Converting unorganized medical data for viewing

Patent metadata

Field	Value
Publication number	US-12080419-B2
Application number	US-202017025914-A
Country	US
Kind code	B2
Filing date	Sep 18, 2020
Priority date	Jan 27, 2016
Publication date	Sep 3, 2024
Grant date	Sep 3, 2024

Abstract

Methods, systems, and devices for wireless patient monitoring and medical sensing are described. The methods, systems, and devices may include functionality for receiving a medical dataset associated with a patient, the medical dataset including a plurality of physiological measurements taken from the patient and a plurality of parameters associated with the plurality of physiological measurements. The methods, systems, and devices may also identify one or more groups of parameters from the plurality of parameters within the medical dataset, determine at least a medical device type from which at least a portion of the plurality of physiological measurements were measured, the determining based at least in part on the one or more identified groups of parameters; and display at least a subset of the plurality of physiological measurements from the medical dataset.

First claim

What is claimed is:

1. A method for visualizing medical data, the method comprising:
receiving, by one or more processor of a computing device and from a medical device, a medical dataset associated with a patient, the medical dataset comprising a plurality of physiological measurements taken from the patient, wherein the medical dataset comprises a first message segment and a second message segment, and wherein the first message segment comprises a first parameter type associated with a first physiological measurement and the second message segment comprises a second parameter type associated with a second physiological measurement;
in response to receiving the medical dataset, comparing, by the one or more processors, a sequence order of the first parameter type of the first message segment and the second parameter type of the second message segment as arranged within the medical dataset with a predetermined sequence order of the first parameter type and the second parameter type as stored in a memory associated with the computing device;
determining, by the one or more processors, a medical device type from which the first message segment and the second message segment was received based at least in part on comparing the sequence order of the first parameter type of the first message segment and the second parameter type of the second message segment with the predetermined sequence order;
detecting, by the one or more processors, reoccurring preferences of a physician based on a plurality of manual inputs by the physician at the computing device; and
causing display, by the one or more processors, of at least the first physiological measurement, the second physiological measurement, or both via a visual format on a display associated with the computing device, wherein the visual format is based on the determined medical device type and the reoccurring preferences of the physician.

2. The method of claim 1 , further comprising:
identifying, by the one or more processors, the medical device type by at least determining that the medical device type is associated with a parameter group that includes the first parameter type and the second parameter type.

3. The method of claim 1 , further comprising:
identifying, by the one or more processors, the medical device type by at least determining that the medical device type is associated with a message segment format that corresponds with the first message segment of the first message segment, the second message segment format of the second message segment, or both.

4. The method of claim 1 , further comprising:
determining, by the one or more processors, a model of the medical device type from which the first message segment and the second message segment were received based at least in part on determining the medical device type, wherein the visual format is based at least in part on determining the model of the medical device type.

5. The method of claim 4 , wherein determining the model of the medical device type further comprises:
comparing, by the one or more processors, the sequence order of the first parameter type and the second parameter type with an additional predetermined sequence order of parameters indicative of the model of the medical device type.

6. The method of claim 1 , further comprising:
determining, by the one or more processors, a manufacturer of the medical device type based at least in part on determining the medical device type, wherein the visual format is based at least in part on determining the manufacturer of the medical device type.

7. The method of claim 6 , wherein determining the manufacturer of the medical device type further comprises:
comparing, by the one or more processors, the sequence order of the first parameter type and the second parameter type with an additional predetermined sequence order of parameters indicative of the manufacturer of the medical device type.

8. The method of claim 1 , wherein the predetermined sequence order is indicative of the medical device type.

9. The method of claim 1 , wherein the medical dataset further comprises a plurality of message segments, wherein each of the plurality of message segments comprises a plurality of parameters that are each separated from each other a respective separator.

10. The method of claim 1 , wherein
the visual format is based at least in part on the medical device type, a physiological system associated with at least one of the plurality of physiological measurements, a condition of the patient, a default visual format of the medical device type, or a default visual format of a model of the medical device type.

11. The method of claim 10 , wherein the visual format is customizable based at least in part on manual inputs by a clinician to produce a customized visual format, and wherein the customized visual format differs from the default visual format of the medical device type or the default visual format of the model of the medical device type.

12. The method of claim 1 , wherein the medical dataset comprises a Health Level 7 (HL 7) dataset.

13. The method of claim 1 , wherein the method comprises using machine-learning algorithms to detect the reoccurring preferences of the physician and to produce the visual format based at least on one or more outputs of the machine-learning algorithms.

14. An apparatus for visualizing medical data, the apparatus comprising:
a memory;
a display;
one or more processors in electronic communication with the memory and the display; and
instructions stored in the memory and operable, when executed by the one or more processors, to cause the one or more processors to:
receive, from a medical device, a medical dataset associated with a patient, the medical dataset comprising a plurality of physiological measurements taken from the patient, wherein the medical dataset comprises a first message segment and a second message segment, and wherein the first message segment comprises a first parameter type associated with a first physiological measurement and the second message segment comprises a second parameter type associated with a second physiological measurement;
in response to receiving the medical dataset, compare a sequence order of the first parameter type of the first message segment and the second parameter type of the second message segment as arranged within the medical dataset with a predetermined sequence order of the first parameter type and the second parameter type as stored in the memory;
determine a medical device type from which the first message segment and the second message segment was received based at least in part on comparing the sequence order of the first parameter type of the first message segment and the second parameter type of the second message segment with the predetermined sequence order;
detect reoccurring preferences of a physician based on a plurality of manual inputs by the physician at the apparatus; and
instruct the display to display an at least the first physiological measurement, the second physiological measurement, or both via a visual format, wherein the visual format is based on the determined medical device type and reoccurring preferences of the physician.

15. The apparatus of claim 14 , wherein the instructions cause the one or more processors to:
identify the medical device type by at least determining that the medical device type is associated with a parameter group that includes the first parameter type and the second parameter type.

16. The apparatus of claim 14 , wherein the instructions cause the one or more processors to:
identify the med

Assignees

• Covidien Lp

Inventors

• Fox David Anthony
• Boyer Robert T

Classifications

• G16H10/60

  for patient-specific data, e.g. for electronic patient records · CPC title

• G16H40/67 Primary

  for remote operation · CPC title