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Improving Diagnosis in Gastrointestinal Cancer: Integrating Prediction Models into Routine Clinical Care

改善胃肠癌的诊断：将预测模型纳入常规临床护理

基本信息

批准号：
10641060
负责人：
Andrew J Read
金额：
$ 27.65万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10641060
关键词：
Address Adopted Adoption Algorithms Attention Award Biometry Blood Vessels Characteristics Chronic Clinical Clinical Trials Colorectal Complete Blood Count Consolidated Framework for Implementation Research Data Data Science Data Scientist Data Set Detection Development Diagnosis Diagnostic Disease Electronic Health Record Electronics Erythrocytes Evaluation Future Gastroenterologist Goals Health Services Healthcare Systems Hemoglobin Hemorrhage Integrated Health Care Systems Interview Investigation Iron Iron deficiency anemia Laboratories Learning Logic Logistic Regressions Machine Learning Malignant neoplasm of esophagus Malignant neoplasm of gastrointestinal tract Maps Medical Methods Michigan Modeling Nature Occult blood screen Patients Pattern Performance Predictive Cancer Model Reminder Systems Research Research Personnel Scientist Small Intestines Specific qualifier value Statistical Algorithm Stomach Structure Surveys System Techniques Testing Theoretical Domains framework Training Translational Research Translations United States Universities Veterans Health Administration Width Work acceptability and feasibility cancer diagnosis career clinical care clinical implementation clinical practice clinical predictive model design digital implementation facilitators implementation framework implementation science implementation strategy improved in silico innovation model development novel prediction algorithm predictive modeling predictive test prospective screening skills tool translational scientist

项目摘要

PROJECT SUMMARY/ABSTRACT Candidate: Andrew J. Read, MD, MS is a gastroenterologist and health services researcher at the University of Michigan with a research focus on improving diagnosis of gastrointestinal (GI) tract cancers. Dr. Read has prior training in biostatistics but not in mixed methods or implementation science. This NCI K08 award will train him to become a leader in the translational science of medical prediction, providing him with the skills to develop tailored implementation strategies for cancer prediction models and test these strategies in future clinical trials. Research Context: Iron deficiency anemia (IDA) is a common sign of many diseases, including GI tract cancers. Despite this important association, IDA is often under-recognized or under-investigated, resulting in delays in diagnosis. Fortunately, the electronic health record (EHR) contains potential diagnostic clues that can be leveraged to improve diagnosis of GI tract cancers. Specifically, algorithms can be developed to detect subtle changes in complete blood count (CBC) parameters to predict GI tract cancers. However, prediction models have rarely been implemented in clinical practice. Identifying the barriers and facilitators to implementing a model can allow for more customized implementation strategies to improve the chances of successful implementation. Research Aims: Dr. Read will (1) Refine a prediction model for detection of GI tract cancers using longitudinal laboratory data from the Veterans Health Administration (VA), the largest integrated healthcare system in the United States; (2) Identify barriers and facilitators to implementation of prediction models in clinical practice using mixed methods with an explanatory sequential design, incorporating a clinician survey followed by semi- structured clinician interviews; and (3) Develop and test components of a prediction model implementation strategy in a clinical setting, using Implementation Mapping. Training Aims: Dr. Read will develop expertise in: (1) Developing advanced longitudinal prediction models using a national dataset; (2) Using and applying mixed methods and implementation science frameworks to identify barriers and facilitators to successful implementation of a prediction model; (3) Applying Implementation Mapping to develop and test an implementation strategy for a novel clinical prediction model.

项目总结/文摘