Abstract
Automation of healthcare facilities represents a challenging task of streamlining a highly information-intensive sector. Modern healthcare processes produce large amounts of data that have great potential for health policymakers and data science researchers. However, a considerable portion of such data is not captured in electronic format and hidden inside the paperwork. A major source of missing data in healthcare is paper-based clinical pathways (CPs). CPs are healthcare plans that detail the interventions for the treatment of patients, and thus are the primary source for healthcare data. However, most CPs are used as paper-based documents and not fully automated. A key contribution towards the full automation of CPs is their proper computer modeling and encoding their data with international clinical terminologies. We present in this research an ontology-based CP automation model in which CP data are standardized with SNOMED CT, thus enabling machine learning algorithms to be applied to CP-based datasets. CPs automated under this model contribute significantly to reducing data missingness problems, enabling detailed statistical analyses on CP data, and improving the results of data analytics algorithms. Our experimental results on predicting the Length of Stay (LOS) of stroke patients using a dataset resulting from an e-clinical pathway demonstrate improved prediction results compared with LOS prediction using traditional EHR-based datasets. Fully automated CPs enrich medical datasets with more CP data and open new opportunities for machine learning algorithms to show their full potential in improving healthcare, reducing costs, and increasing patient satisfaction.
Highlights
Healthcare processes produce a large amount of data that has great potential for healthcare administrators, health policymakers, and big data researchers
As an illustrative numerical example, we show the contribution of this work in improving the prediction accuracy of machine learning algorithms through experiments applied to stroke clinical pathways (CPs) data to predict Length of Stay (LOS) of stroke patients in an acute rehabilitation facility
The ontology is integrated with a Java-based prototype CP management system that we developed based on our proposed framework
Summary
Healthcare processes produce a large amount of data that has great potential for healthcare administrators, health policymakers, and big data researchers. A great portion of such data is not properly captured, missing in electronic format, and hidden inside paperwork and forms It was the hope of Electronic Health Record (EHR) systems to store that vast amount of data in digital format. The authors stressed that ‘‘given the fragmentation of health care and poor EHR interoperability, information exchange and usability, priorities for further investment in health IT will need thoughtful reconsideration’’ [1]. This is not the only study regarding the vast amount of missing healthcare data in HIS. In [3], the authors indicate that missing patients’ data are prevalent in EHRs and are an impedance to utilizing machine learning for predictive and classification tasks in healthcare
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