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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结/文摘