OBJECTIVES: The success of artificial intelligence (AI) and machine learning (ML) approaches in biomedical research depends on the quality of the underlying data. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Data Centric Challenge was designed to address the challenge of making raw clinical research data AI ready, with a focus on type 1 diabetes studies available in the NIDDK Central Repository (NIDDK-CR). This paper aims to present a structured methodology for enhancing the AI readiness of clinical datasets. MATERIALS AND METHODS: We detail a systematic approach for data aggregation and preprocessing, including binning continuous data, processing text features, managing missing values, and encoding for categorical variables while maintaining the data integrity and compatibility with ML algorithms. RESULTS: We applied the proposed methodology to transform raw clinical data from type 1 diabetes studies in the NIDDK-CR into a structured, AI-ready dataset. The evaluation process validated the effectiveness of our AI-readiness enhancement steps and explored the potential use cases in type 1 diabetes research. DISCUSSION: The methodology discussed in this paper will serve as guidance for preparing data for AI-driven clinical research, with the resulting AI-ready data to serve as a training tool for building and improving AI/ML model performance. CONCLUSION: We present a generalizable framework for preparing clinical research data for AI applications. The resulting datasets lay a strong foundation for downstream AI/ML applications, setting the stage for a new era of data-driven discoveries.

OBJECTIVES: The aim of this study was to investigate how healthcare staff intermediaries support Federally Qualified Health Center (FQHC) patients' access to telehealth, how their approaches reflect cognitive load theory (CLT) and determine which approaches FQHC patients find helpful and whether their perceptions suggest cognitive load (CL) reduction. MATERIALS AND METHODS: Semistructured interviews with staff (n = 9) and patients (n = 22) at an FQHC in a Midwestern state. First-cycle coding of interview transcripts was performed inductively to identify helping processes and participants' evaluations of them. Next, these inductive codes were mapped onto deductive codes from CLT. RESULTS: Staff intermediaries used 4 approaches to support access to, and usage of, video visits and patient portals for FQHC patients: (1) shielding patients from cognitive overload; (2) drawing from long-term memory; (3) supporting the development of schemas; and (4) reducing the extraneous load of negative emotions. These approaches could contribute to CL reduction and each was viewed as helpful to at least some patients. For patients, there were beneficial impacts on learning, emotions, and perceptions about the self and technology. Intermediation also resulted in successful visits despite challenges. DISCUSSION: Staff intermediaries made telehealth work for FQHC patients, and emotional support was crucial. Without prior training, staff discovered approaches that aligned with CLT and helped patients access technologies. Future healthcare intermediary interventions may benefit from the application of CLT in their design. Staff providing brief explanations about technical problems and solutions might help patients learn about technologies informally over time. CONCLUSION: CLT can help with developing intermediary approaches for facilitating telehealth access.