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Patent Agency Ranking
公开(公告)号：US20230011880A1
公开(公告)日：2023-01-12
申请号：US17856058
申请日：2022-07-01
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Lasith Adhikari , David Paul Noren , Gregory Boverman , Eran Simhon , Chaitanya Kulkarni , Syamanthaka Balakrishnan , Vikram Shivanna , Larry James Eshelman , Kailash Swaminathan
Abstract: A method for performing, using a patient disposition system, a disposition analysis of a plurality of patients to optimize a discharge planning process for each of the plurality of patients, including: (i) receiving electronic medical record information about each of the plurality of patients; (ii) identifying one of a plurality of different patient types for each of the plurality of patients based on the received electronic medical record information; (iii) selecting a trained multi-state model for each identified patient type; and (iv) determining, based on the selected trained multi-state model, a disposition state for each of the plurality of patients in real-time, wherein each disposition state includes a location to which the patient is to be discharged. The method further includes determining at least one service or assessment that can be deferred to the location to which the patient is to be discharged.
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公开(公告)号：US20210052217A1
公开(公告)日：2021-02-25
申请号：US16919154
申请日：2020-07-02
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Claire Yunzhu Zhao , Bryan Conroy , Mohammad Shahed Sorower , David Paul Noren , Kailash Swaminathan , Chaitanya Kulkarni , Ting Feng , Kristen Tgavalekos , Emma Holdrich Schwager , Erina Ghosh , Vinod Kumar , Vikram Shivanna , Srinivas Hariharan , Daniel Craig McFarlane
Abstract: 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.
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公开(公告)号：US20240127939A1
公开(公告)日：2024-04-18
申请号：US18381236
申请日：2023-10-18
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Lasith Adhikari , David Paul Noren , Gregory Boverman , Eran Simhon , Chaitanya Kulkarni , Moumita Saha , Krishnamoorthy Palanisamy , Gyana Ranjan Mallick , Ahmed Sanin , Claire Yunzhu Zhao
Abstract: A method for predicting simulated patient admissions, comprising: receiving healthcare records for a plurality of patients; adapting the received healthcare records to a common data format; parameterizing the adapted healthcare records to generate a plurality of patient parameters comprising for each patient a day of the week admission parameter, a time of day admission parameter, and a patient type parameter; generating a length of stay parameter for each of the plurality of different patient types; generating a transition probability for each of the plurality of different patient types; predicting, for a time period in the healthcare environment, patient admissions; predicting a care pathway for some or all of the predicted patient admissions during the time period; and reporting, via a user interface, the predicted patient admissions and predicted care pathways.
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公开(公告)号：US20230008936A1
公开(公告)日：2023-01-12
申请号：US17856024
申请日：2022-07-01
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Lasith Adhikari , Chaitanya Kulkarni , David Paul Noren , Eran Simhon , Syamanthaka Balakrishnan , Gregory Boverman
Abstract: A method for performing a demand analysis for a hospital, including: (i) receiving hospital capacity information; (ii) receiving hospital data, the hospital data comprising information on patient admissions, patient discharges, and patient transfers for a previous period of time; (iii) adapting parameters of a machine learning algorithm based on the hospital data; (iv) receiving clinical information about patients currently admitted in the hospital; (v) determining, based on output from the adapted machine learning algorithm and clinical information about the patients currently admitted in the hospital and the hospital capacity information a predicted patient flow for the hospital in real-time; (vi) detecting a deviation between the predicted patient flow and at least one actual data point; and (vii) displaying to at least one user in real-time, the detected deviation for the hospital.
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公开(公告)号：US11925474B2
公开(公告)日：2024-03-12
申请号：US16919154
申请日：2020-07-02
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Claire Yunzhu Zhao , Bryan Conroy , Mohammad Shahed Sorower , David Paul Noren , Kailash Swaminathan , Chaitanya Kulkarni , Ting Feng , Kristen Tgavalekos , Emma Holdrich Schwager , Erina Ghosh , Vinod Kumar , Vikram Shivanna , Srinivas Hariharan , Daniel Craig McFarlane
CPC classification number: A61B5/4842 , A61B5/7225 , A61B5/742 , G16H10/60 , G16H50/20 , G16H50/30 , G16H50/50 , G16H50/70
Abstract: 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.
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