Alzheimer’s disease (AD) is the most common neurodegenerative disorder that leads to a gradual decrease in cognitive abilities . AD contributes to the sixth leading cause of death in the United States and a fifth leading cause in the age group of 65 and over . It is estimated that approximately 5.4 million people have AD in the USA. ,. Alarmingly, the age-adjusted death rate increased by 39% from 2000 through 2010 . For these reasons, the National Institutes of Health (NIH), specifically the National Institute on Aging (NIA), fosters AD research by providing federal funding to support Alzheimer’s disease Centers (ADCs) at medical institutions throughout the USA . Each ADC and their affiliated researchers work to better understand the underlying risk factors of AD and improve patient care. This is done by conducting translational research and by collecting a substantial, longitudinal, standardized clinical and neuropsychological dataset called the Uniform Data Set (UDS), developed and maintained by the National Alzheimer’s Coordinating Center (NACC). As one of 32 ADCs, the KU ADC, through clinical research and critical care, actively works to further the understanding of AD to improve diagnoses, patient care, and educational resources for those affected by AD.
To enhance clinical activities and facilitate decision making strategies, the KU ADC manages many research activities such as study enrollment, patient screening, and research data capture. These activities are exemplified in the KU ADC Clinical Cohort study, which is a prospective, longitudinal study, following around 400 participants, both with and without memory impairment. Each year, participants in the Clinical Cohort complete clinical and neuropsychological evaluations as part of the UDS created by NACC. Organizing data capture and monitoring cross-relationships in different research studies is an important function of the Data Management and Statistics (DMS) Core of the KU ADC. Additional uses for these cross-linked data include generation of recruitment metrics for study sponsors and summary communications to participants and their physicians.
Historically, research data has been collected using the paper-based case report forms (CRFs) and was then entered into a database to generate electronic records . Although the data collection process using CRFs is simple, error checking during entry is demanding and involves an additional validation step to ensure data safety and preserve the quality and integrity of the data . In recent years electronic data capture (EDC) has become more prevalent with advances in hardware, software, and internet connectivity. With the broadening use of EDC, administrative bodies in the US and Europe have provided guidelines to assure data safety, privacy, and data interchangeability ,,,. Thus, the EDC system provides a platform to gather, manage, and store clinical research data more securely than ever. Electronic data capture allows simple access to the data, with security restrictions in place, which complies with regulatory standards, allows for real-time error checking, and helps maintain data integrity ,. It is evident through literature that EDC is more time-efficient for data capture and more time saving when completing data validation, while maintaining comparable error rates to paper-based approaches.,.
The KU ADC uses an open-source EDC system for data capture which has accounted for improved efficiency in the speed of data transfer, consistent and periodic data reporting to the National Alzheimer’s Coordinating Center (NACC) . Additional advantages of this system are that it provides reporting in real-time while maintaining data safety. It is prone to fewer transcription errors, and thus overall improved data quality. Our EDC system provides a swift data management flow from initial contact with patients, to final diagnosis stage, to uploading the data to the NACC database, fulfilling the requirements set by NACC . In addition, to further leverage the capabilities of an EDC based system, we have developed and deployed Web applications through which end users can easily find and summarize key variables and metrics to disseminate the clinical outcome and reports for the patients and physicians.
This manuscript aims to describe the process and implementation of a Shiny app to produce real-time reports for weekly diagnosis meetings. This article attempts to summarize our entire consensus process of the KU ADC from data collection to data management and consensus reporting. Specifically, this paper will advocate the idea to use the open-source software and how to automate the workflow to improve efficiency and compliance, and how this process helps facilitate the consensus meeting. Furthermore, we will discuss how the consensus app helps facilitate sending automated correspondences to both participants and their primary care physicians (PCPs) in the form of a thank-you letter to Clinical Cohort participants for their contribution to the KU ADC research and a diagnostic summary letter to PCPs to provide an update on the participant’s recent research visit.