Structured Narrative

Narrative is a critical factor in evaluating medical evidence, making management decisions, and communicating medical knowledge and is often more accurate,more comprehensive,and provides data complementary to other sources.Medical narrative captures multiple pieces of data that, when used effectively, can reduce length of stay and diminish unnecessary tests.

Well-written narrative can be easier to comprehend, more edifying, and even more convincing than structured data.

A general medicine attending physician working in the hospital receives notification of a new admission from the Emergency Room. The patient is being admitted for an asthma exacerbation. The attending physician accepts the admission and begins to work up the patient. A typical workflow for this scenario is as follows:

1. Review the patient’s history in the electronic health record, focusing on summary-level descriptions of the patient.
2. Visit with the patient, take the history, and conduct the physical examination.
3. Review available clinical data in more detail using the electronic health record.
4. Author a new note documenting the initial encounter, assessment, and plan.

Design

A design in which unstructured text and coded data are fused into a single model called structured narrative.

Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships).

Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry.

Validation

The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules.

Discussion

The proposed model represents all patient information as documents with standardized gross structure (templates).

Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques.

In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale.

NLP

In the structured narrative model, text can be marked up with coded information at any level of detail, down to sentences, phrases, and words.

This function is performed by the natural language processing (NLP) module, which takes text as input and returns XML, marking up medical concepts and their modifiers. The principal innovation of the proposed system is to apply NLP in real time, and use it to improve the entry of documents.

The NLP system analyzes medical text and identifies semantic structures consisting of core concepts (e.g., demographics, diseases, symptoms, medications, and procedures) and their modifiers (e.g., anatomic location, time, frequency, degree, and certainty).

see MedLEE for example of XML format output