Predictive medical journeys for reduction of financial toxicity
Principal Investigator
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-225
Initial CDAS Request Approval
Sep 2, 2016
Title
Predictive medical journeys for reduction of financial toxicity
Summary
TailorMed is developing a predictive medical journey platform.
The tool projects the financial impact across a patient's entire medical journey allowing patients and their treatment team to:
1. Optimize the treatment plan for a specific patient
2. Refer patients to financial assistance programs
3. Optimize insurance plans according to the medical journey forecast
4. Manage direct and indirect medical expenses
TailorMed's uniqueness is in its ability to project the financial implications along the entire medical journey, and not only a single step (e.g. procedure) and the personalization of the cost estimation to the patient's medical condition and insurance benefits.
The need for using advanced machine learning algorithms is rooted in the fact that patients are different in a number of factors, making the task of predicting how an individual patient's medical journey will evolve a difficult task. Many patients, especially old and with an advanced stage disease, have pre-existing conditions (e.g., diabetes, heart disease, coagulation, etc.) which influence their treatment plan and the expected implications of that treatment. In addition, demographic and provider specific variabilities also play a role in how the medical journey evolves.
In order to tackle these problems, TailorMed uses machine learning algorithms on top of medical data to generate personalized predictions of a patient's medical journey.
The tool projects the financial impact across a patient's entire medical journey allowing patients and their treatment team to:
1. Optimize the treatment plan for a specific patient
2. Refer patients to financial assistance programs
3. Optimize insurance plans according to the medical journey forecast
4. Manage direct and indirect medical expenses
TailorMed's uniqueness is in its ability to project the financial implications along the entire medical journey, and not only a single step (e.g. procedure) and the personalization of the cost estimation to the patient's medical condition and insurance benefits.
The need for using advanced machine learning algorithms is rooted in the fact that patients are different in a number of factors, making the task of predicting how an individual patient's medical journey will evolve a difficult task. Many patients, especially old and with an advanced stage disease, have pre-existing conditions (e.g., diabetes, heart disease, coagulation, etc.) which influence their treatment plan and the expected implications of that treatment. In addition, demographic and provider specific variabilities also play a role in how the medical journey evolves.
In order to tackle these problems, TailorMed uses machine learning algorithms on top of medical data to generate personalized predictions of a patient's medical journey.
Aims
Evaluate feasibility of predicting financial and clinical implication for specific treatment plans of colorectal cancer.
For examples: side effects management, repeated surgeries, lengthy hospital stays, financial distress, complications, etc.
Collaborators
Srulik Dvorsky, TailorMed