Medical Experiential Learning Database - Call for Whitepapers

BROAD AGENCY ANNOUNCEMENT W911NF-17-S-0003 REQUEST FOR WHITEPAPERS BAA TOPIC: 6. HUMAN SCIENCES (HS) CAMPAIGN Subtopic: f. CCE-HS-3: Training, vi. Medical Training Project Title: "Medical Experiential Learning Database (MELD)" Introduction Applications to the Fiscal Year 2019 (FY19) "Medical Experiential Learning Database (MELD)" are being solicited for the Defense Health Agency, Research, Development, and Acquisition (DHA RDA) Directorate, by the U.S. Army Research Laboratory (ARL). As directed by the Office of the Assistant Secretary of Defense for Health Affairs, the DHA RDA Directorate manages and executes the Defense Health Program (DHP) Research, Development, Test, and Evaluation (RDT&E) appropriation. The Execution Management Agent for this Broad Agency Announcement (BAA) / Funding Opportunity is the Simulation Technology and Training Center (STTC). Broad Agency Announcement (BAA) W911NF-17-S-0003 was publicized on Federal Business Opportunities and Grants.gov on 01 April 2017. This Sources Sought Notice calls for Whitepaper submissions in reference to the research within the BAA is - Topic 6. HS Campaign, CCE-HS-3: Training', subtopic vi: - Medical Training'. The United States ARL Broad Agency Announcement W911NF-17-S-0003, issued under the provisions of paragraph 6.102(d)(2) of the Federal Acquisition Regulation, provides for the competitive selection of proposals. Proposals submitted in response to this BAA and selected for award are considered to be the result of full and open competition and in full compliance with the provisions of Public Law 98-369, "The Competition in Contracting Act of 1984," and subsequent amendments. Funding of research and development (R&D) within ARL areas of interest will be determined by funding constraints and priorities set during each budget cycle. Awards related to the submission of Whitepapers requested by this Notice are subject to funds availability and priorities. STTC may choose not to select any new awards due to unavailability of funds or other factors. The sequence of steps leading to award is: (1) Request for Whitepaper initiated by STTC (2) Submission of a timely Whitepaper no more than seven (7) pages in length to the POC for the U.S. Army Contracting Command via contact@example.gov and cc: the STTC Technical Point of Contact (POC) identified below in this section (3) Written notification of whitepaper evaluation completion (4) Request of a formal proposal submission initiated by STTC if the Whitepaper merits a proposal request (5) Submission of a timely, formal proposal (6) Evaluation of the formal proposal as per established criteria presented in Section II (7) Contract or grant award for selected proposals based on availability of funds or other factors This sequence allows earliest determination of the potential for funding and minimizes the labor and cost associated with submission of full proposals that have minimal probability of being selected for funding. Note that interested Applicants must submit a Whitepaper electronically in order to be eligible to submit a formal proposal under this Notice. This Notice requires that Whitepapers be submitted electronically no later than 15 OCT 2018, 5:00 P.M. Eastern Daylight Time (EDT). BAA W911NF-17-S-0003 allows several potential instrument types (e.g., contract, grant, cooperative agreement) to result from a successful proposal. For this Notice, the intention of the Government is to award a contract. THOSE SUBMITTING WHITEPAPERS/PROPOSALS ARE CAUTIONED THAT ONLY A GOVERNMENT CONTRACTING OR GRANTS OFFICER CAN OBLIGATE THE GOVERNMENT TO ANY AGREEMENT INVOLVING EXPENDITURE OF GOVERNMENT FUNDS. This Sources Sought Notice for Whitepapers consists of six parts as follows: - Background and Introduction - Part I: Research and Development Objectives of the Requested Whitepaper - Part II: Preparation, Submission, and Evaluation Criteria - Part III: Deadlines - Part IV: Inquiries - Part V: References Technical Points of Contact: Mr. Matthew Hackett, Science and Technology Manager 12423 Research Parkway, Orlando, FL 32826 +1 (555) 000-0000 contact@example.gov (email contact preferred). Contractual Point of Contact: Mr. Justin Woolsey, Contract Specialist US Army Contracting Command 12423 Research Parkway, Orlando, FL 32826. +1 (555) 000-0000 contact@example.gov ? Background The Assistant Secretary of Defense for Health Affairs (ASD(HA)) established the Defense Medical Research and Development Program (DMRDP) to advance the state of medical science in those areas of most pressing need and relevance to today's battlefield experience. The objectives of the DMRDP are to discover and explore innovative approaches to protect, support, and advance the health and welfare of military personnel, families, and communities; to accelerate the transition of medical technologies into deployed products; and to accelerate the translation of advances in knowledge into new standards of care for injury prevention, treatment of casualties, rehabilitation, and training systems that can be applied in theater or in the clinical facilities of the Military Health System (MHS). Several Joint Program Committees were established and assigned programmatic responsibility for various areas. Joint Program Committee-1 (JPC-1) is responsible for Medical Simulation and Information Sciences (MSIS). As part of this ongoing and coordinated effort to ensure the most effective training, and with funding from the DMRDP, the Simulation and Training Technology Center (STTC) is interested in receiving technical proposals to develop and demonstrate personalized learning capabilities for medical simulation and training. Tactical Combat Casualty Care (TC3) is the standard for care of a battlefield casualty (Butler, 2015). TC3 training includes didactic classroom instruction, simulation-based training, and lane-based live training exercises. The current educational paradigm is - one size fits all', providing uniform instruction for all students. However, students differ widely based on their learning preferences, educational history, and innate ability. Some students achieve mastery quickly, while others may struggle and require remediation or simply additional time. As such, a fully standardized educational platform loses the nuance associated with individual student variability. The ability to tailor educational content to a learner's needs would improve this situation, allowing for a more personalized learning experience. This has been suggested a mechanism for improving education(Sharma, Doherty, & Dong, 2017) or allowing flipped classroom instruction(Dong & Sharma, 2015). In fact, the New England Journal of Medicine piloted an adaptive knowledge improvement system known as Knowledge+ to augment continuing medical education (McMahon & Drazen, 2014). However, the concepts listed above do not address all issues with personalized learning. To begin, military and civilian medical education utilize a vast array of training assets, including part-task trainers, medical mannequins, virtual simulations, and didactic instruction. At present, these assets are not synthesized into a cohesive snapshot of a learner's educational history. Instructors and course directors must assess results from each training asset individually, rather than comprehensively. To create a more robust, personalized learning platform, training assets should export common learner data using xAPI, thereby allowing the medical education learning experiences of TC3 students and providers to be captured in single repository. Using this data, competency assessment and recommendations for learning content can be made on an individual basis to best serve student needs. I. RESEARCH OBJECTIVES Within this call for whitepapers, there are four core areas of investigation. This system would be intended to initially augment TC3 training, and would feature an intuitive interface for trainers and trainees. The system should be capable of aggregating learning experience data from virtual simulations, part-task trainers, medical mannequins, mobile applications, and classroom assessments using the xAPI standard. Respondents must address all four core areas of investigation within a single whitepaper. These four areas are detailed below: - Research, develop, and demonstrate xAPI integration of existing and emerging medical training assets, including virtual simulations, mannequins, part-task trainers, and classroom assessments - Research, develop, and demonstrate a learner record store (LRS) for medical learning experiences leveraging existing LRS frameworks - Research, develop, and demonstrate a TC3 competency model based on the learning experiences within the LRS - Research, develop, and demonstrate a recommendation engine which can intelligently choose content for new learning, remediation, and refresher training based on a learner's history The proposed end product should allow for different sources of input training information and should provide modularity and flexibility to incorporate training assets developed by other applications. Identification of components within the product that are open source / open architecture versus proprietary need to be identified. Respondents are expected to outline how to test and demonstrate their prototype within the four core research areas described above (xAPI integration, LRS, competency model, and recommendation engine) using relevant medical training content. Examples of relevant medical training content could include the TC3 content, Combat Medic (68W) content, or similar. Desired capabilities are listed below, though this list is not comprehensive. The proposed system is not required to include all technical components, but will be evaluated for potential usability and usefulness. Consideration will be given towards solutions with a high probability of transition and which have substantial open source / open architecture components, with minimal to no proprietary software components. Key Technical Capabilities: - Ability to store medical training experiences from disparate training assets - Creation of xAPI profiles for a variety of medical simulation training assets and devices - Ability for instructors and trainees to visualize learner educational history - An open source / open architecture LRS, competency model, and recommendation engine - LRS analytics dashboard - Competency framework able to provide weighting to learning experiences and create a holistic assessment of learner knowledge, skills, and abilities - Recommendation engine capable of suggesting multiple content types, such as video, study guide, virtual scenario, part-task trainer scenario, etc. - Recommendation engine which accounts for learner preferences, identified deficiencies, identified proficiencies, and skills decay models - Recommendation engine leverages theories from educational psychology and the science of learning, including spaced repetition - Recommendation engine which employs machine learning to optimize recommendations - Usage of a common metadata tagging schema, such as IEEE 1484.12.1 or similar - Leveraging existing software components where applicable for LRS, competency frameworks, or recommendation engines, such as those provided by the ADL Initiative or similar - Extensible architecture, able to accommodate future learning experiences including on-the-job training or work experiences, such as treating a patient - Evaluation of the system including usability, usefulness, functionality, and educational relevance of the system - Design of the system to accommodate future integration into a government enterprise IT system, including RMF, FEDRAMP, etc. - Ability to support additional educational domains, such as nursing, physical therapy, dental, or physician level training At completion, the system will be able to aggregate medical learning experiences from multiple training assets. The system will provide visualization of a learner's educational history, viewable by instructors and trainees. The system shall be capable of assessing a learner's educational history to identify strengths, weaknesses, and training gaps. The system shall deduce competency from the stored learning experiences. Based upon the competency model and learner preferences, the system shall employ a recommendation engine which is capable of suggesting training most suited to a trainees current knowledge, skills, and abilities. The ultimate vision is a foundational component of the military medical education system, which follows a medical provider throughout their career, providing them with the most appropriate training based on their experience and future needs. Relevance The information provided must be relevant to the following areas: 1. Information must be relevant to, but not exclusive to the military medical population. 2. Information must be relevant to medical personnel and must be applicable in one or more of the potential military medical education and training applications: Role 1: First Responder Care; Role 2: Forward Resuscitative Care; Role 3: Theater hospital care; Role 4: Overseas Definitive Care; & Role 5: U.S. Definitive Care. Plan of Execution The process of execution begins with whitepaper evaluation and a down-selection process. The government will notify the offeror if their whitepaper is selected, and formally request a full proposal. The full proposals undergo a full scientific review and programmatic view prior to being funded. The planned method for accomplishing the research in this area is through a single award or multiple awards not exceeding $5,000,000 total, with a $2,000,000 base effort and an option of up to $3,000,000. Whitepapers submitted should be structured with a technical approach/solution for a twelve (12) month base and twelve (12) month optional period of performance, totaling 24 months. The whitepaper shall include the anticipated period of performance, effort's scientific research objective, approach, relationship to similar research, the nature and extent of the anticipated results and, if known, the manner in which the work will contribute to the accomplishment of the Army's mission and how this contribution would be demonstrated. The cost portion of the whitepaper shall include a brief rough order of magnitude (ROM) cost that consists of estimated research hours, burden, material costs, travel, etc. THIS ANNOUNCEMENT CONSTITUTES THE ENTIRE SOLICITATION FOR THIS EFFORT. DO NOT SUBMIT A FORMAL PROPOSAL AT THIS TIME. II. PREPARATION, SUBMISSION, AND EVALUATION CRITERIA Preparation: Whitepapers shall not exceed seven (7) total pages; five (5) pages for whitepaper technical content, one (1) cover page and a one (1) page addendum. The whitepapers should be submitted via email to contact@example.gov and cc: the STTC Technical Point of Contact (POC) no later than 5:00 PM EDT 15 OCT 2018 deadline identified below. Whitepapers submitted via email must be in a single PDF formatted file as an email attachment. Evaluation Criteria: Whitepapers will be evaluated by a technical review board using the following criteria: 1. Overall scientific and/or technical merits of the proposed research: The Applicant must demonstrate a thorough understanding of the research problem and articulate a clear approach to addressing the research objectives. The proposed approach must be based on feasible and realistic scientific methods and provide the Government with a high level of confidence of successful completion within proposed timeline. 2. Potential contributions of the effort to the U.S. Army Research Laboratory mission: In addition, to showing how the proposed research aligns with the solicited topic, the Applicant must also specify how the proposed research will contribute to or advance the current U.S. Army Research Laboratory medical simulation and training research mission. Upon completion of whitepaper evaluations, Applicants will be notified whitepaper in writing. Applicants whose whitepapers are evaluated as having significant scientific merit may be invited to submit a proposal. If proposals are solicited, proposals are due 30 calendar days following the request for proposal invitation. The requirements for proposal preparation and submission can be found at https://www.arl.army.mil/www/pages/8/W911NF-17-S-0003.pdf. This announcement is an expression of interest only and does not commit the government to reimburse any proposal preparation cost for responding. The cost of proposal preparation in response to this announcement is not considered an allowable expense to the normal bid and proposal indirect costs as specified in FAR 31.205-18. Any request for whitepaper or submission of a full proposal does not guarantee award. The Government reserves the right to cancel this requirement at any time and shall not be liable for any cost of proposal preparation or submission. Within the meaning of the Federal Acquisition Regulation (FAR) at 6.102 and 35.016, this announcement constitutes the Government's solicitation for this effort. There will be no other solicitation issued in regard to this requirement. Applicants should be alert for any BAA amendments that may be published. III. DEADLINES Any responsible offeror capable of satisfying the objectives identified in this announcement may submit a whitepaper. Whitepaper submissions are encouraged as early as possible but must be submitted no later than 5:00 PM EDT 15 OCT 2018. No extensions will be granted. Only unclassified whitepapers will be accepted. IV. INQUIRIES Inquiries for additional information should be made with the ARL POC, Mr. Matthew Hackett, who may be reached at: E-Mail: contact@example.gov Telephone: +1 (555) 000-0000 V. REFERENCES Butler, F. K. (2015). Tactical combat casualty care. Encyclopedia of Trauma Care, 1548-1551. Dong, C., & Sharma, N. (2015). Flipping the classroom with adaptive learning technology. Medical Teacher, 37(10), 976-976. McMahon, G. T., & Drazen, J. M. (2014). Introducing NEJM Knowledge+ and its adaptive personalized learning: Mass Medical Soc. Sharma, N., Doherty, I., & Dong, C. (2017). Adaptive Learning in Medical Education: The Final Piece of Technology Enhanced Learning? Ulster Med J, 86(2), 1-3.