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Introduction Musculoskeletal injuries affect nearly a million service members annually within the DoD, ultimately costing the U.S. Military half a billion dollars in direct patient costs and a significant loss to fleet readiness as many members are assigned days on limited duty (LIMDU) until they are deemed medically fit to return to duty (RTD). The new approach implemented by Navy Medicine in 2022, called “condition-based LIMDU,” aims to drastically impact the time in which Sailors and Marines spend under a provider’s care by assigning LIMDU days based on a standardized set of guidelines. This study provides a quantitative analysis on LIMDU duration, before and after implementation of the new condition-based LIMDU paradigm, to increase the understanding on the effectiveness and impact to fleet readiness and to assess the accuracy of suggested patient outcome timelines. Materials and Methods De-identified and aggregated data were obtained from the Naval Medical Forces Atlantic’s (NMFL) LIMDU Sailor and Marine Readiness Tracker System (SMART) program for all active duty military patients with ICD-10 code for musculoskeletal conditions. Only closed LIMDU cases in which active duty patients were given a final status of RTD were included. This study analyzed top musculoskeletal ICD-10 codes, optimum period (weeks), maximum period (weeks), and average days on LIMDU assigned at NMFL centers (medical and non-medical) for fiscal years 2021 (FY21) and 2022 (FY22). As well as descriptive statistics, t-test analysis was used to test if there was a difference between FY21 and FY22 and at what point the difference was no longer significant. Critical value method was then used to compare the top five most common musculoskeletal injuries to determine the accuracy of recommended LIMDU days to actual average assigned LIMDU per injury type. A color-coded compliance chart was created based on the results. Results The results showed that for RTD population, the implementation of condition-based LIMDU significantly decreased average days assigned on LIMDU by 33%. In fact, there is a 35-day (5-week) difference before we can confidently say that the difference between FY21 and FY22 is no longer statistically significant. This significant decrease in LIMDU days, before and after implementation, is a trend consistent at both medical and non-medical NMFL centers; however, medical centers reported significantly more assigned LIMDU days for both years. The five most common injuries of FY21 and FY22 were low back pain, pain in shoulder, pain in hip, pain in knee, and pain in ankle. Before implementation, all five of these injury types far exceeded the recommended amount of LIMDU days. With the new condition-based LIMDU paradigm, the average assigned LIMDU days for pain in hip, pain in knee, and pain in ankle were all found to be in compliance with the recommended LIMDU days within a 99% confidence level. Conclusions The new condition-based LIMDU paradigm is successful in its aim to improve fleet readiness by returning Sailors and Marines to full duty status significantly faster. Regular assessment of ICD-10 diagnosis codes and update to recommended LIMDU assignment timelines should be conducted to maximize the effectiveness and accuracy for all medical conditions.
Introduction Musculoskeletal injuries affect nearly a million service members annually within the DoD, ultimately costing the U.S. Military half a billion dollars in direct patient costs and a significant loss to fleet readiness as many members are assigned days on limited duty (LIMDU) until they are deemed medically fit to return to duty (RTD). The new approach implemented by Navy Medicine in 2022, called “condition-based LIMDU,” aims to drastically impact the time in which Sailors and Marines spend under a provider’s care by assigning LIMDU days based on a standardized set of guidelines. This study provides a quantitative analysis on LIMDU duration, before and after implementation of the new condition-based LIMDU paradigm, to increase the understanding on the effectiveness and impact to fleet readiness and to assess the accuracy of suggested patient outcome timelines. Materials and Methods De-identified and aggregated data were obtained from the Naval Medical Forces Atlantic’s (NMFL) LIMDU Sailor and Marine Readiness Tracker System (SMART) program for all active duty military patients with ICD-10 code for musculoskeletal conditions. Only closed LIMDU cases in which active duty patients were given a final status of RTD were included. This study analyzed top musculoskeletal ICD-10 codes, optimum period (weeks), maximum period (weeks), and average days on LIMDU assigned at NMFL centers (medical and non-medical) for fiscal years 2021 (FY21) and 2022 (FY22). As well as descriptive statistics, t-test analysis was used to test if there was a difference between FY21 and FY22 and at what point the difference was no longer significant. Critical value method was then used to compare the top five most common musculoskeletal injuries to determine the accuracy of recommended LIMDU days to actual average assigned LIMDU per injury type. A color-coded compliance chart was created based on the results. Results The results showed that for RTD population, the implementation of condition-based LIMDU significantly decreased average days assigned on LIMDU by 33%. In fact, there is a 35-day (5-week) difference before we can confidently say that the difference between FY21 and FY22 is no longer statistically significant. This significant decrease in LIMDU days, before and after implementation, is a trend consistent at both medical and non-medical NMFL centers; however, medical centers reported significantly more assigned LIMDU days for both years. The five most common injuries of FY21 and FY22 were low back pain, pain in shoulder, pain in hip, pain in knee, and pain in ankle. Before implementation, all five of these injury types far exceeded the recommended amount of LIMDU days. With the new condition-based LIMDU paradigm, the average assigned LIMDU days for pain in hip, pain in knee, and pain in ankle were all found to be in compliance with the recommended LIMDU days within a 99% confidence level. Conclusions The new condition-based LIMDU paradigm is successful in its aim to improve fleet readiness by returning Sailors and Marines to full duty status significantly faster. Regular assessment of ICD-10 diagnosis codes and update to recommended LIMDU assignment timelines should be conducted to maximize the effectiveness and accuracy for all medical conditions.
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.
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