The healthcare sector is at a turning point. Today’s organizations are driven by the convergence of data, technology and a new sense of urgency to recruit and retain talent, optimize operations and deliver the best possible patient outcomes.
Yet the sheer volume of healthcare data is almost incomprehensible. He understands 30 percent global data and is expected to grow faster than any other type of data.
Ninety-seven percent health data currently remains unused. But this is changing, with the accessibility to RIGHT data and innovations such as artificial intelligence (AI) are paving the way for transformation.
As healthcare providers and suppliers position themselves for success in the race for value, their ability to utilize cutting-edge data and technology will determine their success in navigating this complex landscape and achieving sustainable growth.
An experienced doctor talks with his hospital administrator or pharmaceutical representative.
getty
Here are three opportunities in health data and technology, along with information on how to leverage them for success.
Real-world data and real-world evidence for clinical research and practice transformation
Easy access to real-world data (RWD) and real-world evidence (RWE) is becoming essential to drive innovation in clinical research, product development and patient care. Actually, ninety percent Global life sciences executives say their organizations are already working to leverage RWE to facilitate decision-making throughout the product lifecycle.
Despite recognizing the value of RWD/RWE, organizations are only beginning to realize its full potential. Many mix and match data sets, lacking clarity on which data is most important for gaining insights to improve research and clinical trials, discover new treatments, and accelerate the development of drugs and medical devices .
Unlocking the power of RWD/RWE starts with RIGHT datasets. Organizations should seek a database which integrates RWD chargemaster, clinical and quality data from thousands of sites. This provides a rich and accurate basis for evidence-based and population-based analyzes of drugs, devices, disease states, and other treatments.
Robust, anonymized patient data sets combined with AI-based technologies take performance to the next level. In November, the National Institutes of Health (NIH) published study results on a new AI algorithm developed by researchers at the NIH National Library of Medicine and the National Cancer Institute. This technology processes patients’ medical and demographic information and matches it to clinical trials for which they are eligible. In the pilot study, the tool accelerated the matchmaking process by more than 40%.
Elsewhere, machine learning (ML) and natural language processing (NLP) are already being hailed as transformative AI technologies for clinical research innovation. (ML) can quickly process mountains of data and recognize patterns – and (NLP) can read and interpret more than two million records per hour.
Together, these capabilities accelerate value creation by removing cost, time, and variability from clinical research and care delivery, including:
- Early identification of patients and previously unidentified disease risk factors.
- Accurate, more efficient clinical trial recruitment and optimized site selection, “flipping the funnel” to recruit based on where patients are versus where relationships exist.
- Predicted performance of drugs and devices across patient populations for appropriate treatment and care.
- Effectiveness of therapeutic products and post-market devices to support efficient regulatory approvals and reimbursements.
Point of proof: RWD combined with AI technologies has enabled clinical trial sponsors to improve recruitment by 1,800% and triple recruitment compared to baseline projections, significantly reducing enrollment times and costs.
Actionable Intelligence for Patient-Centered Performance
Ninety percent of provider executives say healthcare consumerism is a top priority for their organization. As patients increasingly “vote with their feet” based on factors such as accessAffordability, Safety, and Quality of Care: Consumer-centric healthcare organizations generate revenue growth more than twice that of their industry peers with lower patient satisfaction scores.
For patient-centered performance improvement, providers must Focus on what matters and measure it.
To demonstrate their value to payers, patients and local communities, providers today use credentials and information from leading quantitative grading programs as a North Star for competitive analysis and performance improvement. Balanced dashboards including clinical, financial, and operational metrics enable continuous improvements in quality, service marketing, recruiting and talent management activities, and much more.
Additionally, integrating benchmarks and real-time data from sources such as electronic health records (EHR) and enterprise resource planning (ERP) systems creates a unified view of performance and outcomes financial, operational and clinical.
This includes service line analytics capabilities that unite utilization, clinical, and acquisition cost data to visualize costs versus outcomes, reduce variation in care, and determine which products provide the best value. Workforce improvement is another area of opportunity with benchmarking and monitoring capabilities to improve employee productivity and to develop staffing approaches that best serve organizations and their patients.
Advanced analytics combined with AI-powered tools improve data and performance management, integrating predictive modeling, automation deployment, and rapid emergence of actionable insights to accelerate value creation.
Adopt AI-based clinical decision support (CDS) that leverages RWD and evidence-based guidelines. This technology provides a significant opportunity to streamline workflows, engage physicians, and avoid errors and waste. It uses ML to integrate real-time alerts into EHRs and integrate them into routine workflows, so providers receive actionable diagnosis or treatment recommendations for the right patient at the point of care.
Point of proof: Use of CDS saved approximately $1,000 per patient encounter and resulted in better patient outcomes, including shorter lengths of stay, a lower likelihood of 30-day readmissions, and a lower likelihood of lower number of complications.
Data Visibility and Predictive Analytics for Supply Chain Optimization
According to Prime Minister’s 2024 Resilience Surveyfour in five healthcare providers and suppliers expect supply chain challenges to worsen or remain the same over the next year – a slight uptick in respondents’ concerns compared to to the previous year.
Continued disruptions and product shortages are creating immense pressure on healthcare stakeholders. Consider, for example, that more than 86 percent of U.S. suppliers experienced shortages of intravenous fluids, a staple for daily patient care, following the October 2024 hurricanes.
In this environment, it is no surprise that supply chain optimization is a top priority for providers and suppliers.
Investing in a resilient supply chain is not only a matter of patient safety and quality, but also a strategic choice that significantly influences the financial health of organizations. For example, Premier data shows that half of suppliers lost more than 2.5% of their revenue due to their own shortages, while a quarter lost between 2.5 and 6% of their revenue between February 2023 and February 2024.
Organizations are beginning to integrate resilience and optimization strategies into supply chains. However, they often struggle to obtain and leverage data and technology for actionable insights that streamline operations, manage supply chain costs, and protect against supply disruptions and shortages.
Enter ML-powered predictive models – and robust data shared between suppliers and providers – to help provide that much-needed visibility. Consolidating real-time data across all facilities provides a comprehensive and accurate picture of national supply and demand dynamics – as well as optimization strategies.
Armed with this information, suppliers can anticipate increased demand for production planning, inventory management and shortage prevention. Suppliers receive real-time notifications when purchased products are at risk of shortage. The most innovative predictive systems can identify product shortages with greater than 90% accuracy and automatically recommend clinically approved equivalent products, saving staff valuable time and minimizing supply chain disruption for continuity of patient care.
Supply chain analytics further facilitates decision-making with real-time data on compliance, savings tracking, SKU rationalization, and benchmarking – all essential for gaining insights that drive progress cost control, clinical standardization and improved outcomes.
Point of proof: A healthcare system used AI-powered predictive models to proactively address hundreds of potential shortages before patient care was impacted.
Health data and technological advancements are paving the way for a new era of research and clinical care innovation, supply chain resiliency, and a host of operational and performance improvements never before possible .
Strategic application of RIGHT Assets can separate organizations in the marketplace, position them for long-term growth, and deliver optimal value to the patients and communities they serve.
Better, smarter, faster healthcare is within our reach. We just need the right tools and mindset to make it happen.
For more information, get your free copy of the complete guide.