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.

With the rapid advancements in artificial intelligence (AI) and machine learning (ML), the role of data engineering has become increasingly critical due to the growing demands for high-quality, large-scale, and well-structured datasets required to train reliable predictive models. Healthcare is one of the most data-intensive industries and has demonstrated strong potential for AI-driven automation in clinical decision support, diagnostics, and operational efficiency. However, healthcare data is often fragmented across multiple systems, inconsistently formatted, and constrained by privacy and regulatory requirements, creating significant barriers to scalable AI adoption. In this review, we examine recent research on healthcare data engineering and AI applications, focusing on how data pipelines, interoperability, and governance frameworks support or limit real-world deployment. This review examined 68 peer-reviewed studies published between 2018 and 2026 across multiple clinical domains, including oncology, cardiovascular disease, infectious disease, neurological disorders, medical imaging, and algorithmic frameworks for explainability and fairness. The reviewed literature shows that while AI models achieve promising performance across these domains, the lack of standardized data architectures and interoperable infrastructure remains a primary bottleneck. The purpose of this study is to highlight key challenges and emerging solutions in healthcare data engineering and outline the future directions needed to support safe, scalable, and trustworthy AI integration in the United States healthcare system. The intended core contributions of this article are to: (i) identify the need for reliable AI systems for healthcare, (ii) explore challenges associated with implementing AI systems in healthcare from a data engineer’s perspective, and (iii) analyze key limitations of data engineering as it applies to the implementation of AI systems in healthcare. It must be noted that one of the key limitations of this narrative review is that the authors mostly used citations from MDPI journals.