Advancing high-quality cancer care: cancer biomedical informatics grid supports personalized medicine and the electronic health record

Addressing critical issues in the development of an Oncology Information System

PURPOSES

This paper presents the experience on the design and implementation of a user-centered Oncology Information System developed for the Medical Oncology Department at the "Hospital Universitario Virgen de la Victoria", in Málaga, Spain. The project focused on the aspects considered in the literature as critical factors for a successful deployment and usage of a health information system.

METHODS

System usability, adequate technology, integration of clinical routines, real-time statistical analysis of data, information confidentiality and standard protocol-based external interconnection were the key aspects considered.

RESULTS

The developed system is based on a web application with a modular and layered architecture accounting for usability, ease of maintenance and further system development. Evaluation of system usability was carried at three and fifteen months after system deployment to analyze the advantages/disadvantages experienced by the end-users.

CONCLUSIONS

A thorough prior analysis of clinical activities and workflows, the use of the adequate technology, and the availability of data analysis tools will almost guarantee success in the deployment of an Oncology Information System.

Development of data models for nursing assessment of cancer survivors using concept analysis

Objectives

Sharing of cancer-related information among healthcare professionals is crucial to ensuring the quality of long-term 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, 102 value 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.