{"id":657891,"date":"2020-05-29T09:26:29","date_gmt":"2020-05-29T16:26:29","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=657891"},"modified":"2020-06-17T11:03:59","modified_gmt":"2020-06-17T18:03:59","slug":"project-new-hope-clinical-trials-matching","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-new-hope-clinical-trials-matching\/","title":{"rendered":"Project New Hope: Clinical Trials Matching"},"content":{"rendered":"

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A new approach for finding clinical trials<\/h2>

At any moment in time, there are more than 50,000 clinical trials around the world, that are looking to recruit patients for their studies. Clinical trials are critical for developing new medications and treatments.<\/p>

For patients, it is challenging to find clinical trials that are a match. There could be dozens of trials per condition. Clinical trials typically have dozens of complicated eligibility criteria in unstructured medical language. On average, less than 5% of patients are aware of clinical trials relevant for them. At the same time, researchers are struggling to recruit suitable participants for their clinical trials. Patient recruitment is the main cause of delays in trials, and 50% of the trials fail to reach their recruitment target.<\/p>

For patients, taking part in a clinical trial could mean a breakthrough in their treatment. For many patients, this could mean a New Hope.<\/strong><\/p>

Microsoft\u2019s Clinical Trials Matching technology uses state-of-the-art Natural Language Processing (NLP) tools that understand both the clinical trial protocols and patient health records. The technology helps narrow down and prioritize the set of relevant clinical trials to a smaller set of trials that the patient appears to be qualified for.<\/p>

Our mission<\/h2>

Matching patients to suitable clinical trials is difficult. Our mission is to power solutions that enable healthcare organizations and medical professionals to match patients to potentially suitable clinical trials in an intelligent way, based on trials eligibility criteria and patient details.<\/p>

Use cases<\/h2>

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\n\t\tMatch a single patient to a set of suitable clinical trials<\/b>

A patient or someone on behalf of a patient (doctor, family member, etc.) is searching for suitable trials for the patient condition. This can typically be implemented as a web page or by interacting with a chatbot, where the bot presents questions to the user and qualifies it. This use case does not require access to EMR data.<\/p>

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\n\t\tFeasibility assessment of a single clinical trials based on patient repository<\/strong>

A research organization is trying to identify patients that are eligible for a single trial. This use case requires EMR data, and could either be offered as an automated service (automated matching service between patient data and trial eligibility criteria) or by creating a platform that will allow organizations to query the patient data.\t<\/div>\n\t<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\n

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Match multiple clinical trials with multiple patients<\/strong><\/p>

This is a typical provider-site use case, where a site that runs multiple clinical trials searches for patients for all trials recruiting on site.\t<\/div>\n\t<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\n

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Verify single patient eligibility for a single trial and show gaps<\/strong><\/p>

In this use case, the user is typically a trial coordinator that uses a web experience or a bot to screen and qualify a single patient to a specific trial and understands the gaps.\t<\/div>\n\t<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\n

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The technology<\/h2>

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\"Dynamic<\/td>\"Machine<\/td>\"Conversational<\/td>\"Patient<\/td><\/tr>
Dynamic criteria selection<\/td>Machine reading for trials and patient data structuring<\/td>Conversational AI & Language generation<\/td>Patient qualification<\/td><\/tr><\/tbody><\/table>

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Questions?<\/h2>

Contact us at CTMTechQs@microsoft.com<\/a>\t<\/div>\n\t<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\n","protected":false},"excerpt":{"rendered":"

Intelligent Clinical Trials Matching technology, based on trials eligibility, aims to power solutions that match patient to potentially suitable clinical trials quickly and easily.<\/p>\n","protected":false},"featured_media":662760,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13553],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-657891","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-medical-health-genomics","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"2017-07-01","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Hadas Bitran","user_id":36759,"people_section":"Section name 0","alias":"hadasb"},{"type":"guest","display_name":"Shahar Admati","user_id":660321,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Guy Becker","user_id":657915,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Adi Biran","user_id":657912,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Karin Brisker","user_id":660360,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Guy Epshtein","user_id":660366,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Alon Itach","user_id":660387,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Eldar Kohen","user_id":660369,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Yochai Lehman","user_id":660159,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Udi Naveh","user_id":660180,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Ronny Roktel","user_id":660330,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Arie Schwartzman","user_id":657918,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Gil Shacham","user_id":657921,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Orel Sharabi","user_id":660351,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Amir Taub","user_id":657924,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Tom Timianker","user_id":660318,"people_section":"Section name 0","alias":""},{"type":"guest","display_name":"Shahar Yekutiel","user_id":657927,"people_section":"Section name 0","alias":""}],"msr_research_lab":[],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/657891","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":123,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/657891\/revisions"}],"predecessor-version":[{"id":663081,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/657891\/revisions\/663081"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/662760"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=657891"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=657891"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=657891"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=657891"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=657891"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}