🪲 AI Cracks Superbug

Good morning! 

Welcome to Healthcare AI News, your weekly dose of the latest developments and headlines in the world of Healthcare AI.

In this issue, we explore:

✅ Headlines: Tool helps spot invisible brain damage in college athletes

✅ Feature: How AI streamlines prior authorization

✅ Industry: AI identifies five subtypes of heart failure

✅ Tech:  Software bugs that cause real-world harm

✅ Deal Flow: Oshkosh Corporation to acquire AeroTech for $800M

Be sure to read on to see this week's Top headlines, Industry, Tech, and M&A news.

Let's dive in.

HEADLINE ROUNDUP

• Could self-supervised learning be a game-changer for medical image classification? (Read More)

• Leading experts warn of a risk of extinction from AI (Read More)

• Scientists develop new antibiotic to kill superbug using AI (Read More)

• What is ‘Responsible AI’ and why is big tech investing billions in it? (Read More)

• New tool may help spot 'invisible' brain damage in college athletes (Read More)

• AI tool accurately predicts colorectal cancer aggressiveness, patient survival outcomes (Read More)

• Researchers use AI to study Multiple Sclerosis (Read More)

• Will AI help or hurt our aging parents? (Read More)

• Eating disorder helpline takes down chatbot after its advice goes horribly wrong (Read More)

💡 Keep reading to catch up on Industry, Tech & Deal flow

🌟 Advertise With Us 🌟

Boost your brand amongst Healthcare's influential circle! Our diverse subscriber base boasts top executives, key decision makers, and visionary professionals from leading organizations – the ultimate platform for your brand's success. 🔥

Get in touch today

THE FEATURE

How AI Streamlines Prior Authorization

More than 45 years ago, the manufacturing industry in the United States utilized artificial intelligence (AI) in the form of a robotic arm in an assembly line. Nowadays, AI is commonly used in multiple industries, including retail, energy, agriculture, insurance, and more.

Although a little late to the AI game, the healthcare industry continues to integrate technology into an increasing number of processes and procedures. A key reason is the high cost of healthcare in the U.S.

AI Applied to Healthcare

Along with its capability to process large volumes of data to inform decisions that drive health improvements, reduce costs and streamline resource allocation, the combination of AI with key clinical health applications could potentially create $150 billion in annual savings for the U.S. healthcare economy in just the next few years.

Exactly how is AI utilized in healthcare? Top applications include administrative workflow assistance, robot-assisted surgery, virtual nursing assistance, dosage error reduction, and fraud detection. According to some research studies, AI can even perform as well as or better than humans at key healthcare tasks.

With burnout at an all-time high among healthcare workers in the U.S., the elimination—or at least a marked decrease—of manual, paper-based administrative and clinical processes that are often repetitive and labor-intensive is an especially attractive benefit of AI in healthcare. This is especially true in areas such as prior authorization, risk adjustment, and utilization management.

Automating Prior Authorization

Challenges in achieving prior authorization (PA) abound, especially when outdated methods — and systems — are used. The PA process already incurs high administrative overhead for payers, and utilizing primarily manual decision-making often leads to inconsistent clinical determinations. There’s research to back this up.

A prior study from the Council for Affordable Quality Healthcare (CAQH) found that manually reviewing and responding to PA requests costs payers an average of $3.14 per transaction. According to a survey of physicians conducted by the American Medical Association (AMA), more than nine in 10 physicians reported care delays while waiting for insurers to authorize necessary care, and 82% said prior authorization can lead to treatment abandonment. The survey also found that 33% of physicians report that prior authorization has led to a serious adverse event for a patient in their care.

Employing technology to automate PA not only accelerates data exchange between payers and providers but also hastens turnaround times. AI specifically improves the PA process by reducing manual errors and processing time and aiding in real-time clinical decision-making.

Analysis by McKinsey & Company shows that AI-enabled prior authorization can automate 50 to 75 percent of manual tasks, thereby boosting efficiency, reducing costs, and freeing clinicians at both payers and providers to focus on complex cases and actual care delivery and coordination. For example, payers can utilize AI for PA by:

• Combining data from numerous sources to reduce time-consuming low-value tasks

• Integrating the authorization request with the payer’s eligibility database

• Sorting requests by priority level

• Leveraging evidence-based guidelines to combine with claims history

• Reducing the reliance on clinical staff

Taking some of the responsibility off clinical staff is especially important as the healthcare labor shortage continues.

Google Cloud announced recently the launch of its new AI-enabled Claims Acceleration Suite, which is designed to streamline both prior authorization and claims processing. According to the company, it enables faster time-to-insights and easy integration with rule systems and AI models to parse prior authorization requests and reduces manual data entry time. And, its prior authorization review is supposed to augment human-based workflow to expedite the manual review of prior authorization requests.

Promoting More Accurate Risk Adjustment

Risk adjustment is a crucial part of verifying that healthcare payers receive adequate compensation. As the National Health Council notes, it also provides an important opportunity to ensure the sustainability of the exchanges and coverage for patients with chronic conditions.

Two fundamentals of successful risk adjustment are accurate medical charting and coding and complete encounter and supplemental data. Many payers still use outdated and time-consuming processes to conduct these components, though. A prime example is a clinician-conducted chart review to extract unstructured data from patient records and claims.

The problem with these processes is that roughly 80 percent of healthcare data is unstructured, so payers typically can’t achieve a fully comprehensive view of patient risk. However, employing AI enables them to accurately analyze unstructured data, including clinical notes and other patient records, to identify patterns, extract risk-adjustable diagnoses, and predict future outcomes.

In addition to shorter review times and the opportunity to conduct more chart audits, the streamlined analyses AI offers in risk management result in more accurate risk scores and payments, better prediction of healthcare costs, and the closure of gaps in care. One study found that using AI for this purpose improved risk adjustment accuracy by almost eight percent.

New York-based MVP Health Care (MVP) has started using one of AI’s key tools, machine learning, to assess risk adjustment in Medicare Advantage. The goal was to achieve five-star ratings across all of its Medicare Advantage plans. With the technology, MVP was able to accurately assess its care gaps and evaluate its own performance.

Conducting More Reliable Utilization Management

Healthcare utilization management is a common approach for controlling the cost of healthcare benefits by assessing each service’s medical necessity. Along with prior authorization, common utilization management tools used in healthcare include step therapy, predeterminations, case management, concurrent review, and discharge planning.

When AI-powered optimization of utilization management occurs in healthcare, it produces myriad advantages, including advanced algorithms for resource allocation, real-time monitoring and evaluation of patient care, and the reduction of unnecessary treatments and costs. It provides payers with an accurate method for using real-time clinical and financial data.

One of the uses of AI for payers is medication utilization management, through which accurate and timely reports can be created. These reports present patient insurance information and patterns relating to a physician’s prescribing of various specialty drugs, making them yet another data-driven tool that lowers drug costs.

Halifax Health plans to achieve streamlined utilization management workflows across its three hospitals by implementing an AI-based solution. How? By leveraging a combination of a vendor platform and Physician Advisory Services to address administrative challenges and inefficiencies between its utilization management and physician advisor teams. According to the health system, the platform enables real-time clinical data sharing across networks, helping healthcare organizations target inefficiencies, improve provider-payer relations, and reduce the manual work and subjectivity associated with traditional case reviews through advanced case management analytics and reporting capabilities.

Achieving the Advantages of AI in Healthcare

As with any rapidly emerging technology, there are challenges to the use of artificial intelligence in healthcare, including concerns about the privacy and security of patient information. Often referred to as protected health information (PHI), non-compliant disclosure of this data leads to a violation of the Healthcare Insurance Portability and Accountability Act (HIPAA). The result? A civil and/or criminal penalty. Any healthcare entity handling PHI should establish a business associate agreement (BAA) To verify that any vendors with whom they partner use up-to-date data protection standards.

Another potential obstacle to the use of AI in healthcare is the potential for algorithmic bias. It’s defined as the application of an algorithm that compounds existing inequities in socioeconomic status, race, ethnic background, religion, gender, disability, or sexual orientation and amplifies inequities in health systems. To combat this, AI companies should take proactive steps to promote diversity, equity, and inclusion in their data science teams.

When these obstacles are overcome, the vast number of benefits AI offers to payers and other healthcare entities both large and small provide an effective tool for streamlining numerous administrative tasks that lead to burnout. However, they are in no way meant to replace human employees. Rather, they offer an accurate and comprehensive resource for health plans to mitigate unnecessarily repetitive and burdensome tasks, allowing team members to focus on more mission-critical tasks.

INDUSTRY NEWS

• AI in Fitness: Could your future workout buddy be a robot? (Read More)

• A new collaboration between Stanford HAI and the Mayo Clinic will help two scholars explore the use of AI in neurology and cardiology (Read More)

• Groundbreaking cancer treatment has 90% success rate (Read More)

• AI identifies five subtypes of heart failure to predict future risk for individual patients (Read More)

• How successful healthcare organizations keep worker morale up (Read More)

• Ruling clears way for Purdue Pharma to settle opioid claims, protects Sacklers from lawsuits (Read More)

• Creating a sperm or egg from any cell? Reproduction revolution on the horizon (Read More)

• Where CVS, Walmart, Kaiser, Target rank in market share for retail clinics (Read More)

• Over 71% of consumers say unified healthcare platforms improve experience (Read More)

• Australian AI breakthrough predicts patient deterioration 24 hours in advance (Read More)

• GE HealthCare receives FDA clearance for a new deep learning solution for enhanced image quality in PET/CT, advancing its leadership position in AI (Read More)

TECH NEWS

• AI/ML: Considerations of Healthcare’s new frontier (Read More)

• Ransomware attack on US dental insurance giant exposes data of 9 million patients (Read More)

• Software bugs that cause real-world harm (Read More)

• Apple WWDC 2023 is next week, here’s what we expect (Read More)

• An innovative prompting framework for LLMs designed to enable in-context chain-of-thought planning and reasoning (Read More)

• 5 myths about medical AI, debunked (Read More)

• Personal AI vs Private AI, we speak to the founder-CEOs of two contrasting start-ups (Read More)

• The great CISO resignation: Why security leaders are quitting in droves (Read More)

DEAL FLOW

• Oshkosh Corporation to acquire AeroTech business from JBT Corporation for $800M (Read More)

• Strive Health raises $166 million in series C funding from NEA, CVS Health Ventures and Others (Read More)

• GreenLight Biosciences enters into a merger agreement with Consortium led by Fall Line Endurance Fund for Go-Private transaction valued at $45.5M (Read More)

• Hyro secures $20M for its AI-powered, healthcare-focused conversational platform (Read More)

A SPECIAL MESSAGE FROM OUR PETS! 🐾

📢 Help us grow by sharing this AI-mazing Newsletter with your network. 📩

TOP 3 TWEETS OF THE WEEK