AI and RWD: Unlocking Rare Disease Insights, Enhancing Clinical Decision Making, and Improving Patient Care

GE Healthcare

The healthcare industry is becoming more digitalized due to technological innovation. As a result, there is incredible demand for access to HIPAA and GDPR compliant Real-World Data (RWD). RWD provides the foundation for Real-World Evidence (RWE), which is crucial for validating clinical trials.

Beyond clinical trials, RWD has additional potential for significant impact. Artificial intelligence (AI) applications within healthcare are heavily reliant on RWD for training AI or machine learning (ML) models, performing advanced analytics, and generating data visualizations to assist clinicians as they deliver care to patients[1].

RWD and its applications provide only a small preview of the future of healthcare. AI and ML are expected to augment clinician workflows to improve diagnostics, assist in the identification of better treatments or pharmaceuticals, and improve patient prioritization and triage.  Ongoing partnership between AI developers and healthcare providers will be key in achieving these outcomes.

A Network of Provider Relationships is Paving the Way

GE Healthcare has built a strong network with key healthcare players including leading institutions who provide full access to HIPAA and GDPR-compliant RWD sources. These relationships enable rapid data aggregation with seamless access to existing medical devices and Health Information Systems (HIS).

For healthcare providers, RWD fuels a growing number of innovative AI applications available through the Edison™ Software Marketplace. Software developers similarly benefit from exposure to thousands of potential customers, with the support of GE Healthcare’s Edison™ Developer Program.

The Mission for Precision Health in Rare Disease

Legacy care is rooted in the primary care model where patients receive general medical advice or treatment plans for conditions not requiring specialist involvement. This is the current backbone of the US healthcare system. In many instances it is static and reactive, with most medical conditions being well-known and highly treatable. This has led to complacency around data management and analytics adoption, with siloed IT systems and outsourced data collection being the norm[2].

As we transition from the legacy care approach to a new emerging care approach augmented by AI, RWD is vital in enabling innovation and advancement.

Rare disease is most often managed through specialty care and falls within the category of emerging care. Service availability is lower for specialty care patients, due to physician shortages especially in smaller, more isolated markets.  As a result, these patients are much more likely to be under- or mis-diagnosed due to the rarity of their conditions and limited access to the appropriate care. AI assistance has the potential to resolve many of these challenges, with RWD underpinning the development of these solutions[3].

Due to the rarity and severity of these diseases, specialty care providers understand the importance of data-driven insight for clinical decision-making and treatment plan design. The key difference illustrated is in how emerging care for rare diseases focuses on dynamic real-time care delivery. Integrated and multi-source data streaming is used to increase analytical capabilities.

To summarize, the more RWD these rare disease specialists can access, the better the care will be for patients. Similarly, AI developers can create more specialized software to assist with the detection and treatment of these rare diseases. For diverse datasets on clinical cohorts from a growing partner network, and HIPAA and GDPR-compliant data frameworks, look no further than Edison™ Digital Pharma Solutions as your trusted source for RWD and the innovations it can drive.

The New AI Journey for Patients and Providers

As AI grows in prevalence during diagnosis and treatment, it marks the beginning of a new journey for patients and healthcare providers. This will start with increased data integration at the patient level such as with medical prescription data (Rx) and historical patient data (Hx). Healthcare providers can use this data to perform advanced analytics and supplement ultra-rare disease treatment plans for patients.

RWD is enabling the development and subsequent deployment of new AI algorithms. There is immense potential to improve early disease detection, while simultaneously minimizing costs for healthcare providers.

 

 


[1] Davis B, Morgan J, Shah S. “The future of real-world evidence Biopharma companies focus on end-to-end, AI-driven, internally developed solutions.” Available from: https://www2.deloitte.com/us/en/insights/industry/life-sciences/2018-real-world-evidence-benchmarking.html?id=gx:2sm:3tw:4RWE_LSHC18::6Life_Sciences_and_Healthcare:20180711095200:Global&linkId=54041116. Accessed May 25 2021

[2] Sperling, Laurence. 2020. "Silos In Healthcare Are Bad For Us. Here's The Cure.". World Economic Forum. https://www.weforum.org/agenda/2020/11/healthcare-silos-are-bad-for-us-heres-the-cure/

[3] Trishan Panch, Heather Mattie, and Leo Celi. 2019. "The “Inconvenient Truth” About AI In Healthcare". Nature.Com. https://www.nature.com/articles/s41746-019-0155-4