Process Support for Continuous, Distributed, Multi-party Healthcare Processes - Applying Workflow Modelling to an Anticoagulation Monitoring Protocol

McChesney, Ian

doi:10.1007/978-3-319-48746-5_26

Cited by 3 publications

(2 citation statements)

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“…They are based on mathematical specification alongside a mathematical theory for interpretation and analysis. For example, Petri nets have been used for workflow modelling [7]. In addition, stochastic Petri nets [8], including queueing Petri nets, have been developed.…”

Section: Introductionmentioning

confidence: 99%

Using Markov Models to Characterize and Predict Process Target Compliance

McClean

2021

Mathematics

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Processes are everywhere, covering disparate fields such as business, industry, telecommunications, and healthcare. They have previously been analyzed and modelled with the aim of improving understanding and efficiency as well as predicting future events and outcomes. In recent years, process mining has appeared with the aim of uncovering, observing, and improving processes, often based on data obtained from logs. This typically requires task identification, predicting future pathways, or identifying anomalies. We here concentrate on using Markov processes to assess compliance with completion targets or, inversely, we can determine appropriate targets for satisfactory performance. Previous work is extended to processes where there are a number of possible exit options, with potentially different target completion times. In particular, we look at distributions of the number of patients failing to meet targets, through time. The formulae are illustrated using data from a stroke patient unit, where there are multiple discharge destinations for patients, namely death, private nursing home, or the patient’s own home, where different discharge destinations may require disparate targets. Key performance indicators (KPIs) of this sort are commonplace in healthcare, business, and industrial processes. Markov models, or their extensions, have an important role to play in this work where the approach can be extended to include more expressive assumptions, with the aim of assessing compliance in complex scenarios.

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Section: Introductionmentioning

confidence: 99%

Using Markov Models to Characterize and Predict Process Target Compliance

McClean

2021

Mathematics

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“…Unlike some industry standards, they have an exact mathematical definition of their execution semantics, with a well-developed mathematical theory for process analysis and are graphically represented as collections of places drawn as circles, transitions drawn as rectangles, and arcs, which are drawn as arrows between places. For example, Petri Nets have been used for process support for continuous, distributed, multi-party healthcare processes -by applying workflow modelling to an anticoagulation monitoring protocol (McChesney, 2016). Such workflow modelling has found relevance in the analysis and support of a range of healthcare processes, e.g.…”

Section: Introductionmentioning

confidence: 99%

Using Phase-type Models to Monitor and Predict Process Target Compliance

McClean

Stanford

Garg

et al. 2019

Proceedings of the 8th International Conference on Operations Research and Enterprise Systems

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Processes are ubiquitous, spanning diverse areas such as business, production, telecommunications and healthcare. They have been studied and modelled for many years in an attempt to increase understanding, improve efficiency and predict future pathways, events and outcomes. More recently, process mining has emerged with the intention of discovering, monitoring, and improving processes, typically using data extracted from event logs. This may include discovering the tasks within the overall processes, predicting future trajectories, or identifying anomalous tasks. We focus on using phase-type process modelling to measure compliance with known targets and, inversely, determine suitable targets given a threshold percentage required for satisfactory performance. We illustrate the ideas with an application to a stroke patient care process, where there are multiple outcomes for patients, namely discharge to normal residence, nursing home, or death. Various scenarios are explored, with a focus on determining compliance with given targets; such KPIs are commonly used in Healthcare as well as for Business and Industrial processes. We believe that this approach has considerable potential to be extended to include more detailed and explicit models that allow us to assess complex scenarios. Phase-type models have an important role in this work.

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