AI in time will help providers deliver more personalised care by analysing large amounts of patient data and identifying patterns and trends. This will help providers tailor treatment plans to each patient's unique needs, leading to better outcomes and increased patient satisfaction.
Virtual assistants: AI-powered virtual assistants will be able to help patients schedule appointments, answer questions, and provide basic health information. This can reduce wait times and improve accessibility, especially for patients who live in remote or undeserved areas. Managing bias within the training models will be a key challenge here.
Remote monitoring: AI will help providers remotely monitor patients, allowing for more frequent check-ins and adjustments to treatment plans as needed. This will improve patient outcomes and reduce the need for in-person visits, which can be especially beneficial for patients with chronic conditions. The ability to reconcile multiple inputs in real-time, and accordingly react will allow more patients to be managed out of hospital settings in the future.
Predictive analytics: AI will help providers predict potential health issues before they arise, allowing for early intervention and prevention. This will improve health outcomes and reduce healthcare costs by avoiding more costly treatments down the line. Unifying disparate patient data silos will be the main challenge in the journey to achieving this.
Improved efficiency: AI will help providers streamline administrative tasks and reduce paperwork, allowing for more time to focus on patient care. This will improve the patient experience by reducing wait times and increasing the amount of time providers can spend with each patient and reduce costs. Healthcare specific platforms will drive this efficiency.
AI has the potential to significantly improve touchpoints in healthcare by delivering more personalised care, improving accessibility, and increasing efficiency.
Caution is warranted
Successful Artificial Intelligence models need to be trained on data. At present there is relative opacity about exactly where the data comes from to train AI platforms. Part of this is to do with protecting intellectual property on behalf of AI companies. However, a growing concern is to do with copyright. Who owns the eventual outputted 'knowledge', curated from multiple sources of potentially copyrighted training data?
Why is this important? Well, before AI becomes used in day-to-day clinical practice, there needs to be greater clarity and transparency on exactly where the data has come from to train the models. As important, is what data has not been used. This is critical to ensure understanding of any bias the models may have in delivering their output.
Artificial intelligence will play a greater role in clinician-patient interactions of the future. The foundation of these interactions is currently based on trust and the principles of the Hippocratic oath and the derived field of medical ethics. Similarly, trust in AI will need to be proven and earned, before allowing it to simply edge into clinician-patient touchpoints of the future unchecked.