Development of Data Models for Nursing Assessment of Cancer Survivors Using Concept Analysis
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Objectives: Sharing of cancer-related information among healthcare professionals is crucial to ensuring the quality of longterm care for cancer survivors. Appropriate distribution of the essential facts can be achieved using data models. The purpose of this study was to develop and validate suitable data models for use in the nursing assessment of cancer survivors. Methods:The models developed in this study were based on a modification of concept analysis developed by Walker and Avant. Our approach involved determining the purpose of the analysis, identifying data elements, defining these elements and their uses, determining critical attributes, value sets, and cardinalities, and ultimately constructing data models which were examined externally by domain experts. Results: We developed 112 data models with 112 data elements, 29 critical attributes, 102value sets, and 6 data types for the assessment of cancer survivors. External validation revealed that the data elements, critical attributes, and value sets proposed were comprehensive, relevant, and sufficiently useful to encompass nursing issues related to cancer survivors. Conclusions: Data models developed in this study will contribute to ensuring the semantic consistency of data collected from cancer survivors, which will improve the quality of nursing assessments and in turn translate to improved long-term patient care.
Abstract
Objectives: Sharing of cancer-related information among healthcare professionals is crucial to ensuring the quality of longterm care for cancer survivors. Appropriate distribution of the essential facts can be achieved using data models. The purpose of this study was to develop and validate suitable data models for use in the nursing assessment of cancer survivors. Methods:The models developed in this study were based on a modification of concept analysis developed by Walker and Avant. Our approach involved determining the purpose of the analysis, identifying data elements, defining these elements and their uses, determining critical attributes, value sets, and cardinalities, and ultimately constructing data models which were examined externally by domain experts. Results: We developed 112 data models with 112 data elements, 29 critical attributes, 102value sets, and 6 data types for the assessment of cancer survivors. External validation revealed that the data elements, critical attributes, and value sets proposed were comprehensive, relevant, and sufficiently useful to encompass nursing issues related to cancer survivors. Conclusions: Data models developed in this study will contribute to ensuring the semantic consistency of data collected from cancer survivors, which will improve the quality of nursing assessments and in turn translate to improved long-term patient care.
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