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

On Enabling Integrated Process Compliance with Semantic Constraints in Process Management Systems

Key to broad use of process management systems (PrMS) in practice is their ability to foster and ease the implementation, execution, monitoring, and adaptation of business processes while still being able to ensure robust and error-free process enactment. To meet these demands a variety of mechanisms has been developed to prevent errors at the structural level (e.g., deadlocks). In many application domains, however, processes often have to comply with business level rules and policies (i.e., semantic constraints) as well. Hence, to ensure error-free executions at the semantic level, PrMS need certain control mechanisms for validating and ensuring the compliance with semantic constraints. In this paper, we discuss fundamental requirements for a comprehensive support of semantic constraints in PrMS. Moreover, we provide a survey on existing approaches and discuss to what extent they are able to meet the requirements and which challenges still have to be tackled. In order to tackle the particular challenge of providing integrated compliance support over the process lifecycle, we introduce the SeaFlows framework. The framework introduces a behavioural level view on processes which serves a conceptual process representation for constraint specification approaches. Further, it provides general compliance criteria for static compliance validation but also for dealing with process changes. Altogether, the SeaFlows framework can serve as formal basis for realizing integrated support of semantic constraints in PrMS

On mining latent treatment patterns from electronic medical records

Clinical pathway (CP) analysis plays an important role in health-care management in ensuring specialized, standardized, normalized and sophisticated therapy procedures for individual patients. Recently, with the rapid development of hospital information systems, a large volume of electronic medical records (EMRs) has been produced, which provides a comprehensive source for CP analysis. In this paper, we are concerned with the problem of utilizing the heterogeneous EMRs to assist CP analysis and improvement. More specifically, we develop a probabilistic topic model to link patient features and treatment behaviors together to mine treatment patterns hidden in EMRs. Discovered treatment patterns, as actionable knowledge representing the best practice for most patients in most time of their treatment processes, form the backbone of CPs, and can be exploited to help physicians better understand their specialty and learn from previous experiences for CP analysis and improvement. Experimental results on a real collection of 985 EMRs collected from a Chinese hospital show that the proposed approach can effectively identify meaningful treatment patterns from EMRs

Mining Process Execution and Outcomes – Position Paper

Abstract. Organizational processes in general and patient-care processes in particular, change over time. This may be in response to situations unpredicted by a predefined business process model (or clinical guideline), or as a result of new knowledge which has not yet been incorporated into the model. Process mining techniques enable capturing process changes, evaluating the gaps between the predefined model and the practiced process, and modifying the model accordingly. This position paper motivates the extension of process mining in order to capture not only deviations from the process model, but also the outcomes associated with them (e.g., patient improving or deteriorating). These should be taken into account when modifications to the process are made