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Enabling flexible integration of healthcare information using the entity-attribute-value storage model.

Löper D, Klettke M, Bruder I, Heuer A - Health Inf Sci Syst (2013)

Bottom Line: Therefore, a digital patient care record is introduced to establish the foundation for integrating healthcare-related information.The time for traversing the results strongly depends on the number of documents.The underlying database structure is presented, the import process for extracting incoming reports is described and the export process for generating new outgoing standardized reports is briefly illustrated.

View Article: PubMed Central - PubMed

Affiliation: Database Research Group, University of Rostock, 18051 Rostock, Germany.

ABSTRACT

Background: For an optimal care of patients in home healthcare, it is essential to exchange healthcare-related information with other stakeholders. Unfortunately, paper-based documentation procedures as well as the heterogeneity between information systems inhibit a well-regulated communication. Therefore, a digital patient care record is introduced to establish the foundation for integrating healthcare-related information.

Methods: For the digital patient care record, suitable integration techniques are required that store data in a compact way and offer flexibility as well as robustness. For this purpose, a generic storage structure based on the entity-attribute-value (EAV) model is introduced. This storage structure fulfills the stated requirements and incoming information can be stored directly without any loss of data.

Evaluation results and discussions: First performance tests regarding the query response time are given in this paper. The tests measured the connection time, the query execution time, and the time for traversing the result set. The time for executing the query is lowest. The time for traversing the results strongly depends on the number of documents. A concept comparison to other integration techniques is also presented.

Conclusions: This approach offers flexibility concerning different standard types and the evolution in healthcare knowledge and processes. It also allows for highly sparse data to be stored in a compact way. The underlying database structure is presented, the import process for extracting incoming reports is described and the export process for generating new outgoing standardized reports is briefly illustrated.

No MeSH data available.


Related in: MedlinePlus

Export Process. The export process is divided into five subtasks. Based on some prior requirements, some preselected data will be presented to the care giver. The user selects the relevant data manually. It is then transformed into the target format with the help of transformation rules which still need to be defined. It is then checked whether the document is complete. If not, the care giver checks the selected data again and modifies it. If the document is complete, the target format is generated.
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Fig6: Export Process. The export process is divided into five subtasks. Based on some prior requirements, some preselected data will be presented to the care giver. The user selects the relevant data manually. It is then transformed into the target format with the help of transformation rules which still need to be defined. It is then checked whether the document is complete. If not, the care giver checks the selected data again and modifies it. If the document is complete, the target format is generated.

Mentions: Exporting data from the EAV model is more complicated, though. We have to overcome the heterogeneities introduced by the different data sources. Figure 6 shows the subtasks for the export process. In most cases, newly generated reports only contain a fraction of the stored patient data. The selection of the data to be exported can be divided into two steps: The care giver is provided with a preselected list of data, e.g. which are shown on a mobile device, and then chooses the relevant data among this list manually. The most complicated task within the export process is the Transformation. For this task, transformation rules are applied for the mapping of data, which are either prepared (available in the system for known data formats) or have to be extended by the domain expert for new and previously unknown data formats.Figure 6


Enabling flexible integration of healthcare information using the entity-attribute-value storage model.

Löper D, Klettke M, Bruder I, Heuer A - Health Inf Sci Syst (2013)

Export Process. The export process is divided into five subtasks. Based on some prior requirements, some preselected data will be presented to the care giver. The user selects the relevant data manually. It is then transformed into the target format with the help of transformation rules which still need to be defined. It is then checked whether the document is complete. If not, the care giver checks the selected data again and modifies it. If the document is complete, the target format is generated.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4340778&req=5

Fig6: Export Process. The export process is divided into five subtasks. Based on some prior requirements, some preselected data will be presented to the care giver. The user selects the relevant data manually. It is then transformed into the target format with the help of transformation rules which still need to be defined. It is then checked whether the document is complete. If not, the care giver checks the selected data again and modifies it. If the document is complete, the target format is generated.
Mentions: Exporting data from the EAV model is more complicated, though. We have to overcome the heterogeneities introduced by the different data sources. Figure 6 shows the subtasks for the export process. In most cases, newly generated reports only contain a fraction of the stored patient data. The selection of the data to be exported can be divided into two steps: The care giver is provided with a preselected list of data, e.g. which are shown on a mobile device, and then chooses the relevant data among this list manually. The most complicated task within the export process is the Transformation. For this task, transformation rules are applied for the mapping of data, which are either prepared (available in the system for known data formats) or have to be extended by the domain expert for new and previously unknown data formats.Figure 6

Bottom Line: Therefore, a digital patient care record is introduced to establish the foundation for integrating healthcare-related information.The time for traversing the results strongly depends on the number of documents.The underlying database structure is presented, the import process for extracting incoming reports is described and the export process for generating new outgoing standardized reports is briefly illustrated.

View Article: PubMed Central - PubMed

Affiliation: Database Research Group, University of Rostock, 18051 Rostock, Germany.

ABSTRACT

Background: For an optimal care of patients in home healthcare, it is essential to exchange healthcare-related information with other stakeholders. Unfortunately, paper-based documentation procedures as well as the heterogeneity between information systems inhibit a well-regulated communication. Therefore, a digital patient care record is introduced to establish the foundation for integrating healthcare-related information.

Methods: For the digital patient care record, suitable integration techniques are required that store data in a compact way and offer flexibility as well as robustness. For this purpose, a generic storage structure based on the entity-attribute-value (EAV) model is introduced. This storage structure fulfills the stated requirements and incoming information can be stored directly without any loss of data.

Evaluation results and discussions: First performance tests regarding the query response time are given in this paper. The tests measured the connection time, the query execution time, and the time for traversing the result set. The time for executing the query is lowest. The time for traversing the results strongly depends on the number of documents. A concept comparison to other integration techniques is also presented.

Conclusions: This approach offers flexibility concerning different standard types and the evolution in healthcare knowledge and processes. It also allows for highly sparse data to be stored in a compact way. The underlying database structure is presented, the import process for extracting incoming reports is described and the export process for generating new outgoing standardized reports is briefly illustrated.

No MeSH data available.


Related in: MedlinePlus