Centralized hospital monitoring system for automatically detecting upper airway instability and for preventing and aborting adverse drug reactions

• US 20030000522A1
• Filed: 05/17/2002
• Published: 01/02/2003
• Est. Priority Date: 05/17/2001
• Status: Active Grant

• Current Assignee: Eric N Lynn, Lawrence A. Lynn
• Original Assignee: Eric N Lynn, Lawrence A. Lynn
• Inventors: Lynn, Eric N., Lynn, Lawrence A.
• Granted Patent: US 7,081,095 B2
• Time in Patent Office: Days
• US Class Current: 128/200.24
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence

Abstract

A system and method for the automatic diagnosis of obstructive sleep apnea in a centralized hospital critical care monitoring system for the monitoring of a plurality of patients in at least one of a critical care, step down, and cardiac ward by telemetry. The system includes a central processor having a display, and a plurality of telemetry units for mounting with patients, each of the telemetry units has a plurality of sensors for connection with each patient, the telemetry unit is capable of the transmission of multiple signals derived from the sensors to the central processor, in one preferred embodiment the method comprising steps of programming the system to analyze the signals and to automatically identify the presence and severity of obstructive sleep apnea and to provide an indication of the identification.

Claims

1. ) A method of determining specific factors related to the expense associated with a complex process, said process including a plurality of dynamic components defined by a plurality of dynamic measurable parameters, the method comprising steps of, using a processor;

a) repetitively measure at least a first parameter to derive at least one time series of said first parameter, said first parameter being indicative of at least a first component of said process,

b) processing said time series of said first parameter to derive a first time series of objects derived from fragments of said time series of said first parameter, said objects comprising first parameter objects,

c) storing said first time series objects in at least one of a relational database, an objects database, and an objects relational database,

d) derive at least a second time series of expense values, said expense values being associated with said process, said objects comprising expense objects,

e) processing said time series of expense values to derive a second time series of objects derived from fragments of said time series of expense values,

f) storing said second time series of objects in a relational database, an objects database, and an objects relational database,

g) compare said first time series with said second time series to determine a dynamic relationship between said first parameter objects and said expense objects.

2. ) The method of claim 1 further including steps of;

a) repetitively measure at least a second parameter to derive at least a second time series of said second parameter, said second parameter being indicative of at least a second component of said process,

b) processing said time series of said second parameter to derive a third time series of objects derived from fragments of said time series of said second parameter

c) storing said third time series objects with said first parameter objects and said expense objects is said at least one of a relational database, an objects database, and an objects relational database,

d) comparing said time series of said first parameter objects to said time series of said second parameter objects to identify an occurrence of a dynamic pattern of interaction between said first parameter and said second parameter,

e) comparing said time series of said expense objects to said occurrence to determine a relationship between said expense objects and said occurrence.

3. ) The method of claim 1 wherein said process is a pathophysiologic process and said first parameter is a physiologic parameter.

4. ) The method of claim 3 wherein said processor is connected to a patient monitor.

5. ) The method of claim 2 wherein said process is a pathophysiologic process and said first parameter is a physiologic parameter.

6. ) The method of claim 3 wherein said second parameter is a physiologic parameter.

7. ) The system of claim 5 wherein said occurrence comprises a dynamic interaction between airflow and oxygen saturation.

8. ) The system of claim 5 wherein said occurrence comprises a cluster indicative of upper airway instability.

9. ) A method for the automatic diagnosis of obstructive sleep apnea in a centralized hospital critical care monitoring system for the monitoring of a plurality of patients in at least one of a critical care, step down, and cardiac ward by telemetry, the system including a central processor having a display, and a plurality of telemetry units for mounting with said patients, each of said telemetry units having a plurality of sensors for connection with each said patient, each said sensors transmitting a plurality of signals to said central unit, the method comprising steps of;

a) programming said system to analyze said signals and to automatically identify the presence and severity of obstructive sleep apnea,

b) output an indication of said identifying.

10. ) A method of increasing at least one of hospital and physician net revenue, and improving the efficiency of the diagnosis of sleep apnea, the method comprising steps of;

a) disposing, in at least one of a critical care, step down, and cardiac ward, a centralized hospital critical care monitoring system for the monitoring of a plurality of patients by telemetry, said monitoring system including a central processor having a display, and a plurality of telemetry units for mounting with said patients, each of said telemetry units having a plurality of sensors for connection with each said patient, each said sensors transmitting a plurality of signals to said central unit, the method comprising,

b) programming said system to analyze said signals and to automatically identify the presence and severity of obstructive sleep apnea,

c) output an indication of said identifying.

11. ) The method of increasing net revenue of claim 10 further including steps of;

a) disposing a positive pressure delivery system adjacent a patients nose said delivery system being in connection with said monitoring system,

b) automatically adjusting positive pressure through said delivery system based on said signals.