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

Abstract

BackgroundRoutinely 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.ObjectiveThe 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.MethodsData 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.ResultsThe 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.ConclusionsClinical 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.

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

“…Clinical Pathway: in the context of this paper it is defined as an ordered set of patient-centric events and information relevant to a particular clinical condition [3]. It can be considered as a suitable data structure for routine data extracted from EMRs that records the actual journey of the patient for a given condition.…”

“…The activity in this example would, in turn, be part of a pathway, illustrated in Table 1. The pathway data model is defined in more detail in [3].…”

“…A first support system was developed to plot the biomarker trends based on the pathways data model [3]. This allowed the computation of charts showing the complete PSA trend for each patient in the dataset.…”

“…Based on the pathways framework, rules can be devised to inspect individual pathways and determine how complete they might be. For example, in previous work [3] rules pertaining to the availability, positioning and substantiation of the drops in PSA were proposed to determine which pathways would be eligible for further clinical research.…”

“…Hence, an initial challenge is to gather data from multiple EMR systems and construct meaningful data structures that can encompass all of the relevant information pertaining to a given patient and a given disease over time. We have named such data structures clinical pathways and have provided a methodology to build them [2,3]. Note that some researchers refer to clinical pathways as the standardised and normalised therapy pattern recommended for a particular disease [4].…”