Military Health System Total Learning Architecture (MHS TLA)

3 Feb 2021 This is an update to the RFI to publish responses to questions received and extend the RFI response date to 26 Feb. (end) (Original RFI Posting) The MHS currently faces a number of infrastructure challenges pertaining to learning and performance improvement. Currently, the MHS processes and systems-in-place have no ability to report enterprise-wide education, training, and human performance metrics. For example, across the Services and DHA, multiple training and education systems are used to collect data in different formats and methods. Training and education data is stored in systems like EMS, B-Line, and/or DTMS depending on the site. In many cases, this data is not standardized, centralized, or available for analysis. These isolated, disparate, stove-piped systems do not have sufficient connectivity and interoperability to provide data, information exchange, and analytics. This lack of MHS training data infrastructure results in a subpar ability to report enterprise-wide education, training, and human performance metrics. The MHS Total Learning Architecture (TLA) will be a cloud native System-of-Systems with an open architecture which will consist of connected state-of-the-art COTS and/or GOTS technology. The goal of TLA is to enhance interoperability between existing training and education learning management and delivery systems with the goal of providing an integrated system to standardize and centralize training and education data. TLA will integrate training and education data into a holistic system that can track learner progress and competence throughout their career. The system will ultimately provide an ability to track, deliver, and assess the effectiveness of training and education across the MHS enterprise, and facilitate state-of-the-art learning capabilities in concert with the DoD 2019 Digital Modernization Strategy. Customized filters and analytics will enable the discoverability of insights across the enterprise, revealing gaps and opportunities to connect operational goals and available training and education curricula. Data-driven recommendations can be provided that are optimized or standardized for appropriate and timely delivery across the learning continuum or linked to competency levels and readiness measures. SEE ATTACHED RFI