Clinical research helps create new medical treatments and improve patient care. But running clinical trials can take a long time, cost a lot of money, and be very complicated. Problems like finding the right participants, picking good sites, and keeping patients safe often cause delays. In the United States, these problems slow down how fast new treatments reach patients.
Artificial intelligence (AI) is becoming a helpful tool to solve these problems. AI can make clinical trials faster, cheaper, and safer. It is especially useful for matching participants, selecting sites, and managing safety issues. Medical practice managers, owners, and IT staff need to understand how AI fits into their work to use it well.
One major problem in clinical trials is finding and keeping the right participants. About 37% of trial delays happen because recruiting takes too long. This delay can cost millions. Old methods use manual review of patient records and doctor referrals, which take a lot of time and effort.
AI helps by quickly searching large amounts of data like electronic health records, genetic information, and demographics to find patients who fit the trial rules. For example, the National Institutes of Health’s AI tool, TrialGPT, can cut the time doctors spend screening patients by 40% without losing accuracy. This helps bring in the right participants faster.
AI also helps keep participants involved. It sends reminders and notifications to patients at the right times. This lowers dropout rates and improves the data quality. Using mobile apps, telemedicine, and online consent makes it easier for patients to stay connected and follow the trial rules.
In the U.S., where healthcare is divided across different providers, AI tools help find eligible patients even if they see different doctors or live far away. This is important because trials need participants from many backgrounds as they grow more complex.
Choosing good sites for clinical trials is as important as picking the right participants. The site affects how fast patients enroll, how good the data is, and whether all rules are followed. Traditional ways look at past site performance, expert opinion, and limited location data, but this can miss newer or less busy sites that can do well.
AI looks at many factors, such as how many patients live nearby, the site’s past research results, available facilities, and logistics. For example, Novartis uses AI models to predict the best sites, making trials run more smoothly and on time. These models can test different scenarios to find the best locations.
In the U.S., health care setups differ a lot by region. AI helps sponsors reach sites outside big cities, including smaller towns and rural areas where potential patients live. This supports federal goals to make trials more diverse and reachable for all people.
AI streamlines paperwork and resource planning for administrators and IT managers. Sites chosen with AI usually enroll more patients and keep them longer. This helps the trial succeed overall.
Patient safety is very important in clinical trials. Adverse events (AEs) are things like side effects or problems that happen to participants. Detecting and managing these quickly is key for trial safety and following rules.
AI systems can read and analyze patient reports and clinical notes in real time. For example, Bayer uses an AI system that can review up to 10,000 AE reports every day and respond in less than 200 milliseconds. This helps safety teams act faster and reduces delays from manual reports.
AI can also predict possible adverse reactions before they happen by using genetic and clinical data. This helps doctors choose safer treatments for each person. For example, Watson for Oncology gives doctors advice based on patient data and genetics.
In the U.S., clinical research must follow strict FDA and HIPAA rules. AI helps by automating AE reporting and flagging cases needing human checks. Better safety monitoring lowers risks for patients and improves the quality of research data.
Besides helping with participants, sites, and safety, AI also automates many routine tasks in clinical research. This helps administrators, practice owners, and IT staff work more efficiently.
AI can connect with medical records, billing, and scheduling systems to simplify work. For example, it can check patient eligibility, verify insurance benefits, and get prior approvals quickly. Systems like athenahealth and Availity let staff confirm coverage fast, reducing back-and-forth communication.
AI chatbots and digital helpers can answer patient questions 24/7 and send reminders. This means staff spend less time on basic tasks. Patients get information on appointments, medicine use, and trial updates, which helps them stay involved.
AI-based trial management software can create regulatory documents, track compliance, and check data accuracy. This lowers errors and workload, letting clinical teams focus on patient care and research.
In the U.S., where health care providers often have fewer staff and more paperwork, AI automation helps keep operations running smoothly. IT managers can use AI systems that connect different workflows and keep data safe under HIPAA and other rules.
The U.S. leads the world in AI use for clinical trials because of strong pharma research, an aging population with chronic illnesses, and good health care infrastructure. North America holds the largest market share in this technology. Most focus is on cancer, heart disease, and autoimmune illnesses.
The market for AI in clinical trials is growing fast, at about 19% per year, and could reach roughly $21.79 billion globally by 2030. This growth comes from more use of deep learning for medical images, AI for keeping data consistent, and language models for writing and summarizing trial documents.
The FDA supports AI with guidelines, trial programs, and AI tools for reviewing scientific info. This helps speed up approval and acceptance of AI in trials. Medical organizations across the country are adopting these technologies.
Big drug companies like Roche, Pfizer, and Novartis use AI in their trials. Pfizer’s quick COVID-19 vaccine work showed how AI can help speed up drug discovery and find suitable participants in a short time.
These cases show that AI can help clinical trials run better and improve care and staff satisfaction.
Even with benefits, using AI in clinical trials has challenges that administrators and IT staff should consider:
With careful planning, these challenges can be managed, and the long-term benefits for clinical trials are worth it.
Artificial intelligence is becoming an important part of improving and speeding up clinical trials in the United States. It helps automate participant matching, pick better sites, manage safety issues well, and streamline clinical workflows. This helps medical practice managers, owners, and IT workers overcome common obstacles.
AI use is growing thanks to market demand and regulatory support. Handling data quality, privacy, and ethical concerns will be key as the technology grows.
The U.S. health system can gain a lot with faster trials, lower costs, safer patient care, and happier staff. This will help bring new treatments to patients more quickly while keeping high standards.
Agentforce for Health is a library of pre-built AI agent skills designed to augment healthcare teams by automating administrative tasks such as benefits verification, disease surveillance, and clinical trial recruitment, ultimately boosting operational capacity and improving patient outcomes.
Agentforce automates eligibility checks, provider search and scheduling, benefits verification, disease surveillance, clinical trial participant matching, site selection, adverse event triage, and customer service inquiries, streamlining workflows for care teams, payers, public health organizations, and life sciences.
Agentforce assists in matching patients to in-network providers based on preferences and location, schedules appointments directly with integrated systems like athenahealth, provides care coordinators with patient summaries, runs real-time eligibility checks with payers, and verifies pharmacy or DME benefits to reduce treatment delays.
Agentforce helps monitor disease spread with near-real-time data integration from inspections and immunization registries, automates case classification and reporting, aids epidemiologists in tracing outbreaks efficiently, and assists home health agencies in cost estimation and note transcription.
Agentforce speeds identification of eligible clinical trial participants by analyzing structured and unstructured data, assists in clinical trial site selection with feasibility questionnaires and scoring, automates adverse event triage for timely reporting, and flags manufacturing nonconformances to maintain quality.
According to Salesforce research, healthcare staff currently work late weekly due to administrative tasks. Agentforce can save up to 10 hours per week and is believed by 61% of healthcare teams to improve job satisfaction by reducing manual burdens while enhancing operational efficiency.
Agentforce integrates with Salesforce Health Cloud and Life Sciences Cloud, utilizing purpose-built clinical and provider data models, workflows, APIs, and MuleSoft connectors. It leverages a HIPAA-ready platform combined with Data Cloud and the Atlas Reasoning Engine for real-time data reasoning and action.
Agentforce operates on a HIPAA-ready Salesforce platform designed with trust and compliance at its core. It meets CMS Interoperability mandates and ensures secure, compliant real-time data exchanges among providers, payers, and patients.
Agentforce integrates with EMRs like athenahealth, benefits verification providers such as Infinitus.ai, payer platforms like Availity, and ComplianceQuest for quality and safety, enabling real-time data retrieval, eligibility verification, prior authorization decisions, and adverse event processing.
Features like integrated benefits verification, appointment scheduling, provider matching, disease surveillance enhancements, home health skills, and HCP engagement are planned for availability through 2025, expanding AI-driven automation in healthcare services and trials for broader real-time operational support.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
