Healthcare and life sciences
Healthcare and life sciences are industries facing major market uncertainties. To facilitate effective decision-making in controlled business environments, predictive analytics and strategic scenario planning are necessary, supported by Privacy-Preserving Record Linkages (PPRL) of health data at scale.
An industry under transformation
Providers of healthcare services and life science research are undergoing major changes due to market uncertainty. The need for readily available patient and drug data for research, and the risk of privacy breach indemnification are shaping the adoption of transformative technologies in safe data governance and sharing.
Healthcare and life sciences organizations face major market uncertainties that require scenario planning for strategic decision-making
Market uncertainty conditions are driven by shifts in:
- Population demographics and socioeconomic trends.
- Effects of climate change (e.g., population migrations and disaster demand).
- Cost of data privacy compliance with PII, CCPA and GDPR.
- Scale and utility of medical device data, patient data and cohort studies.
- Lowering data availability latency for cohort studies and general R&D.
- Consumer access and trust on health data.
This paradigm generates challenges in strategic planning for investment and product selection. Confronted to this situation, healthcare leaders must base their strategy on assessment of foreseeable scenarios and evaluate the impact of business options. This approach promotes organizational consensus when prioritizing initiatives that are likely to pay off across a broader range of scenarios, and predicting potential deviations and impact.
As healthcare organizations have a pressing need to sense and respond to changes in the environment used for scenario planning, agile Data and Analytics (D&A) teams have become essential. These teams require prompt access to updated data and modern data science technology. Platform engineering, industry cloud, and microservices are the right approaches to facilitate a frictionless experience for consumers and application developers. Learn more about our experience in systems engineering and development.
Rapid medical innovations and digitization initiatives are driving the increase of vendors of data and analytics solutions. This recent development generates an exploding supply of healthcare intelligence, but is also creating challenges for organizations due to the difficulty to assess the value of offerings.
The value of medical data is starting to be assessed as a strategic asset in organizational strategies for innovation and product-related decisions. This changing environment in data supply requires in turn high flexibility in the development and training of AI models for decision-making. Explainability and adaptability are thus critical aspects of AI-based software engineering in healthcare. Learn more about our AI solutions.
Challenges and opportunities of medical data
Contextual decisions are critical in the healthcare and life science industry, from day-to-day care delivery decisions to strategic preparation to disease spread scenarios. Rich data is required to provide this context and be actionable in quasi-real time, however it is often derived from external sources.
Synthetic data is a novel approach to confidential medical data sharing that removes security, regulatory, and ethical overhead
According to Gartner, healthcare industry’s interest in data monetization is growing. The availability of commercial health data platforms, and new entrants in life sciences with capabilities in generating clinical trial data, are accelerating the availability and scale of medical data. Organizations are increasingly prepared to monetize their data, by ensuring datasets are conveniently curated, quality-controlled, and discoverable.
Medical data sharing initiatives are being launched across the industry, creating health data marketplaces that enable organizations to discover new data pools and purchase directly from providers. Privacy-enhancing data processing technologies, especially synthetic data, provide a novel approach to creating Privacy-Preserving Record Linkages (PPRL) of data that removes the security, regulatory, and ethical overhead of anonymizing real patient data. Learn more about our synthetic data capabilities.
In addition, as healthcare organizations connect more medical devices, applications, and data sources, systems protection becomes critical to data management initiatives.
Regulations are proliferating, mandating advanced practices that ensure data privacy, reliability, and governance. This type of secure digital system includes novel methodologies for software design, development, and testing that achieve these objectives efficiently. Learn more about our test automation capabilities.
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