In our previous blog, we discussed the current state of Manufacturing Execution Systems (MES) in the pharmaceutical industry, highlighting the challenges and limitations faced today. As a natural sequel, this blog explores the future advancements in MES, promising greater integration, flexibility, and efficiency, and their potential impact on pharma manufacturing.

Seamless Integration
In the future, we think that MES will be seamlessly integrated with other systems, creating a unified digital ecosystem. Users will be able to interact with a single, cohesive system without realizing the underlying complexities. This seamless integration will eliminate the need for custom-built interfaces and will ensure a smooth data flow across all systems.

Enhanced User Experience
The future MES will also offer an intuitive user experience. Users will be able to interact with the system effortlessly, akin to driving a car without thinking about its mechanics. This user-friendly interface will make it easier for personnel to access and utilize data, thereby improving overall efficiency.

Support for Different Pharma Manufacturing Types
Future MES systems will have the capability to support continuous manufacturing, personalized medicine manufacturing, and existing batch manufacturing.

Shift in Licensing Models
Many pharma manufacturing companies will experiment with and adopt a SaaS licensing model. This shift will also motivate medium and small-sized companies to implement MES solutions.

Advanced Technologies

1. AI and Machine Learning: AI and ML will play a significant role in the future of MES. These technologies will enable predictive analytics and anomaly detection, enhancing decision-making processes. For instance, AI can help identify variability in product output and suggest potential root causes, making it easier to address issues proactively.
2. Generative AI: The integration of generative AI will allow users to interact with MES through natural language, making data retrieval and analysis more accessible. This will enable operators to ask questions and receive insights in a conversational manner, further simplifying the use of MES.
3. Cloud-Based Solutions: The shift towards cloud-based MES may offer greater flexibility and scalability. Cloud solutions can make it easier to deploy and manage MES across multiple sites, providing a unified platform for global operations. As these systems mature, clients will evaluate on-premises vs. cloud risks and consider changes to their strategies to take advantage of benefits outlined above. However, addressing technical constraints like latency and ensuring robust security measures will be crucial for widespread adoption.

Sustainability and Efficiency
Last but not least, Future MES will contribute to sustainability goals by optimizing resource usage and reducing waste. Integrating sustainability metrics into MES will drive greener manufacturing practices. For example, MES can help monitor and reduce energy consumption by optimizing equipment usage and minimizing waste.

Conclusion
The future of MES in the pharmaceutical industry is bright, with advancements in integration, user experience, and technology set to transform manufacturing processes. By embracing these innovations, pharma companies can achieve greater efficiency, flexibility, and sustainability in their operations.

Financial institutions have faced many challenges in recent years concerning their post-trade processing. Despite efforts, it’s proven difficult for market participants to navigate these obstacles while improving efficiency and controlling costs. In this article, we examine industry challenges and discuss why it’s time for further collaboration across the market. We also explore why sharing expertise and costs across the industry benefit participants and unpack what Financial Market Infrastructure providers (FMIs) must do to create a shared industry solution.

Reducing the Cost of Post-Trade Processing in a Challenging Environment

Financial institutions in the post-trade ecosystem have faced challenging circumstances in recent years. Amidst geopolitical shocks and market volatility, institutions have aimed to comply with new regulations, enhance resiliency, and transform processes, all while controlling cost bases. To meet these demands, they’ve sought to leverage a combination of offshoring capabilities, process re-engineering, and new technologies. These efforts have led to incremental results, both in terms of optimizing costs and operational efficiencies. This is because the cost of processing trades is distributed across the trade lifecycle. Cost inefficiencies are embedded throughout the value chain, and individual solutions only remove them from a small part of the process.

Little has changed since 2020, when the Bank of England noted that more than $20 billion is spent on trade processing each year, split across up to 23 services in the example of FX post-trade activities. This shows the scale of these cost inefficiencies and highlights the need for a holistic approach to tackle such a challenge.

Banks have often solved their trade processing problems on their own, resulting in duplicative efforts across the industry. In their drive towards delivering a Common Domain Model, ISDA has previously suggested that if the industry adopted common data standards and addressed fragmentation, there would be an estimated 80-85% reduction from the dealer cost base of approximately $3.2BN . To mutualize costs, the widespread market adoption of standards and automation best practices will be necessary. Banks must look to central industry players such as market utilities and FMIs. These central players can deliver an impact by spreading costs and sharing infrastructure across the industry.

Making the Case for FMIs

FMIs are in a unique position to offer products and services centrally and at scale. The Bank of England Post-Trade Task Force supports this, highlighting that industry coordination mechanisms are required to overcome industry wide challenges. Therefore, operational and cost efficiencies would be best enabled by FMIs delivering a community build for three reasons:

• Scale and community – FMIs serve a broad network of clients, meaning they are in a unique position to encourage and drive industry-wide standardization.
• Expertise – FMIs bring awareness and understanding; they are experienced in helping firms navigate regulatory change. They possess the in-house capability to commercialize that experience in post-trade product management in a way which more commercial competitors cannot.
• Resilience – Recent market volatility means strategic moves from market participants need to be underpinned by a more conservative risk appetite; FMIs carry systemic importance and have always been held to high standards to provide market-leading resilience.

With these natural advantages, FMIs have been pivotal to operational advancements of the industry. However, they have not been without their own challenges, prompting them to evolve and adapt. Their history has been punctuated by three notable waves.

The first wave was the catalyst for the original FMIs, driven by industry participants looking for capital efficiency leading to the rise of trading venues and CCPs. The second wave was regulatory driven, where following the Financial Crisis in 2008, firms were mandated to use their products and services, creating tailwinds for central infrastructure providers. A third wave of activity was more commercially motivated, as market participants sought a better ROI from these providers, giving rise to the development of third-party vendors such as AcadiaSoft and TriOptima. These vendors provided more competitive and technologically innovative offerings. It should be noted, there were also some less successful ventures including attempts to develop centralized KYC services. However, FMIs are still well positioned to lead a new infrastructure-led wave of community-built initiatives.

A Potential New Wave of Collaboration within Capital Markets 

FMIs are ready to take the lead for the post-trade community and drive the collective effort to solve common problems by identifying their priority needs. For large-scale market infrastructure-led solutions to be successful, a detailed understanding of their clients is required. Regular outreach exercises, coordination via product design forums, and knowledge of participant processes all provide immense value here. An analysis of pain points and data collection will be critical to understanding how technology should be used to create efficiencies.

Once achieved, solutions will need to cover three bases. Firstly, any services provided will need to be valuable, demonstrably addressing the challenges of financial institutions and reducing their cost bases, both in the short and long-term. Secondly, FMIs will need to demonstrate how their solutions are viable and can be implemented during an era where many firms are undergoing large-scale platform modernizations. Finally, any solution will need to be simple, and clearly framed to encourage board level buy-in of the strategic initiative. The challenge of driving cost out of the Capital Markets ecosystem is considerable, but through collaboration, there is a path forward for the industry.

Achieving innovative, resilient, and agile supply chain operations requires organizations to combine their technology, expertise, and talent into one team with one goal.

How can organizations move past some of the long-standing challenges in the supply chain sector?

Capgemini recently held a webinar that considered this question, with a focus on how its business ecosystem partnership with Kuehne+Nagel provides an innovative supply chain orchestration capability to drive resilient, efficient, and sustainable supply chain operations.  This unique partnership delivers this through:

• Implementing an integrated, end-to-end supply chain service utilizing technology and data-driven processes
• Leveraging an outcomes-centric model that delivers enhanced business value
• Empowering a new breed of supply chain professional through diversifying workforces.

Shirley Hung, a distinguished analyst from Everest Group, joined Dr Matthias Hodel (Kuehne+Nagel) and Jörg Junghanns (Capgemini) to discuss ways in which organizations can navigate the evolving market supply chain landscape.  This article reflects the discussion they had.

True agility requires resiliance, effeciency, and sustainablity

As supply chain disruption continues to pose new risks, businesses need comprehensive, technology-based solutions that enable them to stay ahead of the competition. This means organizations must find a solution that helps them deliver resilient, efficient, and sustainable supply chain operations  to ensure they remain agile and responsive in today’s disruptive global ecosystem.

This is even more critical in the face of increasingly fragmented supply chain operations, a lack of visibility and accountability across key supply chain operations, siloed planning and execution, and disinterested partners with mindsets far removed from effective decision-making and problem-solving.

But what does all this mean for the future of supply chain orchestration?

Improving communications and lowering costs by breaking barriers

Breaking down the barriers that have fragmented many key supply chain processes is the quickest route to true supply chain agility, resilience, and sustainability for any organization. This ensures technology, expertise, and talent can be combined into one, multi-disciplined, integrated resource, backed by cutting-edge technology and proven data-driven processes.

But, achieving this is easier said than done due to the volatility consistently seen in today’s market.

Capgemini and Kuehne+Nagel’s partnership – the first of its kind on the market – empowers organizations to break down the silos separating technology, expertise, and talent by functioning as a bridge between them, leading to improved communication, lower costs, and faster response times across supply chains.

Additionally, by pooling their vast AI technology and supply chain expertise, these two supply chain giants can handle any market disruption they encounter, freeing them up to highlight the synergies between their clients’ people, processes, technology and data. This, in turn, enables supply chain teams to work as one team with one goal with minimum effort on their part, leading to:

• Accelerated and more secure supply chain workflows
• Optimized planning, capacity allocation, point of sale and manufacturing processes that enable potential shipping issues to be monitored and addressed before they occur
• Improved inventory management which provides increased inventory transparency to organizations, helping them avoid critical supply chain issues as a result.

All of these combine to form a new outcomes-centric supply chain model that delivers enhanced business value and revolutionized supply chain operations to any organization.

But what if organizations want to future-proof their supply chain operations, alone?

Revolutionizing supply chain operations successfully

Capgemini and Kuehne+Nagel stand ready to invest in, and revolutionize, supply chain journeys, either by working directly with organizations, or by working with partners to help you achieve your transformation goals.

However, revolutionizing supply chain processes, enhancing value, and empowering workforces comes down to truly understanding why you need to evolve your operations in the first place, and deciding on a clear objective that will drive everything forward from day one.

Only this approach will help to break down the barriers between technology, expertise, and talent much quicker during any supply chain transformation which, ultimately, will ensure the delivery of the resilient, agile, and future-ready operations that customers expect.

To discuss this further please reach out to: joerg.junghanns@capgemini.com

Capgemini’s Connected Enterprise approach brings people, processes, data, and technology together to drive intelligent, connected, and resilient supply chain operations.

In the last two blogs in this series, we looked at:

• How organizations are connecting their supply chains  through building ecosystems to drive resilience across their supply chains
• And how disruption and data are driving supply chain transformation.

In this blog, we will look at how supply chain organizations can leverage Capgemini’s unique Connected Enterprise approach to help them do this.

Orchestrating an intelligent, connected supply chain ecosystem

At its core, the Connected Enterprise is a vision of business that integrates every component of an organization with integrated, intelligent technologies and ecosystem partners.

It’s about seamlessly orchestrating an intelligent, connected ecosystem of people, processes, data, and technology – with AI, analytics, and GenAI at its heart – to drive sustainable business outcomes, enhanced value, and continuous innovation across the entire value chain.

For supply chains, it’s a shift away from the siloed, single-source dependent models, to an ecosystem partnership model, driven by technology and data, that turns supply chains from a rigid system of dependency, towards an intelligent, connected, and ever-evolving ecosystem focused on delivering sustainability, productivity, and creativity to the consumer.

The Connected Enterprise gives organizations the ability to reduce friction through leveraging technologies such as AI to enhance operations. This helps to drive improved forecast accuracy, while managing sudden shock to the market without impacting inventories and orders. With consumer demand centered around immediate fulfillment and continual availability of products and orders, this is a vital component for building resiliency and market reputation.

Delivering the Connected Enterprise

When preparing an ecosystem roadmap, organizations should start by assessing their current partnerships and processes. A successful supply chain ecosystem depends on being able to leverage a shared strategic vision and implement the technologies necessary to support it.

As robust supply chain operations are ultimately fueled by data, assessing the level of data maturity and technology-enabled automation should also be a priority. Organizations should ask themselves key questions to understand where and how operational improvements can be introduced. These questions could include:

• What do we bring to an ecosystem?
• What are our ecosystem partners doing that we are not?
• Where can automation be introduced to accelerate processes?
• Do employees require upskilling?
• How can we integrate supply chains with enterprise-wide transformation?

With a trusted partner, organizations can receive strategic support in developing concrete resolutions to these questions. By focusing on value-driven outcomes, they can also rest easier knowing that any investment will be directed towards real-world benefits.

Delivering end-to-end, future-ready supply chain orchestration

Capgemini’s unique Connected Enterprise approach makes delivering connected, intelligent supply chain ecosystems easier for any organization – especially when it is backed by Capgemini’s partnership with Kuehne+Nagel.

This unique business ecosystem combines Kuehne+Nagel’s industry leading expertise with Capgemini’s proven Intelligent Supply Chain Operations capabilities to deliver AI-enabled, cognitive, touchless operations and data-driven decision-making. This drives new and improved performance levels across your end-to-end supply chain through seamlessly integrating your planning and logistics management to reduce accountability, data, and intelligence mismatches.

In the final blog in this series, we will discuss the outcomes and benefits organizations can gain through leveraging Capgemini-Kuehne+Nagel partnership.

To discover how Capgemini’s unique partnership with Kuehne+Nagel can help your organization drive improved, end-to-end performance levels across your supply chain, contact: joerg.junghanns@capgemini.com

Analyzing the limits and possibilities of machine learning in industry

Recent technological advancements in Artificial Intelligence (AI), coupled with user-friendly applications featuring text prompts, such as image-generating AIs and chatbots, have garnered widespread attention in both the media and corporate discussions. While AI in manufacturing has been integrated into manufacturing processes for quite some time, its transformative impact has not yet realized its full potential.

What follows is a guide for plant managers and production engineers on how to strategically harness AI’s power. Incorporating the latest technological developments, including foundation models and generative AI, can unlock AI’s true potential and provide a competitive advantage.

AI use cases in manufacturing

In most manufacturing processes, an important part of the job of operators and engineers is to monitor the process, analyze problems, find root-causes, identify the right corrective actions, and implement them efficiently. Figure 1 outlines different levels of AI capabilities that support each of the levels in this workflow: Perceive, analyze, prescribe, and closed loop AI. Moreover, it illustrates how AI can revolutionize manufacturing processes to make them more efficient, sustainable, and safe across different industries. Furthermore, it highlights how these AI capabilities can be built upon one another, facilitating a stepwise development and introduction of AI functionalities.

It is important for business leaders to be intimately acquainted with each level of the workflow and their correlating areas of application. Let’s take a more detailed look at each level.

The AI ecosystem in manufacturing

The introduction of AI requires more than just expertise in ML and software engineering. Manufacturing is already a complex socio-technical system, consisting of manufacturing and automation technology and many human actors in different roles. Figure 2 illustrates how AI systems operate across the major elements of this system.

AI and the manufacturing workforce

As AI transforms manufacturing operations, the role of the workforce undergoes a parallel transformation. In the ML development phase, engineers play a crucial role in teaching the AI by providing data annotations and assisting ML developers in validating model performance. Explainable AI (XAI) empowers manufacturing experts to assess ML models beyond statistical performance, enabling them to evaluate whether the model has learned meaningful concepts from the data.

During AI deployment and operation, workers and engineers become gatekeepers, deciding whether or not to act on AI predictions and translating AI insights into corrective actions. Fully autonomous systems in manufacturing, where AI directly controls automation systems, remains feasible and economically viable only for a limited set of use cases – it is not expected to become common in the near future. AI models can be significantly enhanced with valuable data by simply recording corrective actions and analyzing the manufacturing system’s responses. Here, workers and engineers can provide feedback, offering additional labels for supervised learning or evaluating ML model outputs.

Automation and the IIoT

Crucial data for AI modelling is generated at all levels of automation systems (field, control, supervisory, and planning). Sensors, instruments, analyzers, and quality systems measure physical quantities and monitor manufacturing system states. The Manufacturing Execution System (MES) guides resource allocation in production. Industrial Internet of Things (IIoT) systems and sensors complement data collected by the automation system, enhancing the overall information pool. The key manufacturing shifts and an ageing installed base of automation systems are crying out for green- and brownfield projects. Their introduction would update the manufacturing processing, offering an opportunity to create AI-ready manufacturing systems.

“Companies that use this opportunity to create the foundations for the large-scale application of AI in manufacturing will get ahead of the competition and maintain their advantage by accelerating their AI journey.”

Christian Michalak, Executive Vice President, Head of Intelligent Industry Germany, Capgemini Invent

Development and operations of AI systems 

The data generated by automation and IIoT systems serves as the foundation for AI, encompassing both ML and traditional symbolic AI techniques. Access to real-time manufacturing process data is essential for AI models to perform online tasks, such as pattern recognition, anomaly detection, and predictive capabilities. The development and operation of AI systems in manufacturing are intricately tied to the continuous flow of data from the complex socio-technical ecosystem. 

By understanding and leveraging the interactions within this ecosystem, organizations can maximize the benefits of AI in manufacturing, fostering a collaborative environment where human expertise and AI capabilities synergize for optimal results.  

AI breakthroughs open new opportunities  

The integration of cutting-edge AI technologies into businesses and processes has paved the way for unprecedented possibilities and new opportunities. Let’s examine an overview of the pivotal breakthroughs in the world of AI and the emerging opportunities that have the potential to disrupt the manufacturing sector.  

Data-centric AI 

If sufficient training data is available, deep learning capabilities that extract meaningful features from raw data can completely replace the tedious and error-prone human effort of feature engineering in ML. This capability has also enabled the shift to data-centric AI, which prioritizes the effective use of available data against optimizing model parameters. This approach mitigates typical challenges in AI for manufacturing, such as lack of labels and low data variance. It also supports tedious and expensive data curation activities. Certain methods, such as self-supervised learning or contrastive learning, facilitate the extraction of semantically meaningful features from raw data without the huge labelling effort of early deep learning models. This new paradigm places data center stage and calls for a change in the design and operations of manufacturing systems. Such systems need to be designed and upgraded with the data requirements of modern AI technology in mind. Moreover, it is imperative for the workforce to recognize data recording and documentation as essential components that add value to their work, rather than viewing them as mere inconveniences. 

Gen AI for operations 

Generative AI (Gen AI) is rapidly becoming an era-defining topic. Recent Capgemini research found that 97% of executives are discussing its applications. Gen AI refers to AI models that are capable of generating text, images, videos, or other types of data, often based on user input. In the manufacturing context, this capability enables many different use cases. One of the most noteworthy benefits is Gen AI’s ability to liberate workforces from the completion of tedious manual tasks. For instance, with recorded inputs (photos, audio recordings, or signal data), Gen AI in manufacturing can help operators and maintenance personnel document observations on the shopfloor. This leads to improved data quality for downstream and value-adding AI tasks. Similarly, generative AI in manufacturing can synthesize training data for rare cases (i.e., quality defects or process breakdowns) and thereby improve the performance and robustness of industrial AI systems. Gen AI models can even create working program code to make automation engineers more effective and help to extend the coverage of automation in industrial plants. 

Explainable AI helps to overcome black-box syndrome 

Deep neural networks and large ensemble models (e.g., Gradient Boosting Trees) have outperformed traditional approaches in various manufacturing applications. However, their opacity has led to a lack of trust. This is largely due to the inherent “black-box” nature of such high-performing models. Explainable AI (XAI) methods shed light on these black boxes, providing insights into how ML models arrive at decisions. Certain techniques, such as counterfactual explanations, help to debug ML models and gain new domain insights. XAI is essential in overcoming trust and acceptance issues faced by AI in manufacturing operations, empowering the workforce to effectively fulfill their roles as teachers and gatekeepers. 

Deep reinforcement learning enabled by digital twins 

Deep Reinforcement Learning (DRL) takes a unique approach by learning through interactions with the environment, making it possible to derive an optimal control strategy. DRL’s deployment in real-life manufacturing settings is facilitated by training within a digital twin—a digital counterpart replicating physical products or processes. Digital twins collect and present data from their physical counterparts, facilitating advanced applications in robotics, scheduling optimization, and process control. AI in manufacturing operations and digital twins share infrastructure and functionality, creating a symbiotic relationship that enhances predictive capabilities.

”To a certain extent, AI in manufacturing and digital twins share similar technology stack and overlap in functionality. But the relationship is symbiotic, rather than competitive. On one hand, AI models trained on actual process data are valuable within the digital twin concept to predict behavior, detect anomalies and simulate what-if scenarios. On the other hand, AI training can benefit from the ability to generate artificial training data with sophisticated digital twins.”

Arun Raina, Digital Twin Specialist, Capgemini Invent

AI for manufacturing will span both edge and cloud

Designing an AI environment for manufacturing encounters challenges posed by the data-rich and information-poor nature of the most industrial data. It is not cost effective to send all data, which can often contain little information, to the cloud. Instead, the most economically viable option necessitates filtering data on the edge. Not all machine learning models can run in the cloud. This is because low-latency predictions are vital. Questions revolve around the timing, quality, and sample rate of data required for training or running AI models. Striking a balance between complexity and data efficiency remains a central challenge for organizations in AI implementation. The concept of fog computing, which extends established concepts from cloud computing to devices located on the edge, is an important enabler for AI in manufacturing operations.

Data products as a governance model

Data products (reusable data assets providing trusted data) offer a structured approach to managing the flow of data in AI for manufacturing. Defining freshness and data quality based on consumer requirements is crucial in the designing of data products. Cloud infrastructure is ideal for creating ML models, while edge resources are essential for smartly filtering and aggregating data, providing low-latency runtime for ML models near manufacturing processes.

“Data products are a great way to get answers to the question that data needs to be processed, to structure the flow of data and facilitate reuse across AI development projects.”

Felix Balhorn, Director, Intelligent Industry Data and Analytics, Capgemini Invent

Democratization of AI with no/low-code platforms and AutoML

While AI tools like ChatGPT and Midjourney have made AI approachable, AI’s application in complex manufacturing tasks can still be technically challenging. No/low-code platforms empower domain experts to develop, test, and deploy AI applications efficiently. AutoML takes this further by automating many ML steps, enabling non-ML experts to create models efficiently. By addressing skill shortages in ML and adapting to changing operating conditions, AutoML plays a crucial role in making AI more accessible and adaptable for a wide range of applications.

Mastering AI and ML in manufacturing

To succeed in the AI transformation of manufacturing, three guiding principles are essential:

• Value-centric approach: Prioritize value over technology, avoiding siloed and costly solutions.
• Evolutionary approach: Follow an evolutionary roadmap, gradually increasing AI complexity while delivering value early and allowing a gradual adaptation within organizations.
• User and developer journeys: Align strategies with user and developer experiences, maintaining clarity on value, development efforts, and infrastructure requirements.

Phased approach to AI transformation

The figure below outlines the essential stages for methodically and sustainably integrating AI into manufacturing. Starting from a strong and robust vision for AI in the manufacturing process, the first phase(assess), captures the current state and identifies gaps in the vision. During the second phase(define and design), the future organization and operating models are defined as well as technological blueprints. In the third phase(implement), the organization is ramped-up while refining the operating and governance model. The fourth and final phase combines assessment, definition & design, and implementation. These phases are organized into four workstreams: Transformation, people and AI, data and AI, and automation and IIoT.

Conclusion

Manufacturing companies are yet to fully exploit the potential of AI in manufacturing in reshaping their operational model. AI presents significant opportunities to expand in scale and scope and to accelerate learning. Companies that achieve a robust understanding of AI’s technological advancements and leverage them to support their workforce, invest in leadership and organizational training, and adopt a systematic approach to exploring and leveraging AI (supported by a comprehensive AI data strategy) will not only outperform but potentially replace their less AI-integrated counterparts.

Hey there! We’re excited to share that Capgemini will be at the Pharma MES conference in Berlin. We’ve got a lot to say about Manufacturing Execution Systems (MES) in the pharmaceutical industry, and we’re breaking it down into three blogs. In this first one, we’ll give you a snapshot of what’s happening today, including the main challenges. Keep in mind, this is our view on the current state of MES—there’s no single truth, and perspectives can vary.

New technologies and regulations make collaboration more valuable than ever

In brief:

• New technologies and regulations are changing the healthcare landscape.
• For proactive health payers, these changes carry immense opportunity.
• By partnering with providers and leveraging new technologies, healthcare payers can unlock new value.

New technologies and evolving regulations present opportunities for healthcare payers, and also highlight the need for closer collaboration with providers. Many of the current challenges in healthcare – including simple and transparent payments, consistent quality of care, and data standardization – could be improved if payers and providers had access to the same information. Recently, the Healthcare Financial Management Association (HFMA) cited collaboration between payers and providers as essential. By reducing administrative friction and breaking down information silos, payers and providers can reap the full benefit of the changing healthcare landscape, and further their common goal of delivering high quality care to patients.

Here are four key technological and regulatory changes, and the value that could be gleaned from closer collaboration between insurers and providers:

1. Technology advancements create valuable patient data and personalized care

Advancements in technology such as electronic health records (EHR), wearable devices, and remote patient monitoring (RPM) are expanding the capabilities of personalized care. These are giving rise to new innovations such as Google AI tools that offer a non-invasive, scalable, and cost-effective way to predict cardiovascular risks using retina scans. This enables early detection, personalized treatment, and broader access to care. Current barriers to reaching patients earlier can be overcome by sharing the data responsibly these new technologies produce, in compliance with data and privacy regulations. This will enable payers to collaborate with providers to play an active part in early detection, better define insurance plans, process payments more quickly, and deliver better care earlier before issues progress.

2. Generative AI enables efficiencies for payers and providers alike

Generative AI (Gen AI) has opened a new frontier helping payers automate claims, assess risks, personalize coverage, and support members through chatbots and virtual assistants. For providers, Gen AI is being used in various areas, including supporting clinical decisions, automating routine administrative tasks, and educating patients.

Gen AI has started helping both payers and providers reduce operational costs, streamline processes, and bring efficiencies. However, for members to fully benefit from these innovations, challenges like system integration, data privacy, and security must be addressed. Investments in new technologies can break down data silos and improve information sharing between payers and providers.

3. Changes in Medicare Advantage (MA) create opportunities

Although current enrollments are concentrated between two MA providers with a combined share of 47%, there may be an opportunity for smaller payers to bite off a bigger share of the market. A report from the Kaiser Family Foundation found that 40% of MA beneficiaries underutilized their benefits in 2023. Payers that encouraged customers to better take advantage of those benefits could be rewarded with growth. Also, the pie is growing for all payers. The Congressional Budget Office (CBO) projects that the share of all Medicare beneficiaries enrolled in Medicare Advantage (MA) plans will rise from 54% to 64% by 2034.

Considering these developments, MA plan providers are revisiting their strategies to take advantage of this potential growth. This will ensure improved benefit design and transparency with respect to sharing data with CMS.

4. Regulations for coverage transparency and authorization wait times

In 2024, the health payer industry will undergo significant regulatory changes, focusing on price transparency. Healthcare payers are at various stages of adopting the Transparency in Coverage Rule and the No Surprises Act, both of which are central to these transparency efforts.

The Medicare Advantage and Part D Final Rule will introduce policy updates affecting marketing, prior authorization, and network adequacy. Payers must also adapt to the CMS Advancing Interoperability and Improving Prior Authorization Processes Final Rule, which emphasizes the integration of system functions and coordination across the healthcare ecosystem. These rules address weaknesses in prior authorization processes by:

• Requiring payers to issue decisions within 72 hours for expedited requests, and seven days for standard requests to reduce urgent care wait times, starting in 2026.
• Mandating adoption of HL7 Fast Healthcare Interoperability Resources (FHIR) Prior Authorization API, which will automate authorizations, therefore boosting efficiency.
• Requiring payers to publicly report prior authorization metrics, including denial rates and reasons.
• Requiring payers to upgrade their patient access API to include prior authorization data and implement a provider access API by January 2027.

This will streamline, automate, and bring transparency to the prior authorization process, dramatically reducing patient wait times.

Payers should go beyond the mandate and embrace interoperability

The CMS Advancing Interoperability and Improving Prior Authorization Processes Final Rule should not be limited to prior authorization only. The healthcare payer of tomorrow should treat this as a step towards enhancing the interoperability of healthcare data across systems, improving the transparency and efficiency of all processes, and ultimately ensuring better coordination of care.

The FHIR standard enhances healthcare data exchange and integration, and while most healthcare payers have taken steps towards adopting it, few are benefitting fully. To gain the most value from interoperability will require:

• Embracing cloud-based solutions for scalability and real-time access
• Standards compliance and governance
• Implementing patient-centric interoperability through APIs.

New technologies are worth the investment

For healthcare payers that keep pace with new technologies, these changes represent an opportunity. To support API-based secure data exchange and governance, payers will need to update core administrative systems. Investments in integrated data analytics, predictive modeling solutions, and Gen AI are also crucial for delivering accurate, personalized, real-time information to members.

The health payer ecosystem must be modular to allow for flexible data sharing with external entities, including information about plans, pricing, coverage, members and compliance, as well as analytics derived from the same. Establishing standards, robust auditing, rigorous testing, and regular monitoring is essential for seamless data exchange and governance.

There’s work for providers too. By implementing EHR and interoperability solutions, providers will improve clinical workflows, enhance personalized care plans, and improve patient engagement, ultimately resulting in superior service delivery and coverage.

By focusing on these measures, payers and providers can drive operational excellence, creating a more efficient, responsive, and cost-effective healthcare system.

“We will explore in detail how leveraging digital tools, data analytics, and AI can deliver operational excellence in the health insurer’s complex provider management space. Join us in in the next chapter for understanding how regulatory changes, value-based benefit plans and industry changes will impact your future organizational goals and creating a proactive roadmap. Stay tuned for our next blog.”