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R01LM014731

Project Grant

Overview

Grant Description
AI METHODS FOR LARGE SCALE EPIDEMIOLOGICAL STUDIES USING PATIENT REPORTS OF MEDICATION ADHERENCE AND TOLERABILITY - PROJECT SUMMARY ADHERENCE TO PRESCRIBED MEDICATIONS IS A CRITICAL ASPECT OF EFFECTIVE MEDICAL TREATMENT, ESPECIALLY FOR CHRONIC CON- DITIONS; HOWEVER, THE WORLD HEALTH ORGANIZATION (WHO) ESTIMATES THAT MORE THAN 50% OF PATIENTS WITH CHRONIC CONDITIONS IN THE UNITED STATES DO NOT TAKE THEIR MEDICATIONS AS PRESCRIBED. MEDICATION NON-ADHERENCE IS ASSOCI- ATED WITH WORSENING HEALTH CONDITIONS AND INCREASED COMORBIDITIES, AND IS ESTIMATED TO ANNUALLY ACCOUNT FOR 25% OF HOSPITALIZATIONS, MORE THAN 100,000 PREVENTABLE DEATHS, AND UP TO $500 BILLION IN HEALTHCARE COSTS IN THE UNITED STATES. THE WHO AFFIRMS THAT “INCREASING THE EFFECTIVENESS OF ADHERENCE INTERVENTIONS MAY HAVE A FAR GREATER IMPACT ON THE HEALTH OF THE POPULATION THAN ANY IMPROVEMENT IN SPECIFIC MEDICAL TREATMENTS.” THE CHALLENGE OF INCREASING THE EFFECTIVENESS OF ADHERENCE INTERVENTIONS HAS LIKELY BEEN DUE IN PART TO THE FACT THAT THE MAJORITY OF MEDICATION NON-ADHERENCE IS INTENTIONAL (IN CONTRAST TO UNINTENTIONAL, SUCH AS FORGETFULNESS) AND SOURCES OF DATA FOR UNDERSTANDING THE FACTORS THAT INFLUENCE INTENTIONAL NON-ADHERENCE REMAIN LIMITED. AS ENCOURAGED BY THE UNITED STATES FOOD AND DRUG ADMINISTRATION AND CENTERS FOR DISEASE CONTROL AND PREVENTION, OUR PRIOR WORK—FUNDED FOR THE PAST 10 YEARS BY THE NATIONAL LIBRARY OF MEDICINE (R01LM011176)—HAS DEMONSTRATED THAT PATIENT REPORTS IN REAL-WORLD, NON-TRADITIONAL SOURCES OF DATA CAN BE USED AS A NOVEL, COMPLEMENTARY APPROACH TO POST-MARKETING PHARMACOVIGILANCE. BECAUSE ONLINE PATIENT REPORTS ARE NOT BIASED BY SURVEY QUESTIONS OR INTERVIEWERS, ARE AVAIL- ABLE ON A LARGE SCALE, AND MAY INCLUDE PARTICIPANTS WHO ARE UNDER-REPRESENTED IN TRADITIONAL SOURCES OF DATA, IN A PRELIMINARY QUALITATIVE CONTENT ANALYSIS, WE WERE ABLE TO GAIN NOVEL INSIGHTS ABOUT MEDICATION NON-ADHERENCE THAT WERE NOT WELL-REPRESENTED IN OTHER STUDIES, SUCH AS PATIENTS REPORTING DECHALLENGE (I.E., AN ADVERSE EFFECT STOPPING WHEN THE MEDICATION WAS STOPPED) AND RECHALLENGE (I.E., THE ADVERSE EFFECT RESUMING WHEN THE MEDICATION WAS STARTED AGAIN). VALIDATING OUR APPROACH THROUGH FIVE DISEASE-SPECIFIC CASE STUDIES IN COLLABORATION WITH DOMAIN EXPERTS IN CARDIOVASCULAR DISEASES, GASTROINTESTINAL DISEASES, CANCER, HIV, AND DIABETES (AIM 3), WE PROPOSE TO DEVELOP NOVEL NATURAL LANGUAGE PROCESSING AND ARTIFICIAL INTELLIGENCE METHODS (AN INTELLIGENT AGENT WITH A SPECIAL- IZED LARGE LANGUAGE MODEL AT ITS CORE) TO CAPTURE MEDICATION USE NARRATIVES FROM ONLINE PATIENT REPORTS (AIM 1) AND TO ELUCIDATE ADDITIONAL FACTORS CONTRIBUTING TO MEDICATION NON-ADHERENCE FROM SPONTANEOUS REPORTING SYSTEMS (SRS, E.G., FAERS), SUCH AS DRUG INDICATIONS, THE MAGNITUDE OF ADVERSE EVENTS, AND DRUG-DRUG INTERACTIONS (AIM 2). WE INCORPORATE FINDINGS FROM OTHER SOURCES INTO OUR CASE STUDIES THROUGH SYSTEMATIC REVIEWS (AIM 3), SYNTHE- SIZING AND COMPARING PUBLISHED STUDIES TO WHAT WE LEARN FROM PATIENT REPORTS POSTED ONLINE AND IN SRS. THIS IS THE MOST COMPREHENSIVE STUDY OF ITS KIND EVER ATTEMPTED, BRINGING THE VOICE OF THE PATIENTS DIRECTLY TO RESEARCHERS IN A REPRODUCIBLE, COST-EFFECTIVE MANNER. THIS CAN INFORM ADHERENCE INTERVENTIONS AND REDUCE THE MORBIDITY, MORTALITY, AND FINANCIAL BURDEN ASSOCIATED WITH INTENTIONAL NON-ADHERENCE, ALIGNING WITH THE NLM’S GOAL OF CREATING A FUTURE IN WHICH DATA AND INFORMATION TRANSFORM AND ACCELERATE BIOMEDICAL DISCOVERY AND IMPROVE HEALTH AND HEALTHCARE.
Funding Goals
TO MEET A GROWING NEED FOR INVESTIGATORS TRAINED IN BIOMEDICAL INFORMATICS RESEARCH AND DATA SCIENCE BY TRAINING QUALIFIED PRE- AND POST-DOCTORAL CANDIDATES, TO CONDUCT RESEARCH IN BIOMEDICAL INFORMATICS, BIOINFORMATICS AND RELATED COMPUTER, INFORMATION AND DATA SCIENCES, TO FACILITATE MANAGEMENT OF ELECTRONIC HEALTH RECORDS AND CLINICAL RESEARCH DATA, TO PREPARE SCHOLARLY WORKS IN BIOMEDICINE AND HEALTH, TO ADVANCE BIOCOMPUTING AND BIOINFORMATICS THROUGH PARTICIPATION IN FEDERAL INITIATIVES RELATING TO BIOMEDICAL INFORMATICS, BIOINFORMATICS AND BIOMEDICAL COMPUTING, AND TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, THROUGH SMALL BUSINESS INNOVATION RESEARCH (SBIR) AND SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) GRANTS.
Place of Performance
California United States
Geographic Scope
State-Wide
Cedars-Sinai Medical Center was awarded AI-Driven Medication Adherence Insights from Patient Reports Project Grant R01LM014731 worth $3,484,662 from the National Library of Medicine in September 2025 with work to be completed primarily in California United States. The grant has a duration of 4 years and was awarded through assistance program 93.879 Medical Library Assistance. The Project Grant was awarded through grant opportunity NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).

Status
(Ongoing)

Last Modified 9/24/25

Period of Performance
9/15/25
Start Date
8/31/29
End Date
1.0% Complete

Funding Split
$3.5M
Federal Obligation
$0.0
Non-Federal Obligation
$3.5M
Total Obligated
100.0% Federal Funding
0.0% Non-Federal Funding

Activity Timeline

Interactive chart of timeline of amendments to R01LM014731

Additional Detail

Award ID FAIN
R01LM014731
SAI Number
R01LM014731-1706324557
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Nonprofit With 501(c)(3) IRS Status (Other Than An Institution Of Higher Education)
Awarding Office
75NL00 NIH National Library of Medicine
Funding Office
75NL00 NIH National Library of Medicine
Awardee UEI
NCSMA19DF7E6
Awardee CAGE
2F323
Performance District
CA-90
Senators
Dianne Feinstein
Alejandro Padilla
Modified: 9/24/25