Flatiron Health, Inc.

Lauren Sutton

Angel Leung

Janet Donegan

Alexander Padmos

Dominic Green

Jessie Tseng

Rahul Bafna

Addison Shelley

Edward Liu

Sharang Phadke

Chun-Che Wang

Eitan Meir Konigsburg

Frank Zexi Chen

Victor J Wang

Paul Greenleaf

Anand Kuchibotla

Raman Choudhry

Lucy He

David Light

Peter M Larson

Nisha Singh

Alexander Rich

Shannon Lee

Neal Meropol

Yihua Zhao

Caroline Nightingale

Joshua Buddle

Harvey James Hamrick, Jr

Zexi Chen

Anthony Hogan

Achin Batra

Max Tromanhauser

Adam Gottesman

Alex Deyle

Filip Frahm

Trevor Royce

Barry Leybovich

Forrest Xiao

Maneet Kaur

Claire Saint-Donat

Prakrit Baruah

Nathan Chan

A system for tracking adverse events may include at least one processing device programmed to receive a request from a user to record an adverse event experienced by a patient; receive a search term input by the user; identify, in an adverse event database and based on the search term, at least one database record for an adverse event, wherein the at least one database record includes an adverse event type and at least one characteristic; receive, via an input field, a rating of the at least one characteristic for the patient; generate an adverse event record based on the adverse event type and the rating; and store the adverse event record in an adverse event log.

Systems and methods for tracking patient events

A model-assisted system for identifying a group of patients for a cohort using a generalized biomarker model may include a processor programmed to provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers; receive, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the third biomarker or having been tested for the attribute associated with the first biomarker; determine a likelihood threshold based on a predetermined cohort size associated with the first biomarker and identify, based on the output, a group of the plurality of individuals for inclusion in a cohort, each individual in the group of the plurality of individuals being associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold.

Clinical trial matching system using inferred biomarker status

A computer-implemented system for determining trials using a metastatic condition of a patient may include at least one processor programmed to receive a selection of a patient; access, in response to the selection of the patient, a patient dataset associated with the patient; receive a predicted metastatic condition associated with the patient; cause display of at least a first portion of the patient dataset and the predicted metastatic condition; determine, based on at least a second portion of the patient dataset or the predicted metastatic condition, a subset of trials for the patient, wherein the subset of trials for the patient is determined from a plurality of trials; and cause display of at least the subset of the trials for the patient.

Systems and methods for patient-trial matching

A graphical user interface for displaying an electronic medical record associated with a patient is provided. The graphical user interface may include an area configured to display patient information, which may include at least a name of the patient. The graphical user interface may also include an indicator displayed in association with the name of the patient. The indicator may include information specifying that the patient is potentially eligible for one or more trials, the patient is participating in one or more trials, or the patient has completed one or more trials.

Systems and methods for providing clinical trial status information for patients

Described herein is a graphical intervention test development system. The graphical intervention test development system provides a graphical intervention test development environment that facilitates computer-based design of an intervention test. The graphical intervention test development environment provides a graphical user interface (GUI) and visualizations of various aspects of an intervention test therein. The graphical intervention test development environment further provides a control interface through which a user can manipulate control parameters that affect outcomes of the intervention test.

Graphical intervention test development system

A computer-implemented system for identifying a patient for a trial may include at least one processor. The at least one processor may be programmed to receive an indication of a selected trial, the selected trial being associated with a testing status criterion; access a plurality of patient records associated with a patient of a plurality of patients; determine, using a machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; determine that the genomic testing status satisfies the testing status criterion; and include the patient in a subset of the plurality of patients based on the genomic testing status satisfying the testing status criterion.

Systems and methods for determining a genomic testing status

Systems and methods may manage one or more clinical trials. In one implementation, a system for creating a query for a trial includes at least one processor programmed to: cause a computing device associated with a sponsor of the trial to display a graphical user interface comprising a plurality of patient identifiers associated with the trial; receive, from the computing device, a selection of one of the plurality of patient identifiers; receive, from the computing device, a query comprising an inquiry relating to the selected patient identifier; and transmit the query to a practice site associated with the selected patient identifier.

Systems and methods for managing clinical trials

A computer-implemented system for determining a genomic testing status of a patient may include at least one processor programmed receive, from a source, unstructured information from a plurality of patient records associated with a patient; determine, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing; determine, using a second machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and display a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.

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Cary, NC, United States of America

Lauren Sutton