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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.
TL;DR
- The U.S. healthcare system faces a crisis of rising costs, with healthcare spending reaching $4.1 trillion in 2020. AI can potentially 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 by analyzing medical images and identifying high-risk patients, particularly in radiology.
- By predicting patient demand and optimizing bed management, staffing levels, and inventory, AI can optimize resource allocation and utilization in healthcare.
- AI reduces hospital readmissions and improves post-acute care through readmission risk prediction, enabling proactive interventions, and optimizing care coordination.
Before continuing, check out the full episode here:
Mark Michalski, CEO of Ascertain
In this week's episode of Pioneers, we spoke with Mark Michalski, CEO of Ascertain, about artificial intelligence in health care.
“Healthcare is oftentimes one of the most important things in our lives, especially in the worst days of our lives…so when I came to Ascertai, it was really with the mission of bringing that best-in-class automation technology to healthcare, and I think that's an important mission for all of us.“ — Mark Michalski
Ascertain focuses on areas where healthcare enterprises struggle the most, particularly operational backend processes like revenue cycle management. Their mission is to use automation to make healthcare systems work more efficiently and effectively.
Mark noted there is a major opportunity to leverage automation technology to modernize healthcare systems. However, the healthcare community is very cautious about introducing new technologies like AI.
However, there's a growing awareness of this technology's capabilities as concrete examples appear in this industry. When you can point to these success stories, like many of our case studies, it helps people understand your goals and intentions.
Let's dive deeper into how AI in health care can help reduce costs.
How Healthcare Artificial Intelligence Reduces Costs and Improves Efficiency
The U.S. healthcare system is grappling with an unprecedented crisis of rising costs. According to the Centers for Medicare and Medicaid Services (CMS), healthcare spending 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.
Finding innovative solutions that can reduce healthcare costs without compromising the quality of care is imperative. One promising avenue is the application of artificial intelligence in healthcare.
AI will undoubtedly reshape healthcare, from streamlining administrative tasks to enhancing diagnostic accuracy and optimizing resource allocation. By leveraging AI technology, the healthcare sector can significantly reduce costs, improve efficiency, and ultimately deliver better patient outcomes.
“There's just tremendous opportunities for what this (bringing AI to the healthcare industry) means for the way that patients will receive care.” — Mark Michalski
AI-Driven Automation in Administrative Tasks
One of the most exciting applications of AI in healthcare cost reduction is automating administrative tasks. A Council for Affordable Quality Healthcare (CAQH) study found that automating administrative transactions could save the healthcare industry $13.3 billion annually.
Healthcare organizations face numerous administrative tasks, including billing, coding, scheduling, and paperwork. These tasks are often time-consuming, error-prone, and costly, diverting valuable resources from patient care. They also lead to another big problem in healthcare.
Healthcare staffing shortages, driven by overwork and burnout, have been a massive problem in the last five years. By automating workflow with AI, physicians will focus more on their core medical duties, and nurses will engage more in the specialized tasks for which they are trained.
“Administrative work is a huge chunk of what they do day-to-day, and that is draining…I think we have technology that is much better at taking this off their shoulders than ever before“ — Mark Michalski
AI-powered solutions can automate many of these administrative tasks, reducing manual labor and minimizing the risk of errors. For example, AI can automate appointment scheduling, freeing up healthcare professionals to focus on more value-added tasks.
Similarly, AI can streamline the billing and coding process by automatically extracting relevant information from medical records and assigning the appropriate codes. This saves time and reduces the likelihood of coding errors, which can lead to denied claims and lost revenue.
As Ankur pointed out during this podcast, it would be ideal if AI models were 100% confident 80% of the time, needing human review only for the remaining 20%. Reducing the human loop aspect to just 20% would achieve a high return on investment (ROI).
Mark mentioned another good example of AI-driven automation that they provide in Ascertain: streamlining patient discharge processes. AI-powered solutions can help 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 reduce the time and costs associated with patient discharge significantly.
Enhancing Diagnostic Accuracy and Efficiency with AI
Another area where AI can significantly reduce healthcare costs is medical diagnostics. Accurate and timely diagnosis is critical for effective treatment, can help prevent costly complications down the line, and can save lives.
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.
When asked about the most exciting or transformative AI application he has witnessed or participated in, Mark highlighted its use in radiology for image classification and segmentation. AI technology proved effective in triage, ensuring that doctors prioritized and reviewed the most critical CT head scans first.
Numerous examples of AI-powered diagnostic tools have demonstrated significant cost reduction potential. For instance, a study found that an AI system for diagnosing diabetic retinopathy had higher sensitivity than general ophthalmologists or retina specialists. AI tools can potentially serve as a low-cost, point-of-care solution, saving millions annually by reducing specialist visits and enabling early detection and treatment.
Similarly, AI-powered tools like IBM's Watson for detecting cancer and other conditions have shown promise in reducing costs and improving patient outcomes.
However, it is important to recognize that AI does not replace human expertise in medical diagnostics. Contextual awareness and clinical judgment remain essential for accurate diagnosis and treatment planning.
Organizations should view AI as a tool that augments and supports human decision-making instead of a standalone solution. When healthcare organizations implement AI-powered diagnostic tools, they must properly train their clinicians to interpret and act on these systems' insights.
AI has vast potential in both clinical and operational aspects of healthcare. However, being pragmatic about it is key to unlocking its full potential. AI works best when integrated with existing systems rather than attempting to replace them. Additionally, staff must receive adequate training to ensure proper use of the technology.
"You have to be very pragmatic, work with the stack and individuals trained on that stack, rather than trying to replace everything." — Ankur Patel
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 redefine resource management in healthcare by enabling more accurate demand forecasting, optimizing allocation, and improving utilization. IIt can effectively predict patient demand and optimize 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 said:
AI can also 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.
Inventory Management
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.
Real-time monitoring and alerting can help healthcare leaders respond quickly to changes in demand or supply, ensuring that resources are always allocated efficiently and effectively.
Artificial Intelligence in Health Care Data Management
The healthcare industry has accumulated vast amounts of clinical data, but there has been a lack of technology to manage and interpret this data effectively. AI technologies now parse healthcare data and make meaningful conclusions, improving decision-making processes.
Mark said he hopes that the current advancements in AI will enable us to ingest, analyze, interpret, and contextualize data within the broader clinical context. 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.
Ambient voice technology (AVT) is another amazing AI tool for patient data management that is gaining traction in healthcare.
Ambient Voice Technology
Ambient voice technology (AVT) in healthcare uses advanced voice recognition and natural language processing (NLP) systems to automatically capture and transcribe conversations between healthcare providers and patients.
Operating in the background (hence "ambient"), it documents clinical interactions without manual input from physicians or nurses. This technology can integrate with existing electronic health records (EHR) systems, facilitating easier updates to patient records without disrupting workflow.
Automating documentation allows healthcare providers to focus more on patients, enhancing communication, patient satisfaction, and care quality. AI is being used to develop ambient voice systems acting as scribes, allowing doctors to focus more on patient interaction than documentation.
This technology allows doctors to maintain eye contact and be physically present during consultations, enhancing empathetic patient care.
Reducing Readmissions and Improving Post-Acute Care
Hospital readmissions are a significant driver of healthcare costs in the U.S. According to the Agency for Healthcare Research and Quality (AHRQ), readmissions within 30 days of discharge cost Medicare an estimated $26 billion annually, with $17 billion attributed to potentially avoidable readmissions.
Reducing readmissions and improving post-acute care coordination are crucial for controlling costs and ensuring patient outcomes. AI plays a significant role in this effort by predicting readmission risk, enabling proactive interventions, and optimizing post-acute care management.
One promising application of AI in reducing readmissions is developing predictive models that identify high-risk patients before discharge. By analyzing patient data such as demographics, clinical history, and social determinants of health, these models can flag individuals who may need additional support to prevent readmission. This allows healthcare teams to proactively address potential issues and ensure patients have the necessary resources and follow-up care upon discharge.
"No one actually wants to be in the hospital. But if you're in the hospital, the sooner you get out, the better." — Mark Michalski
AI-powered tools also streamline and optimize post-acute care coordination. For instance, 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 helps ensure patients receive the right level of care in the most cost-effective setting, reducing complications and readmissions, and improving health outcomes.
The Future of AI in Healthcare Cost Reduction
The potential for AI to drive cost reduction in healthcare is immense. AI can automate administrative tasks and enhance diagnostic accuracy. It can also optimize resource allocation and improve post-acute care, innovating 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 adopting 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 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 can bend the cost curve and make high-quality healthcare more affordable and accessible for all.
With the right investments, partnerships, and mindset, we can create a healthcare system that is more efficient, cost-effective, patient-centered, and equitable. The future of healthcare is one in which AI and human expertise work together to drive better outcomes, lower costs, and improved access for all.
