Development of a program to assess and treat distress in glaucoma patients using an automated EHR-derived AI algorithm

使用自动 EHR 衍生的 AI 算法开发一个程序来评估和治疗青光眼患者的痛苦

• 批准号: 10469533
• 负责人: Samuel Isaac Berchuck
• 金额: $ 11.47万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10469533
• 关键词: Address; Algorithms; Anxiety; Artificial Intelligence; Automation; Award; Behavior Therapy; Behavioral Sciences; Bioinformatics; Biometry; Blindness; Calibration; Caring; Characteristics; Chronic; Clinic; Clinical; Clinical Data; Clinical Research; Comprehension; Coping Skills; Data; Data Collection; Data Science; Data Set; Database Management Systems; Databases; Development; Discrimination; Disease; Distress; Effectiveness; Electronic Health Record; Ensure; Familiarity; Focus Groups; Glaucoma; Goals; Gold; Health; Health Care Research; Health Professional; Health Sciences; Healthcare; Image; Intervention; Laboratories; Lead; Left; Light; Measures; Medical; Medical Research; Mental Depression; Mentors; Nature; Oncology; Ophthalmologist; Outcome; Outcome Measure; Patient Care; Patient Outcomes Assessments; Patient Self-Report; Patients; Performance; Personal Satisfaction; Phase; Population; Property; Protocols documentation; Provider; Psychiatry; Quality of life; Questionnaires; Randomized; Randomized Clinical Trials; Recommendation; Records; Registries; Research; Research Design; Research Personnel; Risk Estimate; Risk Factors; Severity of illness; Stress and Coping; Supervision; Surveys; Techniques; Telephone; Testing; Time; Training; Validation; Vision; Visit; Visual Fields; Work; algorithm training; artificial intelligence algorithm; base; career development; clinical care; clinical decision-making; clinical practice; clinical risk; comorbidity; compliance behavior; cost; design; diagnosis standard; evidence base; experience; eye center; follow-up; high risk; improved; innovation; instrument; intervention program; medication compliance; mindfulness-based stress reduction; model development; multidisciplinary; novel strategies; outcome prediction; patient screening; population health; predictive modeling; prevent; programs; prospective; psychosocial; retention rate; screening; screening program; skills; standard measure; statistical and machine learning

PROJECT SUMMARY/ABSTRACT Glaucoma is a disease that results in irreversible blindness and due to its chronic, progressive nature, imposes a psychosocial burden on patients. Appropriately, the focus of ophthalmologists is on controlling the disease to prevent vision loss. Yet, patient’s psychosocial distress during and after therapy has not been routinely addressed and is another important target of care. Psychosocial distress (i.e., anxiety, depression) negatively impacts all outcomes in glaucoma and is associated with poor follow-up and medication adherence, worse vision-related quality-of-life and disease severity, and faster rates of visual field progression. Direct assessment and treatment of psychosocial distress is likely to improve glaucoma outcomes. While uncommon in glaucoma clinics, psychosocial distress screening has been occurring with some consistency in other medical settings (e.g., oncology) for more than a decade, leading to referrals for intervention and improvements in psychosocial distress and subsequently overall health. Our overarching scientific premise is that a screening program for psychosocial distress (i.e., anxiety, depression) in glaucoma clinics would enhance the patient’s adherence to medical recommendations, and quality-of-life, ultimately leading to improvements in vision-related outcomes (e.g., visual field progression). Patient-reported outcome measures are the gold standard measures of distress, however are not routinely collected in patients with glaucoma due to perceived time and cost burdens. To remedy this, the PI proposes an automated pre-screening framework, motivated by preliminary analyses that demonstrate that distress can be reliably identified using predictive modeling based on glaucoma clinical risk factors from electronic health records (EHR) data. This predictive model will be developed in aim 1 using an existing EHR database, the Duke Glaucoma Registry, and will yield automated risk estimates of distress that can be used to inform clinical decision making, regarding the administration of a distress survey; therefore, limiting distress assessment to a subset of high-risk patients. Secondary aims will focus on external validation of the automated technique, and gauging acceptability to distress screening in a glaucoma clinic (aim 2), and the refinement of a behavioral intervention to improve coping skills for distress in patients with glaucoma (aim 3). This research will positively impact patient well- being in glaucoma, serving as an evidence-based assessment of a distress screening program. The proposal also details a training plan to help the PI transition from a postdoctoral scholar to an independent researcher. The mentored phase of the award will be supervised by the primary mentor, Dr. Felipe Medeiros, and multidisciplinary mentoring team including Dr. Tamara Somers (Psychiatry and Behavioral Sciences), Dr. David Page (Biostatistics & Bioinformatics), and Dr. Kevin Weinfurt (Population Health Sciences). Performing the proposed research, formal coursework, and mentored career development will provide the PI with highly sought-after skills and experiences to help ensure a successful transition to independence.

项目概要/摘要 青光眼是一种导致不可逆失明的疾病，并且由于其慢性、进行性的性质， 适当地，眼科医生的重点是控制疾病以减轻其负担。 然而，患者在治疗期间和治疗后的心理困扰并不常见。 已得到解决，并且是心理社会困扰（即焦虑、抑郁）消极的另一个重要目标。 影响青光眼的所有结果，并与不良随访和药物依从性相关，更糟 与视力相关的生活质量和疾病严重程度，以及更快的视野进展速度。 心理社会困扰的评估和治疗可能会改善青光眼的结果，但并不常见。 在青光眼诊所中，社会心理困扰筛查在其他方面具有一定的一致性 医疗机构（例如肿瘤科）十多年来，导致转诊进行干预和治疗 我们的首要科学前提是改善社会困境以及随后的整体健康。 青光眼诊所的社会心理困扰（即焦虑、抑郁）筛查计划将 提高患者对医疗建议的依从性和生活质量，最终导致 视力相关结果的改善（例如，患者报告的结果测量）。 是衡量痛苦的金标准，但由于青光眼患者的情况，并未常规收集这些数据 为了解决这个问题，PI 提出了一个自动预筛选框架， 受到初步分析的启发，这些分析表明可以使用预测来可靠地识别痛苦 这种预测基于电子健康记录 (EHR) 数据中的青光眼临床危险因素进行建模。 目标 1 中将使用现有的 EHR 数据库（杜克青光眼登记处）开发模型，并将产生 痛苦的自动风险评估可用于为临床决策提供信息 进行痛苦调查；因此，将痛苦评估仅限于一部分高风险患者。 次要目标将侧重于自动化技术的外部验证，并衡量自动化技术的可接受性 青光眼诊所的痛苦筛查（目标 2），以及改进行为干预以改善 青光眼患者的痛苦应对技巧（目标 3）。 患有青光眼，作为痛苦筛查计划的循证评估。 还详细介绍了帮助PI从博士后学者过渡到独立研究员的培训计划。 该奖项的指导阶段将由主要导师 Felipe Medeiros 博士进行监督， 多学科指导团队包括 Tamara Somers 博士（精神病学和行为科学）、Dr. David Page（生物统计学和生物信息学）和 Kevin Weinfurt 博士（人口健康科学）。 拟议的研究、正式课程和指导的职业发展将为 PI 提供高度 广受欢迎的技能和经验，以帮助确保成功过渡到独立。