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To inform training needs for the revised Certified Clinical Data Manager (CCDMTM) Exam.

The coronavirus disease 2019 pandemic has resulted in large-scale changes to incorporate telemedicine for the delivery of care. People with cystic fibrosis (CF) have care considerations that pose challenges to telemedicine; they include frequent visits for pulmonary disease progression, medication management, and evaluation by a multidisciplinary team of providers. We share our center's experience with video visits replacing in-person clinic evaluation, using quality improvement strategies to create a replicable workflow [...]

We designed an mHealth application (app) user interface (UI) prototype informed by participatory design sessions, persuasive systems design (PSD) principles, and Lorig and Holman's self-management behavior framework to support self-management activities of Hispanic informal dementia caregivers and assessed their perceptions and preferences regarding features and functions of the app.

The Alzheimer's Therapeutic Research Institute (ATRI) developed a novel clinical data management system, the ATRI electronic data capture system (ATRI EDC), to address the complex regulatory, operational, and data requirements that arise in the conduct of multicenter Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRDs) clinical trials. We describe the system, its utility, and the broader implications for the field of clinical trials and clinical research informatics.

To evaluate the attitudes of the parties involved in the system toward the new features and measure the potential benefits of introducing the use of blockchain and machine learning (ML) to strengthen the in-place methods for safely prescribing medication. The proposed blockchain will strengthen the security and privacy of the patient's prescription information shared in the network. Once the ePrescription is submitted, it is only available in read-only mode. This [...]

Safe care of central venous access devices (CVAD) requires clinicians be able to identify key CVAD properties from insertion until safe removal. Our objective was to design and evaluate interfaces to improve CVAD documentation quality and information retrieval.

A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes.

Surgical instrument oversupply drives cost, confusion, and workload in the operating room. With an estimated 78%-87% of instruments being unused, many health systems have recognized the need for supply refinement. By manually recording instrument use and tasking surgeons to review instrument trays, previous quality improvement initiatives have achieved an average 52% reduction in supply. While demonstrating the degree of instrument oversupply, previous methods for identifying required instruments are qualitative, expensive [...]

Sleep apnea is associated with a broad range of pathophysiology. While electronic health record (EHR) information has the potential for revealing relationships between sleep apnea and associated risk factors and outcomes, practical challenges hinder its use. Our objectives were to develop a sleep apnea phenotyping algorithm that improves the precision of EHR case/control information using natural language processing (NLP); identify novel associations between sleep apnea and comorbidities in a large [...]

To evaluate patient experience with a prenatal telemedicine visit and identify barriers to accessing telemedicine among rural pregnant people in northern New England during the beginning of the COVID-19 pandemic.