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An ontology driven clinical evidence service providing diagnostic decision support in family practice.

Corrigan D - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Formulation of a working diagnostic hypothesis in family practice requires consideration of many differential diagnoses associated with any presenting patient complaint.The solution implements ontology models of evidence accessible to consumers as a web service using open source components and standards.An implementation example is described that consumes the service to drive a diagnostic decision support tool developed for the TRANSFoRm project.

View Article: PubMed Central - PubMed

Affiliation: HRB Centre for Primary Care Research, Royal College of Surgeons in Ireland, Dublin, Ireland.

ABSTRACT
Formulation of a working diagnostic hypothesis in family practice requires consideration of many differential diagnoses associated with any presenting patient complaint. There follows a process of refinement of the differentials to consider, through ruling in or out each candidate differential based on the confirmed presence or absence of diagnostic cues elicited during patient consultation. The patient safety implications of diagnostic error are potentially severe for patient and clinician. This paper describes a clinical evidence service supporting this diagnostic process. It allows decision support consumers to provide coded evidence-based recommendations to assist with diagnostic hypothesis formulation, integrated with an EHR in primary care. The solution implements ontology models of evidence accessible to consumers as a web service using open source components and standards. An implementation example is described that consumes the service to drive a diagnostic decision support tool developed for the TRANSFoRm project.

No MeSH data available.


Related in: MedlinePlus

An XML patient evidence set submitted to the evidence service for a female patient presenting with chest pain and symptoms including fatigue.
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f5-2081312: An XML patient evidence set submitted to the evidence service for a female patient presenting with chest pain and symptoms including fatigue.

Mentions: The client layer provides a client side library used to handle exchange of patient data between the third party consumer with appropriate calls sent to the backend evidence service. The client accepts patient data in the form of a XML patient evidence set describing the patient RFE, demographics and the underlying cues confirmed through consultation with the patient (figure 5). The evidence service returns recommendations in the form of a dynamically updated ranked list of differentials to consider by keeping a cue count for each differential under consideration. This list is based on the presenting RFE and ordered in descending cue count based on the number of patient cues confirmed present for each differential along with the supporting underlying evidence cues for each diagnosis. An interactive and iterative diagnostic conversation can take place between the third party consumer as presence or absence of patient cues are confirmed, appropriate patient contextualised REST queries are executed and the re-ranked diagnosis list is supplied to the consumer tool.


An ontology driven clinical evidence service providing diagnostic decision support in family practice.

Corrigan D - AMIA Jt Summits Transl Sci Proc (2015)

An XML patient evidence set submitted to the evidence service for a female patient presenting with chest pain and symptoms including fatigue.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525252&req=5

f5-2081312: An XML patient evidence set submitted to the evidence service for a female patient presenting with chest pain and symptoms including fatigue.
Mentions: The client layer provides a client side library used to handle exchange of patient data between the third party consumer with appropriate calls sent to the backend evidence service. The client accepts patient data in the form of a XML patient evidence set describing the patient RFE, demographics and the underlying cues confirmed through consultation with the patient (figure 5). The evidence service returns recommendations in the form of a dynamically updated ranked list of differentials to consider by keeping a cue count for each differential under consideration. This list is based on the presenting RFE and ordered in descending cue count based on the number of patient cues confirmed present for each differential along with the supporting underlying evidence cues for each diagnosis. An interactive and iterative diagnostic conversation can take place between the third party consumer as presence or absence of patient cues are confirmed, appropriate patient contextualised REST queries are executed and the re-ranked diagnosis list is supplied to the consumer tool.

Bottom Line: Formulation of a working diagnostic hypothesis in family practice requires consideration of many differential diagnoses associated with any presenting patient complaint.The solution implements ontology models of evidence accessible to consumers as a web service using open source components and standards.An implementation example is described that consumes the service to drive a diagnostic decision support tool developed for the TRANSFoRm project.

View Article: PubMed Central - PubMed

Affiliation: HRB Centre for Primary Care Research, Royal College of Surgeons in Ireland, Dublin, Ireland.

ABSTRACT
Formulation of a working diagnostic hypothesis in family practice requires consideration of many differential diagnoses associated with any presenting patient complaint. There follows a process of refinement of the differentials to consider, through ruling in or out each candidate differential based on the confirmed presence or absence of diagnostic cues elicited during patient consultation. The patient safety implications of diagnostic error are potentially severe for patient and clinician. This paper describes a clinical evidence service supporting this diagnostic process. It allows decision support consumers to provide coded evidence-based recommendations to assist with diagnostic hypothesis formulation, integrated with an EHR in primary care. The solution implements ontology models of evidence accessible to consumers as a web service using open source components and standards. An implementation example is described that consumes the service to drive a diagnostic decision support tool developed for the TRANSFoRm project.

No MeSH data available.


Related in: MedlinePlus