With AI-enabled healthcare operations, R&D, therapies, clinical trials, care planning, and everyday tasks steadily becoming the norm across the industry, the problem of trust in opaque models and unsubstantiated insights holds stakeholders back from fully embracing data-driven care delivery and optimized patient outcomes. Rushabh Padalia, Partner, TheMathCompany, dives into how Explainable AI is the natural answer to this problem of trust, in its ability to create transparency and instill confidence in everyday decision-making.
Rushabh: While AI spending across industries soared exponentially in 2020, healthcare was naturally expected to be at the forefront of this boom. The numbers, however, tell an entirely new story: the AI in Healthcare market is singlehandedly expected to grow at a CAGR of 46.2%, as opposed to a mere 20.1% for all other industries combined.
With AI now instrumental to drug discovery, precision medicine, disease prediction, and many dimensions of healthcare operations, its relevance across global contexts is only set to grow. However, this expansive growth has also turned the spotlight on the idea of transparency and trust in closed-box AI models. As decision-making in the healthcare space often has direct and critical implications for patients’ health outcomes, many stakeholders are, understandably, reluctant to rely on mathematical models for decisioning.
It is gaps like these – between numbers and patient outcomes – that Explainable AI can effectively bridge, pushing new frontiers for healthcare automation. For instance, when AI is used for the identification of high-risk patients for specific diseases, it is important to understand the factors leading to specific individuals being highlighted as high risk vs low risk, to design timely interventions. Typically, in such scenarios, XAI will help HCPs understand the number of visits by a patient in last 6-12 months, the combination of 1 or more acute and/or chronic diseases they may be diagnosed with, the impact of certain treatments on similar risky populations based on past data, and so on, to facilitate comprehensive patient overviews.
The use cases for XAI in this space are truly immense: whether it is for an insurance firm to review a potential fraudulent claim to reduce operational costs; a drug manufacturer to fast-track end-to-end drug discovery phases to control a pandemic; or care management teams to identify the right patient populations for timely intervention, the Explainable component to AI will provide the required confidence for stakeholders to leverage AI across a range of applications and healthcare sub-segments.
Rushabh: With 1. the Centers for Medicare and Medicaid Services’ (CMS) Interoperability mandate of always placing the patient first while accessing data within and across entities (from Payers, Providers, and Pharmacies, for instance), and 2. Fast Healthcare Interoperability Resources (FHIR) defining data exchange protocols and content models, data availability across the healthcare sector has improved continuously and exponentially.
While there is no additional data collection or capturing required specifically for Explainable AI (XAI) – given that it is always better to have varied dimensions of data captured for any kind of AI model – the importance of data/feature engineering increases in the context of XAI. XAI requires the ability to slice and dice the data so that the model output can be presented in a variety of ways. Business-facing design thinking, the ability to trace data back to data patterns, and even having Explainable AI built into blueprints, such as in the case of Co.dx, our proprietary AI-powered platform, has proven to help stakeholders gain confidence with models and their results, enabling faster and more effective decision-making.
Similarly, in the context of healthcare, the aim of building an AI application should always be to reduce the cost of care, improve the quality of this care, enhance patients’ health outcomes, and ensure that the models gain the trust of the stakeholders using them. The following two use cases illustrate situations where XAI would be critical in enabling the successful consumption of an AI model:
1. Automating prior authorization – where an AI model would recommend the approval or denial of a patient’s treatment and payment coverage, based on historical data, to healthcare personnel such as nurses and medical directors – would require decisions to be assisted with XAI components to be truly trustworthy. Such advanced features can draw up a comparison of historical patient requests with similar dimensions; show actual approved vs denied ratios; and visually represent stakeholders’ predicted decisions – enabling any consumer of the model to accept the predicted decision with greater ease, expedite decision-making through clear insights, or even refute the decision predicted using data-backed reasoning.
2. As the emerging field of precision medicine brings together the complex and fascinating dimensions of patient demographics, clinical medicine, and genomics, HCPs will require a holistic view across all 3 dimensions to effectively deliver care. Understanding the impact of each of these three dimensions of precision medicine, for instance, getting a view of lifestyle changes’ interactions with genetic expression, is where XAI can augment HCPs effectively.
Rushabh: A significant problem that most healthcare practitioners today face is that although no two patients can be identical, from a clinical standpoint, they are frequently treated with the same diagnosis for the same disease. And this is precisely the kind of problem that AI can help resolve, with its ability to contextualize decision-making, at speed and scale.
By analyzing large quantities of patient data including demographic information, genomic profiles, environmental factors, prescription drugs, laboratory tests, and hospitalization history, AI models can prescribe customized medication and design-focused therapies unique to each patient’s needs.
However, achieving this level of personalization requires an AI system to be able to explain why it has prescribed a particular drug, or what factors were involved in recommending, for instance, a minimally invasive surgery over medication. As drug and treatment efficacy can differ based on patients’ genetic makeup and biomarkers, explainability will be vital to limiting risks for patients and translating complex ML insights into successful healthcare outcomes.
Explainability ensures that practitioners are more confident in their decision-making and can reduce the time they spend on analyzing scans, for instance. The right explainable system will enable doctors to reliably assess recommendations against a patient’s condition, identify anomalies, and make informed decisions in critical clinical situations.
Rushabh: Besides elevating patient experience and simplifying healthcare access, AI will be key to enhancing healthcare workforces’ efficiency and quality of care delivered. On an operational level, explainability in AI systems can enable more transparency across the healthcare value chain as organizations, providers, and systems benefit from universalized access to patient data and greater visibility into a system’s decision-making rationale, leading to improved workforce interoperability, diagnostic speed, accuracy, and patient outcomes.
With EHRs enabling more connected patient data for life science organizations, healthcare firms, and health insurers, healthcare forces can now better equip themselves to offer proactive care. For instance, context-aware biomedical devices can retrieve context data from sensors and digital patient profiles to recognize the context in which hospital workers perform their tasks. For a nurse, say who has come in for her shift and has to check on a patient admitted in her absence, these contextual elements would include her location, timing of care delivery, reliance on other staff members, and device location and state. Here, a context-sensitive device embedded in hospital beds could enable the bed to be aware of the patient, nurse, and diagnosis to display relevant patient information, prescription history, and next best actions for care. This improved awareness reduces dependency on manual patient records and allows for speedier and targeted care, with personnel largely focusing with patient-critical tasks.
Add to this the enhanced ‘clarity of grounds’ unlocked by XAI, and this will not only translate into quality patient engagements across different touchpoints but also lower the hours healthcare workforces spend on routine, administrative, and documentation-intensive jobs, reducing the risk of burnout.
Rushabh: The key to scaling AI in healthcare is strengthening patient trust in these systems. With healthcare delivery essentially involving a human element, the increased reliance on digital systems often becomes a very fundamental, yet decisive, sticking point in a patient’s trust in AI, combined with differing digital literacy levels, misconceptions, and more. Concerns that AI might make decisions in a biased manner naturally creates poor patient trust, leading to knock-on effects for AI adoption in healthcare systems.
Internally, healthcare organizations can introduce explanability into their systems by unlocking collaborative whitespaces between healthcare teams, practitioners, and solution developers – with stakeholders learning about the data going into a system, potential causes of bias in decision-making, and transparent mechanisms to calibrate trust. This informed approach can then be conveyed by practitioners to patients on how AI-based solutions function and can help make personalized care recommendations unique to their patient profile.
On a leadership level, clearly defining an AI system’s purpose, scope, and operation from an ethical and logical standpoint would allow for well-directed efforts, setting benchmarks for greater responsibility and liability, and providing an unambiguous outlook to incentivize further research into AI innovation and adoption. Constant communication with the general public on new research findings and ensuring transparency for technological solutions used will be vital to reducing patients’ resistance to AI systems.
Lastly, on a larger level, complying with patient healthcare information (PHI) guidelines on patient data, its usage, interoperability, and confidentiality, stipulated by regulations such as HIPAA would also allow patients, practitioners, and healthcare systems to have the same level of trust in new AI solutions that they do in other drugs, devices, and systems that have received similar approvals. Organizations that regularly evaluate their operations against HIPAA regulations and provide certified services while engaging in consistent dialogue to apprise the public can bolster patients’ trust in the promise of AI.