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

Bettencourt-Silva JH, Clark J, Cooper CS, Mills R, Rayward-Smith VJ, de la Iglesia B - JMIR Med Inform (2015)

Bottom Line: The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood.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.This is an important development for the reuse of big data in hospitals.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computing Sciences, University of East Anglia, Norwich, United Kingdom. jhbs@cmp.uea.ac.uk.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Simplification of the data extraction process [14].
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Related In: Results  -  Collection

License 1 - License 2
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figure1: Simplification of the data extraction process [14].

Mentions: The process involves liaising with domain experts (or subject matter experts) to identify data sources where information related to prostate cancer patients is likely to be stored (eg, radiology). In this case study, the team of experts included a urology consultant, prostate cancer geneticists, a consultant oncologist, a histopathologist, and a chemical pathologist. For each data source identified (a EHR or HIS), the data extraction process [14] was followed. The process consists of four key steps and Figure 1 shows this: (1) system understanding, where each data source is investigated and details about the system are gathered; (2) data understanding, where data familiarization, selection, and building the data dictionary occurs; (3) extraction preparation, where data extraction methods are prepared or reviewed; and (4) extraction and evaluation, where data are extracted, validated, and the process is evaluated.


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

Bettencourt-Silva JH, Clark J, Cooper CS, Mills R, Rayward-Smith VJ, de la Iglesia B - JMIR Med Inform (2015)

Simplification of the data extraction process [14].
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526987&req=5

figure1: Simplification of the data extraction process [14].
Mentions: The process involves liaising with domain experts (or subject matter experts) to identify data sources where information related to prostate cancer patients is likely to be stored (eg, radiology). In this case study, the team of experts included a urology consultant, prostate cancer geneticists, a consultant oncologist, a histopathologist, and a chemical pathologist. For each data source identified (a EHR or HIS), the data extraction process [14] was followed. The process consists of four key steps and Figure 1 shows this: (1) system understanding, where each data source is investigated and details about the system are gathered; (2) data understanding, where data familiarization, selection, and building the data dictionary occurs; (3) extraction preparation, where data extraction methods are prepared or reviewed; and (4) extraction and evaluation, where data are extracted, validated, and the process is evaluated.

Bottom Line: The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood.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.This is an important development for the reuse of big data in hospitals.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computing Sciences, University of East Anglia, Norwich, United Kingdom. jhbs@cmp.uea.ac.uk.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus