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

Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges

Modeling Clinicians’ Cognitive and Collaborative Work in Post-Operative Hospital Care

abstract: Clinicians confront formidable challenges with information management and coordination activities. When not properly integrated into clinical workflow, technologies can further burden clinicians’ cognitive resources, which is associated with medical errors and risks to patient safety. An understanding of workflow is necessary to redesign information technologies (IT) that better support clinical processes. This is particularly important in surgical care, which is among the most clinical and resource intensive settings in healthcare, and is associated with a high rate of adverse events. There are a growing number of tools to study workflow; however, few produce the kinds of in-depth analyses needed to understand health IT-mediated workflow. The goals of this research are to: (1) investigate and model workflow and communication processes across technologies and care team members in post-operative hospital care; (2) introduce a mixed-method framework, and (3) demonstrate the framework by examining two health IT-mediated tasks. This research draws on distributed cognition and cognitive engineering theories to develop a micro-analytic strategy in which workflow is broken down into constituent people, artifacts, information, and the interactions between them. It models the interactions that enable information flow across people and artifacts, and identifies dependencies between them. This research found that clinicians manage information in particular ways to facilitate planned and emergent decision-making and coordination processes. Barriers to information flow include frequent information transfers, clinical reasoning absent in documents, conflicting and redundant data across documents and applications, and that clinicians are burdened as information managers. This research also shows there is enormous variation in how clinicians interact with electronic health records (EHRs) to complete routine tasks. Variation is best evidenced by patterns that occur for only one patient case and patterns that contain repeated events. Variation is associated with the users’ experience (EHR and clinical), patient case complexity, and a lack of cognitive support provided by the system to help the user find and synthesize information. The methodology is used to assess how health IT can be improved to better support clinicians’ information management and coordination processes (e.g., context-sensitive design), and to inform how resources can best be allocated for clinician observation and training.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol

Introduction In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as ‘process mining’ has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed. Methods and analysis The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes

Implementation of an interactive pattern mining framework on electronic health record datasets

Large collections of electronic patient records contain a broad range of clinical information highly relevant for data analysis. However, they are maintained primarily for patient administration, and automated methods are required to extract valuable knowledge for predictive, preventive, personalized and participatory medicine. Sequential pattern mining is a fundamental task in data mining which can be used to find statistically relevant, non-trivial temporal dependencies of events such as disease comorbidities. This works objective is to use this mining technique to identify disease associations based on ICD-9-CM codes data of the entire Taiwanese population obtained from Taiwan’s National Health Insurance Research Database. This thesis reports the development and implementation of the Disease Pattern Miner – a pattern mining framework in a medical domain. The framework was designed as a Web application which can be used to run several state-of-the-art sequence mining algorithms on electronic health records, collect and filter the results to reduce the number of patterns to a meaningful size, and visualize the disease associations as an interactive model in a specific population group. This may be crucial to discover new disease associations and offer novel insights to explain disease pathogenesis. A structured evaluation of the data and models are required before medical data-scientist may use this application as a tool for further research to get a better understanding of disease comorbidities