Abstract: A clinical decision support system (CDSS) includes a CDSS module comprising a digital processing device configured to store patient data and a clinical guideline. The clinical guideline comprises connected nodes representing clinical workflow events, actions, or decisions, wherein contiguous groups of the nodes are grouped into care phases. The CDSS module is configured to associate patient data with patient care phases selected from the care phases of the clinical guideline, and determine patient care phase time intervals for the patient care phases based on acquisition date information for the patient data associated with the patient care phases. The selected patient care phase may be a care phase associated with a clinical guideline node associated with the new patient data, or may be a patient care phase that is consistent with the acquisition date for the new patient data.

Abstract: An oncology monitoring system comprises: an image analysis module (42, 44) configured to perform an oncological monitoring operation based on images of a subject, for example acquired by positron emission tomography (PET) and computed tomography (CT); and a clinical guideline support module (10). The clinical guideline support module is configured to: display a graphical flow diagram (GFD) of a clinical therapy protocol for treating the subject comprising graphical blocks (B0, B1, B2, B3, B4, B5, B21, B211, B22, B221, B222, B223, B23, B231, B232) representing therapeutic or monitoring operations of the clinical therapy protocol including at least one monitoring operation performed by the image analysis module; annotate a graphical block of the graphical flow diagram with subject-specific information pertaining to a therapeutic or monitoring operation represented by the graphical block; and display an annotation (POP) of a graphical block (B211) responsive to selection of the graphical block by a user.

Abstract: A clinical decision support system (CDSS) includes a CDSS module (20) comprising a digital processing device (10) configured to store patient data (34) and a clinical guideline (22). The clinical guideline comprises connected nodes representing clinical workflow events, actions, or decisions, wherein contiguous groups of the nodes are grouped into care phases (40). The CDSS module is configured to associate patient data with patient care phases selected from the care phases of the clinical guideline, and determine patient care phase time intervals for the patient care phases based on acquisition date information for the patient data associated with the patient care phases. The selected patient care phase may be a care phase associated with a clinical guideline node associated with the new patient data, or may be a patient care phase that is consistent with the acquisition date for the new patient data.

Abstract: The invention relates to a system (100) for mapping a patient data structure (PD) for describing a patient's case into a guideline data structure (GD) for describing a medical guideline, the system comprising storage (170) for storing: a plurality of data items (DI1; DI2; . . . ; DI99); the patient data structure (PD) comprising data items (DI1; DI3; DI5; DI27; DI47, DI67, DI74) of the plurality of data items (DI1; DI2; . . .

Abstract: A medical information system (170) comprising a validator (140) configured to validate image findings, which are generated based on an image of anatomical structures produced by an imaging apparatus (110), in a first report with at least one of a set of predefined templates or processed data generated based on the set of predefined templates, and an augmenter (160) configured to augment the first report with information from a second report that includes medical findings.

Abstract: An oncology monitoring system comprises: an image analysis module (42, 44) configured to perform an oncological monitoring operation based on images of a subject, for example acquired by positron emission tomography (PET) and computed tomography (CT); and a clinical guideline support module (10). The clinical guideline support module is configured to: display a graphical flow diagram (GFD) of a clinical therapy protocol for treating the subject comprising graphical blocks (B0, B1 B2, B3, B4, B5, B21, B211, B22, B221, B222, B223, B23, B231, B232) representing therapeutic or monitoring operations of the clinical therapy protocol including at least one monitoring operation performed by the image analysis module; annotate a graphical block of the graphical flow diagram with subject-specific information pertaining to a therapeutic or monitoring operation represented by the graphical block; and display an annotation (POP) of a graphical block (B211) responsive to selection of the graphical block by a user.

Abstract: A clinical decision support (CDS) system comprises a patient treatment histories database (10, 32) and a patient case navigation tool (10, 30) operative to select a patient treatment history from the patient treatment histories database and to display a flowchart representation (50) of at least a portion of the selected patient treatment history. Optionally, the navigation tool (10, 30) is further operative to selectively display a flowchart representation (64, 66) of a portion or all of a patient nonspecific treatment guideline not coinciding with the selected patient treatment history. Optionally, the CDS system further comprises a patient records query engine (10, 40) operative to receive a query and apply same against the patient treatment histories database to retrieve query results, the navigation tool (10, 30) being further operative to generate a query responsive to user input and to display query results retrieved for the query.

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.