Clinical trials work best when the right participants join on time and when trial sites can handle the study rules and patient care. In the U.S., about 80% of trials face delays because of slow patient recruitment. These delays often make studies take longer and cost more money. Many trials do not enroll enough participants on schedule, which slows down the approval of new treatments.
Choosing where to run a trial means looking at the quality of the site’s facilities, the makeup of nearby patients, the skills of the researchers, and the site’s past performance. Usually, people do this by checking old records, surveys, and personal opinions. This process takes a long time and can lead to bad choices that slow down the trial.
Medical practice managers and owners often find it hard to handle these tasks along with their daily work. IT managers face their own problems, like combining different sets of data, keeping patient information private following HIPAA rules, and making sure trial teams can work smoothly.
AI helps patient recruitment by automating how it finds who can join and choosing participants using large amounts of data. For example, Verana Health uses AI to look at real patient records and find people who fit specific trials. Their platform, Verana Trial Connect, follows privacy rules like HIPAA. It looks at both clear data, like diagnosis codes, and notes from doctors that people might miss. This cuts down paperwork and speeds up finding patients.
AI also predicts who is most likely to join by studying past patient information, how close people live to the trial site, and if they’ve joined trials before. Verana updates its data every month so recruiters have current information. This helps reduce the number of people who start but do not qualify and brings in more diverse and enough participants.
One good thing for healthcare managers is that AI platforms like Verana’s do not need tricky contracts or software installs. This makes it easier to use without disturbing existing computer systems. This helps busy trial sites in the U.S. start using AI quicker.
AI can quickly study large amounts of trial data to choose the best sites. Novartis uses an AI system that looks at information from over 460,000 trials, 700,000 sites, and 600,000 lead researchers. It scores sites based on facility quality, local patient numbers, and how well investigators perform.
In one test with 1,700 patients at many U.S. sites, sites with top AI scores recruited 2.7 times more Black or African American patients compared to others. Researchers with the best scores recruited 3.4 times more patients than average. This shows AI helps pick sites that are both effective and fair in including diverse people.
A tool called “Unified Ontology” acts like a smart digital model that mixes different data into one analysis. This makes site choices faster and more accurate, cutting selection time from months to minutes. This saves time for those managing trial resources.
AI also helps run trials better by predicting patient enrollment speeds, resource needs, and risks early on. PPD, part of Thermo Fisher, uses machine learning to guess how long it takes to start a study and how many patients will arrive. This helps organize resources to avoid slowdowns and keep trials moving.
These AI predictions can be right about 85% of the time. They also cut down inactive periods in trials by up to half. This makes studies shorter so new treatments can reach patients faster.
Thermo Fisher’s system uses deep learning and special data sets to plan trials, pick sites, and forecast patient sign-ups. Their SMART screening tech automatically checks patients’ records to see who qualifies. This saves time by not reviewing unfit patients and makes sites run smoother.
Using AI can cut trial costs by around 40%. It reduces delays, raises data quality, and automates simple tasks. This is helpful for managers with limited budgets running clinical trials.
The U.S. healthcare system wants clinical trials to include people from different backgrounds. AI helps by choosing sites and participants based on real data to include underrepresented groups better.
Novartis’s test with AI site selection showed nearly three times more enrollment of Black or African American patients. This means AI tools can find sites and researchers good at recruiting diverse groups.
This is important for managers who need to meet federal rules about fairness in trials and improve healthcare access for all.
Besides analyzing data, AI automates simple, repeated tasks in clinical trials. Automation uses software bots or AI helpers to handle scheduling, entering data, checking benefits, and answering questions.
Salesforce’s Agentforce for Health is one example. It has ready-to-use AI tools that handle phone calls and patient questions. It works with health record systems like athenahealth to check insurance and benefits fast. This speeds up approvals and lowers the paperwork load on staff.
Salesforce reports say 87% of healthcare workers often work late because of admin tasks, and 59% feel less happy about their jobs. AI helpers can save workers up to 10 hours a week on these tasks, which boosts productivity and staff mood. For instance, Rush University System for Health found that AI frees staff to focus on more complex patient needs.
AI automation also helps follow laws like HIPAA and CMS rules by safely managing patient data and approvals in real time.
IT managers find that AI works well with their current systems, making trial support easier and smoother.
Clinical trials face many risks like delays in finding patients, breaking rules, and poor site work. AI and machine learning help manage these risks by watching data constantly and alerting teams early.
Prediction models can see trends in patient signing up and spot sites that might have problems before issues happen. This lets managers fix problems early by changing how they recruit or moving resources.
For example, a Phase 3 rare disease trial used AI to collect real-time data from more than 18 sources to check study health. This helps teams act quickly to avoid costly delays.
AI also supports Risk-Based Monitoring, which sets key risk markers based on disease types and locations. This focuses efforts on patient safety and compliance while cutting unnecessary monitoring visits.
Keeping patients safe during trials is very important. AI helps by watching patient data all the time to catch bad reactions early, follow drug interactions, and predict safety issues.
AI-powered digital biomarkers can detect problems with nearly 90% accuracy. They provide near real-time alerts so clinical teams can act faster. This increases data accuracy in trials, which is important for approvals and building trust with participants.
Managers benefit because AI combines correct patient matching, risk checks, and data reviews in one system, which boosts trial quality overall.
Time Savings: AI cuts down manual work like patient screening and site choosing, giving staff more time for patient care and difficult trial tasks.
Cost Efficiency: AI can reduce trial costs by up to 40%, letting managers spend money better on care and quality.
Compliance and Security: AI platforms follow HIPAA rules, keeping patient data safe and following regulations.
Improved Trial Outcomes: Faster patient recruitment and smart site choices lead to better results and successful trials.
Operational Simplification: Automating work with AI reduces admin burdens, helps staff feel better, and improves trial site processes.
Increased Trial Diversity: AI helps find participants from underrepresented groups, helping meet government diversity requirements and making research more inclusive.
As healthcare in the U.S. changes, AI becomes an important tool for improving trials. Medical practice managers, owners, and IT staff need to learn about and use AI for recruiting patients and picking sites. Real-world examples from companies like Novartis, Thermo Fisher, Verana, and Salesforce show that AI works well.
Combining AI with automation makes operations smoother, lowers staff stress, and helps use resources better. This means faster trials, better patient care, and quicker availability of new treatments.
Healthcare groups that use these tools now will be ready to handle modern clinical research challenges, run trials more efficiently, and offer better results for patients across the United States.
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.
