ISG Provider Lens™ Specialty Analytics Services - Life Sciences and HealthCare - Global 2024

AI transforms the life sciences and healthcare sector with high commercial impact and personalized care

For firms in the life sciences and healthcare analytics sector, leveraging advanced analytics has become crucial to remain competitive. The rapid advancements in AI and Generative AI (GenAI) across industries promise quick and informed decision-making by understanding user pain points and providing customized solutions to an organization’s unique problems. Implementing a modern data strategy coupled with advanced analytics and AI is a key factor in this transformation.

A robust data foundation provides a unified, enterprise-wide view of clinical and commercial data by incorporating internal and external sources such as electronic medical records (EMR), clinical trials, insurance claims, CRM systems, academic collaborations and wearable devices. By democratizing access to this multimodal data and applying techniques such as predictive and prescriptive analytics, ML, NLP and computer vision, organizations can accelerate R&D, uncover hidden patterns, forecast trends and optimize responses. This approach helps life sciences and healthcare companies achieve several critical goals as follows:

• Drug discovery and development: Streamlining R&D processes, predicting compound efficacy and anticipating future diseases, leading to fast and targeted research for developing life-saving treatments.
• Sales and forecasting: Enhancing customer segmentation, accurately forecasting demand and driving sales growth through data-driven insights.
• Pharmacovigilance and drug safety: Forecasting and detecting subtle safety signals, addressing potential risks promptly, and proactively managing safety throughout the drug development process, resulting in safe and more effective products.
• Accelerated clinical processes: Utilizing AI to streamline data collection, analysis and reporting in clinical trials, leading to quick trial completion, drug development and launch.
• Market access and pricing: Developing effective strategies and optimizing pricing models using comprehensive data insights.
• Fraud detection: Applying advanced analytics to identify fraudulent activities in payer networks and reducing waste and abuse in claim processing. 
• Improved diagnostics with AI: Using advanced AI algorithms to analyze extensive medical data for more  accurate disease detection and diagnosis, enabling personalized treatment plans and better patient outcomes. 
• Patient and member experience: Using a holistic view of patients and members, AI models to create personalized treatment plans and engagement strategies that enhance risk management.

In this research, ISG mainly focuses on the life sciences segment, covering clinical R&D, drug development, clinical trials, pharmacovigilance and the commercial value chain of pharmaceutical companies, and the healthcare segment, encompassing payers and patient care providers. However, technology areas such as user analytics, medical devices and the medtech ecosystem have applications in both segments.

Specialty analytics providers

Addressing complex and unique challenges in the life sciences and healthcare sector requires more than off-the-shelf intelligent automation and SaaS analytics software. Custom analytics solutions that integrate seamlessly into client ecosystems and offer full customization capabilities are, thus, essential. These solutions, developed with a white-box approach and designed with a human-centered design and deep domain expertise, provide the agility, flexibility and scalability needed to embed data-driven insights into decision-making processes effectively.

Specialty analytics providers are key to enabling life sciences and healthcare enterprises to adopt advanced analytics with clarity and confidence. Organizations can drive innovation and maintain a competitive edge by implementing white-box solutions from these providers whose approach typically involves the following:

• Custom solutions: Developing technology agnostic, tailored analytics solutions that fit each client’s specific needs, objectives and maturity levels. These solutions align with existing capabilities while enabling future growth.
• Collaborative development: Working closely with clients to cocreate solutions that address unique challenges and foster a collaborative culture, ensuring alignment with business goals and maximizing value.
• Scalability: Offering solutions adaptable to varying analytics maturity levels and evolving business needs, from initial implementation to advanced analytics.
• Ethical AI: Ensuring AI models are robust, unbiased and compliant with regulatory standards, incorporating ethical
  considerations throughout the development lifecycle.
• Continuous support: Providing ongoing assistance with implementation, optimization and post-implementation
  maintenance services to ensure smooth operations.
• Talent and R&D augmentation: Establishing CoEs and supporting capability expansion for client development teams.

Specialty analytics providers offer various accelerators and IP-driven assets to expedite deployment and reduce time to value. These include pretrained AI and ML models that are modular, cloud-native and can seamlessly integrate into client systems. Providers often collaborate with major cloud hyperscalers, such as Azure, AWS and Google Cloud, to cocreate and market these solutions. Common accelerators include tools for data management, patient analytics, member experience and sales and marketing analytics.

The providers also focus on the following aspects:

• Partner ecosystems: Collaborating with global hyperscalers to extend digital analytics and data services, enabling
  accelerated client scaling.
• Expanding IP and accelerators: Broadening their portfolio to cover more value chain areas and accelerate time to value.
• AI engineering platforms: Offering comprehensive services, including MLOps and large language model  operations (LLMOps), to ensure scalable and robust AI deployments.
• Emphasizing DataOps: Investing in advanced data integration and orchestration tools to enhance scalability and flexibility and fostering a DataOps ecosystem with key vendors, such as Databricks and Snowflake, and open-source communities.
• GenAI integration: Developing ready-todeploy industry-specific GenAI use cases to improve patient outcomes, clinical trials and operational efficiency.
• Talent building: Recruiting top talent globally and partnering with academic institutions to build and nurture expertise.

Here’s how different segments of life sciences and healthcare organizations are advancing innovation and efficiency by utilizing AI-based solutions:

Life sciences — commercial model for pharmaceutical and medical devices

As pharmaceutical and medical device companies revise their commercial strategy, shifting from simply collecting data to effectively connecting and leveraging it is essential. Specialty providers enhance commercial effectiveness by creating solutions that analyze data from patients, payers and providers, conduct patient journey analyses, and build competitive landscapes for life sciences clients. This allows organizations to improve their market share through healthcare professional targeting and engagement, marketing mix modeling and automating the promotional review process. Some of the key areas where advanced analytics can create a tangible impact are listed below:

• Medical sales support: Enabling sales representatives to have informed interaction with physicians and customers and be aware of competitor developments using GenAI-based assistant.
• Promotional material tagging: Eliminating the need to manually tag assets with the metadata required for a  predictive analytics solution using GenAI.
• Customer segmentation and nextbest-action engine: Marketing outreach personalization based on behavioral markers in terms of frequency and sequence of nudges to increase adoption and engagement and drive loyalty.
• Demand and sales forecasting: Gaining accurate insights into future demand by accounting for shifting market dynamics and customer behavior to optimize inventory management and sales strategies.
• Supply chain visibility: Enhancing transparency and control throughout the supply chain with real-time data and
  analytics and smart recommendations for informed purchase decisions for procurement transformation. 

Clinical R&D, trials and drug safety

Advanced analytics and AI are transforming drug development by accelerating discovery, optimizing clinical trials and enhancing patient monitoring.

• Drug discovery: AI analyzes extensive biological, genetic and clinical data to identify potential drug targets. ML models assess drug toxicity based on historical data, aiding in the selection of safer compounds for drug  development. AI also uncovers new uses for existing drugs by detecting patterns across various diseases, speeding up therapy development.
• Clinical trials management: Specialty providers use advanced analytics, AI and LLMs to streamline clinical trials, from protocol design and patient recruitment to monitoring and compliance. AI tools use EMRs and diagnosis notes to predict patient eligibility. Remote monitoring, adherence tools and advanced analytics help scale trials, while  standardized frameworks and AIdriven solutions integrate and evaluate trial data to manage potential adverse events.
• Drug safety: Life sciences companies use advanced analytics to detect adverse events and improve drug safety through benefit risk assessment. AI solutions analyze vast datasets to uncover patterns, trends and patient  preferences and mitigate the risks associated with adverse events.

Healthcare providers

AI solutions enhance care delivery and operational efficiency for healthcare providers, leading to better patient outcomes and streamlined processes. By analyzing medical records, lab reports, claims and other patient data, AI improves various aspects of the treatment journey, from reducing time to treatment initiation to accurate diagnosis
and ensuring proper engagement. Specialty providers enable healthcare organizations to leverage AI in the following areas:

• Health trajectory analysis: Using a patient’s longitudinal data to predict disease progress, treatment responses and patient outcomes, thereby aiding personalized medicine and proactive healthcare.
• Surgical intelligence solutions: Providing AI-driven tools for surgical planning and decision support and  improving surgery precision, safety and efficiency.
• Clinical decision support systems: Offering evidence-based recommendations for enhancing diagnostic accuracy, treatment effectiveness and patient safety; this includes GenAI solutions for extracting and summarizing clinical notes and assisting in disease codification.
• Patient and population analytics: Analyzing patient demographics and health trends to drive targeted  interventions, improve outcomes and optimize resource use.
• Digital health: Providing a remote care platform that integrates with telehealth solutions and wearable devices, enabling continuous monitoring for critical patients.
• Operations optimization: Balancing nurse-to-patient and doctor-to-patient ratios based on patient needs, staff capacity and performance metrics.
• Patient engagement: Utilizing predictive analytics to segment patients for targeted outreach and offering AI chatbots for self-service options.

Healthcare payers

AI models for healthcare payers are primarily employed in the following areas:

• Sales and marketing:
  • Acquiring and enrolling customers by targeting high-value prospects and personalizing outreach.
  • Enhancing engagement with tailored content and programs, boosting retention by predicting churn and proactively engaging at-risk members to improve renewals.
• Member experience:
  • Using AI to predict engagement propensity and implementing personalized engagement programs to improve member experience.
  • Streamlining digital journeys with automated insights and enhancing acquisition and retention through personalized recommendations.
• Operational efficiency:
  • Extracting and summarizing clinical notes, improving coding accuracy and predicting treatment costs to enhance reimbursement processing and member satisfaction.
  • Optimizing claims operations by predicting denials and automating claim filtering based on reasons for denial.
• Risk management:
  • Reducing care costs by predicting which members are at risk of adverse events and require urgent intervention.
  • Identifying at-risk groups and customizing outreach with GenAI and NLP, improving health outcomes and member care through proactive interventions.

Commercializing GenAI

In recent years, specialty providers have been heavily investing in GenAI and training their LLMs on healthcare-specific datasets. These LLMs prioritize privacy, incorporate domainspecific terminology and minimize bias, thereby
empowering healthcare enterprises to tailor solutions that enhance patient outcomes and decision-making. They can be customized for various use cases, including sales, marketing, R&D and patient care, enabling productivity 
improvements, process optimization and the creation of highly personalized AI assistants.

Some providers offer a comprehensive suite of GenAI solutions and services, including:

• Consulting and advisory: Encompassing use case ideation, investment prioritization, tailored road mapping, CoE setup and operational governance.
• Technical advisory: Covering model selection, architecture assessment, cost management, customization  strategy, model training, fine-tuning and incorporating human feedback.
• Custom solution development: Building PoCs, developing full-scale enterprise solutions and offering LLM services as private versions for on-premises deployment, ensuring no data is shared with cloud service providers.
• LLM quality and testing: Providing testing services for GenAI solutions, including production observability and monitoring.
• LLMOps platform: Automating output validation and monitoring the quality and reliability of GenAI solutions.

Industry outlook

ISG research finds the following key advancements shaping the life sciences and healthcare industry over the next 2–3 years:

• Precision medicine: Healthcare payers and providers are increasingly focusing on personalized care. By utilizing analytics on real-world data (RWD) from sources such as wearables, mobile devices, electronic health records (EHRs), and claims data, they can identify patient subgroups with unique needs and tailor treatments accordingly.
• GenAI experimentation: The development and refinement of LLMs are enhancing efficiencies across the value chain for life sciences and healthcare organizations. The growing availability of LLMOps platforms is advancing  experimentation and innovation in this area.
• Small language models (SLMs): Creating domain-specific and subdomain-specific GenAI models allows healthcare enterprises to host these models internally, addressing data privacy concerns while leveraging the benefits of SLMs.
• Digital health adoption: There is a heightened emphasis on improving patient outcomes through digital solutions, such as telehealth, that support both in-person and remote care environments.
• Expansion into emerging markets: Providers are exploring business opportunities in emerging markets such as APAC and LATAM, which are driven by rapid digital transformation and rising healthcare expenditures, creating a growing demand for healthcare analytics solutions.

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