AI's role in accelerating healthcare innovation

The Kestria Healthcare & Life Sciences Practice Group has gathered esteemed professionals from various fields, bringing together a wealth of knowledge and experience to drive forward healthcare innovation.

Key takeaways:

Transformational AI: AI promises to revolutionize healthcare by improving patient care, operational efficiency, and driving innovation.

Ethical integration: Addressing ethical concerns is crucial for AI's fair and transparent integration into healthcare systems.

Leadership for adoption: Overcoming resistance and embracing AI requires strong leadership and agility within the healthcare sector.

AI real world applications and observations

For Marina Massingham from Canada, CEO at Aifred Health, excitement about AI in healthcare today is witnessing its evolution. AI boosts productivity and efficiency in purchasing, supply chain management, compliance, patient scheduling, and resource allocation, streamlining many processes. AI advances performance, especially in radiology, where its image recognition improves patient diagnosis. Broadly, AI enhances diagnosis and operational efficiencies. The impact of COVID-19 highlighted resource constraints in healthcare, making AI's role in optimizing processes crucial. Automating diagnostics and processing medical imagery maximizes limited resources.

The most thrilling aspect of AI is its ability to do things humans can't. AiFred, for example, predicts patient responses to medications, removing the trial-and-error approach from treatment. This capability is beyond human reach, making AI's potential boundless. Overall, seeing AI adopted to meet healthcare challenges today makes this an incredibly exciting time for the field.

Christos Panagiotopoulos from Greece, Managing Director at Karl Storz Greece, explains that this has been common for over a decade. CAD systems have been used in MRI and CT to differentiate malignancies and avoid missed findings. Mammography was the first to use this technology due to its difficulty for the human eye. Now, similar algorithms are used in ultrasound. AI's initial steps involved comparing images with vast data and biopsies to reduce false negatives and positives, improve sensitivity and identify early-stage, curable pathologies. Early detection saves lives and reduces costs. Today, GI endoscopy uses AI to identify pathologies, ensuring nothing is missed. Karls Storz is developing more advanced AI to suggest actions during live operations, a development expected sooner than we think.

According to Tejinder Jassal from India, General Manager at Convatec, AI's profound impact on healthcare is captivating, influencing various sectors like medical devices and pharmaceutical companies. It spans decision support, drug discovery, electronic health records, genomics, hospital management, and medical imaging. Predictive care driven by data enables cost optimization and personalized treatment. In the coming years, AI-empowered predictive care will play a major role. Networked hospitals and connected care will treat minor ailments at smaller hubs like retail clinics or same-day surgery centers, all linked by a single digital infrastructure. Centralized command centers will monitor supply and demand across the network.

Anna Marsell from Sweden, COO at Olink Proteomics, emphasizes that as better drugs are developed, the aging population also brings additional costs. AI could offer substantial benefits in healthcare economics by enabling precision medicine, delivering tailored treatment to patients when needed, thus enhancing treatment outcomes. Furthermore, AI could potentially optimize resource allocation, freeing up clinicians to focus on their specialties while delegating tasks that AI can manage efficiently. Additionally, AI could facilitate proactive healthcare by forecasting and preventing diseases, ultimately enhancing population health. However, ethical implementation is vital, ensuring fairness and individualized care alongside the optimization of overall public health. Exploring AI's impact on healthcare economics presents intriguing possibilities.

Ensuring ethical AI use in healthcare

Tejinder Jassal notes that in the earlier days of clinical decision support tools, many were standalone solutions that were not well integrated into clinical workflows. ,Today many of these systems are integrated into EHRs, improving deployment and enhancing their value at the bedside. AI enhances this by leveraging EHR and external data, processing larger amounts efficiently and aligning stakeholders to extract detailed insights. Some healthcare players in India use patient data to predict ailments and future risks, upsell preventive treatment plans and reduce costs. As more data is captured, algorithms will improve, increasing efficiency. While there are data concerns, the long-term benefits of AI outweigh the initial challenges. As AI in healthcare matures, it will overcome complexities, leading to more efficient algorithms and better clinical decision-making, ultimately improving outcomes.‘

For Anna Marsell ethics is always an important topic in healthcare discussions. ‚There is an opportunity with AI as an unbiased tool that doesn't consider personal incentives or reimbursements, unlike medical doctors in certain countries who receive specific reimbursements for providing certain treatments. AI can provide an unbiased perspective, independent of preferred companies or available tools and determine the right treatment for a patient at the right time. The unbiased nature of AI can significantly reduce existing biases in healthcare. While the importance of a doctor's opinion should never be neglected and they should always have the final say, AI can greatly improve healthcare outcomes. With the right education, AI presents a significant opportunity for humanity,‘ states Anna Marsell.

How to build public trust

‚To build trust and transparency in AI, it's essential to adhere to existing privacy laws and regulations, such as HIPAA, FDA guidelines and Health Canada standards,‘ says Marina Massingham. ‚Ethical companies, particularly those in regulated medical device sectors, should adhere to baseline regulatory compliance. Transparency in AI can be improved by clearly explaining result derivation and involving clinicians in demystifying the AI process. Crafting patient permissions to clarify data usage and the necessity for the AI model is essential. Equally vital is the ethical collection of data, ensuring only necessary data is collected to build trust and avoid data misuse.‘

As per Tejinder Jassal, the outcomes AI generates should build trust and confidence with regulators. ‚Better clinical outcomes and decisions will naturally build trust. Our goal should be to ensure that the AI models we work on prove their efficacy, transparency, and positive outcomes. If regulators and policymakers see tangible improvements in outcomes and health economics, as Anna mentioned, they will gain confidence in AI's potential to transform patient care and healthcare. Ultimately, AI's results should speak for themselves, reducing the need for us to actively promote its benefits,‘ adds Tejinder Jassal.

‚The best way to be good ambassadors for AI is to communicate its benefits to the public and ensure good practices are followed,‘ notes ChristosPanagiotopoulos. ‚It's important to show that people have more to gain than to fear. Throughout history, major technological breakthroughs, like the telephone, car and mobile phone, initially faced fear but eventually complemented humanity positively. We must ensure proper training for people to adapt and be part of this change. Additionally, establishing frameworks, limits and regulations is crucial. A strong regulatory framework will build trust and transparency over time. With AI, as with past breakthroughs, trust will grow if it is regulated and constrained properly.‘

AI opportunities to streamline Healthcare operations and reduce costs

‚In some Indian hospitals, the low patient-to-nurse ratio is addressed by using robots to handle basic tasks like administering dosages and troubleshooting minor issues,‘ says Tejinder Jassal. ‚This allows nurses to prioritize urgent matters, enhancing efficiency in a constrained environment. AI algorithms streamline the discharge process by assessing criteria for discharge, expediting insurance approvals and ensuring a seamless process. These applications boost customer satisfaction and optimize hospital resource allocation to tackle pressing challenges.‘

Marina Massingham emphasizes that AI can significantly reduce physician burden. ‚Large language models and general NLP automate information entry into EMRs, freeing clinicians for patient care. One company optimizes resource allocation for cancer patients, ensuring efficient use of limited resources. Another improves discharge processes and identifies patients needing more care, enhancing operations and customer satisfaction. These examples highlight AI's dual use: enhancing the quality of care and optimizing resource utilization.‘

As for Christos Panagiotopoulos, a practical example of how AI can streamline healthcare is its ability to handle administrative and secondary tasks efficiently, freeing doctors and nurses to focus on patient care. ‚This addresses the global shortage of medical staff. One specific example is AI-assisted colonoscopy procedures, which can be completed much more quickly. This demonstrates how AI not only handles administrative tasks and data analysis but also speeds up medical interventions, making healthcare more efficient overall,‘ says Christos Panagiotopoulos.

AI's impact on drug discovery and product development

When it comes to product development and clinical trial design, AI offers significant advantages, optimizing study outcomes and speeding up the process. ‚However, this can also lead to exclusions, raising questions about who is left out and the consequences of this exclusion. While faster drug delivery to market is desirable, we must consider the trade-offs and consequences of exclusion. Designing clinical trials always involves trade-offs and narrowing criteria may expedite product release but could also exclude certain groups. It's crucial to address these concerns and ensure that AI platforms do not disproportionately exclude any population,‘ says Anna Marsell.

Tejinder Jassal highlights that driving clinical researchers to adopt new tools for more efficient drug development is crucial due to the high risks and rewards involved. ‚The drug development process, spanning 5 to 10 years and costing billions, poses challenges without FDA approval certainty. AI and related technologies tackle these hurdles by optimizing manufacturing, quality control, and data analysis. They identify overlooked patterns, enhance efficiency, and streamline data gathering, aiding in novel target identification and speeding up drug discovery. Despite my absence from Pharma, I've observed AI's pivotal role in unlocking advances and deriving insights from extensive EHR information.‘

Marina Massingham states that the availability of information in EHRs and the vast amount of patient data presents an intriguing possibility. It raises the question of whether the capabilities of AI can be harnessed to discover new indications for drugs already on the market. This prospect holds promise for pharmaceutical companies seeking to maximize the potential of their existing medications. Expanding the indications for established drugs could offer substantial benefits, considering the challenges and costs associated with bringing new drugs to market.

Future prospects and potentials in Healthcare with AI

‚I strongly advocate for universal healthcare, coming from the UK and residing in Canada,‘ states Marina Massingham. ‚My vision is that leveraging AI to analyze vast amounts of data will significantly enhance population health management in the future. This should be reflected in healthcare policies, optimizing resource utilization while ensuring personalized care for every individual, regardless of their background or location. AI has the potential to revolutionize population health management in public healthcare systems, addressing current shortcomings and catering to diverse demographics. It's my optimistic vision for the future, acknowledging the challenges we face today, including an aging population and limited resources.‘

‚The ideal use case for AI would involve predictive analytics to anticipate patients' needs, ensuring proactive management to prevent crises and alleviate strain on healthcare resources. This would facilitate a connected care model, with centralized hubs managing less urgent cases and directing critical care to specialized centers. Such a system could address various challenges across government policy, insurance, healthcare providers and patient care. Additionally, safeguarding privacy and data integrity, possibly through blockchain technology, is crucial for AI's successful integration into healthcare, ensuring secure and efficient information exchange. This ideal scenario promises transformative outcomes, aligning with our goals for AI in healthcare,‘ adds Tejinder Jassal.

Coming from Sweden and advocating for population health and socialized medicine, Anna Marsell is a big supporter of providing quality healthcare for all. ‚However, I also value individualized care. Precision medicine presents an opportunity to enhance healthcare delivery without contradicting these principles. Currently, treatment pathways often follow a predetermined sequence, which may not always be optimal for individual patients. If AI can identify the most effective treatment upfront, bypassing unnecessary steps, it would benefit both individuals and the broader population. This approach not only saves time and resources but also improves outcomes, offering personalized care tailored to each patient's unique needs.‘

Christos Panagiotopoulos believes efforts are underway to minimize risk, homogenize outcomes and streamline procedures and guidelines. While adhering to guidelines can ensure efficiency, Anna's suggestion of skipping unnecessary steps aligns with this practical approach. In the future, surgical procedures may become more automated and guided, reducing human errors and standardizing care levels. This shift could lessen reliance on individual skills and factors, ensuring more consistent service and care for patients.

Challenges of integrating AI into healthcare infrastructures

According to Christos Panagiotopoulos, the primary challenge lies in healthcare professionals' resistance to change, alongside public skepticism. We need to adapt our education system to meet emerging needs and establish transparent regulatory frameworks to build trust among the public.

Tejinder Jassal notes that the resistance to adopting AI in healthcare stems from several factors. Firstly, there's a lack of belief due to limited data and use cases demonstrating its effectiveness. Secondly, the initial cost of implementing AI is a deterrent, although long-term benefits include cost optimization and improved patient management. Thirdly, the confidence to adopt AI is bolstered by successful implementations in other hospitals, highlighting the importance of building trust through showcasing effective use cases. As AI solutions continue to address healthcare challenges while ensuring patient privacy and security, adoption rates are likely to increase.

Essential leadership skills for AI healthcare professionals

For Anna Marsell, curiosity will be another crucial skill. ‚We can't be experts in everything, but being curious about potential solutions can be valuable. AI presents new possibilities for human development and future approaches. None of us will have all the answers, but fostering curiosity alongside agility will be key. We don't need to know everything, but we can ask questions and explore possibilities,‘ states Anna Marsell.

According to Marina Massingham, people must embrace using technology they don't fully understand. ‚While articulating this as a specific leadership skill is challenging, individuals who require full comprehension before action won't find comfort in AI. Understanding all its mechanics is unrealistic. Instead, one must focus on functionality, be comfortable with observed outcomes and utilize it despite lacking full understanding. This approach involves risk-taking and requires compromise unless one is an expert in the field,‘ notes Marina Massingham.

Summary

The future of healthcare is poised for a revolutionary shift driven by the integration of AI. Expect advancements in patient care, operational efficiency, and innovation across sectors like clinical decision-making and drug discovery. Ethical considerations will be paramount to ensure transparency, data privacy, and trust in AI systems. Effective leadership and adaptability will be essential to navigate the challenges and opportunities that AI brings to healthcare.

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