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Insights From Chapter Author Dr. Antonio Pesqueira

Unlocking the Future of ADHD Care

By Antonio Pesqueira on Feb 26, 2025

As advancements in technology continue to develop at rapid paces, it's important to understand its effects and practical implications on the field of medicine and healthcare. The chapter, "ADHD Healthcare Intelligence: A Synergistic Approach with Big Data and AI for Better Screening, Diagnosis, Treatment, and Monitoring," from the book, Data-Driven Business Intelligence Systems for Socio-Technical Organizations (ISBN: 9798369312100) provides relevant and informative knowledge on the usage of technology in healthcare. Discussing the detection and treatment of ADHD, this chapter provides valuable insights suitable for a wide range of audiences. Chapter author, Dr. Antonio Pesqueira, with a background in healthcare and pharmaceutical supply chain management, provides a deeper look into this evolving field of medical technologies. See his discussion below.

Data-Driven Business Intelligence Systems for Socio-Technical Organizations

Prof. Pantea Keikhosrokiani
© 2024 | 490 pgs. | ISBN13: 9798369312100

Edited By a Previous Teaching Fellow From the National Advanced IPv6 Centre of Excellence (Nav6)
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Covers Topics such as Behavioral Analytics, Deep Learning, & Socio-Technical Dynamics

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Advancements in ADHD Healthcare Intelligence By Dr. Antonio Pesqueira

Attention Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurological condition that can have significant ramifications on individual development, social participation, and overall quality of life. In an evolving healthcare landscape, a recent chapter titled “ADHD Healthcare Intelligence: A Synergistic Approach with Big Data and AI for Better Screening, Diagnosis, Treatment, and Monitoring” proposes a bold step forward in addressing the diagnostic, therapeutic, and monitoring complexities of ADHD. These insights report distills the chapter’s key contributions, demonstrating how the application of Big Data (BD) and Artificial Intelligence (AI) holds promise for transforming ADHD care, and highlighting the dedicated individuals—researchers, clinicians, and medical professionals—whose efforts have paved the way for these advancements.

ADHD manifests primarily through persistent patterns of impulsivity, hyperactivity, and inattention, affecting both children and adults. Traditional treatment models, relying on pharmacotherapy, behavioral interventions, and psychosocial support, often show mixed outcomes due to the disorder’s heterogeneous nature. Over the last decade, the need for a more robust, data-driven approach has become increasingly evident. This chapter addresses that need by advocating a technologically integrated framework, emphasizing the synergy of BD and AI across four pivotal areas of ADHD care: screening, diagnosis, treatment, and monitoring.

One of the most compelling insights from the chapter is the potential of AI-driven tools to streamline early screening and diagnosis. Conventionally, ADHD diagnosis hinges on subjective clinical evaluations, symptom checklists, and observational reports from parents, teachers, or caregivers. While these methods are indispensable, they can lack consistency across different contexts and cultural settings. AI, by contrast, can tap into diverse data sources—from electronic health records and genomic databases to wearable technology outputs and even social media use patterns—to detect subtle behavioral signatures indicative of ADHD. These advanced algorithms can process large volumes of patient data, quickly identifying correlations and red flags that might elude manual screening processes. The result is a more accurate, comprehensive, and timely diagnosis, ensuring that individuals receive interventions when they are most likely to be beneficial.

The chapter underscores the essential role of BD in augmenting these AI algorithms. Big Data, in essence, is about volume, velocity, and variety of information. Applying BD methodologies to ADHD care grants clinicians and researchers the capacity to handle massive datasets encompassing patient demographics, medical histories, psychometric evaluations, and more. This wide-ranging perspective can illuminate nuanced factors, such as genetic predisposition or comorbid mental health conditions, that shape a person’s experience of ADHD. In turn, AI systems become more adept at refining diagnostic criteria and personalizing treatment recommendations, moving beyond one-size-fits-all approaches. Patients stand to benefit significantly from this individualized care paradigm, as treatments become precisely aligned with each person’s unique profile.

Importantly, the chapter highlights how these data-centric tools could transform treatment and ongoing monitoring. ADHD management is rarely linear: medication types may need frequent adjustments, therapy sessions might evolve in focus over time, and environmental modifications may be required in schools or workplaces. AI’s predictive analytics can flag the likelihood of treatment success or detect emergent challenges early—such as declining medication adherence or rising symptom severity—allowing proactive interventions. Continuous data collected via wearable devices, smartphone applications, or telehealth platforms can be fed into AI engines for real-time updates on patient well-being. This enables clinicians to pivot swiftly, tailoring interventions to maintain or improve patient outcomes.

A hallmark contribution of the chapter is its exploration of Multimodal Large Language Models (M-LLMs) like GPT-4 and potential future iterations (sometimes referred to hypothetically as “ChatGPT 5”). These sophisticated AI models can interpret and synthesize text, images, audio, and other data formats, offering an in-depth analytical lens for complex disorders like ADHD. For example, M-LLMs may detect linguistic markers that distinguish ADHD subtypes or identify changes in speech patterns linked to mental health fluctuations. When integrated into telehealth services, these AI models can help bridge geographic gaps, providing remote screening, therapeutic guidance, and education to underserved populations. In addition, language barriers—common obstacles in global healthcare contexts—can be mitigated by AI-driven translation and interpretation features, broadening ADHD care’s accessibility and inclusivity.

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The chapter’s empirical methodology involved extensive literature reviews and an online questionnaire administered to 48 experts from the healthcare sector. These experts—encompassing psychiatrists, psychologists, nurses, researchers, and executive directors in pharmaceutical, clinical, and academic settings—shared their perspectives on where BD and AI could add the most value. The diverse background of respondents ensured that this study was enriched by both clinical acumen and administrative insight. Their feedback illuminated challenges as well: the need to maintain data privacy, mitigate biases in AI-driven tools, and ensure the equitable distribution of technology across different socioeconomic contexts. These hurdles cannot be overlooked. The study’s commitment to addressing them underscores the diplomatic, patient-centered tone that permeates the chapter, reminding us that technology must serve as an enhancer rather than a disruptor of person-focused healthcare.

Ethical concerns figure prominently. AI algorithms depend heavily on the data fed into them, risking the propagation of biases if certain populations are underrepresented. The researchers behind this study call for comprehensive, representative datasets alongside rigorous regulatory guidelines that safeguard patient confidentiality. They also advocate for medical education reforms that equip future clinicians with the expertise to interpret AI outputs responsibly. These forward-thinking recommendations attest to the dedication and foresight of the people behind this research: a collective of professionals driven by the conviction that technology, used wisely, can significantly elevate healthcare outcomes.

Another notable aspect of the study involves the challenges of real-world implementation. The authors stress that interdisciplinary collaboration is vital. Psychiatrists, data scientists, ethicists, neurologists, behavioral therapists, and patients themselves each bring a crucial lens to the conversation. By working in harmony, they can design systems that genuinely respond to patient needs while adhering to high standards of clinical validity and safety. This team-based ethos highlights the humanity at the core of the project: the researchers, clinicians, and technology experts are deeply invested in the well-being of individuals living with ADHD.

Beyond clinical practices, the broader community also stands to gain. Families navigating ADHD can benefit from AI-powered educational tools, digital therapeutics, and user-friendly applications that track daily routines, offering them tailored strategies for improved home life. Schools may implement real-time analytics to understand the concentration patterns of students with ADHD, helping teachers design more supportive learning environments. Employers may use data insights to craft workplace accommodations for adults with ADHD, recognizing their strengths and reducing barriers to productivity. Ultimately, these innovations not only improve individual care but also foster a more inclusive and empathetic society.

By combining large-scale data analytics with sophisticated AI algorithms, healthcare professionals are better positioned to identify, treat, and monitor ADHD in ways that are more precise, empathetic, and responsive to each patient’s evolving needs. This research, driven by the collective passion of diverse experts, transcends mere theoretical discussion. Instead, it charts a course toward actionable innovation that acknowledges the complexities of ADHD while uplifting those most impacted by it. Through diligent collaboration, ethical practice, and continued research, the synergistic power of BD and AI stands to shape a new era of ADHD screening, diagnosis, treatment, and monitoring—one that prioritizes the people behind the condition every step of the way.

Disclaimer: The opinions expressed in this article are the author’s own and do not reflect the views of IGI Global Scientific Publishing.

About the Chapter Author

Antonio Pesqueira is a healthcare and pharmaceutical commercial and supply chain management leader, with an emphasis on technological advancements and process improvement. Concurrently, he holds esteemed academic positions as a professor and research fellow at ISCTE/IUL. Within the Technology and Architecture Research Department, he contributes to the Ph.D. program, focusing his research on digital technologies, healthcare data management, blockchain, and the nexus of innovation and business management. He has notably chaired the Pharmaceutical Supply Chain & Security World Forum and is often invited as a keynote speaker at events such as the Pharma Track & Trace, and Serialization & Labeling Summit. Co-authored more than 20 scholarly articles and book chapters that have been featured in prestigious scientific journals. These include the Journal of Business Research, Future Generations Computer Systems, Journal of Medical Systems, Knowledge Management, WSEAS Transactions on Business and Economics, and Information Systems Frontiers, to name a few. Moreover, his outstanding contributions have been acknowledged at various peer-reviewed international conferences. Furthermore, serves as a reviewer for journals including Pharmaceutical Medicine (PHMA) and BMC Medical Informatics and Decision Making. He possesses certifications in data science and agile coaching and plays an instrumental role in the scientific review committee for the AICONF'23 program at Oxford University.

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