Siemens Healthcare Diagnostics Gmbh

Siemens Healthineers Ag

Siemens Healthcare Gmbh

Ahmet Tuysuzoglu

Shaohua Kevin Zhou

Tommaso Mansi

Yefeng Zheng

Tiziano Passerini

Rui Liao

Luxi Zheng

Eli Gibson

Michael Heydlauf

Alexander Brost

Yue Zhang

Sandeep M Naik

Jeffrey Hoffman

Micheal Heydlauf

A scheduling system includes: a plurality of input devices configured to output medical data, a workforce storage, configured to store working characteristics of a plurality of doctors, and a scheduler configured to receive as input data related to the medical data and the working characteristics, and configured to provide as output a plurality of schedules for the plurality of doctors for analysing the medical data.

Medical data management system, method and computer program

Systems and methods for automatically determining an assessment of a patient are provided. A patient is automatically interacted with, by a first trained machine learning based model, to acquire initial patient data. One or more risk factors associated with the patient are automatically determined, by a second trained machine learning based model, based on the received initial patient data. The patient is automatically interacted with, by the first trained machine learning based model, to acquire additional patient data based on the one or more determined risk factors. An assessment of the patient is automatically determined, by the second trained machine learning based model, based on the initial patient data and the additional patient data. The assessment of the patient is output.

Universal health machine for the automatic assessment of patients

Methods of scheduling maintenance on a plurality of automated testing apparatus and possibly also on ancillary test processing apparatus are provided. The methods include inputting identification data on the plurality of automated testing apparatus to be maintained, inputting maintenance requirement data for the maintenance of each of the apparatus, inputting demand constraint data, operating on the identification data, maintenance requirement data, and demand constraint data using an optimization program, such as mixed integer linear programming (MILP), subject to demand constraints and at least one objective, and outputting an optimized maintenance schedule for a planning period. Systems and apparatus configured to carry out the methods are also provided, as are other aspects.

Methods and apparatus of maintenance scheduling in automated testing over a planning period

Systems and methods include an optimization-based load planning module for laboratory analyzers of bio-fluid samples. The optimization-based load planning module is executable on a computer server and is configured to optimize assay (lab test) assignments across a large number of laboratory analyzers based on one or more of the following user selected and weighted objectives: reduced turn-around-time, load balancing, efficient reagent usage, lower quality assurance costs, and/or improved system robustness. The optimization-based load planning module outputs a load plan comprising computer executable instructions configured to cause a system controller of a laboratory analyzer system to schedule and direct each requested test to be performed at one or more selected laboratory analyzers of the laboratory analyzer system in accordance with the user selected and weighted objectives. Other aspects are also described.

Optimization-based load planning systems and methods for laboratory analyzers

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Jersey City, NJ, United States of America

Ahmet Tuysuzoglu