Health Technology, Digital Healthcare
Article | July 14, 2023
In the ever-evolving healthcare landscape, transparency in pharmacy benefit management (PBM) has emerged as a critical issue. The discussion surrounding driving down prescription drug costs and increasing access to affordable medications has brought attention to the practices of PBMs. However, achieving true transparency requires more than just buzzwords; it necessitates access to real-time data that empowers consumers to make informed decisions about their healthcare. In this piece, we will explore the importance of real-time transparency in PBMs and highlight how Xevant, a leading platform, is revolutionizing the industry.
The Current State of PBM Legislation
With over 100 bills to reform PBM practices, legislative efforts are intensifying to address the business practices associated with PBMs. However, one common concern is the absence of language surrounding real-time automation in many of these bills. The lack of such provisions threatens to undermine the effectiveness of the proposed reforms. It is crucial to examine the available resources and insights to gain a comprehensive understanding of the issue. The current state of PBM legislation and the efforts to reform PBM practices highlight the pressing need for transparency and accountability in the pharmaceutical industry. PBMs play a critical role in the drug pricing ecosystem. Still, concerns about “traditional” PBM business practices, such as lack of transparency and opaque rebate systems, have raised questions about their impact on drug prices and patient access to affordable medications.
Xevant's Groundbreaking Solution
Xevant, led by CEO Brandon Newman, stands at the forefront of the drive for transparency in PBM practices. As the only platform capable of providing PBMs and consumers with real-time, automated, and completely transparent data from the entire pharmacy benefits ecosystem, Xevant is poised to revolutionize the industry against the backdrop of the political landscape.
The absence of language surrounding transparency and real-time automation in many proposed bills threatens the effectiveness of the reforms. Yet, innovative companies like Xevant are leading the charge for openness in PBM practices. Xevant's real-time data automation and optimization capabilities empower consumers with timely, comprehensive, and transparent information, enabling them to make informed decisions about their healthcare and potentially save money.
With the potential passage of these bills, the pharmaceutical industry could see a shift towards greater accountability, fairer pricing practices, and improved access to affordable medications. The reforms could also create a more level playing field for generic drug manufacturers, fostering competition and lowering prices.
Real-Time Data Automation and Optimization
Newman emphasizes that transparency cannot be achieved without access to real-time data automation and optimization. This real-time, customized data enables individuals to compare prices, explore alternatives, and understand the specific cost components related to their medications. By bringing together various parts of lowering drug costs, such as drug rebates, 340B contracts, sell-side discounts, copay assistance, and employer negotiations, Xevant offers a solution that empowers consumers with the information they need when required.
The Implications of Timely Access to Data
The scarcity of timely access to data among many traditional PBMs is a significant challenge in achieving transparency in the pharmaceutical industry. These PBMs typically collect data annually, which leaves a substantial margin of error and can result in millions of dollars lost from consumers' pockets. In contrast, Xevant's capabilities offer a game-changing solution.
With Xevant's platform, consumers gain immediate access to critical information regarding drug rebates, markups during spread pricing, competitive alternatives, and the vast landscape of the pharmaceutical ecosystem. Having these complete datasets available in real-time allows individuals to make informed decisions about their healthcare and potentially save lives. The significance of timely access to data cannot be overstated, as transparency becomes meaningful only when it happens in the present rather than months, or even a year, later than when the impact has already occurred.
Navigating Proposed Legislation and Questionable Business Practices
Another critical aspect of the PBM landscape that Xevant addresses is the moral implications associated with cost-sharing, clawbacks, spread pricing, and the pass-through of rebates. These practices have long been criticized for their opacity and their negative consequences on patients' access to affordable medications. Xevant's transparency-focused approach highlights these practices, allowing stakeholders to evaluate their ethical implications and work towards fairer alternatives.
Xevant recognizes that proposed legislation may have potential cracks that allow for slip-through and the continuation of questionable business practices. Delayed and inaccurate reporting are loopholes that can hinder the effectiveness of reform efforts. By actively engaging with legislators and industry stakeholders, Xevant aims to identify these potential shortcomings and advocate for comprehensive robust legislation that leaves no room for exlploitation
The Future of Healthcare and the Role of Real-Time Automation
As the discussion surrounding PBM reform gains momentum, the future of healthcare in America hangs in the balance. Xevant sets a new standard for efficiency and consumer empowerment in healthcare decision-making by employing AI-driven technology. Xevant's visionary approach to real-time data automation and optimization paves the way for greater transparency and cost savings in the pharmaceutical industry.
Wrapping Up
Transparency in pharmacy benefit management is crucial to addressing the soaring costs of prescription drugs and enhancing access to affordable medications. Without access to real-time data and automation, the pursuit of transparency remains elusive. Xevant's groundbreaking platform solves this pressing challenge, enabling PBMs and consumers to access complete, transparent data in real-time.
As legislative efforts progress, the need for real-time transparency becomes increasingly evident, and Xevant emerges as the leading legal solution for PBMs. When harnessing the power of real-time data automation, the vision of affordable healthcare can be transformed into a reality, benefiting individuals and the entire healthcare ecosystem.
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Digital Healthcare
Article | November 29, 2023
As medical science has improved rapidly, life expectancy around the world also has risen. Still, as longevity increases, healthcare systems are facing a growing demand for advanced services, increased costs, and a struggling workforce to meet various requirements of patients. Demand is driven by many unstoppable forces: a shift in lifestyle choices, shifting patient expectations, population aging, and the never-ending cycle of innovation are a few among others.
Challenges Faced by the Healthcare Industry
According to Mckinsey, one in four people in North America and Europe will be over the age of 65 by 2050. This shows that, soon, the healthcare industry will have to deal with a larger number of patients with more complex requirements. Catering to these patients is expensive and requires health systems for providing long-term focused and proactive care. To remain sustainable, healthcare systems need major transformational and structural changes.
The industry also needs a larger workforce because according to the World Health Organization (WHO), globally there is a shortfall of approximately 9.9 million nurses, physicians, and midwives. Apart from attracting, training, and retaining these healthcare professionals, you also have to ensure that their time and effort add value to patient care. Utilizing the solutions powered by modern technologies, such as Artificial Intelligence (AI) in the healthcare industry, will add perfection and more value to human efforts.
AI in the healthcare industry has the potential required to transform and revolutionize healthcare by addressing the challenges in the industry mentioned earlier. AI can better the outcomes, improve efficiency, and augment productivity in healthcare delivery. This article takes an in-depth look at the impact of AI in healthcare.
Impact of AI in the Healthcare Industry
In the coming years, AI in the healthcare industry will improve the day-to-day life of healthcare practitioners, augment the patient experience, improve care delivery, and can even facilitate life-saving treatments and revolutionize the industry. Additionally, AI will improve population-health management, operations, and strengthen innovations.
According to Statista, the global AI healthcare market will increase to more than US$28 billion by 2025. Here is a detailed look into the areas where and how AI in the healthcare industry will be impactful.
Chronic Care Management
Chronic diseases, such as cancer, diabetes, kidney diseases, are the leading cause of disability and death in the US and the main drivers of the country’s annual health cost. Effectively managing various chronic diseases is an overarching and long-term process. But with the help of the right tool, healthcare providers can meet the needs of these patients without delay.
Artificial intelligence tools in the healthcare industry can help healthcare providers overcome the complexities of chronic disease management and make it more effective and provide quality treatment. AI in the healthcare industry is increasingly being leveraged by organizations to improve chronic disease management, enhance patient health, and drive down costs, which will also eventually result in data-driven and personalized care. AI in the healthcare industry is expected to move the industry toward proactive care delivery from a reactive one and lead the industry to provide more individualized treatments. This is just one of the ways AI in the medical industry is going to revolutionize chronic care management in hospitals.
Care Delivery
Artificial intelligence in the healthcare industry is changing the way care is delivered; it is expected to make healthcare more efficient, accurate, and accessible. Reducing costs and improving health outcomes are the values health systems and hospitals are trying to deliver to patients every day. Hospitals are increasingly incorporating technologies, which are powered by the use of AI in healthcare to meet the challenge.
According to the American Hospital Association (AHA), AI in the healthcare industry has unlimited potential to solve most of the vexing challenges in the industry. They identify AI use cases in the healthcare industry in four broad areas, which are administrative, operational, financial, and clinical areas.
Administrative Use Cases for AI in the Healthcare Industry
• Admission procedures
• Appointment scheduling
• Customer service responses
• Discharge instructions
• Hiring and orientation protocols
• Licensure verification
• Patient check-in procedure
• Prior authorizations
• Quality measure reporting
Operational Use Cases of AI in the Healthcare Industry
• Inventory management
• Materials management
• Supply chain management
• Facilities management
Financial Use Cases for AI in the Healthcare Industry
• Billing and collections
• Claims management
• Insurance eligibility verification
• Revenue cycle management
Clinical Use Cases of AI in the Healthcare Industry
• Predictive technologies
• Interventional technologies
By incorporating and utilizing these scopes with AI in the healthcare industry, the industry can be transformed into a next-gen level in no time. It also allows healthcare practitioners to focus more on patients, which would eventually help in raising staff morale and improving retention.
Clinical Decision Support
Recent advancements in AI in the health industry are capable of enhancing the currently used clinical decision support (CDS) tools to have value-based imaging and to improve patient safety. According to the National Institute of Health (NIH), the synergy between CDS systems and AI in the healthcare industry will be able to:
• Reduce friction in radiology workflows
• Identify relevant imaging features easily
• Generate structured data to develop machine learning algorithms
• Enable an evolution toward decision support for a holistic patient perspective
• Suggest imaging examinations in complex clinical scenarios
• Assist in identifying appropriate imaging opportunities
• Suggest appropriate individualized screening
• Aid health practitioners to ensure continuity of care
AI in the healthcare industry is competent in making CDS a next-gen one, enhancing the experiences of radiologists and providers, and improving patient care.
Diagnostics
Slowly but surely, AI is improving almost every aspect of human life with innovations and advancements. The latest is that AI in the healthcare industry is impending a revolution in medical diagnostics by providing accurate risk assessments, accelerating disease detection, and boosting hospital productivity. By automatically prioritizing urgent cases and accelerating reading time, image recognition AI enhances the workflow of radiologists. It even helps in the prevention of diseases by the early detection of diseases.
In medical images such as x-rays, MRIs, and CT scans, AI-driven software can efficiently be used to accurately spot signs of many diseases, especially in detecting many chronic diseases such as cancer. According to the NIH, AI will be widely applied in the healthcare industry especially for various tasks such as patient engagement and adherence, diagnosis, and treatment recommendations. So, there is no doubt that AI in healthcare will revolutionize the diagnostic process in the approaching years by detecting diseases, classifying diseases, and improving the decision-making process. The application of AI in the healthcare industry will make people live longer.
Triage and Diagnosis
AI can be effectively used to automatically triage cases. AI algorithms will analyze the cases and forward cases to pathologists after determining the priority based on the probability of cases according to the criteria set by labs. This makes the workflow of pathologists easier and efficient. Through the process the algorithm will be able to:
• Verify the digital images attached to the case belong to that case
• Validate the tests ordered and match the specimen type
• Identify cases marked as stat
• Determine the cases, which can be positive or are most likely to be negative
Moreover, AI technologies in the healthcare industry also can be effectively used to provide more accurate and faster diagnoses. This speeds up the entire process of triage and diagnosis and is expected to revolutionize the healthcare industry soon.
The Future Outlook for AI in the Healthcare Industry
Over the next few years, AI in the healthcare industry has the best opportunities in hybrid models to support clinicians in diagnosis, identifying risk factors, and in treatment planning. This scope will result in faster adoption of AI technology in healthcare, which will show measurable improvements in operational efficiency and patient outcomes.
With a plethora of issues to overcome, which are driven by documented factors such as growing rates of chronic diseases and the aging population, it is obvious that the healthcare industry needs new innovative solutions. AI-powered solutions in the healthcare industry will achieve a clear impact on the global healthcare industry in a short time.
Frequently Asked Questions
Which is the best application of AI in the healthcare sector?
Cognitive surgical robotics is the best application of AI in the healthcare sector as it helps practitioners collect data from real surgical processes, which would help in improving existing surgical approaches.
Why is artificial intelligence important in healthcare?
Artificial intelligence in healthcare is vital as it can help make decisions, analyze and manage data, and have conversations. So, AI will drastically change the everyday practices and roles of clinicians.
When was AI-first used in healthcare?
The term, Artificial intelligence (AI) was first described in 1950, but the limitations of the term prevented its acceptance. In the 2000s, these limitations were overcome and people started to accept the term.
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Healthtech Security
Article | November 29, 2023
Government bodies have organized vaccination drives from the days of paper, pen and file folders. Nations across the globe have successfully run vaccination programs on a large scale.
In countries such as India, with the second-largest population, a vaccination campaign to eradicate polio was delivered at specified centers and going door-to-door. India was declared officially polio-free in March 2014. All without technology!
Routine vaccination administration has always been either by a scheduled or walk-in appointment. Vaccinating populations for polio, smallpox or similar diseases has always been a part of a multi-year plan for governments.
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AI
Article | December 21, 2021
COVID-19 has been a catalyst for change, with the diagnostics industry taking centre stage and rising to the challenge of a global pandemic. One of the silver linings of this mammoth task has been the unprecedented time and focus dedicated to finding new technologies and solutions within the sector.
The lessons learned from the pandemic now need to be taken forward to improve breast and cervical cancer detection, prevention and treatment across the UK over the coming years.
In the more immediate term, the diagnostics industry, alongside public health leaders, faces a daunting backlog as screening programmes for breast and cervical cancer were put on pause for months. These two life-saving tests have been some of the most overlooked during the pandemic and getting back on track with screening is critical as we start to turn the corner. We believe innovation in diagnostics, particularly artificial intelligence guided imaging, is a key tool to tackle delays in breast and cervical cancer diagnosis.
The scale of the backlog in missed appointments is vast. In the UK, an estimated 600,000 cervical screening appointments were missed in April and May 2020. And an estimated 986,000 women missed their mammograms, of which an estimated 10,700 could be living with undiagnosed breast cancer. It is clear that hundreds of thousands of women have been affected as COVID-19 resulted in the reprioritisation of healthcare systems and resource allocation.
Both cervical and breast cancer screening are well suited for digital technologies and the application of AI, given both require highly trained medical professionals to identify rare, subtle changes visually –a process that can be tedious, time-consuming and error prone. Artificial intelligence and computer vision are technologies which could help to significantly improve this.
What does AI mean in this context?
Before examining the three specific areas where digitisation and AI can help, it is important to define what we mean by AI. It is the application of AI to medical imaging to help accelerate detection and diagnosis. Digitisation is the vital first step in implementing an AI-driven solution – high quality images demand advanced cloud storage solutions and high resolution. The better the quality of the input, the more effectively trained an AI system will be.
The first area where AI-guided imaging can play a role is workflow prioritisation. AI, along with increased screening units and mammographers, has the potential to increase breast cancer screening capacity, by removing the need for review by two radiologists. When used as part of a screening programme, AI could effectively and efficiently highlight the areas that are of particular interest for the reader, in the case of breast screening, or cytotechnologist when considering cervical screening.
Based on a comparison with the average time taken to read a breast screening image, with AI 13% less time is needed to read mammogram images, improving the efficiency with which images are reviewed. This time saving could mean that radiologists could read more cases a day and potentially clear the backlog more quickly.
For digital cytology for cervical cancer screening, the system is able to evaluate tens of thousands of cells from a single patient in a matter of seconds and present the most relevant diagnostic material to a trained medical professional for the final diagnosis. The job of a cytotechnologist is to build a case based on the cells they see. Utilising these tools, we are finding that cytotechnologists and pathologists are significantly increasing their efficiency without sacrificing accuracy to help alleviate the backlog of cervical screening we are seeing in many countries.
Prioritising the most vulnerable patients
Another key opportunity is applying AI to risk stratification, as it could help to identify women who are particularly at risk and push them further up the queue for regular screening. Conversely, it would also allow the screening interval for those women at lower risk to be extended, creating a more efficient and targeted breast screening programme.
For example, women with dense breast tissue have a greater risk factor than having two immediate family members who have suffered from breast cancer. What’s more, dense breasts make it more difficult to identify cancerous cells in standard mammograms. This means that in some cases cancers will be missed, and in others, women will be unnecessarily recalled for further investigation.
A simple way to ensure that those most at risk of developing breast cancer are prioritised for screening and seen more regularly would be to analyse all women on the waiting list with AI-guided breast density software. This would allow clinicians to retrospectively identify those women most at risk and move them to the top of the waiting list for mammograms.
In the short term, to help tackle the screening backlog, prior mammograms of women on the waiting list could be analysed using the breast density software, so that women at highest risk could be seen first.
Finding new workforce models
Being able to pool resources will allow resource to be matched to demand beyond borders. Globally, more than half a million women are diagnosed with cervical cancer each year and the majority of these occur where there is a lack of guidance to conduct the screening programme. The digital transformation of cervical screening can connect populations that desperately need screening to resources where that expertise exists. For example, developing countries in Africa could collect samples from patients and image these locally, but rely on resources in the UK to support the interpretation of the images and diagnoses. Digital diagnostics brings the promise of a ‘taxi-hailing’ type model to cervical cancer screening – connecting groups with resources (drivers with cars) to those who are in need (passengers): this is an efficient way of connecting laboratory professionals to doctors and patients around the world.
It’s going to take many months to get cancer screening programmes up and running at normal levels again, with continued social distancing measures and additional infection control impacting turnaround times. But diagnostic innovation is on a trajectory that we cannot ignore. It will be key to getting cancer screening programmes get back on track. AI is a fundamental piece of the innovation puzzle and we are proud to be at the forefront of AI solutions for our customers and partners.
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