METHOD AND SYSTEM FOR PERSONALIZED GUIDELINE-BASED THERAPY AUGMENTED BY IMAGING INFORMATION

• US 20110046979A1
• Filed: 05/04/2009
• Published: 02/24/2011
• Est. Priority Date: 05/09/2008
• Status: Active Application

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Tulipano, Paola Karina, Boroczky, Lilla, Opfer, Roland, Lee, Michael C., Lagor, Charles, Carlsen, Ingwer Curt, Vloemans, Victor Paulus Marcellus
• Application Number:
  US12/989,805
• Publication Number:
  US 20110046979A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/20 relating to practices or guidelines

Abstract

When treating a patient, clinical decision support system (CDSS) guidelines are employed to assist a physician in generating a treatment plan. These plans are generated using both imaging and non-imaging data. To accomplish this, the CDSS is interfaced with imaging systems (CADx, CAD, PACS etc.). A data-mining operation is performed to identify relevant patients with similar attributes such as diagnosis, medical history, treatment, etc from imaging and non-imaging data. Natural language processing is employed to extract and encode relevant non-imaging (textual) data from relevant patients'"'"' records. Additionally, an image of a current patient is compared to reference images in a patient database to identify relevant patients. Relevant patients are then identified to a user, and the user selects a relevant patient to view detailed information related to medical history, treatment, guidelines, efficacy, and the like.

Claims

1. A guideline-based clinical decision support system (CDSS) (10), including:

a guideline engine (16) that executes one or more guidelines (28) for treating a current patient; and

an external image system (44) that interfaces with the guideline engine (16).

2. The system according to claim 1, further including a case-based data-mining engine (20) that compares current patient attributes to attributes of reference patients stored in the external imaging system (44) and determines a distance value that describes a level of similarity between the current patient and respective reference patients.

3. The system according to claim 2, further including a guideline authoring tool (26) that receives user input related to the current patient for generating a custom treatment guideline for the current patient.

4. The system according to claim 3, further including a rule-based engine (22) that provides an alert to a user when the custom treatment guideline conflicts with a predefined rule stored in a rule database (24).

5. The system according to claim 3, further including an ontology engine (18) that communicates with one or more clinical information systems (30) to retrieve reference patient attribute information for comparison to attributes associated with the current patient.

6. The system according to claim 5, wherein the one or more clinical information systems (30) include an electronic medical record database (32) and a natural language information database (34) that store information related to reference patients.

7. The system according to claim 6, wherein the case-based data-mining engine (20) is further coupled to and retrieves information from:

the one or more clinical information systems (30);

an external CDSS (36);

one or more evidence links (40); and

one or more external imaging systems (44).

8. The system according to claim 7, wherein the case-based data-mining engine (20) executes a natural language processing codec to retrieve information from the one or more clinical information systems (30), the external CDSS (36), or the one or more evidence links (40).

9. The system according to claim 8, further including a guideline-based CDSS interface (12) that presents current patient information, reference patient information, recommended guideline information, and custom guideline information to the user.

10. The system according to claim 2, wherein the user selects one or more reference patients from a list of reference patients whose patient information has a distance value below a predetermined threshold, in order to view more detailed information related to the selected reference patient.

11. The system according to claim 10, wherein the detailed information includes one or more of patient history, a patient image representation, treatment regimen, efficacy of treatment, dosage, dosing schedule, and side effects experienced by the reference patient.

12. The system according to claim 1, wherein the external imaging system includes at least one of:

a computer-aided detection (CAD) image system (46);

a computer-aided diagnosis (CADx) image system (48); and

a picture archiving and communication systems (PACS) (50).

13. The system according to claim 1, wherein attributes include at least one of size, volume, shape, texture, position, and functional parameters of a tumor or anatomical structure.

14. The system according to claim 1, wherein the guideline engine (16) includes one or more processors configured to:

compare attributes of the current patient to attributes of reference patients retrieved;

determine a distance value for at least one reference patient, the distance value being indicative of a level of similarity between the at least one reference patient and the current patient;

present to a user information associated with the at least one reference patient;

receive treatment guideline input from the user as a function of the reference patient information; and

generate and optimize a custom treatment guideline for the current patient from the received treatment guideline input.

15. A method of incorporating medical image information into clinical decision support system (CDSS) information, including:

comparing attributes of a current patient to attributes of one or more reference patients retrieved from an external imaging system (44); and

generating a custom treatment guideline for the current patient as a function of one or more treatment guidelines associated with the relevant reference patients.

16. The method according to claim 15, further including:

evaluating a level of similarity between the current patient and the one or more reference patients; and

presenting to a user reference patient information for reference patients identified as being relevant for having a level of similarity above a predetermined threshold level.

17. The method according to claim 16, further including retrieving reference patient attribute information from at least one of a computer-aided detection (CAD) imaging system (46), a computer-aided diagnosis (CADx) imaging system (48), or a picture archiving and communication systems (PACS) (50).

18. The method according to claim 15, further including comparing attributes including at least one of size, shape, texture, anatomical location, and functional parameters of a tumor or anatomical structure represented in a current patient image and one or more reference patient images.

19. The method according to claim 16, wherein presenting reference information to the user further includes:

presenting a ranked list of reference patients to the user in order of similarity between the reference patients and the current patient;

presenting at least one of a reference patient image, patient history, treatment regimen, treatment efficacy information, side effect information, dosage, and dosing schedule for a reference patient upon selection of the reference patient by the user.

20. The method according to claim 19, further including recommending a treatment guideline to the user based at least in part on treatment guidelines implemented for a relevant reference patient.

21. The method according to claim 20, further including permitting the user to modify the recommended treatment guideline to create the custom treatment guideline for the current patient.

22. The method according to claim 15, further including optimizing the custom treatment guideline for the current patient as a function of user input related to the one or more treatment guidelines.