Creating hospital-specific customized clinical pathways by applying semantic reasoning to clinical data

AbstractObjectiveClinical pathways (CPs) are widely studied methods to standardize clinical intervention and improve medical quality. However, standard care plans defined in current CPs are too general to execute in a practical healthcare environment. The purpose of this study was to create hospital-specific personalized CPs by explicitly expressing and replenishing the general knowledge of CPs by applying semantic analysis and reasoning to historical clinical data.MethodsA semantic data model was constructed to semantically store clinical data. After querying semantic clinical data, treatment procedures were extracted. Four properties were self-defined for local ontology construction and semantic transformation, and three Jena rules were proposed to achieve error correction and pathway order recognition. Semantic reasoning was utilized to establish the relationship between data orders and pathway orders.ResultsA clinical pathway for deviated nasal septum was used as an example to illustrate how to combine standard care plans and practical treatment procedures. A group of 224 patients with 11,473 orders was transformed to a semantic data model, which was stored in RDF format. Long term order processing and error correction made the treatment procedures more consistent with clinical practice. The percentage of each pathway order with different probabilities was calculated to declare the commonality between the standard care plans and practical treatment procedures. Detailed treatment procedures with pathway orders, deduced pathway orders, and orders with probability greater than 80% were provided to efficiently customize the CPs.ConclusionsThis study contributes to the practical application of pathway specifications recommended by the Ministry of Health of China and provides a generic framework for the hospital-specific customization of standard care plans defined by CPs or clinical guidelines

Ontology-based modelling of stroke clinical pathways

Healthcare spending in Canada is on the rise. One method to reduce healthcare spending is to reduce length of stay (LOS). Clinical Pathways (CPs) are one recommended management technique to reduce LOS. CPs are implementations of medical guidelines in a specific healthcare environment. This may include hospitals, clinics or other healthcare facilities. They represent an evidence-based patient care workflow for a specific disease. The adoption of CPs allows for easier continuity of care across different healthcare settings and medical teams. While the use of CP as part of standard patient care has grown considerably in the past decades, not much progress was made in CP representation and modeling to encode CP data properly within existing Health Information Systems (HIS). One proposed method to achieve this goal is ontological modeling. Ontology is a formal model that represents a certain subject matter. It not only communicates what things exist in a certain domain or field but also how those things relate to each other. This research proposes an ontological model for stroke CP representation and processing. Such a model would allow CPs to be sharable, extendable, and machine-readable, thus enabling greater patient management. The Systematized Nomenclature of Medicine – Clinical Terms (SNOMED – CT) is used to encode medical knowledge described within clinical pathways. The stroke CP Ontology is an extension of a generic CP ontology, with new concepts introduced specific to the domain of stroke. It is able to represent different types of CP activities, occurring over a period of type, referencing medical knowledge contained in SNOMED CT. It is also able to infer new knowledge using the Semantic Web Rule Language (SWRL). This ontology is presented to users through a prototype Clinical Pathway Management System (CPMS). The CPMS is built using Java and the Eclipse IDE. The OWL and SWRL API are used to directly connect to and query ontology files. After completion of a CP, the CPMS generates new ontology files unique to each patient’s CP execution as well as a general output file of patient activities and outcomes. Data analytics can be performed on this output file to determine the most common CP activities, levels of compliancy and similarities between patient CP progressions

Semantic web system for differential diagnosis recommendations

There is a growing realization that healthcare is a knowledge-intensive field. The ability to capture and leverage semantics via inference or query processing is crucial for supporting the various required processes in both primary (e.g. disease diagnosis) and long term care (e.g. predictive and preventive diagnosis). Given the wide canvas and the relatively frequent knowledge changes that occur in this area, we need to take advantage of the new trends in Semantic Web technologies. In particular, the power of ontologies allows us to share medical research and provide suitable support to physician's practices. There is also a need to integrate these technologies within the currently used healthcare practices. In particular the use of semantic web technologies is highly demanded within the clinicians' differential diagnosis process and the clinical pathways disease management procedures as well as to aid the predictive/preventative measures used by healthcare professionals