{"id":30922,"date":"2025-06-21T09:15:03","date_gmt":"2025-06-21T09:15:03","guid":{"rendered":""},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-30T00:00:00","slug":"the-role-of-advanced-technology-in-streamlining-clinical-trials-and-improving-efficiency-in-oncology-research-3689087","status":"publish","type":"post","link":"https:\/\/www.simbo.ai\/blog\/the-role-of-advanced-technology-in-streamlining-clinical-trials-and-improving-efficiency-in-oncology-research-3689087\/","title":{"rendered":"The Role of Advanced Technology in Streamlining Clinical Trials and Improving Efficiency in Oncology Research"},"content":{"rendered":"<p>Oncology trials involve a lot of data and can be very complex. For example, in a Phase III study, about 10,000 data points are collected for each patient. This number grows when genomic and digital health data are added. Handling this large amount of data causes several problems:<\/p>\n<ul>\n<li><b>Duplicative Data Entry:<\/b> More than half of the clinical trial data points are manually re-entered after being recorded in hospital records. This wastes time and can lead to errors.<\/li>\n<li><b>Administrative Burden:<\/b> Clinical research coordinators (CRCs) spend much of their time entering data, fixing errors, and meeting regulations. A study at Memorial Sloan Kettering Cancer Center showed that solving data questions can take up to half of CRCs\u2019 time, leaving less time for patient care and trial work.<\/li>\n<li><b>Cost and Time Pressure:<\/b> Manual work increases the cost of trials. About 20% of clinical trial expenses are related to duplicate data entry and checking. Big oncology trials can cost millions and often take a long time, delaying treatments from reaching patients.<\/li>\n<\/ul>\n<p>These problems make it important for hospitals, research centers, and sponsors to find better methods to manage trials without losing data accuracy or patient safety.<\/p>\n<h2>Integration of EHR and Electronic Data Capture Systems<\/h2>\n<p>One helpful technology in oncology trials combines electronic health records (EHR) with electronic data capture (EDC) systems. This uses eSource technology to transfer patient data automatically from medical records to trial databases. This removes the need to enter the same data twice.<\/p>\n<p><!--smbadstart--><\/p>\n<div class=\"ad-widget case-study-ad\" smbdta=\"smbadid:sc_9;nm:UneQU319I;score:1.6099999999999999;kw:medical-record_0.98_record-request_0.95_record-automation_0.89_patient-data_0.63_data-retrieval_0.57;\">\n<h4>Automate Medical Records Requests using Voice AI Agent<\/h4>\n<p>SimboConnect AI Phone Agent takes medical records requests from patients instantly.<\/p>\n<div class=\"client-info\">\n    <!--<span><\/span>--><br \/>\n    <a href=\"https:\/\/simbo.ai\/schedule-connect\">Start Building Success Now \u2192<\/a>\n  <\/div>\n<\/div>\n<p><!--smbadend--><\/p>\n<h2>Example: Flatiron Health and the University of Colorado Cancer Center Partnership<\/h2>\n<p>Flatiron Health worked with the University of Colorado Cancer Center and UCHealth to link EHR data with EDC systems. Their tool, Clinical Pipe\u2122, connects these systems and is used at more than 85 medical centers and cancer sites across over 300 locations in the U.S.<\/p>\n<p>With this connection:<\/p>\n<ul>\n<li>Clinical teams moved 11,342 data points for 955 case report forms, completing each in about 37 seconds.<\/li>\n<li>Manual and error-prone data entry dropped greatly, making workflow smoother.<\/li>\n<li>Healthcare workers spent more time with patients instead of on paperwork.<\/li>\n<li>The work helped speed up studies and allowed more cancer trials to take place.<\/li>\n<\/ul>\n<p>Dr. Christopher Lieu, Associate Director of Clinical Research at CU Cancer Center, said this technology supports good care while managing data well. Emily Akin from Flatiron Health added that Clinical Pipe\u2122 lessens the workload on trial teams and makes trials easier to run.<\/p>\n<p><!--smbadstart--><\/p>\n<div class=\"ad-widget checklist-ad\" smbdta=\"smbadid:sc_21;nm:AOPWner28;score:1.87;kw:data-entry_0.98_insurance-extraction_0.94_ehr_0.89_sm-process_0.78_form-automation_0.72;\">\n<div class=\"check-icon\">\u2713<\/div>\n<div>\n<h4>AI Call Assistant Skips Data Entry<\/h4>\n<p>SimboConnect extracts insurance details from SMS images &#8211; auto-fills EHR fields.<\/p>\n<p>    <a href=\"https:\/\/simbo.ai\/schedule-connect\" class=\"download-btn\"> Start Building Success Now <\/a>\n  <\/div>\n<\/div>\n<p><!--smbadend--><\/p>\n<h2>Impact on Medical Practice Administration<\/h2>\n<p>Medical administrators and IT managers must update their IT systems to support EHR-EDC integration. Important steps include:<\/p>\n<ul>\n<li>Making sure hospital systems can work with research platforms.<\/li>\n<li>Training staff on new workflows.<\/li>\n<li>Managing data privacy and following regulations during data transfers.<\/li>\n<\/ul>\n<p>These changes help use resources better, lower errors, shorten trial times, and may reduce costs.<\/p>\n<h2>Automation and Cost Reduction through eSource Technology<\/h2>\n<p>Oncology trials include many repeated tasks like checking patient eligibility, collecting data, fixing errors, and verifying documents. Research at Memorial Sloan Kettering Cancer Center shows these tasks take thousands of staff hours. For example, in a trial with 10 patients, manual data entry alone can take around 5,000 hours.<\/p>\n<p>Using eSource technology automates many tasks:<\/p>\n<ul>\n<li>Site staff time drops by over half by automatically capturing data from EHRs into trial databases.<\/li>\n<li>Duplicate data entry stops, saving about $150,000 per study site.<\/li>\n<li>Databases lock faster because less time is needed for data checks.<\/li>\n<\/ul>\n<p>Clinical Research Coordinator Gynet Santiago said that before, searching for patient data took a lot of time and was hard. Automation lets her focus more on patients, making trials better and faster.<\/p>\n<h2>Wider Adoption and Clinical Trial Operations<\/h2>\n<p>Technology use is growing in US oncology research. Companies like Medidata and Advarra offer digital platforms that improve data sharing, quality control, and efficiency. Medidata supports over 30,000 trials with 9 million volunteers, showing the wide use of technology in research.<\/p>\n<p>Advarra helps oncology trials with:<\/p>\n<ul>\n<li>Special regulatory reviews for complex treatments like CAR-T and mRNA therapies.<\/li>\n<li>Clinical trial management systems that improve tracking and scheduling.<\/li>\n<li>Expert staffing and consulting to reduce the work on trial sites.<\/li>\n<\/ul>\n<p>These tools help reduce delays, improve following rules, and keep patients safe during research.<\/p>\n<h2>AI and Workflow Automation: Accelerating Trial Efficiency and Accuracy<\/h2>\n<p>Artificial intelligence (AI) and automation are becoming important in clinical research. AI programs and machine learning can analyze data faster and better than people, helping researchers make quick decisions and catch problems early.<\/p>\n<h2>Applications of AI in Oncology Clinical Trials<\/h2>\n<ul>\n<li><b>Data Quality Monitoring:<\/b> AI checks trial data as it comes in to find problems, missing info, or rule breaks, speeding up error fixing.<\/li>\n<li><b>Patient Recruitment and Retention:<\/b> AI looks at patient data to find eligible people faster, helping with recruitment and diversity.<\/li>\n<li><b>Predictive Analytics:<\/b> AI models forecast treatment results early, allowing trials to adjust and speed up promising treatments.<\/li>\n<li><b>Automated Data Extraction:<\/b> AI tools read notes from doctors to pull needed info, cutting down on manual review.<\/li>\n<\/ul>\n<p>Flatiron Health uses AI and machine learning to link systems and improve cancer care decisions with real-world data.<\/p>\n<h2>Benefits of AI and Automation for Healthcare Administrators<\/h2>\n<ul>\n<li>AI reduces staff workload by automating repetitive and error-prone tasks.<\/li>\n<li>Automation helps follow study rules consistently.<\/li>\n<li>Technology helps grow trial operations without needing much more staff or cost.<\/li>\n<li>Data insights help managers make better decisions about trials and resources.<\/li>\n<\/ul>\n<p>This technology progress is important in places like Colorado and nearby states served by groups like University of Colorado Cancer Center and UCHealth, where clinical trials are part of cancer care.<\/p>\n<h2>Specific Considerations for Medical Practice Administrators, Owners, and IT Managers<\/h2>\n<p>Medical leaders face many challenges when adding advanced technology to oncology research. Some important points are:<\/p>\n<ul>\n<li><b>Infrastructure and Security:<\/b> Strong IT systems with secure networks and controlled access to patient data are needed, following HIPAA and FDA rules.<\/li>\n<li><b>Interoperability:<\/b> Systems like Flatiron Clinical Pipe\u2122, Medidata, and hospital records must work well together without manual fixes.<\/li>\n<li><b>Staff Training and Change Management:<\/b> Staff need training to use new digital workflows. Without it, changes may not be accepted.<\/li>\n<li><b>Data Governance and Quality Control:<\/b> Clear rules for checking and tracking data help keep accuracy and prepare for inspections.<\/li>\n<li><b>Customized Solutions:<\/b> Large health systems with many hospitals and clinics benefit from flexible technology plans that fit their different needs.<\/li>\n<\/ul>\n<p>By handling these areas, organizations can lower trial costs, reduce delays, and improve patient experiences while supporting cancer research.<\/p>\n<p><!--smbadstart--><\/p>\n<div class=\"ad-widget regular-ad\" smbdta=\"smbadid:sc_17;nm:AJerNW453;score:0.99;kw:hipaa_0.99_compliance_0.96_encryption_0.93_data-security_0.85_call-privacy_0.77;\">\n<h4>HIPAA-Compliant Voice AI Agents<\/h4>\n<p>SimboConnect AI Phone Agent encrypts every call end-to-end &#8211; zero compliance worries.<\/p>\n<p>  <a href=\"https:\/\/simbo.ai\/schedule-connect\" class=\"cta-button\">Speak with an Expert \u2192<\/a>\n<\/div>\n<p><!--smbadend--><\/p>\n<section class=\"faq-section\">\n<h2 class=\"section-title\">Frequently Asked Questions<\/h2>\n<div class=\"faq-container\">\n<details>\n<summary>What is the collaboration between the University of Colorado Cancer Center and Flatiron Health?<\/summary>\n<div class=\"faq-content\">\n<p>The University of Colorado Cancer Center and Flatiron Health have announced a collaboration to implement Flatiron Clinical Pipe\u2122, which aims to improve the efficiency of clinical trials by integrating electronic health record (EHR) data directly into electronic data capture (EDC) systems.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What is Flatiron Clinical Pipe\u2122?<\/summary>\n<div class=\"faq-content\">\n<p>Flatiron Clinical Pipe\u2122 is a technology that connects electronic health records with electronic data capture systems, enabling direct transfer of clinical trial data to improve study efficiencies and reduce administrative burdens on clinical trial site teams.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>How does the collaboration impact clinical trial efficiency?<\/summary>\n<div class=\"faq-content\">\n<p>The integration of Flatiron Clinical Pipe\u2122 enables researchers to avoid duplicative, error-prone data entry, thereby improving access and efficiency of clinical studies, which allows for conducting more trials and accelerating evidence generation.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What are the key benefits of using Flatiron Clinical Pipe\u2122?<\/summary>\n<div class=\"faq-content\">\n<p>Key benefits include reduced site burden, streamlined data management, accelerated study timelines, and allowing healthcare providers to focus more on patient interaction instead of administrative tasks.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>How many data points can Flatiron Clinical Pipe\u2122 transfer efficiently?<\/summary>\n<div class=\"faq-content\">\n<p>Research has shown that teams using Flatiron Clinical Pipe\u2122 can transfer 11,342 individual data points in an average time of just 37 seconds for launching, completing, and submitting case report forms.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What role does UCHealth play in this collaboration?<\/summary>\n<div class=\"faq-content\">\n<p>UCHealth serves as the clinical care partner in the collaboration, leveraging its extensive network and facilities to facilitate clinical trials and improve patient care outcomes through innovative technology.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What is the significance of the University of Colorado Cancer Center?<\/summary>\n<div class=\"faq-content\">\n<p>The University of Colorado Cancer Center is Colorado\u2019s only National Cancer Institute-designated Comprehensive Cancer Center, focusing on advancing cancer treatment and patient care through innovative research.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>Who are the stakeholders involved in this initiative?<\/summary>\n<div class=\"faq-content\">\n<p>The key stakeholders include Flatiron Health, the University of Colorado Cancer Center, UCHealth, clinicians, researchers, and patients within the oncology ecosystem.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What research supports the efficacy of Flatiron Clinical Pipe\u2122?<\/summary>\n<div class=\"faq-content\">\n<p>Research published in ESMO Real-World Data and Digital Oncology showcases the efficiency of Flatiron Clinical Pipe\u2122, demonstrating its capability to significantly reduce the time spent on administrative tasks in clinical trials.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>How does Flatiron Health contribute to oncology care?<\/summary>\n<div class=\"faq-content\">\n<p>Flatiron Health utilizes machine learning, artificial intelligence, and real-world evidence to transform oncology clinical research and improve patient care by connecting data and streamlining processes.<\/p>\n<\/p><\/div>\n<\/details><\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Oncology trials involve a lot of data and can be very complex. For example, in a Phase III study, about 10,000 data points are collected for each patient. This number grows when genomic and digital health data are added. Handling this large amount of data causes several problems: Duplicative Data Entry: More than half of [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-30922","post","type-post","status-publish","format-standard","hentry"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/30922","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/comments?post=30922"}],"version-history":[{"count":0,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/30922\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/media?parent=30922"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/categories?post=30922"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/tags?post=30922"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}