It’s no secret healthcare is at a turning point, as artificial intelligence and other emerging technologies are solving problems related to fragmentation and frustration that are so prevalent in the industry .
As health systems manage these fundamental changes, it will be important for delivery organizations to ensure that clinicians and IT decision makers keep their Patient satisfaction comes first.
Mason is a partner at FTV Capital, where he leads healthcare IT and medical technology investments. He led funding rounds for Luma Health and 6 Degrees Health.
We spoke to Mason to discuss how investors view AI in healthcare, how it’s set to drive the acceleration towards value-based care, How AI-powered clinical decision making is becoming the norm and how revenue cycle management processes can streamline payments and advances technical patient engagement number.
Ask. Overall, how do investors view artificial intelligence in healthcare?
ONE. Investors are approaching AI in healthcare with cautious optimism. They are taking a balanced approach, recognizing both the potential for significant progress and the need to carefully consider second-order consequences.
Recent setbacks, including several high-profile AI healthcare projects that failed to meet expectations, have led to a more cautious investment outlook going forward. However, we have also seen many success stories that illustrate the promise of AI when applied to specific, clearly defined use cases and outcomes, making investment in applications very specific and targeted to be more engaging.
At FTV, we believe the most Valuable AI applications are those that drive specific outcomes – clinical, financial, patient- or provider-related outcomes – using targeted AI applications and specific to the use case. At the same time, AI adoption must be done in a way that requires the least amount of change management from users.
For every company we track or consider investing in, our first step is to evaluate the AI use case and how it can make incremental improvements to existing processes. Integrating AI into existing workflows without causing major disruption is critical to minimizing risks and enhancing the appeal of AI solutions to those in the healthcare ecosystem. health – from payers to providers to patients.
Looking to the future, we are closely monitoring data privacy, data sovereignty and general regulation as healthcare is becoming one of the most heavily regulated areas of AI due to concerns about patient privacy.
Innovation and regulation must go hand in hand. Data privacy is very important. However, healthcare data is fundamentally distributed data – it resides across countless systems and applications of many owners. It is important to note that regulation can directly apply technological advances in a very positive way.
The best example of this is how providers – from large health systems to small doctor’s offices – are being pushed to adopt electronic health records at scale thanks to government subsidies government provided by the HITECH Act.
Despite some current challenges, AI will certainly transform healthcare. We think investors remain largely optimistic that as AI technologies develop and prove their effectiveness in real-world settings, they will drive significant improvements in healthcare efficiency and patient outcomes.
Q. How do you think AI can drive the acceleration toward value-based care?
ONE. AI improves the ability to measure and improve patient outcomes. In value-based care models, providers are incentivized to achieve positive health outcomes with minimal subsequent complications, rather than being compensated under the traditional fee-for-service model .
This move to an outcomes-based compensation mechanism allows AI to automate the collection and analysis of patient outcomes data, ensuring reimbursements are closely linked to health improvements achieved. and provide a more accurate assessment of the quality of care.
Furthermore, AI can assist healthcare providers in determining the most effective treatments for each patient by analyzing large data sets from a variety of sources. This allows for a more personalized, tailored and precise approach to patient care, which is important for improving patient outcomes and satisfaction.
Predictive analytics can predict potential health problems before they become serious, allowing for early 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 encounters and process more data, they have the opportunity to continuously refine their output by identifying both positive and negative trends. This leads to increasingly accurate and valuable insights that help further improve value-based care strategies.
For example, AI can be more conservative in setting up reimbursement programs for certain providers, making it a more successful predictor of value-based outcomes. This continuous improvement ensures that healthcare providers can stay ahead of emerging health trends and adapt their operations accordingly.
Q. How can AI simplify revenue cycle management to streamline payments ahead of digital patient engagement?
ONE. By automating repetitive, labor-intensive tasks, improving accuracy, and providing actionable insights, AI can streamline the revenue cycle management process. One of the key benefits of AI in RCM is the ability to automate existing manual functions such as claims processing, eligibility verification, and payment posting.
By reducing the amount of manual work, Not only does AI speed up the revenue cycle, it also reduces errors that lead to claim denials and delays, ultimately improving overall efficiency.
In addition to automation, AI can predict potential revenue leaks and highlight financial inefficiencies. Predictive analytics tools can analyze historical data to identify patterns and anomalies that can indicate problems such as underpayments, declines or late refunds.
By proactively addressing these issues, healthcare providers can optimize their revenue streams and ensure a more stable and faster financial foundation. AI-driven insights also help refine billing practices and contract negotiations, leading to better financial outcomes and pushing our healthcare system from passive to paid active math.
Furthermore, AI enhances the accuracy of coding and billing processes, which is critical for timely and accurate reimbursements. By analyzing patient records and identifying the most appropriate codes, AI helps reduce labor costs and the potential for human error while ensuring compliance with regulatory standards.
This not only speeds up payments but also increases transparency and trust between patients, providers and payers.
Ask. You argue that AI-assisted clinical decision making is becoming the norm. Do you think it’s a little early in the evolution of AI for it to be a part of these decisions? Please explain your point of view more clearly.
ONE. AI will not replace the clinical decisions made by healthcare providers, but it will serve as a powerful tool to support decision making – an AI-enabled model that largely reflects the trends we are seeing in the enterprise AI market. AI excels at collecting complex, high-volume data points and evaluating trends, outcomes, or other analytics.
Physicians can then use this cleaned and contextualized data to diagnose and make patient care decisions. The goal is to supplement, not replace, human interaction with patients and providers.
The integration of AI into clinical decision making has proven beneficial. Through machine learning and natural language processing, AI has demonstrated outstanding accuracy in diagnosing medical conditions from medical records such as images. These AI systems assist clinicians by providing evidence-based recommendations, identifying potential drug interactions, and recommending personalized treatment plans, thereby improving the quality of care. care and reduce the possibility of human error.
The current healthcare environment, with its massive data volumes and complex medical cases, requires the use of AI to effectively manage and interpret information. AI can process and analyze data much faster than humans, making it an invaluable tool in clinical settings.
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 assist in identifying tissue samples that may be indicative 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, especially as AI tools continue to learn and improve.
As always, we look for technology adoption that produces the most positive results, requires minimal change management, delivers sustainable and ongoing ROI, and can be funded consistently . Applying this economic framework to technological advancement is the best predictor of AI success in healthcare.
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