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.

The study aims to present an architecture for a recommendation system based on user items that are transformed into narrow categories. In particular, to identify the movies a user will likely watch based on their favorite items. The recommendation system focuses on the shortest connections between item correlations. The degree of attention paid to user-group relationships provides another valuable piece of information obtained by joining the sub-groups. Various relationships have been used to reduce the data sparsity problem. We reformulate the existing data into several groups of items and users. As part of the calculations and containment of activities, we consider Pearson similarity, cosine similarity, Euclidean distance, the Gaussian distribution rule, matrix factorization, EM algorithm, and k-nearest neighbors (KNN). It is also demonstrated that the proposed methods could moderate possible recommendations from diverse perspectives.

The completeness of event logs and long-distance dependencies are two major challenges for process mining. Until now, most process mining methods have not been able to discover long-distance dependency and assume that the directly-follows relationship in the log is complete. However, due to the existence of high concurrency and the cycle, it is difficult to guarantee that the real-life log is complete regarding the directly-follows relationship. Therefore, process mining needs to be able to deal with incompleteness. In this paper, we propose a method for discovering process models including sequential, exclusive, concurrent, and cyclic structures from incomplete event logs. The method analyzes the co-occurrence class of the log and the model and then uses the technology of combining the behavior profile and co-occurrence class to obtain the communication behavior profile of the co-occurrence class. Furthermore, a method of constructing a substructure from the event log using the co-occurrence class is presented. Finally, the whole process model is built by combining those substructures. The experimental results show that the proposed method can discover process models with complex structures involving cycles from incomplete event logs and also can deal with long-distance dependency in the event log. Meanwhile, the discovered process model has a good degree of consistency with the original model.

Due to greater accessibility, healthcare databases have grown over the years. In this paper, we practice locating and associating data points or observations that pertain to similar entities across several datasets in public healthcare. Based on the methods proposed in this study, all sources are allocated using AI-based approaches to consider non-unique features and calculate similarity indices. Critical components discussed include accuracy assessment, blocking criteria, and linkage processes. Accurate measurements develop methods for manually evaluating and validating matched pairs to purify connecting parameters and boost the process efficacy. This study aims to assess and raise the standard of healthcare datasets that aid doctors’ comprehension of patients’ physical characteristics by using NARX to detect errors and machine learning models for the decision-making process. Consequently, our findings on the mortality rate of patients with COVID-19 revealed a gender bias: female 15.91% and male 22.73%. We also found a gender bias with mild symptoms such as shortness of breath: female 31.82% and male 32.87%. With congestive heart disease symptoms, the bias was as follows: female 5.07% and male 7.58%. Finally, with typical symptoms, the overall mortality rate for both males and females was 13.2%.

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