자료유형  

 학위논문

Control Number  

0017164849

International Standard Book Number  

9798346389378

Dewey Decimal Classification Number  

362.1

Main Entry-Personal Name  

Nguyen, Minh Chau Thanh.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

114 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

General Note  

Advisor: Baiocchi, Mike;Chen, Jonathan.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2024.

Summary, Etc.  

요약Over the past twenty years, emergency department (ED) visits have increased about twice as fast as population growth, and each year, roughly two million ED visits resulted in Intensive Care Unit (ICU) admissions. Everyday, physicians triage patients to determine their acuity levels, the levels of care they need when being admitted to the hospital.These decisions largely depend on human judgment in a high-stakes environment with poor evidence, limited information, and intensive-time pressures. The difficulty of triaging coupled with inherent biases in decision-making highlights the need and opportunity for computer-aided clinical decision support, leveraging electronic health records (EHRs) to manage difficult triage decisions that otherwise place undue pressure on decision-makers with potentially dire consequences.The goal of my research is to develop a framework to support clinical decision making in high-stakes environments with a data-driven approach using statistical and machine learning methods. How can we help physicians to better prioritize patients and decide initial treatments in hospitals?First, I developed machine learning models for initial risk assessments. One model focuses on a specific issue of initial insulin dosing for patients admitted with high blood glucose, whether a patient needs low or higher insulin dose, and how many units, to guide initial interventions. Another model predicts general patient's risk for initial ICU admission.Second, I proposed an inductive and iterative framework for improving model development and assessment, named thick data analytics. Thick data analytics utilizes both quantitative and qualitative approach where experts review discordance cases from related prediction models (e.g. a patient admission with risks predicted at different times within the first 24 hours of admission). The goal is to understand the discordance, improve prediction, and reduce preventable transfers within a short time frame. Thick analytics facilitates model redesign to be more trustworthy in aiding triage.Lastly, I will discuss tie-breaker designs for a pragmatic clinical trial where patients with similar risks could be randomized for admission to ICUs vs non-ICUs. We aim to evaluate the effectiveness of interventions such as ICU admission on patient's health outcomes such as mortality and length of stay. When allowing overriding in such experiments, randomization is an encouragement, which is a form of instrumental variables. Our work will help understand medical decision biases to improve triage process for better patient outcomes.

Subject Added Entry-Topical Term  

Health care access.

Subject Added Entry-Topical Term  

Patients.

Subject Added Entry-Topical Term  

Emergency medical care.

Subject Added Entry-Topical Term  

Electronic health records.

Subject Added Entry-Topical Term  

Success.

Subject Added Entry-Topical Term  

Clinical decision making.

Subject Added Entry-Topical Term  

Design.

Subject Added Entry-Topical Term  

Insulin.

Subject Added Entry-Topical Term  

Intensive care.

Subject Added Entry-Topical Term  

Hospitalization.

Subject Added Entry-Topical Term  

Clinical outcomes.

Subject Added Entry-Topical Term  

Drug dosages.

Subject Added Entry-Topical Term  

Health sciences.

Subject Added Entry-Topical Term  

Information science.

Subject Added Entry-Topical Term  

Pharmaceutical sciences.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 86-05B.

Electronic Location and Access  

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Control Number  

joongbu:656199