Limits...
Clinical Decision Support Systems (CDSS) for preventive management of COPD patients.

Velickovski F, Ceccaroni L, Roca J, Burgos F, Galdiz JB, Marina N, Lluch-Ariet M - J Transl Med (2014)

Bottom Line: Web services were integrated in existing health information system platforms.Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management.Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway.

Objectives: The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow.

Methods: The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team.

Results: A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms.

Conclusions: Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.

Show MeSH

Related in: MedlinePlus

Reasoning paradigm.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4255917&req=5

Figure 2: Reasoning paradigm.

Mentions: Figure 2 is an illustration of the reasoning paradigm implemented by the CDSS. The rule-based engine operates on inserted facts about a patient that are transmitted to the CDSS by the external HIS requiring decision-support services. Facts may be particular clinical findings or measurements or demographic information about the patient (e.g. "forced vital capacity = 3.7 L"; "dyspnea's MRC severity grade = 4"; "gender = male"). Rules represent mathematical or logical knowledge that infers (produces) new facts from currently available facts. Clinical rules are a subclass of rules that represent clinical and medical knowledge that infers new facts or medical recommendations from currently available medical facts. Clinical rules operate within a modular context that allows, at any particular moment, firing only the specific set of rules associated with the specific clinical task at hand (e.g. case-finding, diagnosis, assessment).


Clinical Decision Support Systems (CDSS) for preventive management of COPD patients.

Velickovski F, Ceccaroni L, Roca J, Burgos F, Galdiz JB, Marina N, Lluch-Ariet M - J Transl Med (2014)

Reasoning paradigm.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4255917&req=5

Figure 2: Reasoning paradigm.
Mentions: Figure 2 is an illustration of the reasoning paradigm implemented by the CDSS. The rule-based engine operates on inserted facts about a patient that are transmitted to the CDSS by the external HIS requiring decision-support services. Facts may be particular clinical findings or measurements or demographic information about the patient (e.g. "forced vital capacity = 3.7 L"; "dyspnea's MRC severity grade = 4"; "gender = male"). Rules represent mathematical or logical knowledge that infers (produces) new facts from currently available facts. Clinical rules are a subclass of rules that represent clinical and medical knowledge that infers new facts or medical recommendations from currently available medical facts. Clinical rules operate within a modular context that allows, at any particular moment, firing only the specific set of rules associated with the specific clinical task at hand (e.g. case-finding, diagnosis, assessment).

Bottom Line: Web services were integrated in existing health information system platforms.Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management.Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway.

Objectives: The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow.

Methods: The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team.

Results: A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms.

Conclusions: Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.

Show MeSH
Related in: MedlinePlus