AI in Healthcare: Strategies for Medtech Success
Mike White, principal of Alexander Group, discusses the role of AI in healthcare and how MedTech companies must adapt GTM strategies to harness AI’s potential despite challenges.
Rapid artificial intelligence (AI) technology innovation across industries, including healthcare, is causing companies to adapt their go-to-market (GTM) strategies. These technological advancements in MedTech require a different GTM approach to better support customers and patients. AI integrations in healthcare have already led to notable product innovations in radiology. AI-driven product innovation is also beginning to emerge in other specialties, such as cardiovascular and neurology. According to a recent Alexander Group study, 70% of providers of AI products used in procedures today. But despite its immense potential, AI’s progression in healthcare could be hindered by complex regulatory hurdles and the need for robust – often costly – infrastructure to handle workloads.
This article will explore the dynamic terrain of healthcare AI, address common implementation challenges, and offer actionable recommendations for healthcare companies to leverage AI in their organizations.
See More: Obstacles To Widespread Adoption of AI in the Healthcare Industry
AI-driven Innovations in Healthcare
Integrating AI into healthcare applications has led to a new era of innovation, as evidenced by medical devices. Among the wide array of areas experiencing positive transformations, diagnosis stands out as one of the most significantly enhanced, thanks to AI.
AI is powering next-generation MRI machines and reducing scan times by more than 50%. Groundbreaking endoscopy modules are leveraging AGI to improve adenoma detection rates by as much as 14%. Robotic surgery, which has taken off in recent years, is using AI to transform the way surgeons operate. Alexander Group’s recent provider study showed providers predict the use of robotics will double in 5 years. These advancements are improving the way healthcare facilities operate and providing better care for patients.
There is clearly enthusiasm for AI applications in healthcare. Recent data from the FDA revealed a surge in AI and machine learning-backed devices, with approximately 155 devices approved in a recent 12-month period. Notably, the majority (87%) belonged to radiology. Other specialties include cardiovascular, neurology, and hematology.
This dominance underscores AI’s pivotal role in enhancing diagnostic capabilities across medical specialties. However, AI’s impact extends beyond diagnosis into the equally important categories of hospital operations and patient interactions. Overall, the true essence of AI’s benefits lies in improving products and services, promising a future where healthcare delivery is more precise, personalized, and efficient.
Top Challenges in AI Implementation in Healthcare
With these industry advancements, challenges come too, including regulatory hurdles and hesitancy to adopt new technology among healthcare providers. Obtaining 510(k) clearances from the FDA is essential to ensuring that any new product coming to market will meet all clinical and safety expectations, but the process can be rigorous. While these measures improve patient well-being, there is growing anticipation that government intervention through legislation may help streamline the process of approval on AI medical devices, expediting the integration of AI into clinical practice. With a potentially accelerated timeline, more research must be done to demonstrate the benefits of AI on patient outcomes. This further research will shape regulatory frameworks and dictate how much more AI investment will come from stakeholders.
Another potential roadblock to AI adoption in healthcare will be reluctance among healthcare providers. Physicians, most of whom lack formal training in AI due to its recency, will need access to ongoing education to familiarize themselves with AI technology. For example, these doctors may need to undergo post-med school educational programs that demonstrate how to adapt their practices to AI-based technologies such as surgical robotics.
Challenges with AI adoption will extend beyond physician training and dive into broader hospital infrastructure. This will include both managing patient data privacy and acquiring IT expertise for tech implementation. As healthcare institutions increasingly leverage AI, safeguarding patient information through meticulous management becomes a top priority. Moreover, successful AI integration requires specialized IT expertise. Collaboration between healthcare providers and IT professionals is essential to navigating the intricate implementation landscape within existing hospital frameworks.
How Medtech Companies Can Reevaluate GTM Strategies
Given the new developments and challenges that AI has introduced in the healthcare industry, medtech companies now have an opportunity to reassess their GTM strategies. One of the most important things for healthcare providers to consider is maintaining a collaborative approach when determining ways to partner with medtech companies. Embracing collaboration when building these relationships will help accelerate the pace at which transformative technologies reach the market.
Obtaining these successful collaborations will also involve a fundamental reevaluation of GTM models that better align with the evolving needs of providers and patients. Not only will AI reshape the way patients are cared for, but it will also impact how medtech companies structure customer-facing teams. Recent data from the research show that clinical scientific evidence is the number one reason providers adopt new technology, ranked higher than price, brand, and other factors. Medtech companies must ensure their sales and marketing teams are equipped with clinical data and have the expertise to educate providers. Insights from The Alexander Group’s customer experience research show that 90% of providers rank on-site case coverage and clinical education as the top factors when selecting medtech vendors for high clinical complexity products. Companies must ensure their team has the right level of clinical expertise and deploy the appropriate mix of in-person support teams. Furthermore, because medical products increasingly incorporate software technology, customer success roles will integrate with supporting functions tailored to software deployment to integrate AI-driven solutions into the healthcare system seamlessly.
Medtech companies are being compelled to reevaluate their GTM strategies to effectively navigate the evolving landscape caused by the swift adoption of AI in healthcare. AI promises advancements in many areas. Rapid progression can also introduce new challenges for the industry, including regulatory complexities and questions about provider technology adoption. By addressing these challenges and adopting actionable recommendations, MedTech companies can effectively navigate the new world of AI and unlock its full potential, enhancing patient outcomes and healthcare delivery.
