Clinical research helps create new medical treatments and improve patient care in the United States. But clinical trials often take a lot of time, work, and resources to finish. Recently, artificial intelligence (AI) has started to help improve many parts of clinical research. These include finding the right participants, picking trial sites, and handling adverse events. These changes help make trials faster, more efficient, and less expensive. They also help patients get better results. This article explains how AI is used in these areas and its effects on medical practice administrators, owners, and IT managers.
One big challenge in clinical trials is quickly finding people who qualify to join. In the past, this meant people had to look through patient records by hand and spend a lot of time screening candidates. AI changes this by scanning large amounts of data from electronic health records (EHRs), patient lists, and digital signals to find suitable candidates faster.
AI uses prediction models to rank patients based on how likely they are to join the study. These models review data like lab tests and diagnoses, and also analyze clinical notes using natural language processing (NLP). NLP turns written notes into searchable information, which helps reduce the time needed to enroll patients and lowers the number of failed screenings.
Finding eligible patients faster helps clinical trials start sooner. This allows patients to get new treatments more quickly. AI also helps reach out to groups that are often left out of trials. Including more diverse participants is important for medical administrators and researchers. It makes sure trial results better represent the whole patient population.
Choosing the right sites for clinical trials is important for smooth research. AI helps by looking at feasibility questionnaires and data about site capacity. It scores possible sites based on their clinical experience, patient groups, and logistics. This leads to better site choices based on data.
AI also helps improve clinical trial protocols. Researchers can use AI to run “what-if” simulations that test different rules for who can join, goals of the study, and sample sizes. This helps predict how these choices affect trial results before the final design is set. AI can also create and improve trial protocols, suggest flexible designs that can change during the trial, and recommend changes in sample size or grouping based on past data.
These improvements lower the chance of costly changes during the trial and increase the chance of success. For healthcare IT managers, this means fewer problems and easier resource planning. For medical administrators, better protocols help patients follow the study plan and improve the quality of data, which is important for meeting regulations.
Watching patient safety during trials is a difficult and ongoing job. AI helps by automatically looking for unusual signs in various data like lab tests, imaging, biomarkers, and patient reports. This allows researchers to spot safety issues quickly and take action fast.
AI also helps sort and prioritize adverse events by how serious and urgent they are. Automating reporting and documentation makes it easier to follow rules and respond early. This supports patient safety and keeps trials running well.
Automating event triage lowers the amount of paperwork for research staff. They can spend more time on medical decisions instead of paperwork. This helps reduce stress and improve job satisfaction among healthcare workers who often handle many administrative tasks.
Apart from clinical tasks, AI also helps automate many routine jobs in clinical research. Tasks like scheduling, checking patient eligibility, managing data, and verifying benefits can be done automatically by AI agents. This saves a lot of time and improves efficiency.
For example, AI can:
These automations reduce paperwork for healthcare workers and research coordinators. This lets them focus on patient care and medical decisions.
Research shows healthcare workers spend many extra hours on paperwork. AI can save up to 10 hours per week for these teams. This may help them feel better about their jobs, which is important for medical practice managers who want to reduce staff burnout.
A key point for medical administrators and IT managers is that AI tools must follow strict privacy and regulatory rules. Some platforms, like Salesforce’s Agentforce for Health, show it is possible to use AI while meeting important health data laws. These platforms work within HIPAA-ready frameworks and meet CMS Interoperability rules.
Such AI tools give real-time access to patient insurance coverage, prior authorizations, and demographic data in a secure way. This keeps operations clear and trustworthy. Following these rules is very important to keep patient trust and follow federal health laws.
Many healthcare groups in the U.S. have seen benefits from AI in clinical research and care. Though some AI tools are specialized, their approaches can be used broadly by hospitals and clinics involved in research.
These examples show AI can reduce bottlenecks and help clinical teams give better care while managing complex research studies.
Even though AI has many benefits, there are challenges. Problems with data quality, data coming from many sources, and bias in AI models can affect fairness and accuracy. Also, AI results are sometimes hard to understand. That is why clinical experts must review and approve AI decisions.
The human-in-the-loop method combines AI automation with expert clinical judgment and knowledge of regulations. This method makes sure AI is used ethically and keeps trials safe, reliable, and accurate. It shows AI is a helper, not a replacement, for human professionals.
AI is changing clinical research in the United States in many ways. It improves participant matching, site selection, study design, adverse event handling, and routine workflows. These changes help trials finish faster, recruit better, and keep patients safer. AI also helps healthcare teams reduce paperwork. Understanding and using AI tools can help medical practice administrators, owners, and IT managers run clinical research more efficiently.
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.
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