Healthcare AI
March 20, 2024

AI-Driven Solutions for Healthcare Cost Reduction: Automation, Diagnostics, and Resource Optimization, with Mark Michalski, CEO of Ascertain

Mark Michalski, CEO of Ascertain, discusses how AI can reduce healthcare costs through automation, improved diagnostics, and optimized resource allocation.
Written by Ankur Patel
This is a summary of an episode of Pioneers, an educational podcast on AI led by our founder. Join 2,000+ business leaders and AI enthusiasts and be the first to know when new episodes go live. Subscribe to our newsletter here.

TLDR

  • The U.S. healthcare system faces a crisis of rising costs, with healthcare spending reaching $4.1 trillion in 2020. AI has the potential to reduce costs and improve efficiency across various aspects of healthcare.
  • AI-driven automation can streamline administrative tasks such as billing, coding, and appointment scheduling, potentially saving the healthcare industry billions of dollars annually.
  • AI can enhance diagnostic accuracy and efficiency, particularly in radiology, by analyzing medical images and identifying high-risk patients.
  • AI can optimize resource allocation and utilization in healthcare by predicting patient demand and optimizing bed management, staffing levels, and inventory management.
  • AI can help reduce hospital readmissions and improve post-acute care by predicting readmission risk, enabling proactive interventions, and optimizing care coordination. However, challenges such as data privacy and the need for diverse training data must be addressed.

Introduction: The Need for Cost Reduction in Healthcare

The United States healthcare system is grappling with an unprecedented crisis of rising costs. According to the Centers for Medicare and Medicaid Services (CMS), healthcare spending in the U.S. reached $4.1 trillion in 2020, accounting for 19.7% of the nation's Gross Domestic Product (GDP). These staggering costs have far-reaching consequences for patients, providers, and the economy as a whole. Patients are burdened with high out-of-pocket expenses, leading to financial strain and debt. Providers struggle to deliver quality care while managing tight budgets. The overall economy suffers from the diversion of resources towards healthcare.

It is imperative to find innovative solutions that can reduce healthcare costs without compromising the quality of care. One promising avenue is the application of AI in various aspects of healthcare. AI has the potential to revolutionize the way healthcare is delivered, from streamlining administrative tasks to enhancing diagnostic accuracy and optimizing resource allocation. By leveraging the power of AI, healthcare organizations can significantly reduce costs, improve efficiency, and ultimately deliver better patient outcomes.

We sat down with Mark Michalski, CEO of Ascertain, in this week’s episode of Pioneers to dig into this very topic. Before continuing, check out the full episode here:

AI-Driven Automation in Administrative Tasks

One of the most exciting applications of AI in healthcare cost reduction is the automation of administrative tasks. Healthcare organizations are burdened with myriad administrative responsibilities, from billing and coding to scheduling and paperwork. These tasks are often time-consuming, error-prone, and costly, diverting valuable resources from patient care.

AI-powered solutions can automate many of these administrative tasks, reducing the need for manual labor and minimizing the risk of errors. For example, AI can streamline the billing and coding process by automatically extracting relevant information from medical records and assigning the appropriate codes. This not only saves time but also reduces the likelihood of coding errors, which can lead to denied claims and lost revenue. Similarly, AI can automate appointment scheduling, freeing up staff to focus on more value-added tasks.

A compelling case study of AI-driven automation in healthcare is Ascertain's work on streamlining patient discharge processes. The company has developed an AI-powered solution that helps healthcare organizations manage the complex and often time-consuming process of discharging patients. By automating tasks such as insurance verification, prior authorization, and post-acute care coordination, Ascertain's solution has the potential to significantly reduce the time and costs associated with patient discharge.

The potential cost savings from automating administrative tasks are substantial. A study by the Council for Affordable Quality Healthcare (CAQH) found that automating administrative transactions could save the healthcare industry $13.3 billion annually. Though it should be said, implementing AI-driven automation is not without its challenges. Healthcare organizations must ensure their AI solutions are secure, compliant with regulations, and integrate seamlessly with existing systems. They must also invest in the necessary infrastructure and training to support adopting AI-powered tools.

Enhancing Diagnostic Accuracy and Efficiency with AI

Another area where AI can significantly impact healthcare cost reduction is in medical diagnostics. Accurate and timely diagnosis is critical for effective treatment and can help prevent costly complications down the line. Today, the diagnostic process is often complex, time-consuming, and subject to human error. AI has the potential to revolutionize medical diagnostics by enhancing accuracy, efficiency, and accessibility.

One of the most promising applications of AI in diagnostics is radiology. AI algorithms can analyze medical images, such as X-rays, CT scans, and MRIs, with remarkable speed and accuracy.

As Mark notes, "AI algorithms can process and analyze data more comprehensively and accurately than traditional methods, leading to more precise risk assessments."

By automating the image analysis process, AI can help radiologists work more efficiently and accurately, reducing the risk of missed or delayed diagnoses.

There are numerous examples of AI-powered diagnostic tools that have demonstrated significant cost reduction potential. For instance, a study published in the Journal of the American Medical Association (JAMA) found that an AI system for diagnosing diabetic retinopathy could save the U.S. healthcare system $400 million annually by reducing the need for specialist visits and enabling early detection and treatment. Similarly, AI-powered tools for detecting skin cancer, breast cancer, and other conditions have shown promise in reducing costs and improving patient outcomes.

However, it is important to recognize that AI is not a replacement for human expertise in medical diagnostics. Contextual awareness and clinical judgment remain essential for accurate diagnosis and treatment planning.

AI should be viewed as a tool to augment and support human decision-making, rather than a standalone solution. As healthcare organizations implement AI-powered diagnostic tools, they must ensure that their clinicians are properly trained to interpret and act upon the insights provided by these systems.

Looking to the future, AI has the potential to enable even earlier detection and prevention of disease. By analyzing vast amounts of patient data, including genetic information, medical history, and lifestyle factors, AI algorithms could identify individuals at high risk for certain conditions and recommend proactive interventions. This shift towards preventive care could have a profound impact on reducing healthcare costs in the long run.

Optimizing Resource Allocation and Utilization

Efficient allocation and utilization of healthcare resources are critical for controlling costs and ensuring the sustainability of the healthcare system. Managing resources effectively can be complex and challenging, particularly in large healthcare organizations with multiple facilities and departments. AI can revolutionize resource management in healthcare by enabling more accurate demand forecasting, optimizing allocation, and improving utilization.

AI can be effectively deployed in resource management by predicting patient demand and optimizing bed management. By analyzing historical data on patient admissions, length of stay, and other factors, AI algorithms can forecast future demand and help healthcare organizations allocate beds more efficiently. This can reduce the risk of overcrowding, minimize wait times, and ensure that patients receive the care they need in a timely manner. As Mark emphasizes, "The sooner you get out, the better. And the sooner the hospitals can get that bed to the next patient, the better it is for them, because there's just so many people to take care of."

AI can also be used to optimize staffing levels and scheduling. By analyzing data on patient volume, acuity, and other factors, AI-powered tools can help healthcare organizations determine the optimal number and mix of staff needed at any given time. This can reduce the risk of understaffing, which can lead to decreased quality of care and increased costs due to overtime and agency staff. Conversely, it can also prevent overstaffing, which can result in unnecessary labor costs.

Another area where AI can drive cost savings is in inventory management. Healthcare organizations must maintain adequate supplies of medications, equipment, and other essential items while minimizing waste and avoiding stock issues. AI-powered inventory management systems can analyze usage patterns, predict future demand, and optimize ordering and replenishment processes. This can help reduce inventory carrying costs, minimize expired or obsolete stock, and ensure that critical supplies are always available when needed.

To fully realize the potential of AI in resource optimization, healthcare organizations must prioritize data integration and real-time analytics. By breaking down data silos and creating a unified view of resource utilization across the organization, AI algorithms can provide more accurate and actionable insights. Real-time monitoring and alerting can help healthcare leaders respond quickly to changes in demand or supply, ensuring that resources are always allocated in the most efficient and effective manner.

Reducing Re-Admissions and Improving Post-Acute Care

Hospital re-admissions are a significant driver of healthcare costs in the United States. According to the Agency for Healthcare Research and Quality (AHRQ), re-admissions within 30 days of discharge cost Medicare an estimated $26 billion annually, with $17 billion of that amount attributed to potentially avoidable readmissions. Reducing re-admissions and improving post-acute care coordination are critical for controlling costs and ensuring the best possible outcomes for patients. AI will play a significant role in this effort by predicting re-admission risk, enabling proactive interventions, and optimizing post-acute care management.

One promising application of AI in reducing re-admissions is the development of predictive models that can identify patients at high risk for re-admission before they are discharged. By analyzing a wide range of patient data, including demographics, clinical history, and social determinants of health, these models can flag individuals who may require additional support or intervention to prevent re-admission. This allows healthcare teams to proactively address potential issues and ensure that patients have the necessary resources and follow-up care in place upon discharge.

AI-powered tools are also being used to streamline and optimize post-acute care coordination. For example, some healthcare organizations use AI to match patients with the most appropriate post-acute care setting based on their clinical needs, preferences, and insurance coverage. This can help ensure that patients receive the right level of care in the most cost-effective setting, reducing the risk of complications and re-admissions. AI-powered remote monitoring solutions can also help healthcare teams keep track of patients' progress after discharge, allowing for early detection and intervention if any issues arise.

"No one actually wants to be in the hospital,” says Mark. ”But if you're in the hospital, the sooner you get out, the better."

AI can enable personalized post-acute care plans that are tailored to each patient's unique needs and circumstances. By analyzing patient data and leveraging clinical best practices, AI algorithms can recommend specific interventions, follow-up appointments, and support services that will most likely promote a smooth recovery and prevent re-admissions.

However, implementing AI-driven post-acute care solutions is not without its challenges. Healthcare organizations must ensure their AI models are trained on diverse and representative patient populations to avoid perpetuating biases or disparities in care. They must also consider the potential impact on clinician workflows and ensure that AI-generated recommendations are presented in a way that is actionable and easy to integrate into existing care processes. Finally, there may be concerns about data privacy and security, particularly when sharing patient information across care settings.

Conclusion: The Future of AI in Healthcare Cost Reduction

The potential for AI to drive cost reduction in healthcare is immense. From automating administrative tasks and enhancing diagnostic accuracy to optimizing resource allocation and improving post-acute care, AI can transform virtually every aspect of the healthcare system. As Mark emphasizes, "This is a wave that you want to ride, and your enterprises will be much stronger for having done it. It's not just another hype cycle."

Realizing AI's full potential in healthcare cost reduction will require close collaboration between healthcare providers, technologists, and policymakers. Healthcare organizations must be willing to invest in the necessary infrastructure, talent, and training to support the adoption of AI-powered solutions. Technologists must work closely with clinicians and administrators to ensure that solutions are designed to meet the unique needs and constraints of the healthcare environment. Policymakers must also create a supportive regulatory framework that encourages innovation while protecting patient privacy and safety.

If these stakeholders can come together to harness the power of AI, the impact on healthcare costs will be transformative. By reducing waste, improving efficiency, and enabling more proactive and personalized care, AI has the potential to bend the cost curve and make high-quality healthcare more affordable and accessible for all. As healthcare leaders look to the future, they must embrace AI as a critical tool in their cost-reduction arsenal and work tirelessly to integrate it into their operations.

The journey towards fully realizing these cost reductions will not be easy, but it is a journey that we must undertake. With the right investments, partnerships, and mindset, we can create a healthcare system that is not only more efficient and cost-effective but also more patient-centered and equitable. The future of healthcare is one in which AI and human expertise work hand in hand to drive better outcomes, lower costs, and improved access for all. It is a future that we must embrace with open arms and bold vision.

Want to learn more about AI in healthcare? Check out our episode on responsibly adopting AI in this industry with Dr. Harvey Castro.

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