System and Method to Service Medical Equipment

• US 20090281867A1
• Filed: 05/06/2008
• Published: 11/12/2009
• Est. Priority Date: 05/06/2008
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Sievenpiper, Crispian, Frowein, Richard, Warner, Adrian F.
• Application Number:
  US12/115,680
• Publication Number:
  US 20090281867A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/012 Providing warranty services
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/40 for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades

Abstract

A system and method to facilitate service delivery to a client is provided. In one embodiment, a system may collect service event data corresponding to one or more failure modes from a population of devices and analyze the service event data in accordance with a reliability growth model to detect a trend in occurrences of the one or more failure modes. The system may also predict future client demand for service of the population of devices attributable to the one or more failure modes based at least in part on the detected trend, and generate a report indicating at least one of the detected trend or the predicted future client demand.

Claims

1. A system comprising:

a memory device having a plurality of routines stored therein; and

a processor configured to execute the plurality of routines stored in the one or more memory devices, the plurality of routines comprising;

a routine to collect service event data corresponding to one or more failure modes from a population of medical devices disposed at one or more healthcare facilities;

a routine to analyze the service event data in accordance with a reliability growth model to detect a trend in occurrences of the one or more failure modes; and

a routine to output a report including at least one of the following;

an indication of the detected trend, an indication of a predicted future client demand for service of the population of devices attributable to the one or more failure modes based at least in part on the detected trend, or a recommended resource allocation based at least in part on the predicted future client demand.

2. The system of claim 1, wherein the plurality of routines includes a routine to analyze performance of a service rule with respect to at least one of a detection or a prediction of a given failure mode of the one or more failure modes.

3. The system of claim 1, wherein the routine to collect service event data includes at least one of a routine to receive service event data input by an operator, or a routine to obtain service event data from the population of medical devices.

4. The system of claim 1, wherein the routine to output the report includes a routine to store the report in the memory device or in an additional memory device.

5. The system of claim 4, wherein the routine to output the report includes a routine to display the report.

6. A method comprising the steps of:

collecting service event data corresponding to one or more failure modes from a population of devices disposed at one or more client locations;

analyzing the service event data, via a computer, in accordance with a reliability growth model to detect a trend in occurrences of the one or more failure modes;

predicting future client demand for service of the population of devices attributable to the one or more failure modes based at least in part on the detected trend; and

outputting a report including at least one of an indication of the detected trend or an indication of the predicted future client demand.

7. The method of claim 6, wherein the service event data includes a cumulative number of device failures in the population of devices attributable to a particular failure mode of the one or more failure modes, wherein the cumulative number of device failures includes at least one of a predicted device failure or an actual device failure.

8. The method of claim 7, wherein the cumulative number of device failures includes an actual device failure, and the actual device failure of the cumulative number of device failures attributable to the particular failure mode triggers a service rule configured to detect the particular failure mode and generates an indication of an occurrence of the particular failure mode.

9. The method of claim 8, comprising modifying the service rule based at least in part on the frequency with which the service rule is triggered.

10. The method of claim 7, wherein the cumulative number of device failures includes a predicted device failure, and the predicted device failure of the cumulative number of device failures attributable to the particular failure mode is detected via a service rule configured to predict a future occurrence of the particular failure mode.

11. The method of claim 10, further comprising the step of evaluating the efficacy of the service rule based at least in part on the detected trend.

12. The method of claim 11, wherein evaluating the efficacy of the service rule includes comparing a reliability growth of the population of devices before relative to after deployment of the service rule.

13. The method of claim 7, wherein the cumulative number of device failures includes a device failure, and collecting the service event data includes receiving a client notification of the device failure and associating the device failure with the particular failure mode.

14. The method of claim 6, wherein the reliability growth model comprises a Crow-AMSAA model.

15. The method of claim 6, further comprising the step of adjusting an allocation of resources based at least in part on the report.

16. The method of claim 15, wherein the resources include at least one of human resources or replacement parts for the population of devices.

17. The method of claim 6, wherein the step of generating the report includes creating an indication of the detected trend, and wherein the step of predicting future client demand is based at least in part on the output report.

18. A manufacture comprising:

a computer-readable medium having executable instructions stored thereon, the executable instructions comprising;

instructions to collect data from one or more medical facilities;

instructions to analyze the data in accordance with a reliability growth model to detect a trend in the data; and

instructions adapted to output a report including at least one of the following;

an indication of the detected trend, an indication of predicted future service demand based at least in part on the detected trend, or a suggested resource allocation based at least in part on the predicted future service demand.

19. The manufacture of claim 18, wherein instructions to output the report include:

instructions to create an indication of the detected trend, and

instructions to predict a future client demand based at least in part on the output report.

20. The manufacture of claim 18, wherein the collected data includes patient data, and the instructions to analyze the data are further adapted to calculate a trend indicative of a nosocomial outbreak in a medical facility.