Life Sciences Enterprises’ Sourcing Criteria for Selecting BPO Providers | Market Insights™
September 13, 2023
Life Sciences Enterprise
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Life Sciences Enterprise
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Revenue Management
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Facilitated by Everest Group
The life sciences industry is undergoing significant evolution and growth driven by advancements in technology, changing demographics, shifting regulatory landscapes, and emerging healthcare challenges.
India has emerged as a key location for life sciences organizations due to its vast talent pool of skilled professionals, cost-effectiveness, robust research infrastructure, supportive regulatory environment, and thriving innovation ecosystem.
Join this roundtable on September 12 in Bangalore to participate in conversations with our expert analysts and your peers. Attendees will gain valuable insights into the future potential for the industry, and how India as a leading global delivery location can play an instrumental role in the growth of life sciences organizations.
We’ll uncover key themes in this discussion, such as:
Who should attend?
Roundtable guidelines
The only price of admission is participation. Attendees should be prepared to share their experiences and be willing to engage in discourse. Participation is limited to enterprise leaders (no service providers).
In the past, the manufacturing industry primarily focused on designing standardized manufacturing procedures and managing labor and mechanical systems. However, the advent of Industry 4.0 has led to the widespread adoption of technology across various sectors, unlocking numerous benefits. Nevertheless, the life sciences industry has been slow in embracing technology to modernize its manufacturing setups. The pandemic, regulatory frameworks, and the drive for operational excellence are now propelling the adoption of smart manufacturing services.
Life sciences enterprises are striving to unlock benefits such as cost optimization, increased productivity, visibility, and efficiency through investments in vital use cases such as digital twins and predictive maintenance. They are also exploring high-growth opportunities such as sustainable manufacturing, batch-to-continuous manufacturing, and personalized medicine production. As the industry receives investments in smart manufacturing, providers are assuming the role of end-to-end digital transformation partners by collaboratively developing solutions to assist enterprises in their digital journeys.
This report features 16 life sciences smart manufacturing service provider profiles and includes:
Scope:
The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.
Generative AI
The life sciences industry can reap the many benefits of Generative Artificial Intelligence (GAI) by effectively overcoming challenges in this highly regulated industry to responsibly implement the technology. Discover key implications for technology players and a roadmap for enterprises to successfully adopt GAI for commercial functions.
Help us learn more about the potential of gen AI in the life sciences commercial function by participating in this short survey and receive a complimentary summary of the survey findings.
In the first blog in this series, we explored Gen AI life sciences commercial use cases, shared industry leaders’ skeptical to optimistic perspectives on its potential, and uncovered new technology offerings. Read on for more insights into key risks, repercussions, and recommendations to adopt generative AI in life sciences.
“With great power comes great responsibility.” – Uncle Ben, Spiderman
Undoubtedly, Gen AI has massive potential to disrupt most processes and create new opportunities across industries, including the life sciences commercial function. But the highly regulated nature of this industry brings significant risks and challenges that will need to be overcome to adopt GAI at scale. Let’s explore this further in the illustration below:
“A journey of a thousand miles begins with a single step.” – Lao Tzu
While the Gen AI journey can appear long and daunting, commercial technology players may have a head start over their peers across the life sciences value chain. While certain use cases, such as personalized campaign generation and brand reputation monitoring, will require complex integrations and domain-specific development, other applications like content generation/analytics, market research, and autonomous customer support can be quickly implemented and brought to market.
Next, let’s take a look at six recommendations for life sciences technology providers to seize opportunities that GAI presents.
Enterprises are still evaluating the Gen AI conundrum across the entire life sciences commercial function, including the risks, challenges, costs, return on investment (RoI), talent, and processes. Our five-step GAI tools adoption guide can help enterprises accelerate this process, as illustrated below:
While Gen AI holds immense promise for transforming the life sciences commercial landscape, it comes with its fair share of challenges, including ethical considerations, data quality, interpretability, and integration hurdles that need to be addressed to ensure responsible and successful adoption.
Technology providers can proactively develop strategies and solutions to overcome these obstacles. By crafting a thoughtful roadmap, committing to ethical practices, and focusing on continuous learning and improvement, the life sciences commercial solutions supply ecosystem can harness the power of Gen AI to unlock new opportunities, enhance customer experiences, and drive sustainable industry growth. While the journey to adopt Gen AI may be complex, the rewards for successful navigation are boundless.
Help us as we research the possibilities of Gen AI in the life sciences commercial sector by taking part in this brief survey. As a token of appreciation, you will receive a complimentary summary of the survey results.
To discuss Gen AI in life sciences and its impact on the commercial technology landscape, contact Rohit K, Durga Ambati, Panini K.
Value-based care models have gained significant traction in the market with growing costs of healthcare services and a pronounced gap in the care quality provided under traditional fee-for-service models. Consequently, the Centers for Medicare and Medicaid Services (CMS) plans to drive the adoption of value-based care by transferring all Medicare fee-for-service beneficiaries into a care relationship with accountability for quality and lower total cost of care by 2030. While the pandemic initially drove alternate care delivery models, such as home-based care and virtual care, enterprises are increasingly using them to improve the continuity of care, reduce costs, and drive value for their member/patient base.
However, this push toward value-based care requires further technology investments from both payers and providers for integrated care management and effective utilization management. Providers can fulfill enterprises’ clinical and care management operations requirements by delivering clinical services from cost-effective locations and deploying advanced technology solutions built on a foundation of clinical, claims, and Social Determinants of Health (SDoH) data for personalized care programs and engagement.
In this research, we assess 15 healthcare service providers featured on the Clinical and Care Management Operations PEAK Matrix®. We provide a relative positioning and analysis of the providers’ market shares and evaluate their strengths and limitations. The study will enable healthcare enterprises to identify suitable providers to transform their business processes.
This report features a detailed analysis of 15 healthcare service providers and includes:
Scope:
The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.
Due to decentralized and hybrid designs, the growing clinical trial complexity generates vast data volumes from diverse sources and creates significant data management challenges. In response, sponsors are increasingly employing unified clinical data and analytics (D&A) platforms to centralize and standardize clinical data to gain actionable insights, offering real-time monitoring, predictive analytics, and risk management. These platforms transform disparate datasets into cohesive, structured formats, allowing stakeholders to detect outliers, predict adverse events, and generate on-demand dashboards and reports.
D&A platforms offer centralized repositories, Risk-Based Quality Management (RBQM), and seamless integration with Electronic Health Records (EHRs), wearables, and medical devices. These features improve data access and interoperability, contributing to more informed decision-making and enhanced quality and risk oversight. Clinical D&A platform providers are incorporating cutting-edge AI and generative AI capabilities to automate repetitive tasks, generate insights, and strengthen quality and risk management to address sponsor needs.
In this report, we analyze 18 clinical D&A platform providers featured on the Everest Group’s PEAK Matrix® based on their capabilities, offerings, and market impact. The report will empower buyers to choose the right provider for their sourcing considerations and enable providers to benchmark themselves against their competition.
Contents:
In this report, we:
Scope:
In this report, we assess 14 clinical Data and Analytics (D&A) platform providers. The providers are positioned on Everest Group’s PEAK Matrix®, a composite index of a range of distinct metrics related to the providers’ capabilities and market impact. The study will enable buyers to choose the best-fit provider based on their sourcing considerations, while providers will be able to benchmark their performance against each other.
In this report, we:
Scope:
The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.
The healthcare industry constantly evolves, requiring organizations to continuously adapt and enhance their capabilities to stay competitive. The shift toward value-based care has presented various opportunities, such as telehealth, population data analytics, remote patient monitoring, commercial models based on risk assessment, increased investment in care management, and a greater emphasis on digital initiatives, particularly automation and analytics. To achieve these objectives, healthcare providers are forging robust partnerships within ecosystems, collaborating with top third-party providers, developing innovative technological solutions, and implementing novel approaches such as Business-Process-as-a-Service (BPaaS) and provider solutions for service delivery.
In this report, we analyze 29 healthcare payer operations providers featured on the Healthcare Payer Operations PEAK Matrix® . The report positions the providers’ market shares relative to each other, and evaluates their strengths and limitations. The study will enable healthcare payers to identify suitable providers to transform their business processes and differentiate themselves.
This report analyzes 29 healthcare payer operations providers and includes:
Scope:
The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.
Advances in Generative Artificial Intelligence (GAI) have sparked interest in its potential to drive growth and innovation in the biopharma and medical devices industries. Despite challenges and regulations, the life sciences industry is actively exploring GAI’s possibilities. Learn about the current state of Generative AI adoption, the supplier landscape, and proactive actions stakeholders should take to stay at the forefront of this technology.
Contact us for questions or to have a discussion.
Life sciences, just like all other industries, is actively seeking to understand the intricacies of Generative AI (GAI) to gain a competitive edge. Enterprises in this industry are gearing up to embrace this generational shift in AI-enabled technology. Continue reading for the first part of our series on Generative AI adoption.
GAI is a type of machine learning that uses neural networks to learn patterns in the input data. Based on the input data it was trained on, GAI then generates the most appropriate response. GAI’s promise of delivering significant operational and tactical benefits in the short term and hyper-personalization and intelligent decision support over time is pushing life sciences enterprises to evaluate their Generative AI adoption readiness.
Although GAI can potentially disrupt the life sciences technology ecosystem in many significant ways, navigating the various risks and challenges that come with its implementation in this highly regulated industry will be critical.
Let’s take a look at the potential impact of GAI on the life sciences value chain:
The graphic below explores the short and long-term impact of GAI on various life sciences functions:
GAI can be used to model certain aspects of drug discovery. Some prominent cases include Insilico Medicine’s GAI platform Chemistry42 which generates ideas for novel chemical compounds, and AstraZeneca’s transformer-based model MegaMolBART for reaction prediction, molecular optimization, and de novo molecule creation.
As life sciences enterprises look to unlock GAI’s true value, its various stakeholders have exciting opportunities to collaborate and form next-generation partnerships to successfully drive GAI implementation. The supply ecosystem across the GAI technology stack is illustrated below:
The following stakeholders have key roles in GAI’s future:
These stakeholder groups are uniquely positioned to act as catalysts for Generative AI adoption. The exhibit below looks at the actions each group should take to move forward with GAI and the implications:
Stay tuned for the second part of this blog series, where we will examine the most promising use cases in each area of the life sciences value chain, including their potential risks. We will also present a blueprint to successfully maximize the value of GAI-enabled solutions.
To discuss the future of Generative AI in life sciences, contact Kumar Dhwanit, or Rohit K.
Keep an eye out for our LinkedIn Live, The Possibilities for Generative AI in Sourcing.
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