Collective Health, Inc.

Sergio Martinez-Ortuno

Asif Khalak

Henning Chiv

Michelle Alicia Gard

Emma Louise Wise

Andrew Chang

Chetan Ithal

Nicholas Halpern-Manners

Izac Benjamin Milstein Ross

Yulia Eskin

Erica Leigh Horowitz

Raphael N'Gouan

John George O'Leary

Irene Victoria Tollinger

Xiaowen Ye

Dara Strauss-Albee

Heather Grates

Thomas Bedington

Thomas Bedingten

In an example, a server associated with a healthcare management service provider can receive first instructions defining rule(s) associated attribute(s) of a claim and/or a target member of a healthcare management service provider. The server can receive second instructions defining a schema for translating the rule(s) into logical expression(s). Using the rule(s) and/or the logical expression(s), a first data entry can be identified. A second data entry can be identified using a model. The server can generate an output identifying the first data entry and the second data entry and can perform an operation based at least in part on the output.

Learning expert system

A plan authoring tool can receive user input describing parameters of benefits of a benefit plan. The plan authoring tool can convert the user input into a machine structured plan description (MSPD) that defines the benefit plan. The MSPD can be used by a plan documentation generator to generate one or more plan documents that explain the benefits and/or indicate how to manually adjudicate claims submitted in association with the benefit plan. The MSPD can also be used to automatically adjudicate claims. Plan analysis tools can be used to evaluate an MSPD to validate a new or edited benefit plan and/or provide user feedback on the benefit plan so that users, if desired, can revise the MSPD via the plan authoring tool.

Machine structured plan description

A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Routing claims from automatic adjudication system to user interface

Techniques for identifying data for use in a healthcare application are described. In an example, a server can receive, via a graphical user interface (GUI), first instructions defining rule(s) associated with an outcome that is relevant to a healthcare application and second instructions defining a schema for translating the rule(s) into logical expression(s). The server can access a plurality of data entries and can apply the logical expression(s) to the plurality of data entries to identify a first data entry that satisfies the rule(s). At a substantially same time, the server can apply a model to the plurality of data entries to identify a second data entry that is predicted to be associated with the outcome. The server can generate an output identifying the first data entry and the second data entry and can utilize the output for the healthcare application.

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Oakland, CA, United States of America

Sergio Martinez-Ortuno