Open Source Electronic Medical Records (EMR) and Electronic Health Records (EHR) are widely used in healthcare institutions because it is mostly free and customizable. Generally, EMRs and EHRs are used in healthcare institutions because their adoption reduces costs and improves patient outcomes through increased efficiency. During the adoption of EMRs/EHRs, whether open-source or closed-source, the number one concern of healthcare institutions is their workflow. When adopting any open-source software, there is a lot to consider, "Free does not mean you have to compromise on utility." Process mining helps to discover and analyze the actual process executions of an information system (IS). In this paper, we use process mining to check the conformance of the workflow of Open-Source EMRs (workflow from event logs of an EMR) and the workflow of hospitals (workflow of hospitals based on domain knowledge). We modeled the workflow of hospital processes using business process modeling notation (BPMN) and converted it into a Petri net. Event log extracted from an Open-Source EMR (OpenEMR) was preprocessed for process conformance checking in ProM Framework. We check the conformance of log and model using alignment and replay. We display the results based on four metrics (fitness, precision, simplicity, and generalization). Then, we filter logs to check the conformance of Role-based access controls. Our conformance checking results showed that processes in Open-Source EMR align with the processes executed by hospitals.
Data used for experimentation is from the open-source EHR, Open EMR.
Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.
Data used for experimentation is from the open-source EHR, Open EMR.
Process discovery usually analyses frequent behaviour in event logs to gain an intuitive understanding of processes. However, there are some effective infrequent behaviours that help to improve business processes in real life. Most existing studies either ignore them or treat them as harmful behaviours. To distinguish effective infrequent sequences from noisy activities, this paper proposes an algorithm to analyse the distribution states of activities and the strong transfer relationships between behaviours based on maximum probability paths. The algorithm divides episodic traces into two categories: harmful and useful episodes, namely noisy activities and effective sequences. First, using conditional probability entropy, the infrequent logs are pre-processed to remove individual noisy activities that are extremely irregularly distributed in the traces. Effective sequences are then extracted from the logs based on the state transfer information of the activities. The algorithm is based on a PM4Py implementation and is validated using synthetic and real logs. From the results, the algorithm not only preserves the key structure of the model and reduces noise activity, but also improves the quality of the model.
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