Order Set Schema OS Schema

Order Sets are predefined groups of orders for specific clinical settings, diagnoses or treatments. They provide decision support to help clinicians improve the outcome of treatments by prompting clinical alerts about current patient's condition; diagnosis support by highlighting key symptoms and finding; medication ordering by suggesting appropriate medications and dosages based on the medical condition of a patient; cost reduction by suggesting effective alternative treatments, and medications. Such decision support can be incorporated into computerized systems in a variety of ways:

- Representation and interpretation of data

- Modeling patient information

- Representation of medical concepts

- Abstraction and interpretation of data

- Modeling processes

- Representation of actions

- Organization of plans

- Modeling decisions

- Representation of goals and intentions

In alignment with the proposed enterprise-wide unified content strategy to support consistency, interoperability, and reusability of components that can easily be integrated into applications, the initial step of our modeling process was the definition of an OS (as described above), and the elements involved. It is particularly important to have a clear definition of what is to be modeled, because an information model is about the semantics behind the data. A clear semantic definition promotes semantic interoperability between applications and information systems. Generally speaking, a definition used with a model is considered reasonably clear if the structure of the model and the way the model is used are robust with respect to variation within the range of uncertainty in the definition. The benefits derived from a clear definition also apply to the semantics of the relationships and associations in the model.

The next step in the modeling process was the analysis and categorization of reference content of the two CPOE systems, BICS OE and MGH OE, currently in use at the two main academic medical centers of Partners. In this step we identified and defined all the logically independent components (metadata categories) involved in the OSs of these two CPOE systems. The result was a list of categorized metadata representing the internal structure of the analyzed OSs, as depicted in Fig. 7. The following are the definitions of the identified metadata categories:

- Header - contains general information about the OS as a whole, including OS name, unique identifier, version, description of the purpose of the OS, and intended user.

- Audit Data - includes information that supports the editorial process from creation of content to version management and ownership.

Fig. 7. Overall structure of the XML OS Schema. The plus sign indicates that the element has multiple attributes and can be expanded

- Indication Criteria - describes clinical conditions to which the clinical knowledge applies.

- Clinical Setting - includes venues, and clinical disciplines to which the content applies.

- Intention-Action-Interaction - indicates the type of encounter to which the current content applies, the reasons and goals for the OS, and the processes involved. This category includes an ontology of intentions which will be described below.

- Target Specifics - indicates the suitability of the OS for specific target groups in terms of age, gender, and acuity of symptoms.

- Patient Data - contains patient-specific data, such as past clinical history (e.g., medications, laboratory test results, allergies, previous diagnoses, family history), and current diagnosis.

- Knowledge Rules - provides real-time decision support for medication errors, dosage error, and adverse drug interactions, known patient allergies, calculate dosages based on the patient, and standardized care. It also provides links to alternative, complementary supporting knowledge.

- Notes and Instructions - contains predefined, brief guidelines, recommendations, and instructions for procedures and actions that may be carried out by users.

- Order Group - header grouping similar orders in an OS.

The Schema in Fig. 7 captures the metadata categories of the OS information model. Each category consists of group or cluster of related content elements. As content is grouped and categorized, a taxonomy automatically starts to appear, and terms in the taxonomy become metadata that can be used as an index to retrieve content. Element metadata identifies content at the element level (finer granularity), based on the elements defined in the information model. Element metadata helps authors manage content through the authoring process, further fostering reusability, retrieval, and tracking issues. Following is a more detailed description of the elements in each category.

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