It's no secret that healthcare is undergoing a transformation as artificial intelligence and other emerging technologies solve problems related to fragmentation and frustration that pervade the industry.
As health systems manage these fundamental changes, it's important that delivery organizations ensure that clinicians and IT decision-makers have patient satisfaction at the forefront of their minds, says Alex Mason.
Mason is a Partner at FTV Capital where he leads the firm's healthtech and healthcare information technology investment practice. He led funding rounds for Luma Health and 6 Degrees Health.
We spoke with Mason to discuss how investors view AI in healthcare, how AI will help accelerate value-based care, how AI-assisted clinical decision-making is becoming the norm, and how revenue cycle management processes can streamline payments and drive digital patient engagement.
Q. Overall, how are investors viewing artificial intelligence in healthcare?
A. Investors are both optimistic and cautious about AI in healthcare. They are taking a balanced approach, recognizing both the potential for big breakthroughs and the need to think carefully about second-order effects.
Recent setbacks, including failure of high-profile AI healthcare ventures to live up to expectations, have made the investment outlook more cautious in the near term, but there are also many success stories that demonstrate the promise of AI when applied to specific, well-defined use cases and outcomes, making investments in very specific, targeted applications more attractive.
At FTV, the most important thing is Valuable AI applications are those that use specific, targeted AI applications in a use case to drive a specific outcome, be it clinical, financial, patient-related, or provider-related, while at the same time, the application of AI must be done in a way that minimizes change management by users.
For every company we track and investment we consider, the first step is to evaluate the use case for AI and how it can bring incremental improvements to current processes. Integrating AI into existing workflows without causing major disruptions is essential to mitigate risk and increase the attractiveness of AI solutions to everyone in the healthcare ecosystem, from payers to providers to patients.
Looking to the future, we are closely monitoring data privacy, data sovereignty, and regulation in general, as healthcare is understandably becoming one of the most highly regulated areas for AI given patient privacy concerns.
Innovation and regulation need to work hand in hand. Data privacy is important. But health data is fundamentally distributed data, residing in many systems and applications with many owners. It is important to keep in mind that regulation can guide the adoption of technological advancements in a very positive way.
A prime example is how government subsidies provided as a result of the HITECH Act have led to large-scale adoption of electronic health records by health care providers, from large health systems to small clinics.
Despite some current challenges, AI will inevitably transform healthcare, and investors are generally optimistic that as AI technologies evolve and prove their effectiveness in the real world, they will significantly improve healthcare efficiency and patient outcomes.
Q. How do you think AI can help accelerate value-based care?
A. AI will improve our ability to measure and improve patient outcomes. Value-based care models incentivize healthcare providers to achieve good health outcomes with negligible downstream complications, rather than being rewarded through the traditional fee-for-service model.
The shift towards outcomes-based payment systems will enable AI to automate the collection and analysis of patient outcomes data, ensuring reimbursement is closely aligned with the improvements in health outcomes achieved and allowing for a more accurate assessment of quality of care.
Additionally, by analyzing large datasets from various sources, AI can help healthcare providers identify the most effective treatments for individual patients, enabling a more personalized, relevant and precise approach to patient care, which is essential for improving outcomes and patient satisfaction.
Predictive analytics can anticipate health issues before they become serious, allowing for earlier intervention and better management of chronic conditions. This proactive approach aligns closely with the goals of value-based care, which emphasizes prevention and long-term planning.
As AI models are integrated into more clinical settings and process more data, they will be able to continually fine-tune their output by identifying both positive and negative trends, resulting in more accurate and valuable insights that further refine value-based care strategies.
For example, AI can more carefully tailor reimbursement plans for specific providers, allowing them to more accurately predict value-based outcomes. This continuous improvement allows providers to stay ahead of emerging health trends and adjust their practice accordingly.
Q. How can AI simplify the revenue cycle management process and streamline upfront payments in digital patient engagement?
A. AI can streamline revenue cycle management processes by automating repetitive and labor-intensive tasks, improving accuracy, and providing actionable insights. One of the key benefits of AI in RCM is its ability to automate existing manual functions such as claims processing, eligibility checks, and payment posting.
By reducing the burden of manual work, AI not only accelerates the revenue cycle but also minimizes errors that lead to denied or delayed claims, ultimately improving overall efficiency.
In addition to automation, AI can predict potential revenue leakage points and uncover financial inefficiencies. Predictive analytics tools can analyze historical data to identify patterns and anomalies that may indicate problems such as underpayments, denials, or delayed refunds.
By proactively addressing these issues, healthcare providers can optimize revenue streams and ensure a more stable and agile financial footing. AI-driven insights can also help improve billing practices and contract negotiations, leading to better financial outcomes and propelling healthcare systems from reactive to proactive payment.
moreover, AI increases the accuracy of coding and billing processes, which are essential for timely and accurate reimbursement. By analyzing patient records and identifying the most appropriate code, AI reduces labor costs and the potential for human error, and ensures compliance with regulatory standards.
This not only expedites payments but also increases transparency and trust among patients, providers, and payers.
Q. You say that AI-based clinical decision-making is becoming the norm, but do you think it's too early in the evolution of AI to be part of these decisions? Can you elaborate on where you see this going?
A. While AI will not replace clinical decisions made by healthcare providers, it can serve as a powerful tool to support decision-making. This is an AI-assisted model that largely mirrors the trend we are seeing in the enterprise AI market: AI excels at taking large volumes of complex data points and evaluating trends, outcomes, and other analytics.
Physicians can use this organized and contextualized data to make diagnostic and patient treatment decisions. The goal is to complement, not replace, the human interaction between patient and healthcare provider.
Integrating AI into clinical decision-making is already proving beneficial. Through machine learning and natural language processing, AI has demonstrated great accuracy in diagnosing medical conditions from medical records such as images. These AI systems support clinicians by providing evidence-based recommendations, identifying potential drug interactions, and suggesting individualized treatment plans, improving the quality of care and reducing the chance of human error.
In today's healthcare environment, with its vast amounts of data and complex patient cases, the use of AI is essential to efficiently manage and interpret information. AI can process and analyze data much faster than humans can, making it an invaluable tool in clinical practice.
For example, in radiology, AI can quickly identify abnormalities in image scans, allowing radiologists to focus on more complex diagnostic tasks. Similarly, AI in pathology can help recognize patterns in tissue samples that may be signs of diseases such as cancer.
Despite challenges such as data privacy concerns and the need for seamless integration into existing systems, The trajectory of AI development is promising as AI tools continue to learn and improve.
As always, we aim to adopt technologies that will produce the greatest positive results, minimize change management, provide a durable and sustainable ROI, and can be funded on an ongoing basis. Applying this economic framework to technological advances is the best way to predict the success of AI in healthcare.
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Email: bsiwicki@himss.org
Healthcare IT News is a publication of HIMSS Media.
