The Digital Pillbox: Integrating AI, IoT, and Pharma Solutions (Part 2) E-Book
Editors: Akhil Sharma
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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Sprache: Englisch
Building on the foundations of Part 1, this volume explores advanced integrations of AI, IoT, and data-driven systems in pharmaceutical logistics, drug lifecycle management, clinical trials, and personalized therapeutics. It emphasizes the emergence of connected ecosystems where devices, data, and decisions interact seamlessly to enhance drug delivery, traceability, and compliance. Chapters cover critical topics such as digital drug development, AI-enabled pharmacovigilance, remote patient monitoring, blockchain-secured supply chains, and ethical frameworks for digital pharma solutions. The book also addresses the challenges of interoperability, cybersecurity, and adapting to global healthcare standards. Key Features: Analyze advanced applications of AI and IoT across the pharmaceutical value chain Examine digital transformation in clinical trials, drug tracking, and e-prescriptions Present case studies on AI-driven decision support and blockchain in pharma logistics Discuss regulatory compliance, cybersecurity, and ethical considerations Explore future trends in connected healthcare and smart medication ecosystems.
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Veröffentlichungsjahr: 2025
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FOREWORD
In recent years, we have witnessed a profound transformation in the way healthcare is delivered, experienced, and managed largely driven by the convergence of emerging technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and advanced pharmaceutical innovations. The Digital Pillbox: Integrating AI, IoT, and Pharma Solutions (Part-II) enters this evolving landscape as a timely and essential contribution. Building on the foundational insights provided in Part I, this second volume offers an even deeper exploration of how intelligent systems and interconnected devices are redefining medication management and patient care. The inclusion of topics such as robotic automation, smart pillboxes, and the ethical and regulatory dimensions of digital health reflects a mature, holistic view of innovation not as an isolated advancement, but as an integrated system impacting every layer of modern healthcare.
What sets this work apart is its interdisciplinary approach. The contributing authors spanning fields from clinical practice to biomedical engineering and data science bring to the table a wealth of practical experience and forward-thinking analysis. Together, they explore not only what is possible today but also what must be considered as we design for tomorrow: inclusivity, security, sustainability, and above all, patient empowerment.
This book is more than a scholarly resource. It is a catalyst for collaboration and conversation among healthcare professionals, researchers, technologists, and policymakers. It challenges us to rethink traditional models of care and encourages the co-creation of systems that are not only smarter but also more humane.
As the healthcare industry continues to embrace digital transformation, the insights within these pages will prove invaluable. Whether you are an innovator seeking to bring new tools to market or a practitioner striving to improve patient outcomes, the Digital Pillbox (Part-II) offers both inspiration and guidance.
Let this volume be a touchstone for those shaping the next generation of healthcare—where technology and compassion converge to create a truly connected, patient-centered future.
PREFACE
The Digital Pillbox: Integrating AI, IoT, and Pharma Solutions (Part-II) explores the convergence of cutting-edge technologies shaping the future of healthcare and medication management. Building upon the foundation laid in Part I, this volume dives deeper into advanced topics such as robotic automation, smart pillboxes, ethical frameworks, and the integration of IoT across various pharmaceutical and healthcare platforms. It highlights futuristic trends, the societal impact of digital tools, and strategic approaches for building connected patient-centric systems. The chapters are authored by interdisciplinary experts who examine not only the technological benefits but also the ethical, regulatory, and operational challenges faced in real-world implementation. This book aims to serve as a comprehensive guide for healthcare professionals, technologists, researchers, and policymakers, providing insights into the potential of digital innovation to enhance patient outcomes, streamline care delivery, and transform global health systems. We hope this volume inspires thoughtful dialogue, practical integration, and sustained innovation in digital healthcare.
List of Contributors
Futuristic Trends in Medication Management
N. Anusha1,Akhil Sharma2,Akanksha Sharma2,Sunita3,Shaweta Sharma4,*Abstract
“Futuristic Trends in Medication Management” discusses the cutting-edge developments that are likely to bring forth a revolution in delivering, monitoring, and maximising medication in healthcare systems across the globe. This extensive chapter covers a range of innovations, such as state-of-the-art digital health platforms, precision medicine approaches individualised for each patient’s needs, advanced decision support systems driven by artificial intelligence, and robotic automation that is changing how dispensing and administration are done. Through detailed analysis of these emerging patterns, the chapter provides insight into how this can improve medication adherence and reduce drug errors while at the same time improving therapeutic effects and making healthcare delivery efficient. Furthermore, it critically examines various challenges and ethical concerns linked with integrating these futuristic technologies into conventional medical practices. Drawing from current research findings and expert opinions, this chapter presents an all-encompassing view of how future trends will impact medication management. It is indispensable to policymakers, researchers, and care providers grappling to make sense of the ever-changing, complex world of pharmaceutical care.
INTRODUCTION
The rapidly transforming healthcare scenario encapsulates a dynamic array of futuristic trends in medication management. Thus, these approaches and technologies can change how medications are prescribed, administered, and monitored. These trends represent a confluence of cutting-edge healthcare advancements with technology, promising to disrupt traditional practices and improve patient care outcomes. The advent of digital health platforms, personalised medicine strategies, AI-driven decision support systems, and robotic automation has opened up new possibilities for optimising medication management [1, 2].
A shift towards patient-centred care characterizes the changing landscape of healthcare, where treatments are more individualised based on specific needs and circumstances. A futuristic approach to medication management is precision medicine, which uses genetic and molecular data to customise therapies according to patients’ genomic makeup and biomarker profiles. Such an individualised treatment method enhances efficacy and ensures minimal side effects from drugs, improving patient outcomes [3, 4].
Artificial Intelligence (AI) has been crucial in developing future medication management by improving clinical decision-making. AI-powered decision support systems analyse large amounts of patient data, enabling doctors to make informed decisions concerning their treatments in real-time. Computer programs employing predictive analytics algorithms help forecast patients’ responses to medications while identifying possible drug interactions or deviations from expected outcomes, therefore prompting early intervention measures in addition to risk mitigation [5].
Another transformative trend is robotic automation, reshaping the practice of medication management. In healthcare settings, automated dispensing systems streamline medication distribution workflows, reducing errors and increasing efficiency. Robotic-assisted surgery helps enhance accuracy and precision during surgical procedures, leading to better surgical outcomes and patient recovery rates. It enhances precision while administering drugs, thus leading to better results following surgeries. This is through better administration techniques during surgeries like robotic-assisted surgery that have brought about positive changes. Through telepresence robots, remote patient monitoring allows health providers to keep track of their patients’ intake of medicines and vital signs outside hospitals [6].
Healthcare professionals, researchers, and policymakers must keep up with these technological breakthroughs to stay relevant in this field. Adapting to futuristic trends can help improve patient care, streamline operations, and reduce healthcare costs. However, it also creates problems related to data privacy, regulatory compliance, and equitable access to technology. Such trends drive creativity and cooperation between different departments, leading to better ideas on how medication management can be improved in the future and for everyone [7, 8]. Futuristic trends in medication management across various aspects are summarised in Table 1.
DIGITAL HEALTH PLATFORMS
Today's digital health platforms have become the foundation of new-age medication management, thus changing how healthcare is delivered massively. These platforms comprise different technologies and applications that improve patient outcomes, streamline workflow, and enhance communication between patients and their doctors. Digital health platforms include various systems to deliver health services, which are described below and summarised in Fig. (1).
Fig. (1)) Digital Health Platforms.PRECISION MEDICINE
The healthcare system has seen a complete change due to precision medicine, which is geared towards modifying medical treatment and interventions to suit each individual's genetic makeup, environmental factors, and lifestyle. Regarding medication management, precision medicine demonstrates great potential in optimising therapeutic outcomes, minimising adverse drug reactions, and improving patient satisfaction with better compliance.
Genetic Testing
Genetic testing involves analysing an individual’s DNA to detect differences or mutations that might affect their drug reaction. Genetic testing is vital in forecasting how patients will handle particular medications and if they are prone to reactions or treatment failure during medication management. By examining specific genes linked with drug metabolism and response, healthcare workers can tailor medication regimens to optimise efficacy and safety for every patient [24].
Identification of Genetic Markers
Identifying genetic markers enables health professionals to determine the genetic differences affecting drug metabolism, efficiency, or tolerability. Genetic markers include Single Nucleotide Polymorphisms (SNPs), gene replication or deletion, and other genetic variations well known to influence drug metabolism pathways. Such genetic tests help determine whether patients can respond positively to specific drugs [25].
Tailored Treatment Approaches
Medical professionals can design personalised treatment approaches that consider each patient's individuality using genetic tests and identification of genetic markers. For example, this could mean choosing drugs based on their metabolism efficiency or avoiding any genetic side effects. Furthermore, treatment plans can be changed to obtain optimal results and lower the chances of harmful consequences by adjusting drug amount, frequency, or duration [26].
Personalised Risk Assessments
Personalised risk assessments can be achieved with genetic testing by examining genetic factors that may heighten a patient’s vulnerability to some harmful drug events or treatment complications. Healthcare providers can anticipate and lessen such risks by selecting medicines, monitoring and educating the patients, and evaluating an individual’s predisposition toward certain medication-related risks. Personalised risk assessment helps healthcare professionals mitigate potential complications related to treatment and enhance patient safety and good outcomes [27].
Genetic testing primarily supports medication management by customising treatment approaches based on the genetic profile of each patient. Hence, personalised risk assessment, molecular profiling for therapy selection, and dose determination allow optimisation of medication choice, dosing, and monitoring for improved outcomes in every individual. Nevertheless, there are challenges like cost implications, accessibility issues, and ethical considerations that should be addressed so that all individuals will have equal access to genetic tests and personalised drug regimens [28].
Pharmacogenomics
Pharmacogenomics is the science of how a person's genetic structure influences how they respond to drugs. Pharmacogenomics studies an individual's genes, their effect on drug metabolism, and how this knowledge could optimise drug therapy for each patient [29].
Drug Response Prediction
Pharmacogenomics predicts how individuals will respond to medications based on their genetic constitution. This testing identifies the genetic variations associated with drug metabolising enzymes and drug targets that can help healthcare practitioners predict how patients will metabolise medications and anticipate therapeutic benefits and side effects [30].
Individualised Dosage Adjustments
Health practitioners make dosage adjustments using pharmacogenomics tests based on the genetic characteristics of individual patients. Health providers can adjust drug doses after understanding how genetic variations affect drug metabolism and response to maximise therapeutic outcomes while minimising adverse reactions. Medication dosing is adjusted individually for patients based on their physiological characteristics to get the most benefit with the least harm [31].
Reduction of Adverse Reactions
Pharmacogenomics has one of its main advantages in the potential to decrease the frequency of adverse drug reactions. There is a risk that pharmacogenomic testing can help find patients who are predisposed to side effects or poor treatment outcomes, so medical specialists prevent dangerous drugs and select alternative medications with better tolerance. This medicine management method helps reduce negative reactions, ensure patient security, and maximise general treatment results [32].
Pharmacogenomics signals a revolutionary change in medication management by offering personalised views on individuals' drug reactions based on their genetic data. It is possible that, through predicting drug responses, modifying dosing levels as per individual needs, and lowering unwanted events, pharmacogenomics may be instrumental in enhancing medication potency and safety for each respective client, resulting in improved therapeutic outcomes and increased patient satisfaction. Nevertheless, cost implications, availability issues, and incorporation into clinical practices must be solved to efficiently accomplish this aim while ensuring equal availability of personalised healthcare services for all persons [33].
Personalised Treatment Plans
Tailoring medical interventions is done in medication management for personalised treatment plans, which are based on various factors such as genetic makeup, lifestyle, preferences, and medical history of the individual patients. By considering these personalised factors, healthcare providers can optimise treatment efficacy and safety while minimising adverse effects [34].
Targeted Therapies
Targeted therapies are drugs that narrowly focus on particular molecular pathways or biological processes that cause ailment. Often, such therapies are designed to address a patient’s molecular properties like genetic mutations or biomarker expression profiles. Targeted therapies focus specifically on disease-causing mechanisms, thus offering more precise and efficient treatment with minimal off-target effects on healthy tissues [35].
Customised Medication Regimens
Customised medication regimens are when drug selection, dosage frequency, and administration routes are adjusted to cater to patients' needs. This customisation is based on age, body weight, renal function, liver function, other diseases, drug-drug interactions, and genetic variants in drug metabolism pathways. The customised medication regimen aims to enhance therapeutic outcomes while minimising adverse reactions and improving medication adherence [36].
Improved Efficacy and Safety Profiles
Improved efficacy and safety profiles in medication management result from personalised treatment plans, targeted therapies, and customised medication regimens. By developing individualised treatment approaches for every patient based on their unique characteristics and disease profile, healthcare professionals can optimize therapeutic outcomes and reduce the risk of adverse reactions or complications arising from treatment. By personalising this approach, patients’ quality of life can be improved with better outcomes while harm from the therapy is minimised [37].
Medication management as a form of personalised medicine involves personalised treatment plans, targeted therapies, customised medication regimens, and improved efficacy and safety profiles. It will, therefore, be possible for healthcare providers to adopt tailored treatments that suit the needs of each patient, thereby optimising therapeutic outcomes, improving adherence to medications, and enhancing patient satisfaction, leading to improved quality of life. However, several challenges must be addressed before practising personalised medicine, including integrating genetic data and biomarkers into clinical decision-making processes, access to targeted therapies, and interdisciplinary collaboration among healthcare providers [38, 39].
ARTIFICIAL INTELLIGENCE (AI)
Artificial Intelligence (AI) is basically the simulation of various human processes by machines, especially computer systems. AI is quite instrumental in medication management as it helps to analyze huge datasets, generate useful information and provide support for clinical decision-making to enhance the patient’s outcome.
AI-driven Decision Support
AI is employed in decision support systems that use machine learning algorithms to aid medical professionals in making assessments regarding medication management. In this regard, it considers various factors such as patient history, case studies, and any guidelines that may be there at a given time to ensure best practices are followed. AI-based decision support leads to improved drug selection, dose adjustments, and treatment plans by incorporating individualised information into the latest medical knowledge [40].
Healthcare providers receive real-time guidance and recommendations based on individual patient data and clinical evidence from Artificial Intelligence (AI)-backed Clinical Decision Support Systems (CDSS) [41]. The benefits of CDSS are summarised below in Fig. (2).
Drug Interaction Predictions
AI algorithms analyse patients’ medication lists, medical histories, and physiological parameters to predict possible drug-drug interactions. These AI-based systems enable healthcare providers to prevent adverse drug events and improve patient safety by identifying such interactions and associated risks. Forecasts on drug interaction help guide clinicians in deciding what medicine choice should be made on dosage adjustments while minimising adverse reactions related to therapy [42].
Treatment Recommendations
Artificial intelligence algorithms look at patient information, including their genetic data, pictures taken during diagnosis, and clinical records. This way, they can formulate personalized suggestions for treatment for each patient’s features and past medical history. These recommendations may involve choosing the right medicine, adjusting the dose, and developing a monitoring plan to enable the best therapeutic effect with minimum adverse reactions. AI-driven treatment protocols are useful in improving patient outcomes [43].
Fig. (2)) Benefits of CDSS.AI has played an important role in medication management through its CDSS, drug interaction predictions, and treatment recommendations made by AI-driven decision support systems for physicians. They can make decisions based on real-time data analysis by enabling clinicians with real-time insights, evidence-based recommendations, and decision support tools. By leveraging AI technology, healthcare providers can optimise medication selection, increase patient safety, and improve the overall quality of care provided within their facilities. However, addressing such challenges as algorithm transparency, clinician acceptance, and data privacy is important to ensure the responsible implementation of AI in medication management [43].
Predictive Analytics
Using data mining, statistical modelling, and machine learning algorithms in predictive analytics helps analyse past information to foresee future results or trends. Regarding medication management, predictive analytics can be used in different areas of healthcare delivery to predict patient needs, use resources optimally, and enhance clinical decision-making processes [44].
Forecasting Medication Adherence
Predictive analytics can predict adherence to medicines by analysing trends in patient behaviour, medication refill patterns, and other crucial data sources. Intervention strategies such as patient education, drug reminders, and compliance monitoring programs can improve medication adherence rates and optimise treatment outcomes by identifying non-adherent patients at risk [45].
Early Detection of Medication Errors
Patterns that suggest a possible error in medication administration, such as prescribing blunders, errors in distribution, or administration mistakes, can be detected using predictive analytics. By examining electronic health records, prescription orders, and medical administration records, predictive analytics algorithms may identify possible discrepancies or abnormalities that may indicate faults in the management of medicines. If medication errors are identified soon enough, healthcare professionals can intervene quickly and keep adverse incidents away while at the same time making patients safer [44].
Identifying High-Risk Patients
Patients at risk of medication-related complications, adverse drug events, or treatment failures can be identified through predictive analytics. Analysing their demographics, medical backgrounds, and clinical risks, predictive analytic algorithms will group patients into various categories based on the conditions in which they are likely to experience ill outcomes. This allows healthcare providers to identify high-risk patients and apply targeted interventions such as medication therapy management programs, care coordination, and patient monitoring efforts, thereby reducing risk and enhancing patient outcomes [46].
Predictive analytics provide useful insights and decision-support tools for healthcare providers in medication management by helping forecast medication adherence, facilitating early detection of medication errors, and identifying high-risk patients. Health organisations can improve patient safety and outcomes by using predictive analytics algorithms to optimise medication adherence and prevent medication errors. Nonetheless, there is a need to address challenges such as data quality, interoperability, and algorithm validation to guarantee the accuracy, reliability, and ethical use of predictive analytics in medicine [47].
Machine Learning Algorithms
Machine learning algorithms are a branch of AI that allows computers to learn from data and improve their performance without programming. Machine learning algorithms are used to analyse large datasets for medication management; this aids the identification of patterns and predictions and assists in clinical decision-making processes [48].
Through pattern recognition, predictive modelling, and continuous improvement, machine learning algorithms provide important decision-support tools for healthcare providers in medication management. The possibility of proactive interventions and personalised treatment options tailored for specific patients using patient-data analysis or risk assessment allows MLAs to detect hidden patterns in humans. Addressing such challenges as ethical considerations, model interpretability, and data quality will ensure the responsible use of machine learning technologies in medication therapy management [49].
ROBOTIC AUTOMATION
Medication management is being transformed by robotic automation with automated systems and technologies that streamline medication dispensing, packaging, labelling, and fulfilment processes. These advancements enhance efficiency, accuracy, and patient safety in healthcare settings [50].
Automated Dispensing Systems
Robotic technology is used in automated dispensing systems to dispense medication from pharmacies, hospitals, and other healthcare facilities accurately and efficiently. These automated systems are a way of eliminating the errors that come with manual drug dispensation processes. They store drugs securely and can be programmed to give exact or more accurate doses according to the patient’s needs, improving medication safety and workflow efficiency [51].
Robotic Pharmacies
Automated robot pharmacies are robotic pharmacies that apply robotics to manage and execute medicine orders. They store, fetch, pack, tag, and dispense medicines using robots to reduce human intervention in managing drugs. The work is improved at robotic pharmacies, minimising dispensing errors and making it easier for patients to get their medications by providing 24/7 automated services [52].
Medication Packaging and Labeling
Robotic automation systems automate the packaging and labelling of medication in healthcare facilities and pharmacies. Using robots, they package medications into unit-dose packages, blister packs, or other standardised packaging formats. The latter can also apply labels containing patient-specific information, dosage instructions, and barcode labels to ensure that medication is tracked correctly and administered properly [53].
Prescription Fulfillment
The prescription fulfilment process is made more efficient by robotic automation, which automates medication dispensing, labelling, and packaging tasks. Robotic prescription fulfilment systems use robots to process prescriptions, pick up drugs from automated dispensing systems, and make them ready for patient pick-up or delivery. These enhance efficiency regarding service quality, accuracy, and turn-around times regarding drug fulfilment, thus ensuring patients have access to medicines when needed [54].
Robotic automation in medication management, like automated dispensing systems, robotic pharmacies, medication packaging and labelling systems, and prescription fulfilment systems, has several advantages for healthcare providers and patients. Healthcare facilities can improve medication safety, workflow efficiency, and patient satisfaction using this technology while reducing medication errors and operational costs. However, the benefits of robotic automation in medication management are limited by several factors, including the high initial investment costs, integration with existing systems, and regulatory compliance requirements [55].
Robotic-Assisted Surgery
Robotic-assisted surgery is about using robots to help doctors conduct more precise and more controlled surgeries. Through these systems, surgeons can handle surgical instruments with increased precision and movement, leading to improved surgical practice, minimised risks, and minimised patient recovery period [56].
