Patel Prachi M, Green Maja, Tram Jennifer, Wang Eugene, Murphy Melissa Zhu, Abd-Elsayed Alaa, Chakravarthy Krishnan
Houston Methodist Willowbrook Hospital, Houston, TX, USA.
NXTSTIM, San Diego, CA, USA.
J Pain Res. 2024 Dec 11;17:4223-4237. doi: 10.2147/JPR.S494238. eCollection 2024.
Remote Patient Monitoring (RPM) stands as a pivotal advancement in patient-centered care, offering substantial improvements in the diagnosis, management, and outcomes of chronic conditions. Through the utilization of advanced digital technologies, RPM facilitates the real-time collection and transmission of critical health data, enabling clinicians to make prompt, informed decisions that enhance patient safety and care, particularly within home environments. This narrative review synthesizes evidence from peer-reviewed studies to evaluate the transformative role of RPM, particularly its integration with Artificial Intelligence (AI), in managing chronic conditions such as heart failure, diabetes, and chronic pain. By highlighting advancements in disease-specific RPM applications, the review underscores RPM's versatility and its ability to empower patients through education, shared decision-making, and adherence to therapeutic regimens. The COVID-19 pandemic further emphasized the importance of RPM in ensuring healthcare continuity during systemic disruptions. The integration of AI with RPM has refined these capabilities, enabling personalized, real-time data collection and analysis. While chronic pain management serves as a focal area, the review also examines AI-enhanced RPM applications in cardiology and diabetes. AI-driven systems, such as the NXTSTIM EcoAI™, are highlighted for their potential to revolutionize treatment approaches through continuous monitoring, timely interventions, and improved patient outcomes. This progression from basic wearable devices to sophisticated, AI-driven systems underscores RPM's ability to redefine healthcare delivery, reduce system burdens, and enhance quality of life across multiple chronic conditions. Looking forward, AI-integrated RPM is expected to further refine disease management strategies by offering more personalized and effective treatments. The broader implications, including its applicability to cardiology, diabetes, and pain management, showcase RPM's capacity to deliver automated, data-driven care, thereby reducing healthcare burdens while enhancing patient outcomes and quality of life.
远程患者监测（RPM）是患者中心护理的一项关键进展，在慢性病的诊断、管理和治疗结果方面有显著改善。通过利用先进的数字技术，RPM有助于实时收集和传输关键健康数据，使临床医生能够做出及时、明智的决策，提高患者安全性和护理质量，特别是在家庭环境中。这篇叙述性综述综合了同行评审研究的证据，以评估RPM，特别是其与人工智能（AI）集成在管理心力衰竭、糖尿病和慢性疼痛等慢性病方面的变革性作用。通过强调特定疾病RPM应用的进展，该综述强调了RPM的多功能性及其通过教育、共同决策和坚持治疗方案来增强患者能力的能力。COVID-19大流行进一步凸显了RPM在系统性破坏期间确保医疗保健连续性的重要性。AI与RPM的集成提升了这些能力，实现了个性化、实时数据收集和分析。虽然慢性疼痛管理是一个重点领域，但该综述还研究了AI增强的RPM在心脏病学和糖尿病中的应用。以NXTSTIM EcoAI™等AI驱动系统为例，强调其通过持续监测、及时干预和改善患者治疗结果来彻底改变治疗方法的潜力。从基本可穿戴设备到复杂的AI驱动系统的这一进展凸显了RPM重新定义医疗服务、减轻系统负担和提高多种慢性病患者生活质量的能力。展望未来，集成AI的RPM有望通过提供更个性化和有效的治疗方法进一步完善疾病管理策略。其更广泛的影响，包括在心脏病学、糖尿病和疼痛管理中的适用性，展示了RPM提供自动化、数据驱动护理的能力，从而减轻医疗负担，同时提高患者治疗结果和生活质量。
