Consolidating CCDs from multiple data sources: A modular approach

Masoud Hosseini, Jonathan Meade, Jamie Schnitzius, Brian Dixon

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Background Healthcare providers sometimes receive multiple continuity of care documents (CCDs) for a single patient encompassing the patient's various encounters and medical history recorded in different information systems. It is cumbersome for providers to explore different pages of CCDs to find specific data which can be duplicated or even conflicted. This study describes initial steps toward a modular system that integrates and de-duplicates multiple CCDs into one consolidated document for viewing or processing patient-level data. Materials and Methods The authors developed a prototype system to consolidate and de-duplicate CCDs. The system is engineered to be scalable, extensible, and open source. Using a corpus of 150 de-identified CCDs synthetically generated from a single data source with a common vocabulary to represent 50 unique patients, the authors tested the system's performance and output. Performance was measured based on document throughput and reduction in file size and volume of data. The authors further compared the output of the system with manual consolidation and de-duplication. Testing across multiple vendor systems or implementations was not performed. Results All of the input CCDs was successfully consolidated, and no data were lost. De-duplication significantly reduced the number of entries in different sections (49% in Problems, 60.6% in Medications, and 79% in Allergies) and reduced the size of the documents (57.5%) as well as the number of lines in each document (58%). The system executed at a rate of approximately 0.009-0.03 s per rule depending on the complexity of the rule. Discussion and Conclusion Given increasing adoption and use of health information exchange (HIE) to share data and information across the care continuum, duplication of information is inevitable. A novel system designed to support automated consolidation and de-duplication of information across clinical documents as they are exchanged shows promise. Future work is needed to expand the capabilities of the system and further test it using heterogeneous vocabularies across multiple HIE scenarios.

Original languageEnglish (US)
Pages (from-to)317-323
Number of pages7
JournalJournal of the American Medical Informatics Association
Volume23
Issue number2
DOIs
StatePublished - Mar 1 2016

Fingerprint

Continuity of Patient Care
Information Storage and Retrieval
Vocabulary
Information Systems
Health Personnel
Hypersensitivity

Keywords

  • Consolidation
  • Continuity of care document (ccd)
  • De-duplication
  • Health information exchange (hie)
  • Health level seven (hl7)
  • Meaningful use
  • Medical informatics

ASJC Scopus subject areas

  • Health Informatics

Cite this

Consolidating CCDs from multiple data sources : A modular approach. / Hosseini, Masoud; Meade, Jonathan; Schnitzius, Jamie; Dixon, Brian.

In: Journal of the American Medical Informatics Association, Vol. 23, No. 2, 01.03.2016, p. 317-323.

Research output: Contribution to journalArticle

Hosseini, Masoud ; Meade, Jonathan ; Schnitzius, Jamie ; Dixon, Brian. / Consolidating CCDs from multiple data sources : A modular approach. In: Journal of the American Medical Informatics Association. 2016 ; Vol. 23, No. 2. pp. 317-323.
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abstract = "Background Healthcare providers sometimes receive multiple continuity of care documents (CCDs) for a single patient encompassing the patient's various encounters and medical history recorded in different information systems. It is cumbersome for providers to explore different pages of CCDs to find specific data which can be duplicated or even conflicted. This study describes initial steps toward a modular system that integrates and de-duplicates multiple CCDs into one consolidated document for viewing or processing patient-level data. Materials and Methods The authors developed a prototype system to consolidate and de-duplicate CCDs. The system is engineered to be scalable, extensible, and open source. Using a corpus of 150 de-identified CCDs synthetically generated from a single data source with a common vocabulary to represent 50 unique patients, the authors tested the system's performance and output. Performance was measured based on document throughput and reduction in file size and volume of data. The authors further compared the output of the system with manual consolidation and de-duplication. Testing across multiple vendor systems or implementations was not performed. Results All of the input CCDs was successfully consolidated, and no data were lost. De-duplication significantly reduced the number of entries in different sections (49{\%} in Problems, 60.6{\%} in Medications, and 79{\%} in Allergies) and reduced the size of the documents (57.5{\%}) as well as the number of lines in each document (58{\%}). The system executed at a rate of approximately 0.009-0.03 s per rule depending on the complexity of the rule. Discussion and Conclusion Given increasing adoption and use of health information exchange (HIE) to share data and information across the care continuum, duplication of information is inevitable. A novel system designed to support automated consolidation and de-duplication of information across clinical documents as they are exchanged shows promise. Future work is needed to expand the capabilities of the system and further test it using heterogeneous vocabularies across multiple HIE scenarios.",
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