It’s no secret that healthcare is at an inflection point as artificial intelligence and other emerging technologies address the fragmentation and frustration that plagues the industry.
As healthcare systems manage these fundamental changes, it is significant for provider organizations to ensure that clinicians and IT decision makers place patient satisfaction as paramount, said Alex Mason.
Mason is a partner at FTV Capital, where he leads the health technology and healthcare information technology investment practice. He led the funding rounds for Luma Health and 6 Degrees Health.
We spoke with Mason to discuss how investors view AI in healthcare, how it is poised to catalyze the acceleration toward value-based care, how AI-powered clinical decision-making is becoming the norm, and how the revenue cycle management process can streamline payments and advances digital patient engagement.
Q: How do investors generally view AI in healthcare?
AND. Investors are bullish about artificial intelligence in healthcare. They take a balanced approach, recognizing both the potential for significant progress and the need to consider second-order consequences.
Recent setbacks, including some high-profile AI-based healthcare ventures that failed to meet expectations, have led to a more balanced investment outlook in the near term. However, we have also seen many success stories that illustrate the promise of AI when applied to specific, well-defined apply cases and outcomes, which makes investment in very specific and targeted applications more attractive.
We believe most in FTV Valuable applications of AI are those that produce specific outcomes – clinical, financial, patient or provider outcomes – and that leverage targeted and specific applications of AI in a apply case. At the same time, the application of artificial intelligence must be done in a way that requires the least amount of change management effort from the user.
For any company we track or any investment we consider, our first step is to evaluate the apply case of AI and how it can make incremental improvements to current processes. Integrating AI into existing workflows without causing major disruption is key to mitigating risk and increasing the attractiveness of AI solutions across the healthcare ecosystem – from payers to providers to patients.
Looking ahead, we are closely monitoring data privacy, data sovereignty and overall regulation as healthcare rightly becomes one of the most regulated areas of AI given patient privacy concerns.
Innovation and regulation must work hand in hand. Data privacy is crucial. However, healthcare data is fundamentally distributed data – it resides in multiple systems and applications with multiple owners. It is significant to note that regulations can guide the adoption of technological advances in a very positive way.
A prime example of this is how providers – from huge health systems to petite physician offices – have been forced to adopt electronic health records on a huge scale thanks to government subsidies provided by the HITECH Act.
Despite some current challenges, Artificial intelligence will inevitably change healthcare. We believe investors remain largely bullish that as AI technologies evolve and are demonstrated to be effective in real-world settings, they will drive significant improvements in healthcare efficiency and patient outcomes.
Question: How can AI catalyze the acceleration towards value-based care?
AND. Artificial intelligence improves the ability to measure and improve patient outcomes. In value-based care models, providers are incentivized to achieve positive health outcomes with few downstream complications, rather than being compensated in a conventional fee-for-service model.
This shift to performance-based compensation enables AI to automate the collection and analysis of patient outcomes data, ensuring that reimbursements are closely tied to health improvements achieved and provide a more precise assessment of the quality of care.
Moreover, artificial intelligence can support healthcare providers determine the most effective treatments for individual patients by analyzing huge datasets from a variety of sources. This allows for a more personalized, appropriate and precise approach to patient care, which is crucial to improving patient outcomes and satisfaction.
Predictive analytics can predict potential health problems before they become critical, enabling early intervention and better management of chronic diseases. This proactive approach closely aligns with the goals of value-based care, which emphasizes prevention and long-term planning.
As AI models are incorporated into more clinical encounters and process more data, they have the ability to continually fine-tune their results by identifying both positive and negative trends. This results in increasingly precise and valuable insights that further improve value-based care strategies.
For example, AI can be more judicious in determining reimbursement systems for certain suppliers, making it a more effective predictor of value-based outcomes. Through this continuous improvement, healthcare providers can stay ahead of emerging health trends and adapt their practices accordingly.
Q: How can AI simplify the revenue cycle management process to streamline payments before engaging digital patients ahead of time?
AND. By automating repetitive, labor-intensive tasks, improving accuracy and providing actionable insights, AI can streamline the revenue cycle management process. One of the main benefits of AI in RCM is its ability to automate existing, manual functions such as claims processing, eligibility verification and payment posting.
Reducing the burden of manual work, AI not only accelerates the revenue cycle, but also minimizes errors that lead to 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 may indicate problems such as underpayments, denials, or delayed refunds.
By proactively addressing these issues, healthcare providers can optimize their revenue streams and put themselves on a more stable and faster financial footing. AI-powered information also helps improve billing practices and contract negotiations, leading to better financial outcomes and shifting our health care system from reactive payments to proactive payments.
In addition, Artificial intelligence increases the accuracy of coding and billing processes, which is crucial for timely and precise 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 speeds up payments, but also increases transparency and trust between patients, providers and payers.
Q. You suggest that AI-powered clinical decision-making is becoming the norm. Don’t you think it’s a little early for artificial intelligence to evolve for it to be part of these decisions? Please expand your view.
AND. Artificial intelligence will not replace clinical decisions made by a healthcare provider, but it will be a powerful decision-making tool – an AI-enabled model that largely reflects the trends we are seeing in the enterprise AI market. Artificial intelligence specializes in collecting huge amounts of convoluted data points and assessing trends, results, or other analyses.
Physicians can then apply this cleansed and contextualized data to make diagnoses and make decisions about patient care. The goal is to complement, not replace, human interaction between patient and provider.
Incorporating artificial intelligence into clinical decision-making is already proving beneficial. Thanks to machine learning and natural language processing, artificial intelligence has demonstrated extraordinary accuracy in diagnosing diseases based on 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 improving the quality of care and reducing the likelihood of human error.
The current healthcare environment, with overwhelming amounts of data and convoluted patient cases, requires the apply of artificial intelligence to effectively manage and interpret information. Artificial intelligence can process and analyze data much faster than humans, making it an invaluable tool in clinical settings.
In radiology, for example, AI can quickly identify anomalies in imaging scans, allowing radiologists to focus on more convoluted diagnostic tasks. Similarly, artificial intelligence in pathology can support recognize patterns in tissue samples that may indicate diseases such as cancer.
Despite challenges such as data privacy concerns and the need for seamless integration with existing systems, The trajectory of AI development is promising, especially as AI tools continue to learn and improve.
As always, we are looking for technology that generates the greatest positive results, requires minimal change management, provides sustainable and sustainable return on investment, and can be continuously financed. Applying this economic framework to technological advancements is the best predictor of AI success in healthcare.