This study explores how patients and stakeholders envision integrated digital health systems. Background/Objectives: Integrating artificial intelligence (AI), wearable data, electronic health records (EHRs), and patient-reported outcomes could enable proactive and personalized healthcare. However, current solutions remain fragmented and poorly aligned with user expectations. This study aimed to explore patient and stakeholder needs for AI-driven integration and propose a conceptual framework to inform future system design. Methods: As part of the NSF Innovation Corps (I-Corps) program, we conducted semi-structured interviews with 44 participants representing Health Enthusiasts, Chronic Condition Managers, and Low-Engagement Users. Interviews followed the I-Corps customer discovery framework and were thematically analyzed using a hybrid deductive–inductive approach. Results: Participants highlighted four priorities: (i) interoperability and unification of data from wearables, EHRs, and self-reports; (ii) actionable personalization with predictive insights; (iii) trust and transparency in AI recommendations, often requiring clinician oversight; and (iv) usability through low-friction, intuitive interfaces. Age- and persona-specific differences emerged: younger participants favoring predictive features and older participants emphasizing safety, reassurance, and clinical integration. Conclusions: This exploratory qualitative study identified stakeholder needs that informed a conceptual framework for integrated digital health platforms. While preliminary, the framework provides a blueprint for future technical development and validation of patient- and provider-centered systems.

Background: The digitization of health records was expected to improve data quality and accessibility, yet incompleteness remains a widespread challenge that undermines clinical care, interoperability, and downstream analytics. Problem: Evidence shows that missing and under-recorded elements in electronic health records (EHRs) are largely driven by process gaps across patients, providers, technology, and policy—not solely by technical limitations. Objective: This perspective integrates conceptual foundations of incompleteness, synthesizes cross-country evidence, and examines process-level drivers and consequences, with an emphasis on how missingness propagates bias in AI and machine learning systems. Contribution: We present a unifying taxonomy, highlight complementary approaches (e.g., Record Strength Score, distributional testing, and workflow studies), and we propose a pragmatic agenda for mitigation through technical, organizational, governance, and patient-centered levers. Conclusions: While EHR incompleteness cannot be fully eliminated, it can be systematically mitigated through standards, workflow redesign, patient engagement, and governance—essential steps toward building safe, equitable, and effective learning health systems.

Missing data are a persistent challenge in large healthcare datasets, often undermining both statistical validity and machine learning performance when handled using simplistic assumptions. In this work, we examine how entropy-based diagnostics can guide the selection of imputation strategies for high-dimensional health survey data using the National Health and Nutrition Examination Survey (NHANES) 2021–2023. Shannon entropy is used to identify variables with structurally complex missingness, and a range of classical approaches (mean imputation, k-nearest neighbors, and multiple imputation by chained equations) are evaluated alongside deep generative methods, including variational autoencoders, generative adversarial networks (GANs), Wasserstein GANs (WGANs), and diffusion-based models. All methods are compared under a controlled masked-entry evaluation using root mean square error (RMSE) and Kolmogorov–Smirnov (KS) statistics to capture both reconstruction accuracy and distributional fidelity. Results show that diffusion-based models provide the most consistent balance between numerical accuracy and distributional preservation across high-entropy dietary variables, while WGAN demonstrates competitive performance for selected distributions. Structural equation modeling further indicates that entropy is a useful diagnostic signal for identifying variables that are difficult to reconstruct. Overall, this study provides a reproducible framework for aligning imputation strategy with missingness complexity in healthcare data, with implications for improving reliability in downstream analytics.