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

Modified Needleman-Wunsch algorithm for clinical pathway clustering

Clinical pathways are used to guide clinicians to provide a standardised delivery of care. Because of their standardisation, the aim of clinical pathways is to reduce variation in both care process and patient outcomes. When learning clinical pathways from data through data mining, it is common practice to represent each patient pathway as a string corresponding to their movements through activities. Clustering techniques are popular methods for pathway mining, and therefore this paper focuses on distance metrics applied to string data for k-medoids clustering. The two main aims are to firstly, develop a technique that seamlessly integrates expert information with data and secondly, to develop a string distance metric for the purpose of process data. The overall goal was to allow for more meaningful clustering results to be found by adding context into the string similarity calculation. Eight common distance metrics and their applicability are discussed. These distance metrics prove to give an arbitrary distance, without consideration for context, and each produce different results. As a result, this paper describes the development of a new distance metric, the modified Needleman–Wunsch algorithm, that allows for expert interaction with the calculation by assigning groupings and rankings to activities, which provide context to the strings. This algorithm has been developed in partnership with UK’s National Health Service (NHS) with the focus on a lung cancer pathway, however the handling of the data and algorithm allows for application to any disease type. This method is contained within Sim.Pro.Flow, a publicly available decision support tool

Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining

The knowledge of patient-flow is very important for healthcare organizations, because strongly connected to effectiveness and efficiency of resource allocation. Unfortunately, traditional approaches to process analysis are scarcely effective and low efficient: they are very time-consuming and they may not provide an accurate picture of healthcare processes. Process mining techniques help to overcome these problems. This paper proposes a methodology for building a DRG related patient-flow using process mining. Findings show that it is possible to discover the different sequences of activities associated with a DRG related process. Managerial implications concern both process identification, analysis and improvement. A case study, based on a real open data set, is reported

Relationships Between Nursing Resources, Uncompensated Care, Hospital Profitability, and Quality of Care

The value-based purchase requirement of the Patient Protection and Affordable Care Act puts pressure on hospital leaders to control cost while improving quality of care. The resource dependency theory was the theoretical framework for this correlational study. Archival data from the Centers for Medicare and Medicaid Services collected from 166 acute care urban hospitals for the Fiscal Year 2016. Multiple linear regression analysis was used to determine the relationship between nursing salaries per patient day, cost of uncompensated care as a percentage of net patient revenue, percentage of net income from patient services, and overall patient satisfaction for quality of care received. The multiple regression analysis results indicated the model as a whole to significantly predict overall patient satisfaction for quality of care for the Fiscal Year 2016, F (3,162) = 13.788, p = .000, and R2 = .203. In the final model, all 3 independent variables significantly predicted overall patient satisfaction for quality of care. Nursing salaries per patient day and percentage of net income from patient services were significant positive predictors of overall patient satisfaction for quality of care. Nursing salaries per patient day (� = .366, t = 5.120, p = .000) accounted for a higher contribution to the model than percentage of net income from patient services (� = .169, t = 2.374, p = .019). The cost of uncompensated care as a percentage of net patient revenue displayed a significant negative relationship with overall patient satisfaction for quality of care (� = .176, t = 2.458, p = .015). The implications of this study for positive social change include the potential to enhance the quality of care for patients while maintaining local hospitals\u27 financial viability

Smart simulation and modelling for complex cancer systems

Clinical pathways are an effective and effcient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This research project was funded by KESS2 in collaboration with a company partner - Velindre Cancer Centre (VCC). This thesis develops effcient and sustainable methods for pathway mapping, modelling and improving, within the context of developing a state-of-the-art decision support tool. A particular focus on lung cancer is considered for method construction and investigations. The clinical pathways are mapped through representing each pathway as a string of letters. This enabled the development of the modifed Needleman-Wunsch metric, to allow for consideration of both data and medical expert information, for the use with k-medoids clustering. The key contribution of automating the simulation build and necessary input parameters, is developed. Models can be constructed for four routing procedures, namely Raw Pathways, Full Transitions, Cluster Transitions and Process Medoids, that explore progressively less complex and varied interpretations of the clinical pathways. Improvements can then be investigated for aligning capacity and demand. Combining this amounted to the development of Sim.Pro.Flow, an open access decision support tool, that contains all methods discussed in this thesis. The generalised approach allows for these methods, and Sim.Pro.Flow, to be suitably exible for application with process data from both healthcare and other industries