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1. (WO2019063520) SYSTEM AND METHOD FOR JOINT CLINICAL DECISION FOR PHARMACEUTICALS
注意: このテキストは、OCR 処理によってテキスト化されたものです。法的な用途には PDF 版をご利用ください。

CLAIMS:

1. A clinical therapy system (10), comprising:

a drug delivery device (14) configured to delivery medication to a patient; one or more therapy devices (12, 16, 11, 13, 15, 17) configured to provide therapy or to monitor the patient;

a computer (70) programmed to perform a CDS method (100) including: receiving clinical context data for the patient from a health information system

(HIS) (18);

receiving high fidelity data (20, 22, 24, 60, 62, 64, 66) comprising real time measurements for the patient from one or more of the drug delivery device, one or more vital sign sensors, and at least one device of the one or more therapy devices;

generating a clinical prediction for the patient based on a combination of the clinical context data and the high fidelity data;

outputting a therapy recommendation for the patient based on the clinical prediction for the patient; and

controlling operation of the drug delivery device or one of the therapy devices based on the therapy recommendation.

2. The system of claim 1, wherein the computer (70) includes: a local computer (72); and

a cloud computing resource (82);

wherein the CDS method (100) further includes:

processing the high fidelity data using the local computer to generate coarse summary data at a coarser resolution than the high fidelity data; and

communicating the coarse data from the local computer to the cloud computing resource;

applying one or more physiologic models (30, 32, 34 36, 38) to the combination of the clinical context data and the high fidelity data;

wherein the cloud computing resource performs the applying of the one or more physiological models to the combination of the clinical context data and the high fidelity data represented by the coarse summary data.

3. The system of claim 2, wherein the one or more therapy devices includes a ventilator (12) and the receiving of high fidelity data (20) includes receiving ventilator data from the ventilator; and

the applying includes applying pulmonary and cardiac physiologic models (30) to the combination of the clinical context data and the high fidelity data including the ventilator data to generate the clinical prediction for the patient.

4. The system of claim 3, wherein the ventilator data received from the ventilator (12) includes pressure, volume, and fraction of inspired oxygen (Fi02) data.

5. The system of any one of claims 3 and 4, wherein the clinical prediction for the patient includes cardiac output, oxygen delivery pressure, flow and gas constituency settings for the ventilator (12); and

the controlling includes increasing or decreasing an amount of pressure, flow and gas constituency settings from the ventilator delivered to the patient and increasing or decreasing an amount of a respiratory drug delivered to the patient by the drug delivery device (14).

6. The system of claim 2, wherein the one or more therapy devices includes a fluid regulatory device (11) and the receiving of high fidelity data (60) includes receiving fluid regulatory data from the fluid regulatory device; and

the applying includes applying fluid and renal models (34) to the combination of the clinical context data and the high fidelity data including the fluid regulatory data to generate the clinical prediction for the patient.

7. The system of claim 6, wherein the clinical prediction for the patient includes intravenous fluid delivery dose amount and a delivery rate for the fluid regulatory device (11); and

the controlling includes increasing or decreasing an amount of dose or a delivery rate from the fluid regulatory device delivered to the patient and increasing or decreasing an amount of a respiratory regulatory drug delivered to the patient by the drug delivery device (14).

8. The system of claim 2, wherein the one or more therapy devices includes a cooling blanket (13) and the receiving of high fidelity data (62) includes receiving patient temperature and thermal regulation data from the cooling blanket;

the applying includes applying a patient temperature and a thermal regulation model (36) to the combination of the clinical context data and the high fidelity data including the patient temperature and thermal regulation data to generate the clinical prediction for the patient; and

the controlling includes increasing or decreasing an amount of heat delivered to the patient from the cooling blanket.

9. The system of claim 2, wherein the one or more therapy devices includes a ventricular assist device (15) and the receiving of high fidelity data (64) includes receiving cardiac support data from the ventricular assist device;

the applying includes applying the cardiac model (30) to the combination of the clinical context data and the high fidelity data including the cardiac support data to generate the clinical prediction for the patient; and

the controlling includes increasing or decreasing a speed of the ventricular assist device.

10. The system of claim 2, wherein the one or more therapy devices includes a mechanically adjustable patient bed (17) and the receiving of high fidelity data (66) includes receiving patient position data from the mechanically adjustable patient bed;

the applying includes applying a patient position model (38) to the

combination of the clinical context data and the high fidelity data including the patient position data to generate the clinical prediction for the patient; and

the controlling includes moving a portion of the mechanically adjustable patient bed to adjust a position of the patient.

11. The system of either one of claims 1 and 2, wherein:

the receiving of high fidelity data for the patient includes receiving one or more of ECG data, pulse wave time of flight data, blood pressure data, pulse pressure

variability data, and capnogram data from vital sign sensors providing monitoring of the patient; and

the applying includes applying the one or more physiologic models (30, 32, 34, 36, 38) to the combination of the clinical context data and the high fidelity data to generate a medication dosing recommendation for administering a medication to the patient.

12. The system of claim 11, wherein the outputting of the therapy recommendation for the patient includes outputting the medication dosing recommendation as a control signal to a drug delivery device (14) to cause the drug delivery device to administer the medication to the patient in accord with the medication dosing

recommendation.

13. The system of claim 12, wherein the CDS method (100) further includes continuing to perform the receiving of clinical context data, the receiving of high fidelity data, and the applying of the one or more physiologic models (30, 32, 34, 36, 38) during the administration of the medication to the patient to update the medication dosing

recommendation for the patient during the administration of the medication to the patient.

14. The system of any one of claims 1-13 wherein the clinical context data for a patient received from the HIS (18) includes one or more of laboratory data, arterial blood gas (ABG) data, micro biology data, clinical assessments, procedure reports, and radiology and imaging results.

15. A non-transitory computer readable medium storing instructions executable by at least one electronic processor (70) to perform a clinical decision support method (100), the method comprising:

receiving clinical context data for a patient from a health information system

(HIS) (18);

receiving high fidelity data (20, 22, 24, 60, 62, 64, 66) comprising real time measurements for the patient from one or more of a drug delivery device (14), one or more vital sign sensors, and at least one device of one or more therapy devices(12, 16, 11, 13, 15, applying one or more physiologic models (30, 32, 34 36, 38) to the combination of the clinical context data and the high fidelity data to generate a clinical prediction for the patient;

outputting a therapy recommendation for the patient based on the clinical prediction for the patient; and

controlling operation of the drug delivery device or one of the therapy devices based on the therapy recommendation.

16. The non-transitory computer readable medium of claim 15, wherein the at least one electronic processor computer (70) includes:

a local computer (72); and

a cloud computing resource (82); and

wherein the CDS method (100) further includes:

processing the high fidelity data using the local computer to generate coarse summary data at a coarser resolution than the high fidelity data;

communicating the coarse data from the local computer to the cloud computing resource; and

with the cloud computing resource, performing the applying of the one or more physiological models to the combination of the clinical context data and the high fidelity data represented by the coarse summary data.

17. The non-transitory computer readable medium of either one of claims 15 and 16, wherein the one or more therapy devices includes a ventilator (12) and the receiving of high fidelity data (20) includes receiving ventilator data including pressure, volume, and fraction of inspired oxygen (Fi02) data from the ventilator;

the applying includes applying pulmonary and cardiac physiologic models (30) to the combination of the clinical context data and the high fidelity data including the ventilator data to generate the clinical prediction for the patient, the clinical prediction for the patient including cardiac output, oxygen delivery pressure, flow and gas constituency settings for the ventilator; and

the controlling includes increasing or decreasing an amount of pressure, flow and gas constituency settings from the ventilator delivered to the patient and increasing or decreasing an amount of a respiratory drug delivered to the patient by the drug delivery device (14).

18. The non-transitory computer readable medium of either one of claims 15 and 16, wherein the one or more therapy devices includes a fluid regulatory device (11) and the receiving of high fidelity data (60) includes receiving fluid regulatory data from the fluid regulatory device;

the applying includes applying fluid and renal models (34) to the combination of the clinical context data and the high fidelity data including the fluid regulatory data to generate the clinical prediction for the patient, the clinical prediction for the patient including intravenous fluid delivery dose amount and a delivery rate for the fluid regulatory device; and the controlling includes increasing or decreasing an amount of dose or a delivery rate from the fluid regulatory device delivered to the patient and increasing or decreasing an amount of a respiratory regulatory drug delivered to the patient by the drug delivery device

(14) .

19. The non-transitory computer readable medium of either one of claims 15 and 16, wherein the one or more therapy devices includes a mechanically adjustable patient bed (17) and the receiving of high fidelity data (66) includes receiving patient position data from the mechanically adjustable patient bed;

the applying includes applying a patient position model (38) to the combination of the clinical context data and the high fidelity data including the patient position data to generate the clinical prediction for the patient; and

the controlling includes moving a portion of the mechanically adjustable patient bed to adjust a position of the patient.

20. A clinical therapy system (10), comprising:

a drug delivery device (14) configured to delivery medication to a patient; a plurality of therapy devices (12, 16, 11, 13, 15, 17) configured to provide therapy or to monitor the patient, the therapy devices including a ventilator (12), a patient monitor (16), a fluid regulatory device (11), a cooling blanket (13), a ventricular assist device

(15) , and a mechanically adjustable patient bed (17);

a computer (70) including a local computer (72) and a cloud computing resource (82);

wherein the computer is programmed to perform a CDS method (100)

including:

with the local computer, receiving clinical context data for the patient from a health information system (HIS) (18);

with the local computer, receiving high fidelity data (20, 22, 24, 60, 62, 64, 66) comprising real time measurements for the patient from one or more of the drug delivery device, one or more vital sign sensors, and at least one device of the one or more therapy devices;

with the cloud computing resource, applying one or more physiologic models (30, 32, 34 36, 38) to the combination of the clinical context data and the high fidelity data to generate a clinical prediction for the patient;

outputting a therapy recommendation for the patient based on the clinical prediction for the patient; and

controlling operation of the drug delivery device or one of the therapy devices based on the therapy recommendation.