As the buzz around ChatGPT and generative-AI continues, it’s important for health leaders to look past their excitement around these new tools and remain committed to ensuring patient trust is a top priority. Consider that six out of every ten patients are reportedly uncomfortable with providers relying solely on AI for specific healthcare needs, and while ChatGPT may appear to provide impressive responses, there’s still room for error and data misinformation, which can result in patient dissatisfaction and overall negative healthcare outcomes. 

To ensure patient information is utilized in a proper, secure manner, a cautious approach is needed when implementing generative-AI solutions. Though useful, these tools won’t solve all of healthcare’s problems. Instead, implementing a healthy mix of human touch and precautionary measures, like strengthened AI regulations around patient data, is a safer way to create significant improvements throughout the entire healthcare ecosystem. 

Acknowledging existing limitations within generative-AI tools like ChatGPT 

Currently, AI-models like ChatGPT primarily possess the ability to reference specific data they’re trained on; at the same time, they lack some of the cognition to understand meaning. One survey found that of the ChatGPT-generated responses used to develop medical content, 47% were fabricated, 46% were authentic with inaccuracies, and only 7% of responses proved completely authentic and accurate.

AI-models like ChatGPT also face a multitude of other complications when it comes to language and meaning, which must be addressed in order to avoid negative consequences. When these models are asked a question with complex and specific word choices, the response may lack both true reasoning power and accuracy, proving potentially detrimental to a patient’s health. Only 38% of U.S. adults feel utilizing AI like ChatGPT to diagnose diseases and recommend treatments would lead to better health outcomes, with 33% feeling the tool would result in worsened healthcare outcomes. For example, if someone is looking for answers about a rare condition that is not within the ChatGPT’s data training wheelhouse, its responses could result in a misdiagnosis, negatively impacting the patient’s health condition.

Adopting caution to improve overall patient outcomes 

Patient trust is paramount, and right now, that trust is lacking. Reportedly, 50% of patients are not fully sold on the medical advice provided to them through AI, but they are open to a combination of the tool with guided human input, striking a cautionary balance between utility and safety when it comes to handling a patient’s personal healthcare information. Providers can use their medical training and background, as well as their innate understanding of humans, to weed out inaccuracies provided by ChatGPT responses. The right combination of AI and human interaction can potentially improve a patient’s overall healthcare journey. 

Another way to improve a patient’s experience and outcome when working with these tools is by molding generative-AI models, like chatbots, to fit a specific health system’s need. This combination is a win-win for patients and providers, reducing administrative burden by improving simple tasks like appointment scheduling, pre-and post-visit intake forms, billing and statements, etc., and providing patients with a seamless way to access non-urgent healthcare questions in a timely manner. 

Current regulation landscape 

There are still gaps across the industry when it comes to regulating generative-AI, like ChatGPT, in healthcare, which can be detrimental to a patient and potentially risk breached data and public exposure of private, sensitive healthcare information, protected by HIPAA laws. There are existing ways of utilizing this tool in a HIPAA-compliant manner to properly secure patients’ personal data and provide an added barrier of security and peace of mind.

While there is still much to be learned about generative-AI models, these tools can be useful to healthcare workers if introduced and utilized properly. Through a careful combination of providers practicing caution when using these tools, including looking for any data errors or misinformation, we can begin to see an improvement in overall patient healthcare outcomes and satisfaction.  

About Matt Cohen
Matt Cohen, Director of AI at Loyal, is passionate about improving the healthcare experience through intelligent software. Before Matt joined Loyal, he spent several years performing research in areas that include machine learning, speech, signal and audio signal processing at MIT Lincoln Laboratory and the University of Maryland, College Park. He worked as a software engineer and application support engineer at MathWorks, with a focus on machine learning, and was initially hired as a Software Engineer, Applied Machine Learning, at Loyal. As the Director of AI, Matt oversees the company’s machine learning strategy and the AI team, finding new ways to “…provide technology that guides individualized healthcare actions at scale, and creates efficiency within operations.”

What You Should Know: 

– A significant 87% of digital health investors stated that generative AI solutions have influenced their investment strategies, with nearly 1 out of 5 respondents indicating that it is significantly affecting their strategy,  according to the results of an online survey from GSR Ventures. 

– GSR Ventures, an early-stage digital health investment firm with over $3.5B in assets under management, conducted an online poll in September 2023 with over 40 participants from leading firms in the investment community. The survey covered topics such as deal volume, valuations, industry opportunities, and sectors of opportunity for startups.

Key Findings of the report include: 

– Investment Trends: About 62.5% of investors expect to make the same number or more tech investments for the rest of 2023 compared to 2022.

– Lower Valuation Expectations: Most investors anticipate lower valuations for seed stage, Series A, and Series B companies, with decreases of 20% or more compared to 2022. The majority expect this trend to continue for the remainder of 2023.

– Promising Sectors: According to 55% of investors, the oncology specialty holds the most promise for startups, followed by cardiovascular care (37.5%). However, ophthalmology is seen as offering the least opportunity by nearly one-third (31%) of investors.

– Challenges and Opportunities: The shortage and burnout crisis among healthcare providers are seen as the most significant problem-solving opportunity for startup companies by 50% of investors, followed by changing reimbursement models at 26.3%.

“We expected that generative AI innovations had impacted the digital health investment community and our poll results certainly confirmed it, with nearly 1 out of 5 respondents attesting that it is ‘significantly’ influencing their strategy,” said Justin Norden, MD, MBA, a partner at GSR Ventures and adjunct professor at Stanford Medicine where he teaches courses on the subject to medical students. “It will be exciting to witness over the remainder of 2023 into 2024 the many new generative AI healthcare startups that enter the market. Hopefully, investor enthusiasm drives greater adoption of such technologies across an industry that would massively benefit from greater automation and efficiency.”

To review the full survey results, click here.

Last year, only 8% of healthcare executives said their RCM or billing departments were adequately staffed, and a stunning 48% said the employee shortage was “severe,” according to a survey cited in MedCityNews.

RCM teams are the engine that allows the machinery of healthcare organizations to run. Without effective billing and payment processes that deliver positive patient financial experiences, everything grinds to a halt.

These staffing woes can’t continue. The cascade of problems that can develop from overworked RCM payments teams includes errors, loss of revenue, patients contesting bills, and organization-wide revenue shortfalls that affect care.

Frantic hiring and retention efforts haven’t delivered. It’s time for a new strategy.

The good news: if this employment crisis had happened even five years ago, we’d be in serious trouble. But we’re at exactly the right moment to meet the challenge thanks to the power of artificial intelligence (AI).

Since the launch of ChatGPT, there’s been a lot of buzz about the effect AI will have on jobs, coupled with a fear that machines will rob people of their employment.

A better way to think of it is how AI will shift our jobs, and in the case of healthcare RCM workflows, it’s shifts we would be celebrating even if there weren’t an ongoing staffing shortage.

In terms of satisfaction on both ends of the payments equation – RCM team members and patients – AI is positioned to ease any number of pain points. 

Anything you are doing several times a day or that you spend several hours in a month doing is something you should figure out how you can automate.

For patient financial representatives, positive aspects of the job can be overshadowed by rote administrative responsibilities. But repetitive, predictable tasks are exactly what machines excel at. Looking for opportunities to hand those dull but necessary workflows over to AI can transform a representative’s workday.

Take, for example, post-interaction notes. During a conversation with a patient about billing and payments, representatives are frequently simultaneously documenting the details, a task they finish before connecting with the next patient.

So they’re distracted during the conversation and then have to spend non-patient time typing up summaries.

Imagine that same interaction if the representative didn’t have to multitask. 

Imagine the patient experience if the representative could focus solely on empathizing and explaining. 

Imagine representatives’ reduced stress levels if they are able to focus on one issue at a time.

Today’s generative AI can deliver that improvement. Automated call notes, produced in real time, remove a tedious task from the representative, freeing them up to do what people do best – connect, problem-solve, explain, empathize – making their job more engaging, which can drive retention, and the patient’s experience more valuable.

And without that additional responsibility, average handle time goes down, allowing representatives to connect with more patients.

As a bonus, because AI works on rules, the notes are standardized, consistent, and clear, allowing a representative to get up to speed quickly during the next interaction with that same patient, and opening the door for managers to research, look for opportunities to train representatives and patterns to improve processes.

That’s just one small task that, when handed over to AI, delivers enormous benefits at multiple touchpoints. The same is true of tasks like automating QA and compliance, delivering training opportunities and improvement metrics, patient self-service opportunities, and decreasing new representative ramp time.

We’ve already seen the impact of AI-powered solutions on coding and billing, which has eased the staffing shortage on that end of the RCM process, allowing for increased productivity and decreased simple data entry to more challenging work.

So it’s time to give ourselves the same gift throughout the RCM process. Let’s consider all the ways we can use AI throughout the process to increase productivity without demanding employees “do more with less,” further increasing their stress and the odds that they will leave for another job, or even another field.

A Becker’s roundup of 63 health system executives’ 2023 investment plans showed that the first issue on many of their minds was people. Investment in recruitment and retention is vital to keep healthcare moving forward.

The same issues of burnout and understaffing affecting clinical care teams have also hit RCM teams – hard. Investing in the right AI is one way to cure RCM payment pains so teams can keep powering the healthcare engine.

About Shantanu Gangal

Shantanu Gangal is the CEO and co-founder of Prodigal, which uses artificial intelligence to illuminate conversations across borrowing, lending, and repayment, including banking, healthcare RCM, and more. Prodigal’s solutions turn analysis into action with quality and compliance automation, business insights for process improvement, real-time agent assistance, and automated notes.

He has an MBA from Wharton and is a Computer Science gold medalist from IIT Bombay. Shantanu has spent years innovating at the intersection of lending and data. Prior to co-founding Prodigal, he led data teams at Fundbox and worked with various banks during his time at Boston Consulting Group and Blackstone.

What You Should Know: 

– Covera Health, an AI-enabled diagnostic technology company, has announced securing up to $50M in additional Series C funding. This substantial funding aims to support their mission of reshaping the role of radiology on a national scale.

– The company’s platform employs advanced data analytics and AI to enhance the quality and efficiency of radiology services. Their innovative approach focuses on improving radiology outcomes while reducing costs and unnecessary imaging tests.

CoRead Acquisition

The company also finalized the acquisition of CoRead, a leading AI quality assurance company currently being used by more than 2,000 hospital sites nationwide. CoRead stands at the forefront of generative AI focused on performance improvement in radiology, and its capabilities will be integrated into Covera Health’s existing best-in-class quality platform. , Lawrence Ngo, former CEO of CoRead, will join Covera Health as Senior Vice President, Quality Assurance Platform, reporting to President Aaron Friedkin.

Covera Health Background

Covera Health collaborates with radiologists, payers, and providers to create a network of high-performing radiology centers. By using data-driven insights, they help guide patients to the most suitable and effective imaging facilities, thereby improving patient care and cost-effectiveness.

Covera Health’s innovative approach not only improves radiology outcomes but also aims to reduce costs and unnecessary imaging tests. By ensuring patients receive the right tests at the right facilities, they contribute to better healthcare outcomes and cost-effectiveness. 

National Impact

This funding round signifies a significant step in Covera Health’s journey to transform radiology services across the country, ultimately leading to better healthcare outcomes and a more efficient healthcare system.

What You Should Know:

– A new survey conducted by AHIMA reveals that 66% of health information (HI) professionals have experienced ongoing staffing shortages in their workplaces over the past two years. These shortages were notably prevalent in areas such as data quality, consumer health information, revenue cycle management, privacy, risk, and compliance, and data analytics.

– The report, “Health Information Workforce: Survey Results on Workforce Challenges and the Role of Emerging Technologies” also finds that despite workforce shortages, HI professionals see promise in artificial intelligence (AI) and machine learning (ML) technologies for alleviating some of the workforce burdens. However, this increased reliance on AI and ML also calls for upskilling within the profession.

Impact of Health Information Professionals Shortage

83% of respondents have witnessed an increase or persistence in unfilled HI positions within their organizations over the past year, indicating a pressing need for targeted interventions. The shortages in the HI profession have had far-reaching consequences, including reduced reimbursement, increased claims denials, lower patient data quality, and slower information releases. These issues have had a direct impact on healthcare quality and align with broader workforce trends in the healthcare sector.

The Promise of AI/ML Adoption

The survey indicated that 45% of respondents have adopted AI and ML in their departments, but this adoption comes with challenges, such as increased technical demands and the need for enhanced oversight. As a result, 75% of respondents consider upskilling the HI workforce as essential given the growing adoption of AI and ML tools.

Why It Matters

These findings from the survey are crucial in shaping the future management of patient health data and determining the necessary workforce to navigate emerging technologies. The Biden-Harris Administration and the US Congress are actively exploring the implications of AI and emerging technologies on the US workforce, and AHIMA intends to use these survey findings and recommendations to prepare the HI workforce through policy discussions, research, education, and training to ensure the secure management of patient health data.

Report Background/Methodology

AHIMA commissioned NORC at the University of Chicago to conduct the survey to examine the workforce challenges impacting HI professionals and assess the role of emerging and evolving technologies, such as AI and ML, in reshaping the HI workforce. AHIMA will use this information to improve data quality, increase productivity, and reduce administrative burden. With insights from 2,500 respondents, including AHIMA members and non-members, drawn from a vast pool of 35,000 in August 2023, the study spotlights the urgent need for action.

What You Should Know: 

– Emory Healthcare, a comprehensive academic health system in Georgia, is collaborating with healthcare technology pioneer DrFirst to help patients access and adhere to affordable, appropriate prescription medications for their health conditions and ailments. 

– As part of the collaboration, Emory Healthcare will leverage three medication management services offered within Fuzion by DrFirst, a software platform that uses clinical-grade artificial intelligence (AI) to streamline clinical workflows to boost workflow efficiency and medication adherence.

Assist Patients with Medication Adherence and Affordability

Research shows that medication adherence is a primary determinant of success in patient outcomes, while non-adherence can lead to a worsening of diseases and disorders, increased healthcare costs and even death. Key benefits of the collaboration include: 

• Medication history with clinical-grade AI provides the most comprehensive information available and eliminates the need for manual data entry in workflows. This means patients will have less chance of adverse drug events, clinicians will have more complete data at the point of care, and pharmacists will save time on medication reconciliation, which is the process of making sure the list of medications for a patient matches what the patient is actually taking.

• Prescription price transparency allows providers to know patients’ pharmacy benefits, including their out-of-pocket costs, before they leave the doctor’s office or hospital. Knowing these costs up front, with options to switch to an affordable therapeutic equivalent if too costly, saves time and money for patients. This increases medication adherence and means patients are less likely to abandon high-cost prescriptions at the pharmacy.

• Coming soon: Automated messaging will alert patients when their clinician sends an electronic prescription to their pharmacy. The HIPAA-compliant functionality will allow patients to review their prescription and pharmacy information, as well as get relevant educational information and financial savings for their medication.

Patients will automatically benefit from the prescription messaging when they opt-in for communications from Emory. Additionally, patients can choose to opt-out of receiving text messaging alerts.

“The collaboration with DrFirst will put patients first in enhancing medication prescribing and management, from an encounter with the clinician to picking up the prescription at the pharmacy,” says Alistair Erskine, MD, chief information and digital officer for Emory Healthcare. “These clinical solutions will assist our providers with their decisions in providing a higher level of care for our patients.”

What You Should Know:

– AdaptX, a self-serve clinical management solution company raises $10M in funding led by Cercano Management and included participation from notable entities, such as Memorial Hermann Health System, Morningside Ventures, and returning investors Founders’ Co-op, Fortson VC, Star Equity, and WRF Capital.

The company’s focus is on using AI-driven technology to enable clinical leaders to make data-informed decisions and optimize patient care processes with a positive financial impact on healthcare institutions.

AI-Powered Self-Serve Clinical Management

AdaptX offers self-serve solutions designed to empower clinical leaders in assessing, enhancing, and managing patient care. They achieve this by leveraging data from electronic medical records (EMRs) to inform decision-making and care management. With the help of AdaptX, clinicians can quickly adapt treatments and workflows. This leads to improved patient care while simultaneously delivering an immediate return on investment (ROI) for healthcare institutions.

AdaptX plans to utilize the newly acquired funds to support its rapid growth and to further invest in its AI-driven technology known as Adaptive Clinical Management™.

What You Should Know: 

– Annalise.ai, a global leader in the radiology imaging sector, announced the launch of Annalise Triage, an AI-powered worklist solution for chest X-rays and head CTs tailored specifically for the U.S. market. 

– This innovative software will transform the radiology field by empowering radiologists to efficiently triage studies and prioritize those with suspected critical findings, ultimately streamlining their workflow, and supporting faster time-to-diagnosis for urgent conditions. AI-enhanced worklist triaging has been shown to decrease both the time a radiologist needs to read a report, and the time from request until a report is available for critical cases.

Annalise Triage Overview

Annalise Triage includes an array of 12 FDA-cleared findings, including 5 for chest X-ray (CXR) and 7 for non-contrast head CT (CTB). Readily available for use across the United States, this comprehensive offering received the 510(k) clearance and the coveted breakthrough designation for obstructive hydrocephalus. Notably, Annalise Triage is the only radiology triage device to have received this distinction, which is exclusively granted to devices and products that fulfil specific criteria, which include enhancing the diagnosis or treatment of life-threatening or irreversibly debilitating conditions, offering breakthrough technology, presenting a significant advantage over existing alternatives, and being in the best interest of patients.² 

AI-powered Worklist Triage for Head CT

By implementing Annalise Triage, radiologists can swiftly and accurately detect a range of critical, time-sensitive conditions from unenhanced head CT studies, including acute subdural hematoma, acute subarachnoid hemorrhage, intra-axial hemorrhage, intraventricular hemorrhage, obstructive hydrocephalus, mass effect, and vasogenic edema. Furthermore, it enables identification of pleural effusion, pneumoperitoneum, vertebral compression fracture, pneumothorax, and tension pneumothorax on chest X-rays. With this list of 12 time-sensitive and critical findings, Annalise Triage is the most comprehensive AI triage tool for CXR and non-contrast CTB available in the US. 

 “Our advanced algorithms support radiologists by facilitating prioritization of studies with suspected critical findings, thereby optimizing radiology workflow. With its set of clearances, Annalise.ai promotes faster report turnaround times by identifying and elevating critical cases for immediate attention,” said Dr Rick Abramson, Chief Medical Officer at Annalise.ai.   

What You Should Know: 

– Healthcare automation company Olive today announced it will shut down its business after selling its Clearinghouse and Patient Access business units to Waystar and its Prior Authorization business unit to Humata Health. Olive sold off its utilization management solution and business unit to Availity and business intelligence solution to BurstIQ earlier this year.

– Founded in 2012, Olive is a developer of an artificial intelligence workforce, automated revenue cycle and claims management for the healthcare industry. The company secured over $900M in funding reaching a $4B valuation. Olive’s enterprise AI has been implemented at more than 900 hospitals across 40 U.S. states, including more than 20 of the top 100 U.S. health systems.

Healthcare providers are feeling the industry’s talent shortage in their administrative functions as well as clinical areas. One case in point: revenue cycle management. In a recent study, 63% of healthcare providers reported an inability to fill key revenue management roles. Reduced revenues and cash reserves are almost inevitable when providers don’t have sufficient staff to follow up on claims, manage appeals and help patients understand their financial responsibilities and options. 

Fortunately for providers, revenue cycle automation offers timely, cost-effective solutions to the talent shortage. Robotic process automation (RPA) is a proven technology for automating rote, repetitive processes that involve multiple steps and systems and/or substantial human and machine interaction. Using software bots, RPA essentially mimics the keystrokes of human operators. Many bots can be developed and deployed in a matter of weeks. They then work tirelessly and accurately, including during off-hours. At one institution, a software bot cleared a backlog of thousands of claims status checks in just a single weekend. It would have taken humans hundreds of days to accomplish that task. 

RPA is just the start. With software bots streamlining processes and improving data accuracy, the foundation is set for creating more sophisticated automation solutions built on AI and ML models. These can tackle more complex tasks that involve following business rules and making decisions based on the models’ data analysis.  These AI/ML solutions often are more expensive and take longer to implement than RPA. They are best suited for providers that already have standardized processes and cleaner data from their existing automation. 

Unlock revenue fast with robotic process automation

Automating RCM tasks frees up revenue management professionals to take on other, more complex activities, such as providing financial counseling during patient pre-registration activities. RPA solutions will also enable providers to improve revenue cycle productivity without adding additional employees. Many RCM tasks are excellent candidates for automation via software bots and RPA, including: 

Claims status checks. Bots can look up claims data and other information in payer portals, then update systems and even initiate next steps, eliminating these rote tasks and returning time to revenue professionals.

Automated patient pre-registration. RPA bots can link applications and systems together to automate more complex transactions and extract more value from them. Take a patient-facing, self-service registration portal. After a patient agrees to interact digitally with the provider, RPA bots can download patient registration requests; retrieve patient data from an electronic health record (EHR); then update the patient engagement system. The update can trigger the engagement system to send the patient a self-registration and payment link. When the patient completes those steps, the RPA bots can access the payment and patient demographic data and update the provider’s EHR. 

Digitally enabled prior authorization. Bots can easily retrieve patient data, insurance details, CPT codes, physician details, diagnosis codes and schedules from an EHR; flag cases requiring prior authorization; and submit them digitally to a payer portal. Then bots can update records with approved requests. They can also automatically route denied cases requiring additional information to the right clinicians, then refile them when updated. 

What about AI? 

RPA software bots essentially follow sets of rules. While a rule set can be complicated and involve several systems, software bots generally make preprogrammed if/then decisions. Outliers can be routed to finance professionals for follow-up. 

In contrast, automation solutions that incorporate AI and machine learning algorithms can evolve and eventually make autonomous decisions. Put very simply, an ML algorithm learns from the data sets to which it’s exposed, finding patterns and relationships. This makes ML potentially very powerful. ML algorithms can stratify patient accounts by a propensity to pay and automate financial assistance applications. That would reduce costs to collect while improving revenue realization. ML could also identify missing charges and help avoid revenue loss. Those applications, however, are complex. In general, the more advanced the technology, the more time and expense required to implement it. That’s not to take these options off the table. While they undoubtedly will play a role in coding and other tasks, ML and AI applications often are more than many providers need to solve immediate staffing and revenue realization issues. Healthcare organizations must carefully select which processes to automate to ensure the results meet their needs. 

Moving forward with automation

Providers must be clear about what they hope to achieve by automating their revenue cycles and realistic about the time and resources they have available to allocate to the project. The following steps can help guide decisions about which revenue cycle processes to tackle.

• Choose low-hanging opportunities first. Providers should build organizational automation experience before attempting more ambitious projects. The provider that solved its claims status check backlog with RPA initially applied the solution to claims from its largest payer. After succeeding there, the provider then expanded the initiative to claims from its other payers. 

• Choose opportunities that minimize IT involvement. Provider IT professionals often have many competing priorities. Developing RPA software bots requires minimal IT input. 

• Choose a vendor that understands healthcare revenue cycle services. Working with a skilled, experienced vendor helps minimize the time and input a provider’s revenue professionals must give to the engagement. Vendors with RPA and healthcare revenue expertise can build flexible bot frameworks so bots can be extended to other applications with minimal programming.

• Prioritize opportunities that tangibly improve patient and employee experiences. Improving the patient financial experience is a growing priority. Automating tedious, repetitive tasks reduces errors and frees revenue staff to work on more complex issues that deliver more value to patients and the organization. 

• Evaluate the return on investment. Most RPA projects should deliver a return 2 to 3 times greater than the investment. Reconsider projects that have lower anticipated returns or that indicate a long time to ROI. 

The societal changes that have shrunk healthcare’s labor pool are here to stay. Automating the revenue cycle will position providers to improve cash flow, enable their revenue professionals to work at the top of their abilities and offer patients the streamlined digital experiences they increasingly expect. Most importantly, providers will have more of the financial resources they need to focus on their true expertise, delivering patient care and improving outcomes.

About Noel A. Felipe, CRCR
With over 38 years of experience in healthcare accounts receivable management, Noel has a proven track record of developing progressive client-based solutions and building strong cross-functional teams to implement those solutions and maximize client results. At Firstsource Noel maintains direct account management responsibility for strategic clients and leads the development team for Firstsource’s digital collection and digital pre-service collection platforms.  Noel attended Miami Dade College, is a member of the American Association of Health Administration Management (AAHAM); has served two terms as president of the Florida Chapter of HFMA and was appointed to HFMA’s National Advisory Council for Revenue Cycle.

What You Should Know: 

– InfoBionic, a digital health company providing AI-powered diagnostic remote patient monitoring solutions has entered into an agreement with Mayo Clinic to incorporate its extensive know-how in cardiac patient monitoring, AI-ECG, and virtual telemetry to optimize and build upon its existing monitoring platform.

– The collaboration aims to enhance InfoBionic’s current proprietary AI algorithms and analytics capabilities to provide more personalized and predictive insights to both patients and clinicians. 

– In addition, the agreement will help augment current development efforts in virtual cardiac telemetry to enable a seamless cardiac monitoring continuum of care from hospital to home.

Breast cancer awareness is essential for early detection, which is critical for improving survival rates and reducing the burden of this disease. October is Breast Cancer Awareness Month, but education and awareness should not be limited to just one month of the year. A large portion of the global population will experience or be impacted by breast cancer; one in eight women will face a diagnosis in their lives, and this affects both them and their families.

Exciting advancements in the realm of breast cancer detection, such as predictive medicine, value-based care, and artificial intelligence (AI), are on the horizon. There is enormous potential to revolutionize early detection methods, offering not just a reduction in pain and suffering, but also in the financial burdens associated with cancer care and treatment. Annual mammography has long been seen as the gold standard for reducing breast cancer deaths since its introduction in the 1980s, but now the landscape is shifting, and the new era of breast cancer detection is here.

Software and the latest technological advancements enable personalized, optimized, and automated breast cancer detection. AI holds a promising role in the future of prediction and prevention, breaking barriers by turning subjective decisions into objective ones. This advanced technology can take on tasks like repetitive quantification in areas such as image-quality analytics, audit preparations, automated breast density assessment, and evidence-based risk assessment to improve early detection methods.

There is a significant barrier that hinders the promises made by technology to improve breast cancer detection: awareness, particularly in younger women. In the United States, the median age of breast cancer diagnosis is 62 years, but around 9% of cases are diagnosed in women under the age of 45 and these women often have more aggressive cancers diagnosed at a later stage, when treatment is less successful. When routine screening for breast cancer does not begin until the ages of 40-45, women under 40 are at risk of not receiving the care they need. The experts agree that cancer risk assessments should be completed by the age of 25 yet this is not routinely done in the US. Younger women should have the knowledge and support to complete these assessments as they can help preserve their well-being and understand their bodies.

Breast cancer awareness and early detection are essential for improving survival rates and reducing the burden of this disease. New technologies, such as AI, have the potential to revolutionize breast cancer detection, but it is important to ensure that everyone has access to these technologies and the knowledge they need to make informed decisions about their health.

About Teri Thomas, CEO and Managing Director at Volpara Health

Thomas is an American, based in NZ . She has a long history of executive management in healthcare IT ranging from strategy and operations to running global sales & marketing teams. This includes a 20-year career at Epic, a global healthcare systems provider.  Thomas was instrumental in growing the company from under 100 employees with little market recognition to over 10,000 employees. Thomas is also a registered nurse with a Master of Science degree, which gives her a unique perspective on the intersection of IT, clinician workflow and patient experience. Previously, she held executive roles with population health firm Orion Health and IT innovation at University of North Carolina Health Care. Thomas was a key member of the decision-making team behind Volpara successfully acquiring Harvard spin-out CRA Health in February 2021. Her focus is now helping the company to serve its purpose of saving lives, while driving profitable growth.

What You Should Know:

– CipherHealth, a provider of patient-centered engagement launches a new strategic initiative to leverage Google Cloud’s Vertex AI platform to revolutionize hospital operations and patient care.

– CipherHealth is building, training, deploying, and managing machine learning and AI models across products and across the spectrum of care.

Google Cloud’s Vertex AI to Accelerate Development of AI and Machine Learning Models

CipherHealth selected Google Cloud to facilitate a holistic approach to machine learning. Through Vertex AI, sitting on a foundation of secure, interoperable data, Cipherhealth’s data science team can fully construct, fine-tune, oversee and integrate machine learning models into various business processes. They are designed to cater to diverse modalities, such as free text (natural language), voice, vision, and tabular data. The entire initiative exists on CipherHealth’s HiTrust-certified Evolve Platform.

These AI solutions will aim to dramatically accelerate issue resolution, power sophisticated patient experiences, enhance automation, and lead with bi-directional communication. Ultimately, CipherHealth plans to use AI to create personalized care pathways for patients at all stages of care. The highly tuned model will engage, analyze, and adapt to the patient’s inputs to deliver timely insights to clinicians and an intuitive, empathetic experience for patients.

“We’re thrilled to leverage the full power of AI and machine learning to help healthcare leaders improve patient experience, staff satisfaction, and hospital operations,” said Nate Perry-Thistle, CipherHealth Chief Product & Technology Officer. “This important step will empower us to change the paradigm of patient engagement and help hospitals drive transformative growth.”

What You Should Know:

– Abridge, a company leading the way in generative AI for healthcare raises $30M in Series B funding led by Spark Capital, along with several leading healthcare innovators, including Mayo Clinic, Kaiser Permanente Ventures, CVS Health Ventures, UC Investments, Lifepoint Health, SCAN Health Plan, and the American College of Cardiology.

– Abridge’s generative AI scribe automatically converts a patient-clinician conversation into a structured clinical note draft in real-time, leveraging their proprietary AI pipeline.

“I want to care for patients, not paperwork.”

The solution has been proven to save clinicians over two hours per day from administrative burdens that previously took time away from patients and detracted from clinician work-life balance. In recent implementations, over 91% of notes across over 40 specialties were drafted solely with their AI, requiring minimal input from the clinician. The solution has been uniquely designed with trust and auditability, providing the ability to quickly see source data from any AI-generated summary.

Expansion Plans

The company plans to use the latest round of funding to support large-scale health system rollouts and accelerate product advances that will create additional value for patients, clinicians, and health systems.

“The investment and support of these iconic institutions is a game-changer for Abridge, dramatically accelerating what we can do to solve a critical issue facing every health system,” said Dr. Shiv Rao, co-founder and CEO of Abridge. “We are grateful to have an extraordinary and rapidly expanding group of partners who share our ambition to preserve the empathy and humanity in healthcare by enabling clinicians to be more present with their patients.”

What You Should Know:

– Found, a medically-assisted weight loss program launches Found Assistant, a generative AI guide within the Found app that offers members the ability to ask questions and discover instant and relevant information on nutrition, movement, health habits, and more.

– Found Assistant is available 24/7 to provide personalized guidance throughout members’ journeys and further support them in reaching their weight and health goals. During its soft launch in September, nearly one-third of beta users had more than one conversation with Found Assistant.

Power Instant Personalized Weight Guidance with Launch of Found Assistant

Over 70% of adults aged 20 and above in the U.S. have excess weight or obesity and are typically provided with the same outdated medical advice by well-intentioned primary care providers: balanced diet, exercise, and willpower. Just 50% of people adhere to long-term medical treatment plans, including taking prescribed medication, following a balanced diet, and executing lifestyle changes. Difficulty creating and keeping new routines, especially when it relates to weight care, happens for various reasons: some find it challenging to maintain a balanced diet while on the go, while others may need further guidance to fit movement into their daily schedules.

To better address these issues, Found Assistant supports the most challenging moments of member weight care journeys. Eliminating the overwhelm and labor associated with scattershot internet search results, Found Assistant instantly provides relevant educational information and personalized fitness routines, new recipes, lifestyle tips, curated guidance, and more based on member prompts, health histories, and personal preferences. For a member staying in a hotel with limited gym equipment, Found Assistant support and guidance is easily accessible and malleable. For example, it can automatically provide workouts that adapt to the member’s environmental change and help prevent the member from veering off their fitness routine.

“Found Assistant generative AI technology adapts to addressing behavior change and the user’s personal needs faster than any obesity practitioner in a brick or mortar setting or any digital health setting,” said Alex Linares, Chief Product Officer of Found. “Weight care journeys are inherently challenging, requiring dedication to new routines and guidance to achieve intended health outcomes. Found Assistant delivers an instant, personalized member experience that integrates well with the rest of the member experience at Found.”

Within the Found app, members can leverage an open-type field to prompt the Found Assistant with specific questions or use preset buttons to populate prompts for recipes, workout routines, meal plans, and habit-building. Members can review past saved conversations within the app, and all conversations with Found Assistant are kept anonymous. Found Assistant will continue to advance and provide opportunities to reshape and refine the weight loss industry for the better.