Plain Language Summary

What is it about? The US Military Health System faces elevated rates of chronic pain among active duty service members, which significantly impacts both individual outcomes and military readiness. Addressing this problem effectively requires coordinated care across multiple healthcare levels, but traditional research methods cannot ethically or feasibly test all possible interventions. This study fills a critical knowledge gap by developing a simulation-based digital decision support platform that can test hypothetical scenarios without risking patient outcomes. The platform models patient journeys through the Defense Health Agency Stepped Care Model for Pain to provide data-driven guidance for both clinical care and policy decisions. This innovative approach contributes to healthcare optimization by enabling testing of resource allocation and policy changes before implementation.

How was the research conducted? The research team designed a discrete-event simulation platform that simulates how patients move through different types of healthcare for pain management. They applied this methodology by creating 11 interconnected nodes representing key decision points in pain treatment pathways, such as care escalation and emergency encounters. Researchers analyzed Military Health System Data Repository records from 2016-2019 for patients with pain-related conditions to develop statistical models for each node. The study examined adult patients ages 18-65 enrolled in TRICARE Prime who received pain-related diagnoses and had multiple healthcare encounters. This approach was chosen because simulation modeling allows for testing hypothetical scenarios that cannot be ethically evaluated through clinical trials.

What were the results? The primary outcome was a verified and validated simulation platform capable of modeling patient journeys through pain management pathways in the Military Health System. The platform successfully incorporated multiple statistical approaches to determine the next encounter type, care system (military versus civilian facilities), appointment timing, pain intervention receipt, prescription patterns, and pain episode conclusion. Initial simulations revealed that junior enlisted service members were more likely to have emergency room encounters but less likely to receive opioid prescriptions or experience recurrent pain episodes. Patients who escalated to secondary care (like physical therapy) on their second encounter were less likely to require emergency room visits.

Why are the results important? These results have specific significance for improving pain management in the Military Health System by enabling data-driven decisions about resource allocation and policy implementation. The findings could concretely change clinical practice by informing clinic staffing structures, such as embedding physical therapists in primary care settings to reduce emergency room visits. Military healthcare providers, administrators, and patients will benefit from more efficient care pathways and potentially increased quality of life. Long-term, this approach could transform healthcare decision making by allowing leaders to test policy changes before implementation, potentially leading to improved patient outcomes and military readiness.

What are the strengths and weaknesses of this study? A major strength of this study is its foundation in existing healthcare data and policies, which enhances credibility and practical applicability in real-world settings. However, the study has limitations regarding unobservable factors that affect healthcare access and clinical decision making, such as pain severity and transportation availability, which were not included in the model. Future research could incorporate patient engagement and partnership to improve model accuracy and ensure it reflects patients' lived experiences, while also expanding the data timeframe beyond 2019 to account for more recent healthcare patterns.

Note: This content was created with assistance from artificial intelligence (AI) and has been reviewed and edited by ISPOR staff. For more information or for inquiries on ISPOR’s AI policy, click here or contact us at info@ispor.org.