Enhancing the Management of Deployment-Limiting Medical Conditions in the U.S. Department of Navy Leveraging Electronic Health Records and Predictive Analytics Methods.

INTRODUCTION: Deployment-limiting medical conditions (DLMCs) such as debilitating injuries and conditions may interfere with the ability of military service members (SMs) to deploy. SMs in the United States (U.S.) Department of the Navy (DoN) with DLMCs who are not deployable should be placed in the medically restricted status of limited duty (LIMDU) or referred to the Physical Evaluation Board (PEB) for Service retention determination. It is critical to identify SMs correctly and promptly with DLMCs and predict their return-to-duty (RTD) to ensure the combat readiness of the U.S. Military. In this study, an algorithmic approach was developed to identify DoN SMs with previously unidentified DLMCs and predict whether SMs on LIMDU will be able to RTD. MATERIALS AND METHODS: Five years of historical data (2016-2022) were obtained from inpatient and outpatient datasets across direct and purchased care from the Military Health System (MHS) Data Repository (MDR). Key fields included International Classification of Diseases diagnosis and procedure codes, Current Procedure Terminology codes, prescription medications, and demographics information such as age, rank, gender, and service. The data consisted of 44,580,668 medical encounters across 1,065,224 SMs. To identify SMs with unidentified DLMCs, we developed an ensemble model combining outputs from multiple machine learning (ML) algorithms. When the ML ensemble model predicted a SM to have high risk scores, despite appearing healthy on administrative reports, their case was reviewed by expert clinicians to investigate for previously unidentified DLMCs; and such feedback served to validate the developed algorithms. In addition, leveraging 1,735,422 encounters (60,433 SMs) from LIMDU periods, we developed four separate ML models to estimate RTD probabilities for SMs after each medical encounter and predict the final LIMDU outcome. RESULTS: The ensemble model had 0.91 area under the receiver operating characteristic curve (AUROC). Out of 236 (round one) and 314 (round two) SMs reviewed by clinicians, 127 (54%) and 208 (66%) SMs were identified with a previously unidentified or undocumented DLMC, respectively. Regarding predicting RTD for SMs placed on LIMDU, the best performing ML model achieved 0.76 AUROC, 68% sensitivity, and 71% specificity. CONCLUSION: Our research highlighted potential benefits of using predictive analytics in a medical assessment to identify SMs with DLMCs and to predict RTD outcomes once placed on LIMDU. This capability is being deployed for real-time clinical decision support to enhance health care provider's deployability assessment capability, improve accuracy of the DLMC population, and enhance combat readiness of the U.S Military.

Distributed Maritime Operations and the Medical Challenge: How a Medical Common Operating Picture Can Help Bridge the Gap.

This work explores the challenges of delivering medical care in the geographically dispersed and resource-constrained environment of Distributed Maritime Operations (DMO) and Expeditionary Advanced Base Operations (EABO). Traditional medical planning approaches may struggle to adapt to the vast operational space, extended evacuation times, and limited medical force present in these scenarios. The concept of a Medical Common Operating Picture (COP) emerges as a potential solution. By providing a shared view of the medical situation across the theater, encompassing logistics, personnel, and patient data, a medical COP has the potential to facilitate medical command and control (MED C2) in DMO/EABO. The implementation of a medical COP has the potential to optimize resource allocation, enhance situational awareness, streamline medical evacuation, and reduce healthcare provider moral injury in large-scale combat operations. A medical COP will allow medical planners to make informed decisions on triage, resupply, and evacuation, ensuring the best use of limited medical resources. This is done by leveraging a comprehensive understanding of the medical landscape, enabling informed clinical and operational decision-making by humanitarian and combat personnel respectively. A fully realized medical COP system will enable a dynamic theater evacuation policy, balancing the conflicting needs of patient care at higher echelons with the operational expediency of returning servicemembers to their operational units, thereby maximizing evacuation effectiveness. It will further enable medical personnel to perform dynamic casualty triage based on operational realities, mitigating potential ethical dilemmas. Implementing such a medical COP system will require overcoming communication limitations to facilitate data exchange and potentially integrating clinical decision support tools for real-time data analysis and recommendations. It will also require the rapid adoption of modernized operational medicine documentation solutions by medical assets within the operational forces. Ultimately, this work suggests that a medical COP has the potential to bridge the gap between traditional medical planning and the unique demands of DMO/EABO, ultimately optimizing casualty care, maximizing resource efficiency, and preserving the fighting force.

Military Medical Readiness and Patient Experience with Access to Care.

OBJECTIVES: Introduction: Medical readiness is an integral component of total readiness and a prime indicator of an individual's overall fitness to deploy. Promoting medical readiness is the prime directive for military medical departments; however, there are few studies evaluating specific factors of care delivery that will improve medical readiness. In this study, we evaluated one of the common patient perceptions that access to routine and specialty care will have a positive effect on military medical readiness. Surprisingly, there appeared to be a reverse relationship between a patient's perception of access to care and the correlation to their medical readiness. MATERIALS AND METHODS: This study uses the Joint Outpatient Experience Survey data of Army active duty soldiers (December 2017 through May 2018) to investigate the relationship between access to care and medical readiness. Medical readiness scores were examined a month before and a month after a medical encounter. Medical Readiness Categories (MRC) were collected from the Army Medical Operational Data System Mainframe. Respondents of the survey were matched to MRC data. Comparisons were made using chi-square tests and Wilcoxon rank-sum non-parametric tests to determine whether there were differences in readiness and patient experience ratings before and after the encounter. Logistic regressions were also conducted to predict the odds of non-readiness based on the type of health care visit. RESULTS: Soldiers who were medically non-ready were more likely to be above age 35 years or have specialty care encounters. Results indicated those meeting all medical readiness requirements or having minor medical issues that could be resolved quickly, generally rated access to care slightly lower compared to those who were medically non-ready. Musculoskeletal Injuries (MSKIs) are the leading cause of medical non-readiness. As a result, this study explored access to care for MSKIs. Although there were no statistical differences in access ratings for those with MSKIs compared to those without MSKIs, there were statistically significant differences in self-reported health. Individuals with MSKIs tended to report poorer health status. Those with specialty care visits had 1.79 times significantly greater odds (p is less than .05) of being non-medically ready compared to those with primary care. For visits related to MSKI (e.g., physical medicine, orthopedic, or chiropractic etc.), those with an orthopedic or occupational therapy visit had 1.25 and 1.59 significantly greater odds (p is less than .05) of being considered not medically ready compared to all other MSKI related visits before the encounter. However, after the encounter, those with orthopedic care had significantly higher odds of improved readiness. CONCLUSIONS: Findings from this study help contextualize who is considered medically non-ready as well as differences in access to care experiences for this group. The lowest scoring areas for improving access to care include ease of making appointment, time between scheduling an appointment and the visit, and being seen past the scheduled time. Given that musculoskeletal injuries tend to require long term specialized treatments such as physical and occupational therapy, findings from the logistic regressions suggest that access and adherence to such treatments, particularly for orthopedic care, are helpful in improving medical readiness.