Military Injury Cause Coding

Purpose

The Medical Technology Enterprise Consortium (MTEC) is excited to post this announcement for a Request for Project Information (RPI) focused on surveying the current state of developing, testing, and implementing artificial intelligence (AI)-driven processes aimed at improving electronic health record (EHR) injury cause coding without overburdening those responsible for documenting military injuries. The information gathered by this RPI will be shared with relevant interested government parties to help inform future development and procurement efforts.

Scope of Work

Injuries are the leading medical impediment to force readiness within the Department of War, accounting for 39% of all medical encounters and costing an estimated $7 billion annually in direct and indirect costs associated with treatment, rehabilitation, lost duty time, and limited duty time. Effective injury prevention and mitigation efforts depend upon the timely and accurate capture of injury cause coding data; however, the current manual ICD-10-CM coding process is resource-intensive, inconsistent across clinics, and places additional administrative burden on clinicians and medical coders operating in already constrained environments, resulting in incomplete and non-specific coding.

Recent analyses indicate that only approximately 10% of all injury-related encounters include an associated cause-code, resulting in a significant data gap that limits meaningful surveillance, trend analysis, hazard identification, and readiness assessments. Incomplete injury cause coding data also reduces the effectiveness of force health protection activities and inhibits data-informed planning and decision making.

The Defense Safety Oversight Council (DSOC) Military Injuries Working Group (MIWG) and Defense Health Agency (DHA) have identified the need for scalable, enterprise-capable solutions that can:

Minimize administrative and documentation burden on clinicians and medical support staff;

Improve the completeness, specificity, and timeliness of injury cause coding data;

Enhance enterprise injury surveillance and epidemiological analysis capabilities;

Support hazard identification, risk management, and readiness reporting efforts; and

Integrate effectively within existing Military Health System workflows and technical environments.

The Government is particularly interested in innovative approaches leveraging Artificial Intelligence (AI), Machine Learning (ML), and/or Natural Language Processing (NLP) capabilities to analyze free-text clinical documentation within Electronic Health Record (EHR) systems and automatically identify or recommend appropriate ICD-10-CM external cause of injury codes in real time. Desired capabilities may include extraction and recommendation of ICD-10-CM external cause of injury codes (V00-Y99) assigned for the specific:

Injury mechanism;

Activity being performed at the time of injury;

Place or environment of occurrence; and

Duty status (i.e. external cause status).

Solutions should minimize workflow disruption while improving coding completeness, specificity, accuracy, and consistency. The Government is interested in approaches that support clinician decision-making and documentation efficiency rather than increasing manual data entry requirements.

To this end, the Government is interested in receiving information papers related to solutions that support this need. Proposed solutions must be capable of aligning and interoperating with Military Health System (MHS) electronic health record (EHR) environments, including MHS GENESIS, purchased care EHR systems, and deployment-related EHR platforms. Responses should support the collection and use of injury cause coding data to enhance safety management and injury prevention analytics.

Interested parties are invited to submit information papers detailing:

The current maturity and development status of the proposed solution;

Existing operational, clinical, or commercial deployments;

Technical interoperability approach;

AI/ML/NLP methodologies utilized;

Anticipated implementation approach required to integrate the solution within the current MHS environment;

Cybersecurity and compliance considerations;

Expected performance outcomes, limitations, and scalability considerations; and

Estimated schedule / timeline required to achieve operational capability.

Estimated cost and level of effort required to achieve operational capability.

Government furnished Information (GFI) needed to enable development of the capability

The Government intends to use submitted information to better understand the current state of industry capabilities, assess technical feasibility, inform future acquisition strategies, and identify potential approaches for improving injury surveillance and readiness reporting across the Military Health System enterprise.

Points of Contact

For inquiries, please direct your correspondence to the following contacts:

Technical and membership questions should be directed to the MTEC Senior Technology Program Manager, Dr. Chuck Hutti, Ph.D., chuck.hutti@mtec-sc.org

All other questions should be directed to the MTEC Program Manager, Mr. Daniel Vala, daniel.vala@mtec-sc.org