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Investigating Disparities in Treatment Quality and Decision-Making in the UC Health System
Investigating Disparities in Treatment Quality and Decision-Making in the UC Health System
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103204
- ISBN
- 9798280748675
- DDC
- 615
- 서명/저자
- Investigating Disparities in Treatment Quality and Decision-Making in the UC Health System
- 발행사항
- [Sl] : University of California, San Francisco, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 150 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Butte, Atul J.;Tony Capra, John A.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Francisco, 2025.
- 초록/해제
- 요약Social Determinants of Health (SDOH) are non-medical factors including socioeconomic status, chronic disease burden, and mental health, that significantly influence healthcare access and outcomes. While these factors are important for understanding disease risk and disparities, they remain underrepresented in Electronic Health Records (EHRs). EHRs primarily capture relevant clinical data such as diagnoses, medications, and lab results. However, integrating SDOH offers a transformative approach to understanding health outcomes, disease risk, and healthcare disparities, particularly those shaped by socioeconomic and environmental conditions, through computational research and artificial intelligence.This dissertation highlights the importance of incorporating SDOH into real-world evidence (RWE) studies to advance research on disease risk and health outcomes. Improvements in EHR infrastructure, including diagnosis codes, wearable technology, and census tract information, allow for deeper insights into health disparities. This work examines the challenges and opportunities related to integrating SDOH into EHRs, explores their growing role in RWE, and identifies pathways for equitable and actionable reporting of study outcomes.Using de-identified data from the University of California Health Data Warehouse, this dissertation applies statistical modeling, machine learning, and generative artificial intelligence to investigate treatment disparities in two conditions: Type 2 diabetes and Multiple Myeloma. In the diabetes study, patients with lower socioeconomic status were more likely to receive less optimal second-line therapies, highlighting disparities in treatment pathways. In the Multiple Myeloma study, access to CAR-T therapy was significantly associated with treatment location and patient race and ethnicity, highlighting barriers to innovative therapies for diverse population groups.This dissertation further examines the impact of SDOH on treatment decisions, unmet clinical needs, and barriers to equitable care. By integrating SDOH metrics with EHR data, this work informs data-driven strategies to improve access to advanced therapies and address healthcare disparities. These findings aim is to inform policies that ensure all patient populations have fair and consistent access to advanced and innovative medical treatments.
- 일반주제명
- Pharmaceutical sciences
- 일반주제명
- Bioinformatics
- 일반주제명
- Health sciences
- 키워드
- Multiple Myeloma
- 키워드
- Type 2 diabetes
- 기타저자
- University of California, San Francisco Pharmaceutical Sciences and Pharmacogenomics
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798280748675
■035 ▼a(MiAaPQ)AAI31999697
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a615
■1001 ▼aDavidson, Jayson.▼0(orcid)0000-0001-9066-9872
■24510▼aInvestigating Disparities in Treatment Quality and Decision-Making in the UC Health System
■260 ▼a[Sl]▼bUniversity of California, San Francisco▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a150 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Butte, Atul J.;Tony Capra, John A.
■5021 ▼aThesis (Ph.D.)--University of California, San Francisco, 2025.
■520 ▼aSocial Determinants of Health (SDOH) are non-medical factors including socioeconomic status, chronic disease burden, and mental health, that significantly influence healthcare access and outcomes. While these factors are important for understanding disease risk and disparities, they remain underrepresented in Electronic Health Records (EHRs). EHRs primarily capture relevant clinical data such as diagnoses, medications, and lab results. However, integrating SDOH offers a transformative approach to understanding health outcomes, disease risk, and healthcare disparities, particularly those shaped by socioeconomic and environmental conditions, through computational research and artificial intelligence.This dissertation highlights the importance of incorporating SDOH into real-world evidence (RWE) studies to advance research on disease risk and health outcomes. Improvements in EHR infrastructure, including diagnosis codes, wearable technology, and census tract information, allow for deeper insights into health disparities. This work examines the challenges and opportunities related to integrating SDOH into EHRs, explores their growing role in RWE, and identifies pathways for equitable and actionable reporting of study outcomes.Using de-identified data from the University of California Health Data Warehouse, this dissertation applies statistical modeling, machine learning, and generative artificial intelligence to investigate treatment disparities in two conditions: Type 2 diabetes and Multiple Myeloma. In the diabetes study, patients with lower socioeconomic status were more likely to receive less optimal second-line therapies, highlighting disparities in treatment pathways. In the Multiple Myeloma study, access to CAR-T therapy was significantly associated with treatment location and patient race and ethnicity, highlighting barriers to innovative therapies for diverse population groups.This dissertation further examines the impact of SDOH on treatment decisions, unmet clinical needs, and barriers to equitable care. By integrating SDOH metrics with EHR data, this work informs data-driven strategies to improve access to advanced therapies and address healthcare disparities. These findings aim is to inform policies that ensure all patient populations have fair and consistent access to advanced and innovative medical treatments.
■590 ▼aSchool code: 0034.
■650 4▼aPharmaceutical sciences
■650 4▼aBioinformatics
■650 4▼aHealth sciences
■653 ▼aHealth disparities
■653 ▼aLarge language models
■653 ▼aMultiple Myeloma
■653 ▼aSocial Determinants of Health
■653 ▼aType 2 diabetes
■690 ▼a0572
■690 ▼a0715
■690 ▼a0566
■71020▼aUniversity of California, San Francisco▼bPharmaceutical Sciences and Pharmacogenomics.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0034
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357301▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
