SHARES: Substance use HeAlth REcord Sharing

项目总结 综合护理是至关重要的，而且正在变得越来越普遍，但平衡 物质使用障碍(SUD)患者和通过数据访问提高患者安全性 是对实现其全部潜力的挑战。我们之前的奖项(R01MH108992)发现：1)a 普遍缺乏关于sud数据隐私和机密性的研究；2)很少有关于如何 SUD数据共享保护影响护理；3)SUD数据跨学科研究不足 保护；4)将患者和倡导者最少包括在SUD数据隐私文献中；5) 关于SUD数据隐私的稀疏定量和定性研究(意见和法律审查 以及6)电子健康记录敏感数据分割的精度较低 由药物滥用和精神健康服务管理局赞助的软件 (SAMHSA)。 重点是放大患者和提供者的声音 物质使用健康记录 共享(共享)。 研究问题：阐明SUD数据共享面临的挑战和 自动化数据分割的可行性，以提高数据互操作性和医疗保健 那些有SUD的人。研究地点：三个综合诊所、一个卫生系统、一个SUD治疗诊所、一个 健康计划和一个州的健康信息交换。具体目标： 目标1：探查患者 以及提供商对SUD数据共享和医疗保健的看法，以向有关SUD数据的政策提供信息 隐私和机密性；目标2：展示自动化的可行性和准确性 通过提出新的信息学方法和工具进行SUD数据分割 自动SUD数据分割；目标3：评估临床准确性和影响 自动SUD数据分割展示了患者控制的数据共享 改善服务交付和SUD患者结局。影响：Share引入了新奇、 基于标准、独立于机构、不受电子病历限制且可扩展的敏感健康数据 提高数据共享和互操作性的分割方法和技术 医疗保健机构和服务交付以及患者预后。

PROJECT SUMMARY Integrated care is critical and becoming more common, but balancing the privacy needs of patients with substance use disorders (SUD) and increasing patient safety through data access is a challenge to realizing its full potential. Our previous award (R01MH108992) found: 1) a general dearth of research on SUD data privacy and confidentiality; 2) few publications on how SUD data sharing protections impact care; 3) inadequate interdisciplinary research on SUD data protections; 4) minimal inclusion of patients and advocates in SUD data privacy literature; 5) sparse quantitative and qualitative research on SUD data privacy (opinions and legal reviews are predominant); and 6) low accuracy for electronic health record sensitive data segmentation software sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA). The focus is to amplify patients’ and providers' voices on Substance use HeAlth REcord Sharing (SHARES). Research question: Elucidate SUD data sharing challenges and the feasibility of automated data segmentation to improve data interoperability and health care for those with SUD. Study sites: three integrated clinics, a health system, a SUD treatment clinic, a health plan and a state’s health information exchange. Specific aims: Aim 1: Explore patient and provider views on SUD data sharing and health care to inform policies on SUD data privacy and confidentiality; Aim 2: Demonstrate the feasibility and accuracy of automated SUD data segmentation by proposing novel informatics methods and tools that advance automated SUD data segmentation; Aim 3: Evaluate the clinical accuracy and impact of automated SUD data segmentation demonstrating that patient-controlled data sharing improves service delivery and SUD patient outcomes. Impact: SHARES introduces novel, standard-based, institution-independent, EHR-agnostic and scalable sensitive health data segmentation methods and technology to improve data sharing and interoperability between healthcare institutions and service delivery and patient outcomes for those with SUD.