Keep patient satisfaction top of mind with AI, says healthcare IT investor

It’s no secret that healthcare is at an inflection point, as artificial intelligence and other emerging technologies solve the problems associated with the fragmentation and frustration so prevalent in the industry.

As healthcare systems manage these fundamental changes, it is important for healthcare organizations to ensure that physicians and IT decision makers keep patient satisfaction top of mind, said Alex Mason.

Mason is a partner at FTV Capital, where he leads the health technology and healthcare information technology investment practice. He led funding rounds for Luma Health and 6 Degrees Health.

We sat down with Mason to discuss how investors view AI in healthcare, how it will catalyze the acceleration toward value-based care, how AI-enabled clinical decision making will become the norm, and how the revenue cycle management process can streamline payments and drive progress. digital patient engagement.

Q. How do investors view artificial intelligence in healthcare in general?

A. Investors are approaching AI in healthcare with optimistic caution. They take a balanced approach, recognizing both the potential for significant progress and the need to carefully consider second-order consequences.

Recent setbacks, including some high-profile healthcare AI ventures that fell short of expectations, have led to a more muted near-term investment outlook. However, we have also seen numerous success stories that illustrate the promise of AI when applied to specific, well-defined use cases and outcomes, making investments with highly specific and targeted applications more attractive.

At FTV we believe in it the most Valuable AI applications are those that deliver specific outcomes – clinical, financial, patient-related, or provider-related outcomes – using a targeted and specific application of AI in the use case. At the same time, the application of AI must be done in a way that requires as little change management from the user as possible.

For every company we follow 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 critical to mitigating risks and increasing the attractiveness of AI solutions to those in the healthcare ecosystem – from payers to providers to patients.

As we look to the future, we are keeping a close eye on data privacy, data sovereignty, and overall regulations, as healthcare is rightly becoming one of the most regulated areas of AI given patient privacy concerns.

Innovation and regulation must go hand in hand. Data privacy is critical. However, healthcare data is fundamentally distributed data: it resides in many systems and applications and under many owners. It is important to note that regulations can drive the adoption of technological advances in a very positive way.

The best example of this is how providers—from large health care systems to small doctor’s offices—were pushed to adopt electronic health records at scale by the government subsidies provided as a result of the HITECH Act.

Despite some of the current challenges, AI will inevitably transform healthcare. We believe investors remain largely optimistic that as AI technologies evolve and demonstrate their effectiveness in practice, they will deliver 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 under a traditional reimbursement model.

This shift to an outcomes-based compensation system enables AI to automate the collection and analysis of patient outcomes data, ensuring reimbursement closely matches health improvements achieved and enabling more accurate assessment of quality of care.

Additionally, AI can help healthcare providers identify the most effective treatments for individual patients by analyzing large data sets from a diverse set of sources. This enables a more personalized, appropriate and accurate approach to patient care, which is crucial for improving outcomes and patient satisfaction.

Predictive analytics can predict potential health problems before they become critical, enabling early intervention and better management of chronic conditions. This proactive approach is closely aligned 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 ability to continually refine their results 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 determining reimbursement arrangements 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 practices accordingly.

Q. How can AI simplify the revenue cycle management process to streamline payments prior to digital patient engagement?

A. 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 manual workload, AI not only accelerates the revenue cycle, but also minimizes 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 could indicate problems such as underpayments, denials, or delayed refunds.

By proactively addressing these issues, healthcare providers can optimize their revenue streams and ensure a more stable and faster financial foundation. AI-powered 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.

Further, AI improves the accuracy of coding and billing processes, which is critical for timely and accurate refunds. By analyzing patient records and identifying the most appropriate codes, AI reduces 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, healthcare providers and payers.

Q. You suggest that AI-enabled clinical decision making is becoming the norm. Don’t you think it’s a bit early in the evolution of AI to be part of these decisions? Please explain your vision.

A. AI will not replace a healthcare provider’s clinical decisions, but will serve as a powerful tool to aid in decision-making – an AI enablement model that largely reflects the trends we see in the enterprise AI market. AI excels at taking large volumes, complex data points and assessing trends, outcomes or other analyses.

Clinicians can then use this cleaned and contextualized data to inform their diagnoses and patient care decisions. The goal is to complement, not replace, the human interaction between patient and healthcare provider.

The integration of AI into clinical decision-making is already proving to be useful. Through machine learning and natural language processing, AI has shown remarkable accuracy in diagnosing conditions based on medical records such as imaging. These AI systems support physicians by providing evidence-based recommendations, identifying potential drug interactions and suggesting personalized treatment plans, improving the quality of care and reducing the potential for human error.

Today’s healthcare environment, with overwhelming data volumes and complex patient cases, necessitates the use of AI to efficiently manage and interpret information. AI can process and analyze data much faster than humans, making it an invaluable tool in a clinical setting.

In radiology, for example, AI can quickly identify abnormalities in image scans, allowing radiologists to focus on more complex diagnostic tasks. Similarly, in pathology, AI can help recognize patterns in 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 to adopt technology that generates the greatest positive results, requires minimal change management, provides sustainable and ongoing ROI, and can be funded consistently. Applying this economic framework to technological advancements is the best predictor of AI success in healthcare.

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