Koninklijke Philips Corporation N.v.

David Paul Noren

Erina Ghosh

Mohammad Shahed Sorower

Larry James Eshelman

Bryan Conroy

Eric Thomas Carlson

Srinivas Hariharan

Bo Liu

Kailash Swaminathan

Emma Holdrich Schwager

Claire Yunzhu Zhao

Vinod Kumar

Daniel Craig McFarlane

Chaitanya Kulkarni

Kristen Tgavalekos

Lasith Adhikari

Vikram Shivanna

Ting Feng

Gregory Boverman

Syamanthaka Balakrishnan

Disclosed herein is a system for generating real time staffing recommendations for a hospital. The system receives data, including transition data for multiple patients, medical data for the multiple patients, and staffing data. Using the data, the system determines a transition probability for each patient and a predicted workload for each patient. The system simulates unit workloads for multiple units in the hospital using the transition probability and predicted workload for each patient. The system generates staffing recommendations using the simulated unit workloads and displays the staffing recommendations on a user display.

Systems and methods for real time workload balancing

The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Methods and systems for patient baseline estimation

A method () for training a scoring system () comprising the steps of: (i) providing () a scoring system comprising a scoring module (); (ii) receiving () a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.

Method and system for automated inclusion or exclusion criteria detection

Growing community of inventors

Sharon, MA, United States of America

David Paul Noren