Rep. Schweikert on Government Data Sharing and Healthcare Technology

Modernizing Government Data Sharing

• 💡 Rep. Schweikert advocates for moving beyond outdated technology and paper-based processes in government agencies.
• 🚀 The goal is to implement modern solutions, like AI and translational programs, to enable efficient data sharing and actual implementation, rather than just discussing ideas.
• ⚠️ Challenges in government include funding, expertise, and the ingrained status quo of how things have always been done.
• 💰 High-cost talent and compensation schedules are identified as reasons why government struggles to retain skilled individuals who could implement these changes.

Centralized Data and AI Implementation

• 🎯 A vision is presented for a common data area where various government entities host their databases, enabling a central AI shop to process data across agencies.
• 🧩 This approach could allow machine learning packages to identify abnormalities and provide optionality on data sets, improving efficiency and security.
• 🔒 Technology exists to create role-based permissions for secure access within a centralized system, even surpassing some private sector databases in security.
• 🚧 Barriers to implementation are largely political and social, stemming from existing rules and the need for agencies to prioritize their core customer delivery functions.

Healthcare Technology and Legislation

• 🏥 Three key areas for technological improvement in healthcare are proposed: automation of Medicaid eligibility, automated diagnosis coding across Medicare, and electronic prior authorization data submission.
• 🤖 Automating Medicaid eligibility would move it from a manual process to a technological one, similar to online banking, potentially using data matching.
• 📈 Automated diagnosis coding aims to standardize coding across Medicare programs, fixing it at the point of delivery to improve accuracy and reduce fraud.
• ✉️ Electronic submission of prior authorization data is crucial, as the current process is still largely manual, hindering integration at the point of care.

Innovation and Future Systems

• 🧠 The concept of a living data set is crucial for combating fraud, requiring constant updates to identify new scams and threats, similar to cybersecurity models.
• 🤝 Creating an ecosystem where new models and threat signatures are shared across agencies through a centralized analytic center is proposed.
• ⚠️ Unrealistic implementation timelines and the rapid doubling of medical knowledge present challenges for keeping government systems up-to-date.
• 💡 A potential model for centralized analytics to fight fraud is suggested, drawing parallels to existing programs like PACE.