Countries are shifting from paper/legacy systems to optimized, person-centric electronic health record systems (EHRs). With this shift to digitalization at the primary care level, technical partners redundantly translate clinical practice guidelines across multiple EHRs. There are continuous updates to health domain content on each software platform as new clinical guidelines and best practices are issued, necessitating a move from human-readable guidelines to computer/machine readable guidelines, or "Computable Care Guidelines" (CCGs), to facilitate the adoption of guidelines. WHO and CDC have been working together and with partners to help advance the adoption of CCGs. WHO is working with partners such as United Nations Population Fund (UNFPA), the Digital Square, and Hamilton Health Sciences (HHS) to create standardized documentation through "digital accelerator kits" and a "handbook" to guide countries through the digitalization and optimization of their client record systems. These products are targeted to health program managers and business/systems analysts who are often responsible for translating clinical guidelines into client record systems. CDC is working with a variety of partners across the continuum of guideline development and implementation on incorporating downstream perspectives (e.g., informatics, implementation) into guideline development to help apply the recommendations into practice more easily, quickly, accurately, and consistently. Both WHO and CDC are working with standards development organizations, Health Level 7 (HL7) and Integrating the Health Enterprise (IHE), to create standards for CCGs, used by software development teams and health informaticians to implement into EHRs. CCGs undergo scientific rigor in their translation into standards-compliant logic expressed in machine-readable code (e.g., using CQL and HL7 FHIR), including the data model, terminology/code sets, and value sets that are ready to be implemented. Panelists will provide an overview of the CCG work - overall vision, existing parallel initiatives, collaboration mechanisms, challenges, linkages to machine learning and artificial intelligence, and the path forward.