It’s no secret healthcare is at a turning point, as artificial intelligence and other emerging technologies are solving problems associated with the fragmentation and frustration so prevalent in the industry.
As health systems manage these fundamental changes, it’s important for provider organizations to ensure that clinicians and IT decision-makers are keeping patient satisfaction top of mind, said Alex Mason.
Mason is a partner at FTV Capital, where he leads the health tech and healthcare information technology investment practice. He spearheaded funding rounds for Luma Health and 6 Degrees Health.
We spoke with Mason to discuss how investors view AI in healthcare, how it’s set to catalyze the acceleration toward value-based care, how AI-assisted clinical decision-making is becoming the norm and how the revenue cycle management process can streamline payments and advance digital patient engagement.
Q. Overall, how are investors looking at artificial intelligence in healthcare?
A. Investors are approaching AI in healthcare with optimistic caution. They are taking a balanced approach, recognizing both the potential for significant advancements and the need to be thoughtful about second-order consequences.
Recent setbacks, including some high-profile AI healthcare ventures that failed to meet expectations, have led to a more measured investment outlook in the near term. However, we’ve also seen plenty of success stories that illustrate the promise of AI when applied to specific, well-defined use cases and outcomes, which make investments with very specific and targeted applications more appealing.
At FTV, we believe the most valuable AI applications are those that drive specific outcomes – clinical, financial, patient-related or provider-related outcomes – that use a targeted and specific application of AI in the use case. At the same time, the application of AI has to be done in a way that requires the least amount of change management from the user.
For every company we track or investment we consider, our first step is to evaluate the use case for AI and how it can make incremental improvements to current processes. Integrating AI into existing workflows without causing major disruptions is crucial to mitigate risks and enhance the attractiveness of AI solutions to those in the healthcare ecosystem – from payers to providers to patients.
Looking to the future, we’re closely monitoring data privacy, data sovereignty and general regulation since healthcare is rightly becoming one of the most regulated areas of AI given patient privacy concerns.
Innovation and regulation must work hand in hand. Data privacy is critical. However, healthcare data is fundamentally distributed data – it sits across a multitude of systems and applications across a multitude of owners. It is important to note that regulation can direct adoption of technological advancement in a very positive way.
The best example of this is how providers – from large health systems to small physician offices – were driven to large-scale adoption of electronic health records by the government subsidies provided as a result of the HITECH Act.
Despite some of the present challenges, AI will inevitably transform healthcare. We think investors largely remain optimistic that as AI technologies evolve and demonstrate their efficacy in real-world settings, they will drive significant improvements in healthcare efficiency and patient outcomes.
Q. How do you think AI can catalyze the acceleration toward value-based care?
A. AI improves the ability to measure and improve patient outcomes. In value-based care models, providers are incentivized to achieve positive health outcomes with negligible downstream complications, rather than being compensated on a traditional fee-for-service model.
This shift to an outcome-based compensation scheme enables AI to automate the collection and analysis of patient outcome data, ensuring reimbursements are closely aligned with the health improvements achieved and providing a more accurate assessment of care quality.
Moreover, AI can assist healthcare providers in identifying the most effective treatments for individual patients by analyzing large datasets from a diverse set of sources. This allows for a more personalized, appropriate and accurate approach to patient care, which is crucial for improving outcomes and patient satisfaction.
Predictive analytics can forecast potential health issues before they become critical, enabling early intervention and better management of chronic conditions. This proactive approach closely aligns 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 fine-tune their outputs by identifying both positive and negative trends. This results in increasingly precise and valuable insights that further refine value-based care strategies.
For example, AI can be more judicious in setting reimbursement schemes 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 adjust their practices accordingly.
Q. How can AI simplify the revenue cycle management process to streamline payments in advance digital patient engagement?
A. By automating repetitive, labor-intensive tasks, enhancing accuracy and providing actionable insights, AI can streamline the revenue cycle management process. One of the primary benefits of AI in RCM is its ability to automate existing, manual functions such as claims processing, eligibility verification and payment posting.
By reducing manual workloads, AI not only speeds up the revenue cycle but also minimizes errors that lead to claim rejections and delays, ultimately improving overall efficiency.
In addition to automation, AI can predict potential revenue leakage points and highlight financial inefficiencies. Predictive analytics tools can analyze historical data to identify patterns and anomalies that might indicate issues such as underpayments, denials or delayed reimbursements.
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 reactive payments to proactive payments.
Furthermore, AI enhances the accuracy of coding and billing processes, which is critical for timely and correct reimbursements. By analyzing patient records and identifying the most appropriate codes, AI reduces labor costs and the likelihood of human error while ensuring compliance with regulatory standards.
This not only accelerates payments but also enhances transparency and trust between patients, providers and payers.
Q. You suggest AI-assisted clinical decision-making is becoming the norm. Don’t you think it’s a little early in the evolution of AI for it to be part of these decisions? Please elaborate on your outlook.
A. AI won’t replace clinical decisions made by a healthcare provider, but it will serve as a strong tool to assist in decision-making – an AI-assist model that largely mirrors the trends we are seeing in the enterprise AI market. AI excels at taking high-volume, complex data points and assessing trends, outcomes or other analyses.
Physicians can then use this cleansed and contextualized data for their diagnoses and patient care decisions. The goal is to complement, not replace, the human interaction between a patient and provider.
AI’s integration into clinical decision-making already is proving beneficial. Through machine learning and natural language processing, AI has demonstrated remarkable accuracy in diagnosing conditions from medical records such as imaging. These AI systems support clinicians by providing evidence-based recommendations, identifying potential drug interactions and suggesting personalized treatment plans, thereby enhancing the quality of care and reducing the likelihood of human error.
The current healthcare environment, with overwhelming data volumes and complex patient cases, necessitates the use of AI to manage and interpret information efficiently. AI can process and analyze data much faster than humans, making it an invaluable tool in a clinical setting.
For example, in radiology, AI can quickly identify anomalies in imaging scans, allowing radiologists to focus on more complex diagnostic tasks. Similarly, AI in pathology can assist in recognizing patterns in tissue samples that may be indicative of diseases like 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 the adoption of technology that generates the greatest positive outcomes, requires minimal change management, offers durable and persistent ROI, and can be funded consistently. Applying this economic framework to technological advancements is the best predictor of AI’s success in healthcare.
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Email him: bsiwicki@himss.org
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