SAFE-FLOW : a systematic approach for safety analysis of clinical workflows

The increasing use of technology in delivering clinical services brings substantial benefits to the healthcare industry. At the same time, it introduces potential new complications to clinical workflows that generate new risks and hazards with the potential to affect patients’ safety. These workflows are safety critical and can have a damaging impact on all the involved parties if they fail.Due to the large number of processes included in the delivery of a clinical service, it can be difficult to determine the individuals or the processes that are responsible for adverse events. Using methodological approaches and automated tools to carry out an analysis of the workflow can help in determining the origins of potential adverse events and consequently help in avoiding preventable errors. There is a scarcity of studies addressing this problem; this was a partial motivation for this thesis.The main aim of the research is to demonstrate the potential value of computer science based dependability approaches to healthcare and in particular, the appropriateness and benefits of these dependability approaches to overall clinical workflows. A particular focus is to show that model-based safety analysis techniques can be usefully applied to such areas and then to evaluate this application.This thesis develops the SAFE-FLOW approach for safety analysis of clinical workflows in order to establish the relevance of such application. SAFE-FLOW detailed steps and guidelines for its application are explained. Then, SAFE-FLOW is applied to a case study and is systematically evaluated. The proposed evaluation design provides a generic evaluation strategy that can be used to evaluate the adoption of safety analysis methods in healthcare.It is concluded that safety of clinical workflows can be significantly improved by performing safety analysis on workflow models. The evaluation results show that SAFE-FLOW is feasible and it has the potential to provide various benefits; it provides a mechanism for a systematic identification of both adverse events and safeguards, which is helpful in terms of identifying the causes of possible adverse events before they happen and can assist in the design of workflows to avoid such occurrences. The clear definition of the workflow including its processes and tasks provides a valuable opportunity for formulation of safety improvement strategies

An approach to safety analysis of clinical workflows

A clinical workflow considers the information and processes that are involved in providing a clinical service. They are safety critical since even minor faults have the potential to propagate and consequently cause harm to a patient, or even for a patient's life to be lost. Experiencing these kinds of failures has a destructive impact on all the involved parties. Due to the large number of processes and tasks included in the delivery of a clinical service, it can be difficult to determine the individuals or the processes that are responsible for adverse events, since such an analysis is typically complex and slow to do manually. Using automated tools to carry out an analysis can help in determining the root causes of potential adverse events and consequently help in avoiding preventable errors through either the alteration of existing workflows, or the design of a new workflow. This paper describes a technical approach to safety analysis of clinical workflows, utilising a safety analysis tool (Hierarchically-Performed Hazard Origin and Propagation Studies (HiP-HOPS)) that is already in use in the field of mechanical systems. The paper then demonstrates the applicability of the approach to clinical workflows by applying it to analyse the workflow in a radiology department. We conclude that the approach is applicable to this area of healthcare and provides a mechanism both for the systematic identification of adverse events and for the introduction of possible safeguards in clinical workflows

Safety analysis of clinical workflows: The case of the workflow within a radiology department

This paper was originally presented at the Science and Information Conference, 2014, 27-29 August, London. Abstract: Radiology Information Systems (RIS) and Picture Archiving and Communication systems (PACS) are used widely to help in the workflow management in radiology departments. Effective safety analysis tools are needed to ensure the reliability of these high-risk workflows, because errors that may happen through routine workflow propagate within the workflow to result in harmful failures of the system's output. This paper showed how to apply a software technology called Hierarchically-Performed Hazard Origin and Propagation Studies (HiP-HOPS) to analyse the safety of RIS/PACS workflows. The results comprised identification of the root causes of hazardous workflow failures that may put patient's life at risk. We concluded that HiP-HOPS is applicable to this area of healthcare and is able to present benefits through the detailed information on possible failures both their causes and effects. Therefore, it has the potential to improve the safety of RIS/PACS workflows and other clinical workflows

Towards Antifragility of Cloud Systems: An Adaptive Chaos driven Framework

ContextUnlike resilience, antifragility describes systems that get stronger rather than weaker under stress and chaos. Antifragile systems have the capacity to overcome stressors and come out stronger, whereas resilient systems are focused on their capacity to return to their previous state following a failure. As technology environments become increasingly complex, there is a great need for developing software systems that can benefit from failures while continuously improving. Most applications nowadays operate in cloud environments. Thus, with this increasing adoption of Cloud-Native Systems they require antifragility due to their distributed nature.ObjectiveThe paper proposes UNFRAGILE framework, which facilitates the transformation of existing systems into antifragile systems. The framework employs chaos engineering to introduce failures incrementally and assess the system's response under such perturbation and improves the quality of system response by removing fragilities and introducing adaptive fault tolerance strategies.MethodThe UNFRAGILE framework's feasibility has been validated by applying it to a cloud-native using a real-world architecture to enhance its antifragility towards long outbound service latencies. The empirical investigation of fragility is undertaken, and the results show how chaos affects application performance metrics and causes disturbances in them. To deal with chaotic network latency, an adaptation phase is put into effect.ResultsThe findings indicate that the steady stage's behaviour is like the antifragile stage's behaviour. This suggests that the system could self-stabilise during the chaos without the need to define a static configuration after determining from the context of the environment that the dependent system was experiencing difficulties.ConclusionOverall, this paper contributes to ongoing efforts to develop antifragile software capable of adapting to the rapidly changing complex environment. Overall, the research provides an operational framework for engineering software systems that learn and improve through exposure to failures rather than just surviving them