Decision Sciences Institute 2019 - New Orleans, LA
Nov
23
9:00 AM09:00

Decision Sciences Institute 2019 - New Orleans, LA

Accounting for the Patient in Data-Driven Physician Decision Support Systems

Due to the ever-increasing complexity of the situations faced by physicians, there is a need for decision support systems.  There have been two assumptions for these models: that patients with similar medical diagnoses should be treated identically, and physician decisions are made rationally according to standard medical practices.  However, we know that acknowledgment of patient uniqueness is required for an optimal outcome, and with each interaction, a physician has the opportunity to update their medical decisions based on different patient characteristics.  We posit that socio-economic conditions also play a role in a physician’s decision-making process.  Therefore, to make appropriate medical treatment suggestions, a DSS needs to adapt for both patient demographics and medical diagnoses.  The goal of this work is to understand if physicians do adapt their decision-making strategies for different patient types, as this would allow us to create a more accurate data-driven model by accounting for prior patient encounters.

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INFORMS Healthcare 2019 - Cambridge, MA
Jul
27
2:00 PM14:00

INFORMS Healthcare 2019 - Cambridge, MA

Accounting for Patient Learning in Iterative Physician Decision Support Systems

Due to the ever-increasing complexity of the situations faced by physicians, there is a need for decision support systems.  There have been two assumptions for these models: that patients with similar medical diagnoses should be treated identically, and physician decisions are made rationally according to standard medical practices.  However, we know that acknowledgment of patient uniqueness is required for an optimal outcome, and with each interaction, a physician has the opportunity to update their medical decisions based on different patient characteristics.  We posit that socio-economic conditions also play a role in a physician’s decision-making process.  Therefore, to make appropriate medical treatment suggestions, a DSS needs to adapt for both patient demographics and medical diagnoses.  The goal of this work is to understand if physicians do adapt their decision-making strategies for different patient types, as this would allow us to create a more accurate data-driven model by accounting for prior patient encounters.

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INFORMS Healthcare 2019 - Cambridge, MA
Jul
27
2:00 PM14:00

INFORMS Healthcare 2019 - Cambridge, MA

Utilizing Data-Driven Decision Support Systems to Reduce Readmission Rates for Patients with Congestive Heart Failure

Physicians currently attempt to identify CHF patients that are likely to be readmitted within 30 days. However, the readmission rate for these patients remains over 20%.  Data-driven decision support systems can provide an additional tool to understand which patients are at high-risk of readmission.  We create a statistical model to utilize patient-specific information to not only more accurately identify the patients most likely to be readmitted, but also why – whether for condition-related reasons or not. This allows physicians to suggest patient-specific readmission prevention strategies.

 

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DSI 2017 - Washington, DC
Nov
18
1:00 PM13:00

DSI 2017 - Washington, DC

SESSION: Healthcare Management - Decision Analysis (Concourse - Cabinet)

PAPER: Inferring Doctor Decisions: Post-Stent Patient Streaming Using Personalized Medicine
Kellas R. Cameron, Nitin Joglekar, Nachiketa Sahoo, Jugnu Jain

We develop a structural econometric model to analyze doctors’ choice of medication after cardiac stent surgery. The estimated model shows the role of patient’s medical and economic conditions in their choice. The model can be used to stream those who will benefit the most from a new genomic test.

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