As the digital transformation of healthcare continues to evolve, health systems are realizing the immense potential of their vast data sets. These institutions are no longer just places where patients seek care; they are becoming critical sources of valuable insights that can drive advancements in medical research, precision medicine, and clinical decision-making. By monetizing health data, health systems can generate new revenue streams, while simultaneously contributing to the future of patient care.
In this blog post, we explore seven key types of data products that health systems are developing and monetizing, and how these products are transforming the healthcare industry.
1. De-Identified Patient Data Sets for Research and Development
Overview: Health systems generate large-scale de-identified patient data sets that are of great interest to pharmaceutical companies, biotech firms, and academic researchers. These datasets typically include detailed clinical information such as diagnoses, treatments, lab results, and outcomes across diverse patient populations, providing invaluable insights for research and development.
Development: Data is collected from electronic health records (EHRs), lab information systems (LIS), and other clinical sources, then meticulously cleaned and standardized to ensure consistency. Advanced de-identification techniques are applied to remove personally identifiable information (PII), complying with privacy regulations like HIPAA. Once de-identified, the data is structured into usable formats, categorized by clinical factors such as disease state or treatment type.
Use Case: Pharmaceutical companies use these datasets to conduct real-world evidence (RWE) studies, which complement clinical trial data. By analyzing treatment patterns and outcomes in real-world settings, companies can better understand the effectiveness of their drugs, identify new therapeutic opportunities, and support regulatory submissions.
Value:
Revenue: Health systems can generate significant revenue by licensing these data sets, often through long-term contracts.
Advancement in Medical Research: These datasets help drive innovation in drug development, leading to new therapies and better patient outcomes.
Cost Reduction: Insights from these data sets enable more effective treatments, potentially lowering healthcare costs.
Example: Mayo Clinic's partnership with nference involves licensing de-identified EHR data to help pharmaceutical companies accelerate drug discovery.
2. Genomic Data Sets for Precision Medicine
Overview: Health systems with genomic sequencing capabilities are creating large genomic datasets that can be linked to clinical outcomes, which are particularly valuable for precision medicine initiatives. These data sets are sold or licensed to biotech firms, pharmaceutical companies, and research institutions.
Development: Genomic data is collected through patient DNA sequencing, often conducted during routine care or research programs. This data is then integrated with clinical information from EHRs, creating a comprehensive dataset. Like clinical data, genomic data is anonymized to protect patient identity.
Use Case: Biotech firms use genomic data sets to discover biomarkers, develop targeted therapies, and design personalized treatment plans. For example, in oncology, genomic data can identify mutations that specific drugs can target, resulting in more effective, personalized cancer treatments.
Value:
Revenue: Licensing genomic data provides a lucrative revenue stream for health systems.
Personalized Medicine: Insights from genomic data enable the development of personalized therapies that improve patient outcomes.
R&D Acceleration: Large genomic datasets help biotech firms accelerate the drug discovery process.
Example: Geisinger Health System partnered with Tempus to provide genomic and clinical data that supports the development of personalized cancer treatments.
3. Analytics as a Service (AaaS) for Affiliated Providers
Overview: Health systems are extending their advanced analytics capabilities to affiliated facilities and providers through an "Analytics as a Service" (AaaS) offering. This service enables affiliated healthcare organizations, such as clinics, physician groups, and community hospitals, to access powerful, cloud-based analytics tools that help them gain actionable insights from their own clinical and operational data. By leveraging the data infrastructure of health systems, these facilities and providers can improve care outcomes, optimize resource utilization, and reduce costs without needing to invest in expensive in-house analytics capabilities.
Development:
Data Integration: Health systems aggregate clinical, financial, and operational data from affiliated facilities through secure data-sharing agreements. This data is then integrated into the health system’s centralized analytics platform.
Cloud-Based Delivery: The AaaS platform is cloud-based, ensuring scalability and easy access for affiliated providers across different locations. Data can be securely uploaded, processed, and analyzed in real-time.
Advanced Analytics Tools: Using machine learning and AI-driven algorithms, the platform offers predictive analytics, data visualization, and real-time reporting capabilities. Affiliated facilities can use these tools to predict patient outcomes, optimize staffing, and improve population health management.
Self-Service Access: Affiliated providers can access a self-service analytics interface that allows them to run custom reports and queries. Health systems may also offer consulting services or pre-configured dashboards tailored to the needs of individual facilities.
Use Case: A community hospital affiliated with a larger health system can use AaaS to analyze readmission patterns and identify at-risk patients. By implementing predictive analytics, they can create targeted intervention programs that reduce readmissions, improving patient outcomes while minimizing costs. Physician groups may use AaaS to assess their patient population's chronic disease trends and allocate resources more effectively for preventative care programs.
Value:
Shared Revenue Stream: Health systems can generate revenue by offering AaaS to affiliated facilities through a subscription model or a pay-per-use basis. This also strengthens relationships with affiliated providers by offering them valuable services.
Improved Care Coordination: AaaS enables affiliated providers to tap into the same advanced analytics capabilities as the health system, facilitating better care coordination and patient management across the network.
Reduced Costs for Affiliates: Affiliated facilities avoid the high costs of building their own data infrastructure and analytics teams by leveraging the health system’s existing platform. The insights gained can also lead to operational efficiencies and cost reductions.
Enhanced Population Health Management: The ability to analyze large-scale patient data helps affiliated providers manage chronic conditions, predict disease outbreaks, and optimize resource allocation, ultimately leading to better population health outcomes.
Example: Northwell Health offers an AaaS platform to its network of affiliated facilities, allowing community hospitals and clinics to use advanced predictive analytics for population health management. With real-time data integration and AI-driven insights, affiliated providers can improve patient outcomes while reducing operational costs by using the same analytics tools as the larger health system.
4. Care Coordination Platforms for Population Health Management
Overview: Data-driven care coordination platforms are critical for managing patients across different care settings. These platforms enable health systems to track patient progress, share data across care teams, and ensure that patients receive the right care at the right time.
Development:
Interoperability: Health systems develop platforms that integrate data from multiple sources, such as EHRs, lab results, pharmacy records, and social services data.
Real-Time Analytics: These platforms use real-time data to monitor patient progress, track adherence to care plans, and flag any issues that may require immediate intervention.
Collaborative Tools: Care coordination platforms often include collaboration tools that allow care teams to communicate, share information, and adjust treatment plans as necessary.
Use Case: For value-based care, these platforms enable seamless transitions of care from hospital to home, ensuring that patients receive follow-up care and reducing the risk of complications. For example, a care team can monitor a patient discharged after surgery to ensure they adhere to their recovery plan, take medications as prescribed, and attend follow-up appointments.
Value:
Improved Care Continuity: By ensuring that patient data is accessible and shared across care teams, care coordination platforms improve the continuity of care.
Reduced Readmissions: Better coordination helps reduce the risk of preventable readmissions, a key metric in value-based care models.
Revenue Potential: Health systems can offer these platforms to other providers or affiliates to enhance care management across networks.
Example:The Mount Sinai Health System uses its proprietary care coordination platform to manage patients across its various hospitals, outpatient clinics, and home care services. The platform has significantly reduced readmissions and improved care transitions for patients in their value-based care programs.
5. Clinical Trial Data as a Service
Overview: Health systems with robust clinical trial programs can monetize the data generated from these trials. Pharmaceutical companies, contract research organizations (CROs), and academic researchers find this data invaluable.
Development: Data from clinical trials is collected, standardized according to regulatory requirements, and offered as a service. Health systems may sell access to completed trial data or provide real-time access to ongoing trials.
Use Case: Pharmaceutical companies use clinical trial data to support regulatory submissions, refine drug development processes, and gain insights into patient populations.
Value:
Revenue: Monetizing clinical trial data provides substantial revenue for health systems, especially those with high trial volumes.
Accelerated Drug Development: High-quality trial data helps pharmaceutical companies bring new treatments to market more quickly.
Improved Patient Access: Health systems offering clinical trials give patients access to cutting-edge treatments.
Example: Massachusetts General Hospital and Partners HealthCare provide access to de-identified trial data, helping pharmaceutical companies accelerate drug development.
6. Health Data Exchange Services
Overview: Health systems are creating health data exchange services that enable other healthcare providers, researchers, and insurers to access de-identified patient data. These services facilitate secure data sharing agreements and offer advanced analytics capabilities.
Development: Health systems invest in secure, scalable infrastructure to support data sharing. Data exchange services are built using interoperability standards like FHIR and HL7, ensuring data can be shared across systems.
Use Case: Healthcare providers use data exchange services to gain insights into patient populations and improve care coordination. Insurers leverage the data to develop more accurate risk models.
Value:
Revenue: Monetizing data exchange services provides a consistent revenue stream, often through subscriptions.
Improved Healthcare Outcomes: Data exchanges enhance care coordination, improving patient outcomes.
Cost Reduction: Access to comprehensive data reduces healthcare costs through better risk management.
Example: Indiana Health Information Exchange (IHIE) supports data exchange across providers, enhancing care coordination and public health monitoring.
7. AI-Powered Clinical Decision Support Tools
Overview: AI-powered clinical decision support (CDS) tools developed and / or managed by health systems analyze vast amounts of clinical data to provide evidence-based recommendations at the point of care.
Development: Health systems train AI models using their clinical data to predict outcomes and recommend specific patient-level treatments. These tools are integrated with EHRs and undergo rigorous testing before deployment.
Use Case: Clinicians use AI-powered CDS tools to improve diagnostic accuracy, optimize treatment plans, and reduce the risk of medical errors.
Value:
Revenue: Licensing AI-powered CDS tools creates a new revenue stream for health systems.
Enhanced Patient Care: These tools support clinicians in making faster, more accurate decisions.
Efficiency Gains: AI-powered CDS tools reduce the time and resources needed for diagnosis and treatment planning.
Example: Mount Sinai Health System partnered with Butterfly Network to integrate AI into imaging workflows, improving diagnostic accuracy.
Conclusion
Health systems are recognizing the immense value of their data and are exploring innovative ways to monetize it. From de-identified patient data sets to AI-powered clinical decision support tools, these data products not only provide significant new revenue streams but also advance medical research, personalized medicine, and healthcare delivery.
As the healthcare landscape continues to evolve, systems that successfully navigate data monetization will play a crucial role in shaping the future of patient care. With the right infrastructure, partnerships, and ethical frameworks in place, health systems can thrive financially while contributing to the next generation of medical innovation.
Adaptive Product
At Adaptive Product, we specialize in helping health systems bring groundbreaking digital health solutions to life. Our expertise in healthcare digital transformation, data monetization and data product management, coupled with our deep understanding of healthcare data and analytics, positions us uniquely to navigate the complexities of driving digital tranformation as well as building and launching cutting-edge healthcare data products. From strategic planning to technical delivery, our team ensures that high-value digital health solutions are delivered on time and within budget.
Whether you need healthcare digital transformation, data monetization or product management consulting, Adaptive Product is your trusted partner in driving the future of healthcare. Contact us today to learn how we can help you unlock the full potential of digital technology to drive success in the future of healthcare.
Visit us at Adaptive Product or call us at 800-391-3840.
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