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 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.

[1]  Noémie Elhadad,et al.  Automated methods for the summarization of electronic health records , 2015, J. Am. Medical Informatics Assoc..

[2]  Ben Shneiderman,et al.  LifeLines: visualizing personal histories , 1996, CHI.

[3]  George Hripcsak,et al.  Temporal trends of hemoglobin A1c testing , 2014, J. Am. Medical Informatics Assoc..

[4]  Pradeep Kumar Ray,et al.  Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature , 2013, Int. J. Medical Informatics.

[5]  Griffin M. Weber,et al.  Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2) , 2010, J. Am. Medical Informatics Assoc..

[6]  Vivian West,et al.  Innovative information visualization of electronic health record data: a systematic review , 2014, J. Am. Medical Informatics Assoc..

[7]  V. Rayward-Smith,et al.  On Creating a Patient-centric Database from Multiple Hospital Information Systems , 2011, Methods of Information in Medicine.

[8]  Nicolette de Keizer,et al.  The quality of evidence in health informatics: How did the quality of healthcare IT evaluation publications develop from 1982 to 2005? , 2008, Int. J. Medical Informatics.

[9]  Krzysztof J. Cios,et al.  Uniqueness of medical data mining , 2002, Artif. Intell. Medicine.

[10]  Jana Zvárová,et al.  Medical guidelines presentation and comparing with Electronic Health Record , 2006, Int. J. Medical Informatics.

[11]  Chunhua Weng,et al.  Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research , 2013, J. Am. Medical Informatics Assoc..

[12]  Olga Brazhnik,et al.  Anatomy of data integration , 2007, J. Biomed. Informatics.

[13]  Ricardo Seguel,et al.  Process Mining Manifesto , 2011, Business Process Management Workshops.

[14]  P. Carroll,et al.  Recommendations for defining and treating high risk localized prostate cancer. , 2006, The Journal of urology.

[15]  Kazunobu Yamauchi,et al.  What are the standard functions of electronic clinical pathways? , 2009, Int. J. Medical Informatics.

[16]  Pär Stattin,et al.  Prostate specific antigen for early detection of prostate cancer: longitudinal study , 2009, BMJ : British Medical Journal.

[17]  Cord Spreckelsen,et al.  Present Situation and Prospect of Medical Knowledge Based Systems in German-speaking Countries , 2012, Methods of Information in Medicine.

[18]  Walter V. Sujansky,et al.  Heterogeneous Database Integration in Biomedicine , 2001, J. Biomed. Informatics.

[19]  Thomas Lee,et al.  Critical Pathways as a Strategy for Improving Care: Problems and Potential , 1995, Annals of Internal Medicine.

[20]  Wil M. P. van der Aalst,et al.  Application of Process Mining in Healthcare - A Case Study in a Dutch Hospital , 2008, BIOSTEC.

[21]  K. Vanhaecht,et al.  An overview on the history and concept of care pathways as complex interventions , 2010 .

[22]  George Hripcsak,et al.  A Generalized Relational Schema for an Integrated Clinical Patient Database. , 1990 .

[23]  Hans-Ulrich Prokosch,et al.  Process Mining for Clinical Workflows: Challenges and Current Limitations , 2008, MIE.

[24]  Stuart Cable E-Pathways Computers and the Patient’s Journey Through CareE-Pathways Computers and the Patient’s Journey Through Care , 2003 .

[25]  Noémie Elhadad,et al.  Identifying and mitigating biases in EHR laboratory tests , 2014, J. Biomed. Informatics.

[26]  David K. Vawdrey,et al.  HARVEST, a longitudinal patient record summarizer , 2014, J. Am. Medical Informatics Assoc..

[27]  Charles Safran,et al.  Management of Information in Integrated Delivery Networks , 2001 .

[28]  C. Roehrborn,et al.  The economic burden of prostate cancer , 2011, BJU international.

[29]  Martin J. O'Connor,et al.  Knowledge-data integration for temporal reasoning in a clinical trial system , 2009, Int. J. Medical Informatics.

[30]  Huilong Duan,et al.  On mining clinical pathway patterns from medical behaviors , 2012, Artif. Intell. Medicine.

[31]  Wil M. P. van der Aalst,et al.  Process Mining Techniques: an Application to Stroke Care , 2008, MIE.

[32]  Kathryn De Luc,et al.  E-Pathways Computers and the Patient's Journey Through Care Luc Kathryn de Todd Julian E-Pathways Computers and the Patient's Journey Through Care 226pp Radcliffe Medical Press 9781857759037 1857759036. , 2003, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[33]  F. Hamdy,et al.  Prostate-cancer mortality in the USA and UK in 1975-2004: an ecological study. , 2008, The Lancet. Oncology.