SBIR Phase I: An Opioid Taper Clinical Decision Support Tool for Use in a Primary Care Outpatient Setting

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是减少阿片类药物依赖。长期使用阿片类药物的一个关键挑战是在确定最佳剂量的基础上平衡利益和风险，以管理相关风险和副作用。阿片类药物可用于控制这种风险，并可持续数周至数年。然而，在自愿减少长期阿片类药物剂量后，许多患者报告功能、睡眠、焦虑和情绪得到改善，而疼痛没有恶化，甚至疼痛水平也没有下降。此外，锥体通常用于控制阿片类药物耐受性，痛觉过敏，在手术前更有效地控制疼痛，或过渡到另一种形式的阿片类药物或非阿片类药物。近1800万美国人可能受益于阿片类药物的减少，以改善慢性疼痛的长期管理，提高慢性阿片类药物治疗的安全性和有效性。特别是在农村社区，患者可能无法接触到疼痛专家，而依赖于他们的初级保健医生进行慢性疼痛管理。拟议的技术为农村初级保健医生提供了阿片类药物逐渐减少的关键工具，其中几乎一半的阿片类药物是处方的。使用临床决策支持工具，初级保健医生将有一个虚拟的疼痛专家建议预期的预测结果和行动，以确保成功的逐渐减少患者的直接反馈。这个小企业创新研究（SBIR）第一阶段项目将应用机器学习技术开发算法，使用一个数据集来描述成功和不成功的锥形手术，包括临床数据、医疗决策、患者体验和医生/患者互动和沟通。该预测模型将根据患者的身体、心理、社会和情感体验的实时数据，告知医疗决策者患者连续事件的可能性及其对逐渐减少结果的影响。这项创新将在智能手机平台上实施，将医生/患者医疗决策模型从临床环境扩展到患者环境。这将创建一个以患者为中心的锥度的新模型，其中通过优化患者体验来了解估计的临床结果和相关行动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to reduce opioid dependence. A critical challenge in the use of opioids on a long-term basis is balancing benefit and risk, based on determining the optimal dosage to manage the associated risks against side effects. Opioid tapers are used to manage this risk and may last from several weeks to several years. However, following the voluntary reduction of long-term opioid dosages, many patients report improvements in function, sleep, anxiety, and mood without worsening pain or even with decreased pain levels. Furthermore, tapers are typically used to manage opioid tolerance, hyperalgesia, enable more effective pain management prior to surgical procedures, or transition to another form of opioids or non-opioid pain medication. Nearly 18 million Americans may benefit from an opioid taper to improve the long-term management of their chronic pain and improve the safety and effectiveness of chronic opioid therapy. In particular, in rural communities, patients may not have access to pain specialists and rely on their primary care physicians for chronic pain management. The proposed technology provides a critical tool for opioid tapering to rural primary care physicians, where almost half of the opioids are prescribed. Using the clinical decision support tool, primary care physicians will have a virtual pain specialist advising of the anticipated predictive outcomes and actions to ensure a successful taper with direct feedback from the patient.This Small Business Innovation Research (SBIR) Phase I project will apply machine learning techniques to develop algorithms using a dataset that describes successful and unsuccessful tapers with clinical data, medical decision making, patient experience, and physician /patient interaction and communication. The predictive model will inform the medical decision-maker of the likelihood of successive patient events and their impact on the taper outcome based on real-time data about the patient's physical, psychological, social, and emotional experience. This innovation will be implemented on a smartphone platform to extend the physician / patient medical decision model beyond the clinical setting into the patient's environment. This will create a new model of a patient-centric taper wherein the estimated clinical outcomes and associated actions are informed by optimizing the patient experience.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.