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Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer

BACKGROUND: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden pa...

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Autores principales: Bettencourt-Silva, Joao H, Clark, Jeremy, Cooper, Colin S, Mills, Robert, Rayward-Smith, Victor J, de la Iglesia, Beatriz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Gunther Eysenbach 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4526987/
https://www.ncbi.nlm.nih.gov/pubmed/26162314
http://dx.doi.org/10.2196/medinform.4221
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author Bettencourt-Silva, Joao H
Clark, Jeremy
Cooper, Colin S
Mills, Robert
Rayward-Smith, Victor J
de la Iglesia, Beatriz
author_facet Bettencourt-Silva, Joao H
Clark, Jeremy
Cooper, Colin S
Mills, Robert
Rayward-Smith, Victor J
de la Iglesia, Beatriz
author_sort Bettencourt-Silva, Joao H
collection PubMed
description BACKGROUND: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. OBJECTIVE: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. METHODS: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. RESULTS: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. CONCLUSIONS: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals.
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spelling pubmed-45269872015-08-11 Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer Bettencourt-Silva, Joao H Clark, Jeremy Cooper, Colin S Mills, Robert Rayward-Smith, Victor J de la Iglesia, Beatriz JMIR Med Inform Original Paper BACKGROUND: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. OBJECTIVE: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. METHODS: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. RESULTS: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. CONCLUSIONS: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals. Gunther Eysenbach 2015-07-10 /pmc/articles/PMC4526987/ /pubmed/26162314 http://dx.doi.org/10.2196/medinform.4221 Text en ©Joao H Bettencourt-Silva, Jeremy Clark, Colin S Cooper, Robert Mills, Victor J Rayward-Smith, Beatriz de la Iglesia. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 10.07.2015. https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/ (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Bettencourt-Silva, Joao H
Clark, Jeremy
Cooper, Colin S
Mills, Robert
Rayward-Smith, Victor J
de la Iglesia, Beatriz
Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer
title Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer
title_full Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer
title_fullStr Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer
title_full_unstemmed Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer
title_short Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer
title_sort building data-driven pathways from routinely collected hospital data: a case study on prostate cancer
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4526987/
https://www.ncbi.nlm.nih.gov/pubmed/26162314
http://dx.doi.org/10.2196/medinform.4221
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