What You Should Know:

– A new study by researchers at Mass General Brigham suggests large language models (LLMs),  a type of artificial intelligence (AI), could be a helpful tool for doctors to streamline communication with patients. However, the study also highlights the importance of human oversight to ensure patient safety.

–  The findings, published in The Lancet Digital Health, emphasize the need for a measured approach to LLM implementation.

The Burden of Physician Communication

Physicians today face increasing administrative tasks, including responding to patient portal messages. This can contribute to burnout and hinder patient care. The study investigated the potential of AI to assist doctors in drafting replies to patient messages.

AI Generates Draft Responses for Review

Researchers used a powerful LLM called GPT-4 to create draft responses to 100 hypothetical patient questions about cancer. Radiation oncologists then reviewed and edited these AI-generated responses.

Promising Results, But Caution Needed

The study found that:

• Doctors perceived AI assistance as efficient.
• Over 80% of AI-generated responses were deemed safe by doctors.
• Nearly 60% of these responses required no further editing before sending to patients.

However, the study also identified potential risks:

• A small percentage (7.1%) of unedited AI responses could mislead patients and pose health risks.
• In rare cases (0.6%), these responses could even delay essential medical care.

Importance of Maintaining Human Oversight

Interestingly, physicians often retained the educational content generated by the AI when editing responses. While this highlights the potential benefit of AI-generated patient education, the study emphasizes the importance of human oversight to mitigate safety risks.

Mass General Brigham Committment to Responsible AI

Mass General Brigham is committed to responsible AI development and implementation. They are currently conducting a pilot program integrating AI message drafting into their electronic health records system. Future research will focus on patient perception of AI-generated communication and potential bias in AI algorithms.
“Generative AI has the potential to provide a ‘best of both worlds’ scenario of reducing burden on the clinician and better educating the patient in the process,” said corresponding author Danielle Bitterman, MD, a faculty member in the Artificial Intelligence in Medicine (AIM) Program at Mass General Brigham and a physician in the Department of Radiation Oncology at Brigham and Women’s Hospital. “However, based on our team’s experience working with LLMs, we have concerns about the potential risks associated with integrating LLMs into messaging systems. With LLM-integration into EHRs becoming increasingly common, our goal in this study was to identify relevant benefits and shortcomings.”

What You Should Know:

– Intermountain Health, a leading healthcare system serving the Intermountain West, announced today the enterprise-wide deployment of Nuance Communications‘ Dragon® Ambient eXperience™ (DAX™) Copilot, a generative AI solution aimed at improving clinical documentation efficiency and reducing administrative burdens.

– Intermountain Health emphasizes the responsible development and application of AI technology. DAX Copilot incorporates Microsoft’s responsible AI principles, ensuring ethical and trustworthy implementation within the healthcare setting.

Addressing Urgent Challenges in Healthcare

This move reflects Intermountain Health’s commitment to tackling the critical issues facing healthcare systems nationwide. Clinician burnout, staff shortages, and operational complexities are placing a significant strain on healthcare delivery. DAX Copilot offers a powerful solution to these challenges.

Expanding Access to Care Through AI

By automating clinical documentation and administrative tasks, DAX Copilot frees up valuable clinician time, allowing them to focus on patient interaction and expand access to care across Intermountain Health’s service area, spanning seven states. This is particularly crucial considering the alarming rise in physician practice departures each year.

Reducing Documentation Burden with Conversational AI

The American Medical Association reports that physicians dedicate nearly two hours of administrative and documentation tasks for every hour spent with patients. DAX Copilot leverages conversational, ambient, and generative AI to automatically generate clinical documentation during patient visits, saving physicians significant time and reducing burnout.

“We are deploying DAX Copilot to physicians and APPs across our system to help automate and streamline documentation and other time-consuming tasks, so they have more high-quality, personalized time with patients and less time following up on documentation following their daily medical shift,” said Rob Allen, Intermountain Health president and CEO. “We’re pleased to collaborate with Microsoft in developing next-generation solutions to empower our teams with tools that best support our nurses and other caregivers and to transform healthcare experiences for the patients and communities we serve for years to come.”

What You Should Know: 

– Augmedix, a frontrunner in AI-powered medical documentation, has unveiled Augmedix Go – the first-ever fully automated GenAI solution designed specifically for Emergency Department (ED) documentation. 

– The nationwide roll-out follows a successful pilot program with HCA Healthcare, a leading US healthcare provider.

Revolutionizing ED Workflows

Augmedix Go streamlines the documentation process for ED physicians, instantly generating medical notes from patient interactions. This innovation aims to enhance patient care, boost clinician productivity, and alleviate documentation burdens.

Pilot Program Proves Efficacy

The HCA Healthcare pilot program yielded positive results. ED physicians across four hospitals utilized Augmedix Go to automate documentation, significantly reducing note-taking time. Clinician feedback and gathered data confirmed the solution’s effectiveness in achieving its core objectives.

Tailored for the ED Environment

Augmedix’s vast experience in acute care settings empowered them to refine their AI technology for the unique challenges of ED documentation. The system caters to:

• Multiple Interactions: Captures complex conversations encompassing various, non-sequential interactions with a single patient.
• Medical Decision Making: Documents the thought processes behind medical decisions.
• Re-evaluations and Progress Updates: Tracks changes in patient condition throughout their ED stay.
• Noisy Environments: Functions effectively even in the often-chaotic ED environment.

Seamless Capture and High Accuracy

Augmedix Go offers a hands-free option for clinicians. The mobile app, paired with a Bluetooth microphone stored conveniently in a lab coat pocket, discreetly captures patient conversations. At a remarkable 99% acceptance rate, patients readily consent to Augmedix’s use. Clinicians reported satisfaction with audio capture, note quality, and AI accuracy.

Transparency and Trust

Augmedix Go’s user interface showcases the key steps involved in note creation, fostering trust among clinicians. The system utilizes a combination of Augmedix’s proprietary fine-tuned large language models (LLMs) and leading foundational LLMs, such as Google Cloud’s MedLM, to ensure high-quality medical documentation across various specialties and note sections.

“The launch of Augmedix Go in the ED represents our unwavering commitment to develop solutions for health systems that improve their workflows. Our agility and flexible architecture, exhibited by our broad product suite and open network platform, help us address the complex and varying needs of clinicians and providers,” said Manny Krakaris, CEO of Augmedix. “We have built a sophisticated platform designed to enable clinicians to be present with their patients while trusting that Augmedix technology is doing administrative work unobtrusively in the background.” 

What You Should Know: 

– John Snow Labs, the AI for healthcare company announced the findings of the inaugural Generative AI in Healthcare Survey. Conducted by Gradient Flow, the research explores the trends, tools, and behaviors around generative artificial intelligence (GenAI) use among healthcare and life sciences practitioners. 

– Findings reveal a significant increase in GenAI budgets across the board, with one-fifth of all technical leaders witnessing a more than 300% budget growth, reflecting strong advocacy and investment.

Key findings from the survey include: 

Growing Adoption and Investment

• The survey reveals a significant increase in GenAI budgets across the board.
• Technical Leaders, who champion GenAI’s potential, are witnessing the highest budget hikes.
• Large companies are most likely to be evaluating use cases, while medium-sized companies are focused on experimenting and developing AI models.

Healthcare-Specific Language Models Take Center Stage

• Survey respondents favor custom-built, task-specific language models designed for healthcare needs.
• Open-source models are also popular due to cost-effectiveness and adaptability.

Broad Applications for LLMs in Healthcare

• Common use cases include answering patient questions, deploying medical chatbots, and information extraction/data abstraction.
• Technical Leaders prioritize information extraction and biomedical research applications.

LLMs: A Transformative Force for Patient Care

• Respondents believe LLMs will significantly impact transcribing doctor-patient conversations, medical chatbots, and answering patient questions.
• Smaller companies have higher expectations for LLMs, potentially due to their agility in adopting new technologies.

Accuracy, Security, and Privacy Remain Top Priorities

• When evaluating LLMs, accuracy, security, and privacy risks are paramount concerns, with cost being less important.
• Technical Leaders place an even greater emphasis on these criteria, demonstrating a deeper understanding of potential risks and benefits.

Challenges and Roadblocks to Adoption

• Lack of accuracy and potential legal/reputational risks are the biggest limitations to GenAI adoption.
• Smaller companies view cost as a more significant roadblock compared to larger companies.

Human Oversight Remains Crucial

• “Human-in-the-loop” is the most common approach for testing and improving LLM models, highlighting the importance of human intervention.

Testing Focuses on Fairness and Transparency

• Fairness, explainability, and private data leakage are the most commonly tested requirements for LLM solutions.
• Technical Leaders prioritize private data leakage and potential for misinformation, reflecting their awareness of technical risks.

Future of GenAI in Healthcare

The survey paints an optimistic picture for the future of GenAI in healthcare, with the potential to transform patient care, streamline workflows, and accelerate research. However, successful implementation will require addressing accuracy, bias, and industry-specific needs. Collaboration between technical experts and healthcare professionals will be critical in navigating these challenges and ensuring ethical development of GenAI solutions.

“Healthcare practitioners are already investing heavily in GenAI, but while budgets may not be a top concern, it’s clear that accuracy, privacy, and healthcare domain expertise are all critical,” said David Talby, CTO, John Snow Labs. “The survey results shine the light on the importance of healthcare-specific, task-specific language models, along with human-in-the-loop workflows as important techniques to enable the accurate, compliant, and responsible use of the technology.”

What You Should Know: 

– MemorialCare, a healthcare system in Southern California, announced today a partnership with Abridge, a company pioneering the use of generative AI for clinical documentation. 

– The strategic collaboration aims to streamline administrative tasks for physicians and improve patient care experiences.

Reducing Burden and Rekindling Human Connection

MemorialCare anticipates Abridge’s AI technology to significantly reduce the administrative burden of medical record keeping for its physicians. By automating note generation, Abridge frees up valuable time for doctors to focus on the human aspect of patient care – building rapport and providing personalized attention.

The Abridge platform integrates seamlessly into MemorialCare’s existing Epic workflow, minimizing disruptions and easing adoption for physicians. Notably, this partnership follows successful deployments of Abridge technology at numerous prestigious health systems across the US.

Early Success and Diverse Support

Physicians at MemorialCare have been piloting Abridge for several months, reporting positive experiences and a tangible reduction in documentation time. Importantly, the Abridge platform caters to MemorialCare’s diverse patient population by supporting over 50 specialties and facilitating communication in 14 languages.

A critical feature of Abridge is its “Linked Evidence” technology. This allows clinicians to easily verify any portion of the AI-generated summary by referencing the original patient conversation transcript and audio recording. This transparency builds trust and ensures the accuracy of medical records.

“Watching Abridge work in real-time sometimes feels like watching a science-fiction movie—it’s magical. It is a privilege for me to be part of the effort to bring Abridge into MemorialCare and the broader healthcare community,” said Dr. David Kim, EVP and CEO, MemorialCare Medical Foundation. “I have a bold prediction for how Abridge will impact us—⅓ of clinicians will go home sooner, ⅓ will use Abridge to sustain the strains of today’s healthcare pressures, and ⅓ will be able to see more critical patients who need timely attention.”

What You Should Know: 

– A new survey by Wolters Kluwer Health reveals a significant shift in US physicians’ attitudes towards generative AI (GenAI). 

– The study reveals that 40% of doctors are now prepared to use GenAI tools when interacting with patients at the point of care. This marks a rapid rise in acceptance, with nearly 70% of respondents admitting their views on GenAI have become more positive over the past year.

GenAI Seen as a Time-Saving and Collaborative Tool

The survey highlights the potential benefits of GenAI in addressing physician burnout and improving care delivery:

• Improved Care Team Interaction: 81% of doctors believe GenAI can enhance collaboration between care teams and patients.
• Time Efficiency: Over half (59%) of respondents believe GenAI can save them over 20% of their time, with functionalities like summarizing patient data from electronic health records (EHRs) and streamlining literature searches.

Doctor-Patient Divide on GenAI Adoption

The survey reveals a potential gap in perception between doctors and patients regarding GenAI:

• Confidence in GenAI Results: Two-thirds of physicians believe patients would trust GenAI-based clinical decisions, whereas only half of patients expressed such confidence.
• Patient Concerns About Diagnosis: Only 20% of physicians believe patients would be worried about GenAI involvement in diagnosis, while 80% of patients reported such concerns.

Physicians Prioritize Transparency and Medical Expertise in GenAI

The survey underscores the need for clear guidelines and responsible development of GenAI for healthcare:

• Limited Existing Guidelines: Over a third of physicians reported a lack of established guidelines for GenAI use within their organizations.
• Medical Expertise in Content Creation: The most crucial factor for 58% of doctors when selecting a GenAI tool is the involvement of medical professionals in content creation and training.
• Transparency and Vendor Reputation: Nearly 90% of doctors said they would be more likely to use GenAI if vendors provided clear information about data sources and were reputable companies in the healthcare sector.

“Physicians’ are open to using generative AI in a clinical setting provided that applications are useful and trustworthy,” said Dr. Peter Bonis, Chief Medical Officer, Wolters Kluwer Health. “The source of content and transparency are key considerations.”

What You Should Know: 

– The World Health Organization (WHO) unveils S.A.R.A.H., an AI-powered digital health promoter, available 24/7 in eight languages via video or text. 

– S.A.R.A.H. provides tips to destress, eat right, quit tobacco and e-cigarettes, be safer on the roads as well as give information on several other areas of health.

About Sarah: WHO’s First Digital Health Promoter

S.A.R.A.H. (Smart AI Resource Assistant for Health) leverages cutting-edge generative AI technology:

• Enhanced Empathetic Response: S.A.R.A.H. goes beyond scripted interactions, engaging in dynamic conversations that provide nuanced and empathetic support.
• Personalized Health Information: Access information on various health topics, including mental health and healthy habits, tailored to your specific needs.
• 24/7 Availability: S.A.R.A.H. is available anytime, anywhere, on any device, empowering you to take charge of your health journey.

S.A.R.A.H. empowers individuals to:

• Understand Health Risks: Gain insights into risk factors for leading causes of death like cancer, heart disease, and diabetes.
• Make Informed Decisions: Access up-to-date information on quitting tobacco, maintaining a healthy diet, staying active, and managing stress.
• Realize Your Health Rights: S.A.R.A.H. is a valuable tool to access reliable health information, promoting health equity worldwide.

A Call for Continued Development

The S.A.R.A.H. project prioritizes continuous learning and refinement to ensure the highest ethical standards and evidence-based information. The WHO calls upon researchers, policymakers, and healthcare providers to collaborate on responsible AI development for global health benefit. Previous iterations of S.A.R.A.H. were used to disseminate critical public health messages, under the name Florence, during the COVID-19 pandemic on the virus, vaccines, tobacco use, healthy eating and physical activity.

“The future of health is digital, and supporting countries to harness the power of digital technologies for health is a priority for WHO,” said WHO Director-General Dr Tedros Adhanom Ghebreyesus. “S.A.R.A.H. gives us a glimpse of how artificial intelligence could be used in future to improve access to health information in a more interactive way. I call on the research community to help us continue to explore how this technology could narrow inequities and help people access up-to-date, reliable health information.” 

What You Should Know:

– Pager Health, a virtual-care platform, announces the development of three new generative AI (GenAI) applications designed to streamline care navigation and personalize member experiences.

– The strategic integration partnership with Google Cloud empowers Pager to address the “quadruple aim” of healthcare: improved population health, reduced costs, better patient experience, and improved provider experience.

GenAI Applications Overview

The three GenAI applications focus on:

• Reduced Administrative Burden:
  • Chat Summation: This feature summarizes member interactions, generating accurate notes and eliminating documentation redundancies, saving care teams 10-15 minutes per encounter.
• Enhanced Member Experience:
  • FAQ Bot: This AI-powered bot expedites solutions to common health and benefit questions, reducing unnecessary call center interactions and providing immediate guidance to members.
• Improved Care Team Insights:
  • Sentiment Analysis: This tool analyzes member chat interactions to identify emotions like frustration, anxiety, or happiness. This empowers care teams to tailor their approach and provide better support.

Benefits of Pager’s GenAI Applications:

• Reduced Clinician Burnout: By automating repetitive tasks, GenAI frees up clinicians to focus on providing high-quality care.
• Improved Resource Allocation: Streamlined processes enable better allocation of staff time and resources.
• Personalized Member Care: GenAI personalizes member experiences by providing immediate information and addressing individual needs.
• Stronger Payer-Member Relationships: GenAI fosters stronger connections between payers and members through more efficient and personalized communication.

Availability

Pager’s GenAI applications are expected to be commercially available later in 2024.

“With the development of any AI capability, our goal is not to replace the human touch in healthcare but to make healthcare more human,” said Rita Sharma, chief product officer at Pager. "By leveraging advanced AI models, such as GenAI, we’re able to automate time-consuming, manually intense processes without compromising member and employee privacy or experience – reducing clinician burnout and better allocating staff time and resources where they are needed most.”

What You Should Know:

– John Snow Labs launches Generative AI Lab, a first-of-its-kind platform empowering healthcare professionals, even those without coding experience, to harness the power of artificial intelligence.

– By eliminating coding barriers and prioritizing human oversight, John Snow Labs is paving the way for a future where AI can be a powerful tool for improving healthcare outcomes.

Building Better Models Without the Code

Traditionally, building and training AI models has been a complex process requiring programming expertise and data science resources. The Generative AI Lab breaks down these barriers by offering a no-code interface. Healthcare domain experts can now train, fine-tune, test, and share AI models without needing to write a single line of code.

Boosting Efficiency and Accuracy

One key application of the Generative AI Lab is “bootstrapping” smaller, task-specific models from large language models (LLMs). Here’s how it works:

• Experts can provide simple prompts (zero-shot prompts) and see how the model performs on real healthcare documents.
• Feedback on the model’s performance is used to improve accuracy over time.
• This not only boosts accuracy but also creates smaller models that are more cost-effective to run.

Security and Compliance: A Top Priority

Designed specifically for healthcare environments with strict data privacy regulations, the Generative AI Lab operates entirely behind an organization’s firewall. This eliminates the need for internet access or reliance on third-party APIs.

Collaboration and Knowledge Sharing

The platform fosters collaboration by allowing teams to securely share models, prompts, and guidelines within a central hub. This central repository empowers teams to:

• Search and filter models
• Test and publish models
• Import and export models

Human Expertise Remains Central

The Generative AI Lab embraces a “human-in-the-loop” approach. While AI handles the repetitive tasks, human experts can:

• Validate model results
• Train better models based on ongoing feedback
• Utilize task management tools and audit trails
• Leverage custom review and approval workflows

This ensures that regulatory-grade accuracy remains a priority in critical healthcare applications.

“​The Generative AI Lab was built to support the need for small, fine-tuned healthcare-specific models, and human-in-the-loop workflows to improve them,” said David Talby, CTO, John Snow Labs. “As a no-code tool, the Generative AI Lab empowers domain experts, like doctors and lawyers, to maximize the value generative AI can provide, ultimately leading to better experiences in healthcare and other high-compliance environments.”

What You Should Know:

– Scrut Automation, a provider of cloud-based Governance, Risk, and Compliance (GRC) platforms, has secured $10M in funding from existing investors Lightspeed, MassMutual Ventures, and Endiya Partners. This latest round brings the company’s total venture funding to $20.5M since its inception in 2021.

Empowering Mid-Market Businesses in a Complex Landscape

Scrut Automation was specifically designed to address the unique challenges faced by tech-first mid-market businesses in highly regulated industries such as healthcare, fintech, etc. These companies struggle with:

• Strict compliance demands from regulators and industry bodies
• Pressure to manage risk
• Limited budgets and understaffed teams

The situation is further complicated by factors such as:

• A rapidly evolving threat landscape fueled by advancements like generative AI
• Increased cybersecurity layoffs
• A widening cybersecurity skills gap

Scrut Offers a Scalable, Automated Solution

Scrut Automation provides an alternative approach, enabling companies to build scalable GRC programs tailored to their specific needs. The platform offers:

• Consolidation and Contextualization: Streamline compliance and risk management processes while identifying relevant risks and eliminating duplication of effort.
• Automation for Efficiency: Automate control monitoring with deep automation capabilities and a proprietary unifying control framework. Integrations with over 75 products and automation of over 70% of control tests minimize manual effort.
• Real-Time Visibility: Gain near real-time insights into your risk and compliance posture, allowing for proactive risk mitigation.
• Unified Compliance Framework: Eliminate the need for repetitive compliance demonstrations by linking controls to specific compliance requirements.

The Future of GRC: AI-Powered Concierge Services

Looking ahead, Scrut Automation aims to leverage AI to create an industry-first GRC concierge service for mid-market businesses. This AI-powered solution will empower companies to build robust risk and compliance practices with a reduced reliance on human expertise.

“Mid-market organizations have limited options,” says Aayush Ghosh Choudhury, Co-founder and CEO of Scrut Automation. “They can buy off-the-shelf compliance automation tools that offer a one-size-fits-all approach to compliance, disconnected from the organizational risks; or invest in expensive enterprise-grade tools with year-long implementation and underutilized features.”

Worldwide, today’s leading healthcare organizations are embracing digital transformation. The implementation of Configure, Price, Quote (CPQ) solutions is a major component of these business evolutions. Any organization aiming to enhance operational efficiency and bolster patient care now sees a finely-tuned CPQ system as table stakes. With an eye toward the future, healthcare and life sciences (HLS) organizations integrating CPQ are set to fundamentally alter traditional care delivery and management models.

With that in mind, let’s take a look at eight key trends and features reshaping CPQ in healthcare.

1. AI-Driven Personalization for Enhanced Patient Care

The integration of Generative Artificial Intelligence (GenAI) with CPQ systems marks a significant shift toward delivering personalized healthcare services. By leveraging algorithms to analyze comprehensive patient data sets—from medical histories to lifestyle preferences—CPQ platforms can now help craft tailored treatment quotes. This advancement ensures that healthcare services are individualized to a patient’s unique health profile and delivered in a manner that prioritizes empathy and patient-centricity.

Example: Leveraging GenAI, a CPQ system processes a patient’s complete medical profile to propose a customized treatment plan for diabetes management. The plan includes medication, lifestyle modifications, and scheduled consultations, all priced dynamically based on the patient’s insurance coverage and eligibility for financial assistance programs.

Remember: Integrating AI into CPQ systems ensures that healthcare services are customized to the individual, elevating the standard of patient care.

2. Leveraging Predictive Analytics for Dynamic Pricing

Predictive analytics has transformed CPQ systems into dynamic pricing engines capable of adjusting healthcare service costs in real time. By analyzing data on market trends, regulatory updates, and patient demographics, CPQ platforms can fine-tune pricing models to remain competitive while ensuring services are accessible to a diverse patient base.

Example: A healthcare provider (HCP) uses predictive analytics with GenAI/CPQ to adjust the cost of elective surgeries based on seasonality, insurance rebates, and patient influx, ensuring optimal occupancy rates and patient affordability.

Remember: Dynamic pricing through predictive analytics allows healthcare providers to offer competitively priced services tailored to the market and patient needs.

3. CPQ’s Crucial Role in Value-Based Care

In today’s shift towards value-based care, CPQ systems emerge as essential tools for designing service bundles that prioritize patient outcomes over traditional service-based models. This approach aligns pricing and treatment plans with the overarching goal of improving patient health outcomes, marking a departure from fee-for-service models.

Example: A CPQ platform enables a cardiology clinic to bundle diagnostic tests, consultations, and follow-up care into a single, outcome-focused package. This bundled approach simplifies billing for patients and aligns the clinic’s services with the goal of reducing hospital readmissions for heart disease patients.

Remember: By aligning service offerings with value-based care principles, CPQ systems are pivotal in prioritizing patient health outcomes.

4. Virtual CPQ Solutions Bridge the Accessibility Gap

The expansion of telehealth services and remote consultations necessitates the development of virtual CPQ solutions adept at managing online healthcare delivery. These platforms ensure precise billing and service documentation for telemedicine, accommodating the unique requirements of remote care delivery and enhancing access to medical services.

Example: A mental health service provider employs a virtual CPQ system to manage and bill for a series of cognitive behavioral therapy (CBT) sessions delivered via telehealth. This system facilitates seamless service delivery to patients in remote areas, eliminating geographical barriers to access.

Remember: Virtual CPQ solutions are instrumental in extending healthcare services to underserved populations, ensuring quality care is accessible to all.

5. Blockchain for Secure and Transparent Healthcare Transactions

Blockchain technology introduces a new era of security and transparency to CPQ systems in healthcare. By creating a decentralized ledger for all transactions, blockchain ensures that patient data, billing information, and contractual agreements are secure, immutable, and transparent. This level of security protects sensitive information and builds trust among patients and providers by guaranteeing the integrity of every transaction.

Example: Implementing blockchain technology, a hospital’s CPQ system securely records patient consent, treatment plans, and billing details for surgical procedures. This record is immutable and easily verifiable, providing patients with transparent access to their treatment histories and financial transactions.

Remember: Blockchain integration within CPQ systems enhances the security and transparency of healthcare transactions, fostering trust in digital healthcare services.

6. Navigating Regulatory Compliance with Agile CPQ Systems

With healthcare regulations constantly evolving, CPQ systems have become pivotal in ensuring that organizations remain compliant. These systems are designed to be agile and easily updated to reflect new documentation standards, privacy laws, and compliance measures. This agility helps healthcare providers quickly adapt to regulatory changes, safeguard patient data, and maintain operational integrity.

Example: A CPQ system is updated to include new General Data Protection Regulation (GDPR) compliance measures, automatically incorporating enhanced patient data protection protocols into every quote and agreement process. This ensures the organization stays ahead of compliance requirements, protects patient information, and avoids penalties.

Remember: The agility of CPQ systems in adapting to regulatory changes is essential for maintaining the compliance and integrity of healthcare operations.

7. Enhancing User Experience with Intuitive CPQ Platforms

Modern CPQ systems prioritize the user experience (UX), featuring intuitive interfaces, mobile compatibility, and advanced collaboration tools. These enhancements make CPQ platforms more accessible to healthcare professionals, streamlining the quoting process and facilitating better patient interactions. An optimized UX encourages wider adoption of CPQ systems, ultimately improving the efficiency and effectiveness of healthcare service delivery.

Example: A CPQ platform with a user-friendly interface allows an HCP to quickly generate a quote for a patient’s treatment plan on a tablet during a consultation. This immediacy improves patient engagement and satisfaction by providing straightforward, upfront information about treatment options and costs.

Remember: An intuitive and user-friendly CPQ system improves operational efficiency and patient interactions, enhancing the overall quality of healthcare services.

8. Promoting Industry Collaboration through CPQ Interoperability

The push for interoperability in healthcare is mirrored in CPQ systems designed for seamless integration with other digital health platforms. This interconnectedness allows for a collaborative approach to patient care, facilitating the exchange of information and coordination of services across different providers. CPQ interoperability breaks down silos within the healthcare system, enabling a more cohesive and efficient patient care experience.

Example: An interoperable CPQ system enables seamless data exchange between a hospital’s Electronic Health Record (EHR) system and a specialist clinic’s treatment planning tool. This ensures that a patient’s care plan is comprehensive and coordinated across multiple touchpoints, improving treatment outcomes and patient satisfaction.

Remember: Interoperable CPQ systems foster collaboration within the healthcare ecosystem, ensuring that patients receive well-coordinated and comprehensive care.

Future of CPQ Systems in Healthcare

The transformative potential of CPQ systems in healthcare is immense. They offer a pathway to more personalized, efficient, and transparent care delivery. As healthcare providers embrace these emerging trends, they unlock new possibilities for improving patient outcomes, operational efficiency, and regulatory compliance. The future of healthcare, powered by advanced CPQ solutions, promises a landscape where technology and care converge to meet the needs of patients and providers alike.

About Sri Tella

Sri Tella is the Senior Director of Technical Product Management at McKesson, and leads transformative CPQ initiatives for Healthcare and Life Sciences organizations. She specializes in legacy migrations and the optimization of cloud technologies for sales and operations teams.

What You Should Know:

– A new report by HFS Research, in collaboration with Cognizant, sheds light on critical trends shaping the future of healthcare in the United States. The report, titled “Re-energize Health Consumer Engagement and Bridge the Payer-Provider Divide with GenAI,” explores the potential of Generative Artificial Intelligence (GenAI) and vertical integration to improve healthcare delivery.

– The report also emphasizes that the healthcare system must embrace new approaches and technologies like GenAI to address current challenges and create a future with improved health outcomes and a more engaged patient population.

Key Findings and Challenges

The report surveyed C-suite executives and senior leaders from 350 US health plans and health systems. Key findings include:

• Disconnects Between Payers and Providers: A significant gap exists between health plans (payers) and health systems (providers) in understanding member/patient needs. While 50% of health plans believe they possess the digital tools to meet these needs, only 40% claim to truly understand them. This disconnect can negatively impact patient engagement and health outcomes.
• Deteriorating Health Outcomes: The US healthcare system faces declining life expectancy, rising chronic conditions, and mental health epidemics. These factors necessitate a focus on the “triple aim” of healthcare: reducing costs, enhancing patient experience, and improving health outcomes.
• GenAI’s Potential: 25% of providers plan to leverage GenAI for patient engagement, including personalization in acute care settings, customized recovery and wellness content, and generating medical summaries. However, a lack of talent with skills in programming, data science, and domain expertise remains a hurdle to widespread adoption.
• Leadership Ambivalence: While healthcare leadership expresses interest in GenAI, concerns exist about its actual impact.

6 Actionable Opportunities for Health Systems

The report highlights six actionable opportunities for the healthcare system:

1. Embrace GenAI as a Strategic Asset: GenAI can be a valuable tool for driving value from investments in areas like vertical integration.
2. Bridge the Payer-Provider Divide: Collaboration and improved communication are crucial for a more patient-centric healthcare system.
3. Invest in Digital Transformation: Modernizing technology and processes is essential for meeting evolving consumer needs.
4. Develop a GenAI Adoption Strategy: Healthcare organizations need a clear roadmap for integrating GenAI while addressing talent gaps.
5. Focus on the Triple Aim: Prioritize initiatives that address cost reduction, improve patient experience, and achieve better health outcomes.
6. Leverage New Care Models: Explore innovative care delivery models, such as the resurgence of digitally-driven primary care.

“The report highlights the disconnect between payers and providers and its impact on patient engagement and health outcomes,” said Patricia Birch SVP and Chief Strategy Officer for Cognizant Health Sciences and report co-author. “We believe this research will encourage healthcare stakeholders to embrace gen AI as a strategic asset for driving value from high priority investments, including vertical integration.”

Autonomous inpatient coding. This may sound like a pipe dream for revenue cycle leaders, whose hopes were likely raised and crushed in the early aughts. But it’s coming sooner than most think.

Why is true inpatient coding automation the “holy grail” – as Kerry Gillespie, a former CFO at Intermountain Health and now an executive consultant at Warbird Consulting Partners, said to me at a Healthcare Financial Management Association roundtable last year?

For decades, inpatient services have been the backbone of many provider organizations and, simultaneously, a significant pain point for those managing hospital operations. First, the financial imperative is clear: although inpatient represents a much smaller share of patient volumes, it typically drives 60% of total revenues, according to MD Clarity.

Despite the financial magnitude, managing the revenue cycle for inpatient visits has proven more challenging. While providers have struggled with staffing shortages for medical coders of all stripes, certified coding capacity for inpatient admissions has been especially limited. According to recent Glassdoor data, this shortage is reflected in labor costs, with inpatient coder wages often 20%+ higher than outpatient wages. An MGMA Stat poll found that 34% of medical group leaders cited medical coders as the most difficult revenue cycle position to hire for.

With so much value on the line, coding accuracy and speed are much higher stakes. Inpatient coders must grapple with some of the most complex medical cases, typically involving longer stays and more complicated care plans – meaning a much larger volume of medical documentation to review and code. Fully automating coding for even a portion of inpatient cases would thus significantly reduce the burden on coders. Indeed, solving inpatient coding with AI, an elusive goal, would be a massive win for providers – perhaps one of the most impactful operational opportunities in a generation.

So what’s changed?

Tech advancements offer renewed promise

If you’re skeptical of meaningful automation on the inpatient side, you’d be right to think that way. But recent developments have upended what’s possible. To make sense of that, let’s take a quick look at what the past few decades have tried to deliver.

The history of coding automation is defined by four key phases:

• The arrival of natural language processing (NLP)
• The transition to ICD-10
• The advent of deep learning 
• The development of large language models (LLMs)
  

NLP is a type of AI that can be traced back to the 1940s. It enables computers to understand, interpret, and generate human language in a natural and meaningful way. In the early aughts, pioneers like CodeRyte and A-Life (now owned by other companies) led the way in coding productivity based on NLP, achieving rates of 70% automation for low-complexity encounters and 30% automation for moderate complexity. But these capabilities hit their limit – and then coding got harder.

On October 1, 2015, ICD-10 went live in the US. And with it, the number of possible diagnosis codes ballooned from 13,000 to 69,000. This explosion of codes crushed the promise of early NLP, as it couldn’t handle the larger volume and complexity of codes. As medical coding teams struggled to keep their heads above water and contend with 5x the number of codes, automation rates from available tools plummeted, negatively impacting the revenue cycle.

A light on the horizon appeared in 2018 with advances in deep learning. This approach combined mountains of data and extraordinary computing power to create AI that figures out its own rules, translating into super-high automation rates, significant cost reductions, and broad specialty coverage for health systems.

Underpinned by deep learning, autonomous coding has rapidly expanded across high-volume outpatient specialties, delivering automation rates of 85-90%+ or even pushing 99% in specialties like radiology. Prompted partly by broader AI agendas, autonomous coding is becoming the norm for many outpatient settings. This same technology is now turning to inpatient care.

But to make inpatient autonomous coding robust, the last boon has been the arrival of LLMs. This generative AI technology – known as the basis of ChatGPT and other popular tools – complements deep learning to handle last-mile issues, enabling near 100% automation rates. With deep learning and LLMs working in tandem, providers can at last realize true inpatient coding automation.

What to expect and how to prepare

From my vantage point, true autonomous coding for inpatient will arrive later in 2024, providing hyper-accurate automation for the majority of patient admissions. In 2025 and beyond, inpatient capabilities will catch up to where outpatient is today.

This advancement is not too far away. So, how can health system leaders ready their organizations to take on meaningful inpatient automation? A great place to start is by bringing autonomous coding to outpatient departments. Besides reaping the financial and operational benefits – including heightened accuracy, reduced labor costs, improved revenue capture, and decreased administrative burden – getting started on the outpatient side enables leaders to build a relationship with a trusted vendor and sets the stage for expansion to inpatient. Completing implementation for one or more outpatient specialties will also build confidence in the approach and enable revenue cycle and HIM teams to move more quickly on inpatient down the road.

In addition to paving the way for inpatient automation, securing experience with autonomous coding for outpatient helps to promote the organization’s broader AI ambitions. As providers pursue different models for building AI competency – hiring Chief AI Officers, forming cross-functional committees, or appointing in-house experts – locking in concrete projects such as outpatient automation helps to increase momentum and skill-building. Visibility into these AI efforts may even help systems to attract top talent – who, according to BCG, increasingly expect AI to help with their day-to-day roles – in tough labor markets.

Capturing the ‘holy grail’

The impending arrival of autonomous inpatient coding is a remarkable breakthrough for health systems. Recent advancements in deep learning and LLMs mean this transformative technology is closer than ever before. And starting now, the provider organizations that proactively set themselves up for this monumental shift will reap the most benefits.

About Andrew Lockhart

Andrew Lockhart is CEO of Fathom, the leader in autonomous medical coding. Andrew earned his MBA from Stanford University and his BA from the University of Toronto. He is an avid speaker and has presented at HFMA, Academy Forum, Stanford Medical School, and HBMA events.

What You Should Know:

–  Verix, a leader in AI-powered commercial operations solutions for the pharmaceutical industry, has announced the launch of its revolutionary GenAI Database Explorer.

– The GenAI-powered tool empowers sales reps with the ability to explore complex commercial data using natural language processing (NLP), marking a significant step forward in data accessibility and insights generation.

Empowering Sales Teams with Natural Language Queries

Verix’s flagship platform, Tovana, utilizes machine learning to extract valuable insights from commercial data, aiding pharmaceutical sales teams in understanding healthcare provider (HCP) behavior, preferences, and patient needs. The new GenAI Database Explorer integrates seamlessly with Tovana, allowing sales reps to:

• Craft targeted sales strategies through intuitive natural language queries.
• Tailor their approach to individual HCPs based on data-driven insights.
• Maximize HCP engagement with targeted communication and strategies.
• Effortlessly analyze performance and identify areas for improvement.

Unveiling Hidden Insights Through Advanced Technology

The GenAI Database Explorer leverages the power of natural language processing (NLP) and Verix’s robust data foundation. This allows users to extract actionable insights without prior data structure knowledge, ensuring compliance with the latest industry standards.

“Our GenAI Database Explorer empowers users to easily access valuable information and generate insights catering to their own business needs. It increases data exploitation, informed decision-making, and helps field teams better prepare for their next engagement,” said Doron Aspitz, CEO of Verix. We have already started receiving excellent feedback from our pilot customers and look forward to rolling this out to all our customers and clients today.”

What You Should Know:

– CentralReach, the leading provider of software for ABA and multi-disciplinary providers treating Autism and IDD,  announced the appointment of David Stevens as its first-ever Head of GenerativeAI (GenAI).

– In this newly created role, Stevens will lead a team dedicated to designing, developing, and piloting GenAI features for CentralReach’s software suite, cari. The company currently boasts over 20 GenAI solutions in various stages of development, with a focus on streamlining workflows and improving financial outcomes for providers.