Enhancing the Fulfillment of Clinical Practice with Evidence-Based Generative AI

Dr Takeshi Iimura, Chief Medical Officer, Elsevier Japan

One of the main challenges global clinicians face today is the increasing difficulty in processing the vast amounts of scientific research now available. To stay abreast of the latest evidence-based information and practice even within their own specialty, clinicians are required to navigate multiple sources, including journals, articles, and books.

Today, cognitive demands and time pressures have become increasingly common challenges for clinicians across the globe. The task of managing vast amounts of medical information while providing patient care has necessitated the need to find more efficient ways of accessing and applying knowledge.

Many doctors now rely on search engines as part of their daily routine to find answers in the context of specific patients. However, these tools present significant challenges. Sifting through numerous search results can be time-consuming and mentally exhausting.

Conversational AI platforms like ChatGPT have emerged as a potential solution, offering quick summaries of information and streamlining the search process. However, these generative AI tools come with their own set of risks. Without proper vetting, they can easily propagate misinformation and provide unreliable answers.

Clinicians require a solution that fits seamlessly into their workflow and helps them manage the vast amount of data available by providing efficient access to trustworthy and evidence-based information. Not only would this free them from excavating clinical answers from thousands of topics, or resolving conflicting online sources, but ultimately it would enable them to deliver care more efficiently, which is tailored to each individual patient case.

The Need for Innovation in Clinical Support

There is a pressing need for new innovations to help clinicians manage current system pressures. An increase in non-patient-facing tasks, such as paperwork and data entry, is leading to clinician burnout and job dissatisfaction. Compounding this issue, clinicians' workloads are further strained by workforce shortages, leading to increased patient loads and longer hours.

KLAS research Wird in neuem Tab/Fenster geöffnet shows that US doctors rank staffing shortages and too many bureaucratic tasks as the top two contributors to burnout. Elsevier’s Clinician of the Future 2023 report Wird in neuem Tab/Fenster geöffnet further demonstrates the significance of the global workforce challenge, revealing that the shortage of nurses is the top priority clinicians believe should be addressed in the next 2-3 years, with the shortage of doctors ranking fifth.

In light of these challenges, the new generation of AI-powered solutions available to clinicians holds great promise for improving workflow efficiencies. Elsevier’s recent Insights: Clinician of the Future attitudes toward AI Wird in neuem Tab/Fenster geöffnet report reveals that 94% of clinicians believe AI will increase their efficiency, while 85% say it could free up their time for higher-value work. However, the adoption of such technologies is often offset by the need for reassurance that the clinical content is accurate and derived from trusted, evidence-based sources. This is further highlighted in the report as 73% of clinicians agree that the results generated from generative AI-dependent tools should be based only on high-quality and trusted sources.

Therefore, it is imperative that the content used in AI-powered solutions is grounded in reputable sources such as medical journals and textbooks. Moreover, the successful integration of AI in healthcare isn't just dependent on technology itself but hinges on maintaining a balance between cutting-edge advancements and the assurance of using evidence-based, authoritative content.

Desired attributes of AI: Trust, Reliability and Usability

Clinicians must have confidence that the tools they use are accurate, reliable, and based on the latest and most comprehensive medical knowledge. This trust is built by ensuring that AI algorithms are rigorously tested and validated against established clinical standards and that the data they utilise is sourced from reputable, evidence-based medical research.

Ensuring the technology provides consistent and dependable results is crucial. AI algorithms must be tested and validated against established clinical standards to ensure they demonstrate reliability and perform accurately across various scenarios and patient populations.

Even the most advanced AI tools will fail to gain traction if they are not user-friendly and seamlessly integrated into the clinician's workflow. These tools must be designed with usability in mind, offering intuitive interfaces and practical features that enhance clinical practice.

Leveraging proven clinical information is key to fostering confidence among clinicians in adopting these technologies. By committing to the use of reliable and trustworthy sources, we can ensure that AI-powered solutions support clinicians in delivering optimal patient care, addressing systemic pressures without sacrificing the quality of information or care. This forward-thinking approach not only enhances clinician workflow but also paves the way for a more efficient and effective healthcare system.

The promise of AI to support optimal clinical decision-making

Against the global backdrop of workforce shortages and the rise of non-patient-facing tasks, AI-powered platforms in practice, such as ClinicalKey AI Wird in neuem Tab/Fenster geöffnet, emerge as innovative solutions that support clinicians with reliable and trusted content. Combining conversational search tool functionality with accurate evidence-based information, ClinicalKey AI enables clinicians to access actionable and dependable answers, tailored to individual patient cases, and more efficiently at the point of care.

Efficient information retrieval is a key benefit of AI tools. By quickly accessing relevant, evidence-based information, clinicians can dedicate more time to patient care and professional development, which can help alleviate their cognitive burden. This streamlined access to information can significantly reduce the time spent searching through multiple sources, allowing for more focused and effective patient interactions.

Complex case management is another area where AI-assisted tools show promise, aiming to equip healthcare professionals with comprehensive information to aid in differential diagnosis and potentially reduce unnecessary referrals. By providing access to accurate evidence-based information, tools like ClinicalKey AI can support clinicians in making more informed decisions, particularly in challenging or unusual cases.

Continuous learning is facilitated through conversational interfaces and recommended prompts offered by AI platforms. These features can aid ongoing education, helping clinicians stay current with best practices, and allowing them to spend more time deepening their understanding of core principles.

While ClinicalKey AI provides clinicians with clear citations to evidence-based information, it is there to support and not substitute the irreplaceable role of the clinician in delivering patient-centric care. In fact, the strength of this technology is in its ability to augment clinical decision-making when combined with professional judgment.

By leveraging both AI and clinical expertise, clinicians can achieve a more comprehensive and reliable approach to patient care. ClinicalKey AI serves as a powerful information retrieval and synthesis tool, but it's the clinician who interprets this information within the context of each unique patient case, applying their critical thinking skills, clinical experience, and foundational knowledge.

The Role of Evidence-Based Generative AI

In this continually evolving healthcare landscape, generative AI stands out as a transformative catalyst for positive change in clinical practices and has a role to play in helping to enhance clinical fulfilment.

Empowering clinicians with access to accurate, evidence-based information at the point of care will enable them to make more informed decisions efficiently, alleviating some of the administrative burden that can lead to burnout. This efficiency allows clinicians to redirect their mental energy and processing power towards more impactful aspects of patient care, such as listening to and educating patients.

Clinicians can also invest more time in collaborative engagement with their broader healthcare team, fostering stronger relationships and a cohesive environment built on shared understanding. These strengthened, trusted relationships within the team lead to increased collaboration and communication, which are crucial for preventing medical errors through collective effort rather than relying solely on individual clinicians. Ultimately, this streamlined approach allows healthcare professionals to dedicate more time and energy to delivering compassionate and patient-centric care.

As we look to the future of global healthcare, where we continue to refine and integrate these tools more seamlessly into current workflows, we move ever closer to a future where technology and human expertise work in harmony to provide the best possible outcomes for every person.