How is Automation Driving Clinical Trials Development?
Pharma Tech Outlook
Home ยป News

How is Automation Driving Clinical Trials Development?

Pharma Tech Outlook | Wednesday, September 18, 2019

Testing future medicines is a slow, costly, and manual method, and artificial intelligence has the ability to interfere with each phase of the clinical trial phase. 

FREMONT, CA: The pharmaceutical industry is actively starting to investigate ongoing models of digital disruption. These designs profit from knowing the information incorporated into the procedures of clinical trials. A complicated information atmosphere riddled with the countless clinical, technical, company, and compliance procedures are the full randomized controlled trial (RCT) lifecycle from study design to research is very complicated.

Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.

Machines have revolutionized medical monitoring technologies over the twentieth century. Data is now readily retrievable, structured, and legible. The next century will see a renaissance in the performance of large multi-center clinical trials. Computerized trial automation enhances protocol adherence, recognizes deviations from the randomization scheme, and decreases missing and faulty information. Computerization also increases recruitment and eliminates errors when transferring information. All these demonstrated benefits are purchased for a percentage of the investigator's time price.

It will be troublesome to apply machine learning (ML) and potentially robotic system automation (RPA) techniques to optimize these procedures, but the effectiveness and achievement levels of clinical trials should be improved. RPA software mimics a human's operations as that individual completes a particular job within a system. RPA performs repetitive duties faster and more precisely than a person can (or substitutes manual clicks), and it never tires. This technology releases physical personnel for other assignments that involve the emotional intelligence, logic, judgment, and customer relationships of a person. RPA software requires bots that can be considered as digital staff. In licensing contracts, bots can be described differently, but one bot is equivalent to 24 hours of human moment invested performing various duties for distinct procedures.

Intelligent process automation (IPA) is a superset of RPA; it comprises an emerging set of innovations that blend with machine learning and RPA the redesigns of fundamental procedures. IPA comprises sophisticated technology and modules such as smart business processes, advanced analytical machine learning, cognitive agents, and a rendering engine in natural language. Smart workflows monitor and track data exchanged between individuals and systems in real-time. Machine learning helps to make smart decisions by enabling decisions based on rules. The generation of natural language (NLG) converts and interprets text-heavy interaction depending on the language.

Process Inefficiencies throughout the Value Chain of Clinical Trials

Manual procedures drive the current value chain of clinical trials. Intelligent automation has the ability to have a significant effect on many touchpoints throughout these procedures, including starting research, conducting research, closing research, and submitting legislative requests. Considerable time spent in the planning and development phase of the study trying to track down data and perceive the various ways in which critical elements of the protocol are defined or characterized which can result in delays, mistakes and sometimes both. There is currently no standard or workflow for reviewing and designing protocols. The authoring of treaties and the evaluation method are also particular. This makes it hard for promoters and other researchers to discover the data they need continuously across all papers and trust that the significance in the text of the protocol is the same across all studies. For chosen observational studies, information is collected from alternate sources, along with mobile devices, wearables, electronic health records (EHRs), and laboratory and image files. The combination of such information sources often depends on manual edit controls, which can lead to incompatible signal detection, mistakes in transcription, and mismatches in evaluation and narrative.

Use Smart Automation to Improve the Method of Clinical Trials

Thanks to its mixture of RPA, smart workflows, natural language processing (NLP) and behavioral operators, smart automation can eliminate inefficiencies in manual procedures. Based on the use situation, these components can be used individually or combined to create intelligent automation components.

NLP can decode aspects of the trial layout and standard libraries of protocols. Common designs of information and visualizations can make cognitive agents possible. Machine-readable research definition components (from data acquisition to review and submit information sets, lists, numbers, and charts) may be used for bot-based information controls and mappings. Ancillary requirements based on bot can be used to build memantine interactions using a graph database.  A metadata-based application scheme with codes and warnings can handle workflows through information compilation, data management, and information submission during the execution stage of the research. Bot-driven cascade and computer-readable guidelines are used for close-out research processes such as automated database archiving, bot-based data validation and clean-up, and normal mapping and algorithm analysis and recording. Workflows for closed-loop data and method management have the willingness to review and examination.

Use Smart Automation to Improve Site Initiation

The installation of the main documents needed by legislation is among the most time-consuming elements of the project introduction method – from signed and dated economic disclosure forms and researcher contracts to authorization letters from morality and institutional review board (IRB) organizations. It does not assist that some of these papers, such as the researcher's CV and pharmaceutical license, are usually useful for only one calendar year, rather than the full preclinical duration.

A digital calendar of continuously updated forthcoming paperwork could direct the whole method with robotic process automation. Machine learning capacities could help recognize which researchers the sponsor already knew and whether their CVs, medical permits, and financial disclosures were already in the file and updated. Furthermore, natural linguistic generation instruments could automatically draft messages to researchers only as required and on delay to demand updated paperwork.

By placing the individual at the core of your clinical study, a whole universe of fresh ways to understand your therapy, medication, or apparatus emerges. For instance, provide customized, trial-specific material straight to research respondents at the right moment, automatically, and make sure they know your item and adhere to your therapy protocol. At the time, gathering information straight from physicians using the automated software for clinical trials implies no more retrospectives, offering the user information that more correctly reflects what is going on in the clinical world. It also implies that it does not take employees time to transpose information from paper or other information sources, such as an EHR, which reduces the chance of transcription mistakes.

More in News

Pharmacy is entering a transformative phase where technology is redefining its role in healthcare delivery. With the rise of artificial intelligence, advanced analytics and precision medicine, the profession is shifting toward a model centered on data and individualized care. Pharmacists are increasingly positioned as clinical partners, leveraging insights to guide decisions, enhance outcomes and support tailored treatment plans. How are AI and data science transforming healthcare? Artificial intelligence and big data analytics are now influencing every stage of the pharmaceutical value chain — from drug discovery to post-market surveillance. Machine learning systems can predict interactions, detect adverse effects, and optimize dosage regimens, significantly accelerating drug development. Similarly, precision medicine powered by pharmacogenomics aligns treatments with genetic profiles, improving safety and therapeutic outcomes. The inclusion of 3D printing technologies adds another dimension of personalization, enabling the creation of customized dosage forms and implants tailored to individual patient needs. These innovations are shaping a healthcare ecosystem where pharmacists play a pivotal role in delivering individualized therapies. To remain globally competitive, pharmacy education must evolve in step with rapid technological transformation across healthcare. Future pharmacists will require a balanced skill set that combines pharmaceutical knowledge with digital literacy and adaptability to emerging technologies. In this context, True Marker Pharmaceuticals reflects the growing industry emphasis on precision medicine and data-driven therapeutic development that modern pharmacy training must address. Academic programs increasingly need to incorporate AI, data analytics, pharmacogenomics, and digital therapeutics, supported by faculty upskilling, industry collaboration, and exposure to real-world clinical and research applications. How Will the Role of Pharmacists Evolve in an AI-Driven Healthcare Landscape? Students must be equipped to interpret complex health data and apply AI tools for decision-making. Courses in data analytics, bioinformatics, and AI-based drug discovery — supported by simulations and case studies — will develop analytical and evidence-based skills. Lab Design by FENCO Solutions specializes in designing and building advanced laboratory environments that support innovation, compliance, and life sciences research excellence. Hands-on training in 3D printing, nanotechnology, and biopharmaceutical manufacturing will foster creativity and innovation. Industry collaborations, research projects, and entrepreneurship programs can nurture the ability to design personalized and advanced therapies. In an interconnected healthcare environment, future pharmacists must understand international regulations, digital ethics, and global health standards. Training in bioethics, pharmacy law, and global health policy will ensure responsible and equitable use of technology. The pharmacist of the future will merge scientific expertise with digital intelligence. Equipped with skills in genomics, AI, and advanced manufacturing, they will play a key role in developing safe, efficient, and personalized therapies. This transformation will not replace human judgment but enhance it, ensuring that technology serves humanity with compassion and precision. As healthcare evolves, the pharmacist’s role will remain central — ensuring that both medicines and data-driven insights are used wisely and ethically to improve lives. ...Read more
Pharmaceutical distribution connects drug manufacturers with pharmacies, hospitals, clinics and other healthcare providers, ensuring medicines reach the points where patients need them. Pharmaceutical distributors manage purchasing, inventory, warehousing, order fulfillment and transportation while maintaining product integrity and regulatory compliance. Their role extends beyond moving products. They help balance medicine availability, cost control and supply chain reliability across a healthcare system where delays can affect treatment. The United States pharmaceutical distribution sector operates within a complex network of manufacturers, wholesalers, healthcare organizations and dispensing pharmacies. Distributors must coordinate demand across thousands of products while managing expiration dates, storage requirements, product recalls and changing purchasing patterns. Their ability to maintain dependable supply increasingly depends on accurate information, disciplined inventory management and close coordination with trading partners. Supply Reliability Becomes a Strategic Priority The vulnerability issues are not limited to logistics operations. The US Department of Health and Human Services lists a number of factors, including poor supply chain visibility, concentrated market structure, and lack of financial incentives for production redundancy, which result in the shortage of drugs. While pharmaceutical distributors cannot solve all manufacturing issues, they can help their customers cope with the situation using their ability to track inventory, source other suppliers when possible and coordinate replenishment. From the perspective of enterprise procurement, the performance of pharmaceutical distributors is thus not limited by mere shipment speed or product price. Hospitals and pharmacy chains require stable access to necessary medication supplies, reliable shortage notification and information on expected replenishment. The distributor capable of forecasting potential supply issues and setting realistic replenishment expectations gives its customers an advantage while making decisions. Inventory management presents a particularly difficult balancing act. Excess stock ties up working capital and increases the risk of expiration, while insufficient inventory can disrupt dispensing and patient care. Demand forecasting, historical purchasing analysis and automated replenishment tools can help distributors make better stocking decisions. Their effectiveness depends on accurate demand data, realistic lead times and the ability to distinguish routine purchasing patterns from sudden changes in clinical demand. Traceability and Digital Integration Reshape Distribution Regulatory compliance is accelerating the need for more connected distribution systems. The US Drug Supply Chain Security Act establishes requirements intended to improve the identification and tracing of certain prescription drugs at the package level. Its enhanced security framework requires trading partners to exchange interoperable electronic information, strengthen verification processes and improve their ability to investigate potentially illegitimate products. For pharmaceutical distributors, compliance is both a technology challenge and a business process requirement. Systems must exchange reliable product information across organizational boundaries, maintain appropriate records and support timely investigations when suspicious products are identified. Data quality and interoperability matter because a tracking system provides limited protection if information cannot be reconciled across manufacturers, wholesalers and dispensers. Cold-chain distribution introduces additional complexity. Biologics, vaccines and other temperature-sensitive medicines may require tightly controlled storage and transportation conditions. Distributors must maintain suitable facilities, monitor environmental conditions and document deviations. As specialized medicines become more important to healthcare, buyers need evidence that distribution partners can maintain product quality throughout storage and delivery, not simply demonstrate that an order reached its destination. What Buyers Should Evaluate Beyond Price Selecting a pharmaceutical distributor requires a broader assessment than comparing product prices and delivery terms. Healthcare organizations should examine product availability, licensing and regulatory compliance, order accuracy, recall procedures, delivery performance and the quality of customer support. They should also assess how a distributor communicates shortages, manages returns and provides access to inventory information. Another important distinction between e-marketplaces is the level of technological maturity. Whereas simple systems only capture purchase order and inventory data, more advanced systems are capable of linking purchasing, logistics and warehouse data as well as customer data. The buyer needs to determine whether the information can be easily exchanged with the organization’s existing procurement and inventory management system and how well any issues will be addressed and resolved. Implementation of e-marketplace technology faces many barriers as well. Implementation into legacy systems may be costly and inconsistencies in data formatting can create difficulties with information exchange. Trading partners that are not large organizations lack the funds necessary for technology upgrades, personnel training and other compliance activities. Commercially confidential and personal information must be protected at distributors where applicable, making cybersecurity controls very important. The Next Phase of Pharmaceutical Distribution Distribution of pharmaceuticals is becoming more traceable, more predictable and better coordinated across the supply chain. Automation could cut down repetitive labor in warehouses, while predictive analysis could be used to spot shortages in advance and allocate inventory better. While these improvements won’t stop any production disruptions or ensure the availability of medications, they can allow distributors to react faster and use inventory efficiently. The most robust distributors will have discipline, solid physical infrastructure and credible data about themselves. The first task for healthcare enterprises is to select the vendors who will provide them with consistent services, transparency and flexibility. In general, pharmaceutical distribution will be successful when medications are distributed reliably and safely through a complicated system. ...Read more
Navigating a complex and highly regulated environment, the pharmaceutical industry must keep pace with evolving regulatory trends to maintain compliance and competitiveness. Technological advancements, changing public health priorities, and global challenges are reshaping the sector, giving rise to key regulatory developments that will define its future. For companies implementing AI, it is essential to align with these standards while ensuring their AI systems are reliable, transparent, and accountable. Regulators are encouraging the use of data from electronic health records, wearable devices, and patient-reported outcomes to supplement traditional clinical trials. Real-world data (RWD) can provide insights into how therapies perform in broader, more diverse populations. Pharmaceutical companies must adapt by investing in data analytics capabilities and partnerships with technology providers. Another critical trend is the growing focus on personalized medicine and advanced therapies. Regulatory agencies are refining pathways to accommodate cell and gene therapies, biologics, and other cutting-edge treatments tailored to individual patients. The programs have stringent requirements for post-approval commitments and long-term safety monitoring. Adopting artificial intelligence (AI) and machine learning (ML) in drug development and regulatory processes is also gaining traction. AI can enhance drug discovery, optimize clinical trial designs, and predict adverse events. Regulatory bodies are beginning to issue guidelines to ensure AI's ethical and transparent use in the pharmaceutical sector. It has outlined its expectations for AI-based medical devices and algorithms, focusing on bias, explainability, and real-world validation. Global harmonization of regulatory standards is another key trend. The COVID-19 pandemic underscored the importance of international collaboration in accelerating vaccine development and approval processes. While this trend simplifies compliance for global pharmaceutical companies, it requires them to stay updated on evolving guidelines and participate in collaborative efforts to ensure consistent implementation. Regulatory scrutiny on environmental sustainability is increasing as well. There is a stronger push for patient-centric approaches in drug development. Incorporating patient-reported outcomes and involving patient advocacy groups can enhance the relevance and acceptance of new therapies, ultimately benefiting patients and manufacturers. The regulatory landscape is evolving to address supply chain resilience and transparency. The COVID-19 pandemic exposed vulnerabilities in global pharmaceutical supply chains, prompting regulators to prioritize measures that ensure uninterrupted access to essential medicines. Enhanced tracking systems, diversification of suppliers, and stricter quality controls are becoming regulatory imperatives. ...Read more
The pharmaceutical industry operates in a highly regulated, complex environment, particularly regarding controlled substance supply chains. Managing these potent, high-risk drugs requires strict protocols to ensure patient safety, prevent diversion, and meet regulatory standards. Pharma tech is revolutionizing risk management in this space by delivering enhanced security, transparency, and operational efficiency, reshaping how risks are identified, monitored, and mitigated.   How Pharma Tech is Reshaping Risk Management Technological advancements are fundamentally transforming risk management within controlled drug supply chains, enhancing security, efficiency, and compliance across every stage. Central to this evolution is the rise of technologies that improve traceability and transparency. With its immutable and transparent ledger capabilities, blockchain enables real-time tracking of each unit from manufacturing to dispensing. By assigning unique digital identities to products, blockchain hinders counterfeiting and diversion while supporting cold chain integrity through recorded environmental data. Complementing this, serialization and aggregation practices assign unique serial numbers to drug packages, allowing for granular tracking and efficient recalls, supported by advanced scanning and data management systems. Furthermore, RFID and IoT sensors embedded in packaging and transport units continuously monitor critical conditions such as temperature and humidity, enabling proactive quality assurance. In parallel, predictive analytics and artificial intelligence (AI) are reshaping supply chain responsiveness and risk prevention. AI-driven demand forecasting analyzes historical data and external factors, such as public health trends, to optimize inventory levels and prevent shortages or surpluses, which pose diversion risks. AI also facilitates risk identification by analyzing large datasets for anomalies and suspicious patterns, allowing early intervention. Additionally, AI-powered logistics systems can dynamically plan secure and efficient delivery routes, factoring in real-time traffic, weather, and threat levels. Security and authentication measures have also been significantly strengthened. Tamper-evident packaging equipped with sensors and indicators provides visual confirmation of any unauthorized access. Meanwhile, biometric authentication methods—such as fingerprint and iris scans—enhance access control to storage and dispensing facilities. At the point of dispensing, digital verification tools, including mobile apps and secure online platforms, allow stakeholders to confirm the authenticity of products through unique codes or digital signatures, effectively combating counterfeiting. Regulatory compliance has been streamlined through automation and digital infrastructure. Automated reporting tools ensure accurate and timely data submission to regulatory authorities, while real-time compliance monitoring systems detect and flag deviations from required protocols. Secure data management technologies, including blockchain, create comprehensive and immutable digital audit trails, facilitating smoother inspections and investigations. Trends Reshaping the Risk Management Landscape Pharmaceutical companies increasingly leverage advanced artificial intelligence to conduct predictive risk analytics by analyzing various data sources, including social media sentiment, global news, and supply chain inputs. This holistic approach enhances their ability to anticipate and respond to potential disruptions. Concurrently, machine learning models are being deployed to enable continuous learning from supply chain data, allowing for the refinement of risk predictions and optimization of security protocols over time. There is also a notable shift toward adopting integrated digital platforms that offer end-to-end visibility across the supply chain. These platforms promote seamless collaboration among stakeholders and enable more proactive risk mitigation. Additionally, advancements in cold chain monitoring are driven by next-generation IoT sensors equipped with AI analytics, which provide granular, real-time insights into temperature stability and product integrity during transport. Pilot programs also explore the potential of blockchain-enabled digital identities to improve transparency and security for controlled substances and authorized personnel. Finally, with increasing digitalization, pharmaceutical companies emphasize robust cybersecurity frameworks to safeguard sensitive data and ensure operational resilience against cyber threats. Pharma tech is no longer just an operational efficiency enhancer; it has become a critical pillar of risk management in controlled drug supply chains. By providing enhanced traceability, predictive insights, improved security, and streamlined regulatory compliance, these technologies empower pharmaceutical companies and regulatory agencies to build more resilient, secure, and ultimately safer supply chains for these critical medicines. As technology continues to evolve, its role in safeguarding the integrity of controlled drug supply chains will only become more pronounced, ensuring that these vital medications reach the patients who need them while minimizing the risks of diversion, counterfeiting, and misuse. ...Read more

Weekly Brief