Navigating the AI Frontier: Building Trust and Enhancing the Patient Experience

This blog is contributed by guest author, Dr. Malcolm Thatcher.

In my previous post, The Dawn of AI in Healthcare: Promise and Peril, I explored the burgeoning role of Artificial Intelligence in medicine and the critical need for robust regulatory frameworks and organisational AI governance. Building upon that foundation, this blog delves deeper into the profound impact of AI on patient experience, specifically focusing on the important trust relationship between patients and clinicians.

The promise of AI in patient experience

AI promises a revolution in patient care, moving towards a truly personalised and seamless healthcare experience. Imagine a world where your needs are intuitively understood across all touchpoints, from initial consultation to follow-up care, and where clinical interventions achieve high levels of efficacy. Imagine AI-powered drug discovery that identifies the most effective medications for a specific patient profile. Or consider AI-powered wearable devices and remote monitoring systems that track your physiology including vital signs and alert you and healthcare providers to potential problems before they escalate. AI can also be used to create bots and mobile apps, that can answer common patient questions, schedule appointments and provide post-operative care instructions. This will provide patients with 24/7 access to information and help free up clinician time. This vision of an omnichannel, AI-powered healthcare ecosystem holds immense potential for enhancing the patient experience.

The importance of clinician-patient trust

The clinician-patient relationship is built on a bedrock of trust, a bond considered sacrosanct in healthcare. As patients, we entrust our very lives to the expertise and care of doctors, nurses, midwives and allied health professionals. This trust is not merely a preference; it’s a fundamental requirement for effective healthcare delivery. AI introduces a third-party into that relationship, whose influence has the potential to undermine that trust.

AI’s impact on trust: ethical considerations

Introducing AI into clinical settings inevitably raises complex ethical questions. Issues such as algorithmic bias stemming from poorly trained models and the critical need for clear accountability become paramount. Patients rightfully demand transparency, seeking to understand the rationale behind AI-driven diagnoses and treatment plans, mirroring their expectations of clinician explanations.

Data privacy, security and informed consent

Beyond transparency and accountability, AI necessitates a renewed focus on data privacy and security. Healthcare providers have long navigated the complexities of informed consent regarding procedures, financial matters and research participation. The integration of AI potentially impacts data privacy and security via the use of patient data within AI algorithms, necessitating robust privacy safeguards, including patient consent considerations.

Preserving trust in an AI-driven healthcare landscape

The erosion of patient trust can have severe consequences. If data security is compromised, if AI’s decision-making processes remain opaque, or if clinician oversight is diminished, patient confidence will falter. Ultimately, patient experience hinges on trust in their clinicians and the perceived quality of care. AI can be a powerful tool to enhance these elements, but only if implemented thoughtfully.

From black box to transparent system

Treating AI as a “digital black box” is a surefire way to undermine trust. Irrespective of industry, organisations must prioritise transparency. Building upon the internal governance structures discussed in my previous blog, investing in sophisticated observability tools is crucial. These tools provide comprehensive visibility into AI systems, encompassing data usage, security protocols and real-time performance within clinical and patient settings. By illuminating the inner workings of AI, we can identify anomalies and foster a deeper understanding, thereby strengthening trust among clinicians and patients alike.

Meeting AI’s data demands

With AI, comes huge demand for data that is secure, reliable, accurate and trusted. As discussed in my first blog, ensuring observability across all system components is vital. When it comes to AI, enterprise-wide observability of data flows provides organisations with important tools to prevent bottle-necks in algorithm-driven data flows and proactively address any unexpected data issues. I have also commented that organisations now have little justification for not investing in observability solutions that provide scalable and affordable tools for optimising network and application performance, managing digital experiences, proactively detecting and resolving issues, and providing comprehensive, end-to-end monitoring and management of enterprise systems. When using AI, organisations should consider these enterprise observability tools as an insurance policy against a poor AI experience or worse, a loss of trust.

Conclusion

AI holds immense promise for transforming healthcare, driving efficiency and improving patient outcomes. However, it also presents a unique challenge to the established trust relationship between clinicians and patients. While robust AI governance is essential, cultivating transparency and explainability through advanced observability tools is equally critical. By prioritising these elements, we can harness the power of AI to enhance patient experience while preserving the trust relationship that underpins effective healthcare.

Dr. Thatcher is CEO and Founder of Strategance Group, a firm specialising in digital strategy, risk and governance services to assist organisations with their digital investments. Dr.Thatcher is a published author and has held senior executive roles in large public sector and private sector organisations. Notable roles included Chief Technology Officer of the Australian Digital Health Agency, Chief Health Information Officer for Queensland Health; CEO of eHealth Queensland; and Chief Information Officer and Executive Director Facilities for the Mater Health Group. Dr. Thatcher was also formerly a Professor of Digital Practice in the QUT Graduate School of Business.