Process mining for healthcare: Characteristics and challenges.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Process-oriented metrics to provide feedback and assess the performance of students who are learning surgical procedures: The percutaneous dilatational tracheostomy case.

PURPOSE: Assessing competency in surgical procedures is key for instructors to distinguish whether a resident is qualified to perform them on patients. Currently, assessment techniques do not always focus on providing feedback about the order in which the activities need to be performed. In this research, using a Process Mining approach, process-oriented metrics are proposed to assess the training of residents in a Percutaneous Dilatational Tracheostomy (PDT) simulator, identifying the critical points in the execution of the surgical process. MATERIALS AND METHODS: A reference process model of the procedure was defined, and video recordings of student training sessions in the PDT simulator were collected and tagged to generate event logs. Three process-oriented metrics were proposed to assess the performance of the residents in training. RESULTS: Although the students were proficient in classic metrics, they did not reach the optimum in process-oriented metrics. Only in 25% of the stages the optimum was achieved in the last session. In these stages, the four more challenging activities were also identified, which account for 32% of the process-oriented metrics errors. CONCLUSIONS: Process-oriented metrics offer a new perspective on surgical procedures performance, providing a more granular perspective, which enables a more specific and actionable feedback for both students and instructors.

Development of a comprehensive Percutaneous Dilatational Tracheostomy process model for procedural training: A Delphi-based experts consensus.

BACKGROUND: Deconstructing a complex procedure improves skills learning, but no model has covered all relevant Percutaneous Dilatational Tracheostomy (PDT) procedural aspects. Moreover, the heterogeneity of techniques described may hinder trainees' competency acquisition. Our objective was to develop a PDT model for procedural training that includes a comprehensive step-by-step design. METHODS: Procedural descriptions were retrieved after a structured search in medical databases. Activities were extracted and the adherence to McKinley's dimensions of procedural competence was analyzed. We developed a comprehensive PDT model, which was further validated through a Delphi-based consensus of Spanish-speaking international experts. RESULTS: The 14 descriptions retrieved for analysis presented a median [interquartile range] of 18 [11-22] steps, covering 3 [2-4] of McKinley's dimensions. The Delphi panel's first model included all McKinley's dimensions, and was answered by 25 experts from nine countries, ending in the second round. The final model included 59 activities divided into six stages (51 from the initial model and eight proposed by experts) and performed by two operators (bronchoscopy and tracheostomy). CONCLUSIONS: We have presented a PDT model that includes necessary competence dimensions to be considered complete. The model was validated by an experts' consensus, allowing to improve procedural training to promote safer patient care.

Analysis of the relationship between treatment networks and the evolution of patients with Type 2 Diabetes Mellitus.

Type 2 Diabetes Mellitus (T2DM) is a chronic disease that has been increasing in prevalence in recent years and that can cause severe complications. To ensure patient care is administered correctly, it is necessary for medical treatment teams to be both multidisciplinary and cohesive. The analysis of health processes is a constant challenge due to their high variability and complexity. This paper proposes a method based on the analysis of social networks to detect treatment networks, and to identify a relationship between these networks and patient evolution, as measured by glycated hemoglobin (HbA1c) levels. The networks were segmented based on patient adherence to their medical appointments and their mean time of delay. We applied this method on a sample of 1574 patients diagnosed with T2DM. Results show that participatory treatment -in which a patient sees a particular group of professionals on a recurrent basis - together with high levels of adherence are associated to those patients who improve their HbA1c levels in the case of high levels of adherence, while those who continually experience referrals to different professionals, remain unstable and, in some cases, get worse. On the other hand, in order to maintain a patient as stable, continuous control of the patient is enough, regardless of the recurrence to the same professionals.

Control-flow analysis of procedural skills competencies in medical training through process mining.

BACKGROUND: Procedural skills are key to good clinical results, and training in them involves a significant amount of resources. Control-flow analysis (ie, the order in which a process is performed) can provide new information for those who train and plan procedural training. This study outlines the steps required for control-flow analysis using process mining techniques in training in an ultrasound-guided internal jugular central venous catheter placement using a simulation. METHODS: A reference process model was defined through a Delphi study, and execution data (event logs) were collected from video recordings from pretraining (PRE), post-training (POST) and expert (EXP) procedure executions. The analysis was performed to outline differences between the model and executions. We analysed rework (activity repetition), alignment-based fitness (conformance with the ideal model) and trace alignment analysis (visual ordering pattern similarities). RESULTS: Expert executions do not present repetition of activities (rework). The POST rework is lower than the PRE, concentrated in the steps of the venous puncture and guidewire placement. The adjustment to the ideal model measure as alignment-based fitness, expressed as a median (25th-75th percentile) of PRE 0.74 (0.68-0.78) is less than POST 0.82 (0.76-0.86) and EXP 0.87 (0.82-0.87). There are no significant differences between POST and EXP. The graphic analysis of alignment and executions shows a progressive increase in order from PRE to EXP executions. CONCLUSION: Process mining analysis is able to pinpoint more difficult steps, assess the concordance between reference mode and executions, and identify control-flow patterns in procedural training courses.

Discovering role interaction models in the Emergency Room using Process Mining.

OBJECTIVES: A coordinated collaboration among different healthcare professionals in Emergency Room (ER) processes is critical to promptly care for patients who arrive at the hospital in a delicate health condition, claiming for an immediate attention. The aims of this study are (i) to discover role interaction models in (ER) processes using process mining techniques; (ii) to understand how healthcare professionals are currently collaborating; and (iii) to provide useful knowledge that can help to improve ER processes. METHODS: A four step method based on process mining techniques is proposed. An ER process of a university hospital was considered as a case study, using 7160 episodes that contains specific ER episode attributes. RESULTS: Insights about how healthcare professionals collaborate in the ER was discovered, including the identification of a prevalent role interaction model along the major triage categories and specific role interaction models for different diagnoses. Also, common and exceptional professional interaction models were discovered at the role level. CONCLUSIONS: This study allows the discovery of role interaction models through the use of real-life clinical data and process mining techniques. Results show a useful way of providing relevant insights about how healthcare professionals collaborate, uncovering opportunities for process improvement.

Multidisciplinary collaboration in primary care: a systematic review.

Background: Several studies have discussed the benefits of multidisciplinary collaboration in primary care. However, what remains unclear is how collaboration is undertaken in a multidisciplinary manner in concrete terms. Objective: To identify how multidisciplinary teams in primary care collaborate, in regards to the professionals involved in the teams and the collaborative activities that take place, and determine whether these characteristics and practices are present across disciplines and whether collaboration affects clinical outcomes. Methods: A systematic literature review of past research, using the MEDLINE, ScienceDirect and Web of Science databases. Results: Four types of team composition were identified: specialized teams, highly multidisciplinary teams, doctor-nurse-pharmacist triad and physician-nurse centred teams. Four types of collaboration within teams were identified: co-located collaboration, non-hierarchical collaboration, collaboration through shared consultations and collaboration via referral and counter-referral. Two combinations were commonly repeated: non-hierarchical collaboration in highly multidisciplinary teams and co-located collaboration in specialist teams. Fifty-two per cent of articles reported positive results when comparing collaboration against the non-collaborative alternative, whereas 16% showed no difference and 32% did not present a comparison. Conclusion: Overall, collaboration was found to be positive or neutral in every study that compared collaboration with a non-collaborative alternative. A collaboration typology based on objective measures was devised, in contrast to typologies that involve interviews, perception-based questionnaires and other subjective instruments.

Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining.

BACKGROUND: Public health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes. OBJECTIVE: Health care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients. METHODS: This paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise. RESULTS: The method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner. CONCLUSIONS: By discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.

Process mining in healthcare: A literature review.

Process Mining focuses on extracting knowledge from data generated and stored in corporate information systems in order to analyze executed processes. In the healthcare domain, process mining has been used in different case studies, with promising results. Accordingly, we have conducted a literature review of the usage of process mining in healthcare. The scope of this review covers 74 papers with associated case studies, all of which were analyzed according to eleven main aspects, including: process and data types; frequently posed questions; process mining techniques, perspectives and tools; methodologies; implementation and analysis strategies; geographical analysis; and medical fields. The most commonly used categories and emerging topics have been identified, as well as future trends, such as enhancing Hospital Information Systems to become process-aware. This review can: (i) provide a useful overview of the current work being undertaken in this field; (ii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches for their own applications; and (iii) highlight the use of process mining to improve healthcare processes.

The Sequence of Steps: A Key Concept Missing in Surgical Training-A Systematic Review and Recommendations to Include It.

BACKGROUND: Surgical procedures have an inherent feature, which is the sequence of steps. Moreover, studies have shown variability in surgeons' performances, which is valuable to expose residents to different ways to perform a procedure. However, it is unclear how to include the sequence of steps in training programs. METHODS: We conducted a systematic review, including studies reporting explicit teaching of a standard sequence of steps, where assessment considered adherence to a standard sequence, and where faculty or students at any level participated. We searched for articles on PubMed, EMBASE, CINAHL, Web of Science, and Google Scholar databases. RESULTS: We selected nine articles that met the inclusion criteria. The main strategy to teach the sequence was to use videos to demonstrate the procedure. The simulation was the main strategy to assess the learning of the sequence of steps. Non-standardized scoring protocols and written tests with variable validity evidence were the instruments used to assess the learning, and were focused on adherence to a standard sequence and the omission of steps. CONCLUSIONS: Teaching and learning assessment of a standard sequence of steps is scarcely reported in procedural skills training literature. More research is needed to evaluate whether the new strategies to teach and assess the order of steps work. We recommend the use of Surgical Process Models and Surgical Data Science to incorporate the sequence of steps when teaching and assessing procedural skills.

ProDeM: A Process-Oriented Delphi Method for systematic asynchronous and consensual surgical process modelling.

Surgical process models support improving healthcare provision by facilitating communication and reasoning about processes in the medical domain. Modelling surgical processes is challenging as it requires integrating information that might be fragmented, scattered, and not process-oriented. These challenges can be faced by involving healthcare domain experts during process modelling. This paper presents ProDeM: a novel Process-Oriented Delphi Method for the systematic, asynchronous, and consensual modelling of surgical processes. ProDeM is an adaptable and flexible method that acknowledges that: (i) domain experts have busy calendars and might be geographically dispersed, and (ii) various elements of the process model need to be assessed to ensure model quality. The contribution of the paper is twofold as it outlines ProDeM, but also demonstrates its operationalisation in the context of a well-known surgical process. Besides showing the method's feasibility in practice, we also present an evaluation of the method by the experts involved in the demonstration.

Building Process-Oriented Data Science Solutions for Real-World Healthcare.

The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real-world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.

Simulation-Based Mastery Learning of Bronchoscopy-Guided Percutaneous Dilatational Tracheostomy: Competency Acquisition and Skills Transfer to a Cadaveric Model.

INTRODUCTION: Although simulation-based training has demonstrated improvement of procedural skills and clinical outcomes in different procedures, there are no published training protocols for bronchoscopy-guided percutaneous dilatational tracheostomy (BG-PDT). The objective of this study was to assess the acquisition of BG-PDT procedural competency with a simulation-based mastery learning training program, and skills transfer into cadaveric models. METHODS: Using a prospective interventional design, 8 trainees naive to the procedure were trained in a simulation-based mastery learning BG-PDT program. Students were assessed using a multimodal approach, including blind global rating scale (GRS) scores of video-recorded executions, total procedural time, and hand-motion tracking-derived parameters. The BG-PDT mastery was defined as proficient tracheostomy (successful procedural performance, with less than 3 puncture attempts, and no complications) with GRS scores higher than 21 points (of 25). After mastery was achieved in the simulator, residents performed 1 BG-PDT execution in a cadaveric model. RESULTS: Compared with baseline, in the final training session, residents presented a higher procedural proficiency (0% vs. 100%, P < 0.001), with higher GRS scores [8 (6-8) vs. 25 (24-25), P = 0.01] performed in less time [563 (408-600) vs. 246 (214-267), P = 0.01] and with higher movement economy. Procedural skills were further transferred to the cadaveric model. CONCLUSIONS: Residents successfully acquired BG-PDT procedural skills with a simulation-based mastery learning training program, and skills were effectively transferred to a cadaveric model. This easily replicable program is the first simulation-based BG-PDT training experience reported in the literature, enhancing safe competency acquisition, to further improve patient care.

Team Collaboration Networks and Multidisciplinarity in Diabetes Care: Implications for Patient Outcomes.

Prevalence of type 2 diabetes mellitus (T2DM) has almost doubled in recent decades and commonly presents comorbidities and complications. T2DM is a multisystemic disease, requiring multidisciplinary treatment provided by teams working in a coordinated and collaborative manner. The application of social network analysis techniques in the healthcare domain has allowed researchers to analyze interaction between professionals and their roles inside care teams. We studied whether the structure of care teams, modeled as complex social networks, is associated with patient progression. For this, we illustrate a data-driven methodology and use existing social network analysis metrics and metrics proposed for this research. We analyzed appointment and HbA1c blood test result data from patients treated at three primary health care centers, representing six different practices. Patients with good metabolic control during the analyzed period were treated by teams that were more interactive, collaborative and multidisciplinary, whereas patients with worsening or unstable metabolic control were treated by teams with less collaboration and more continuity breakdowns. Results from the proposed metrics were consistent with the previous literature and reveal relevant aspects of collaboration and multidisciplinarity.

Delphi Method to Achieve Clinical Consensus for a BPMN Representation of the Central Venous Access Placement for Training Purposes.

Proper teaching of the technical skills necessary to perform a medical procedure begins with its breakdown into its constituent steps. Currently available methodologies require substantial resources and their results may be biased. Therefore, it is difficult to generate the necessary breakdown capable of supporting a procedural curriculum. The aim of our work was to breakdown the steps required for ultrasound guided Central Venous Catheter (CVC) placement and represent this procedure graphically using the standard BPMN notation. METHODS: We performed the first breakdown based on the activities defined in validated evaluation checklists, which were then graphically represented in BPMN. In order to establish clinical consensus, we used the Delphi method by conducting an online survey in which experts were asked to score the suitability of the proposed activities and eventually propose new activities. RESULTS: Surveys were answered by 13 experts from three medical specialties and eight different institutions in two rounds. The final model included 28 activities proposed in the initial model and four new activities proposed by the experts; seven activities from the initial model were excluded. CONCLUSIONS: The proposed methodology proved to be simple and effective, generating a graphic representation to represent activities, decision points, and alternative paths. This approach is complementary to more classical representations for the development of a solid knowledge base that allows the standardization of medical procedures for teaching purposes.

Mapping the Patient's Journey in Healthcare through Process Mining.

Nowadays, assessing and improving customer experience has become a priority, and has emerged as a key differentiator for business and organizations worldwide. A customer journey (CJ) is a strategic tool, a map of the steps customers follow when engaging with a company or organization to obtain a product or service. The increase of the need to obtain knowledge about customers' perceptions and feelings when interacting with participants, touchpoints, and channels through different stages of the customer life cycle. This study aims to describe the application of process mining techniques in healthcare as a tool to asses customer journeys. The appropriateness of the approach presented is illustrated through a case study of a key healthcare process. Results depict how a healthcare process can be mapped through the CJ components, and its analysis can serve to understand and improve the patient's experience.

Process-Oriented Instrument and Taxonomy for Teaching Surgical Procedures in Medical Training: The Ultrasound-Guided Insertion of Central Venous Catheter.

Procedural training is relevant for physicians who perform surgical procedures. In the medical education field, instructors who teach surgical procedures need to understand how their students are learning to give them feedback and assess them objectively. The sequence of steps of surgical procedures is an aspect rarely considered in medical education, and state-of-the-art tools for giving feedback and assessing students do not focus on this perspective. Process Mining can help to include this perspective in this field since it has recently been used successfully in some applications. However, these previous developments are more centred on students than on instructors. This paper presents the use of Process Mining to fill this gap, generating a taxonomy of activities and a process-oriented instrument. We evaluated both tools with instructors who teach central venous catheter insertion. The results show that the instructors found both tools useful to provide objective feedback and objective assessment. We concluded that the instructors understood the information provided by the instrument since it provides helpful information to understand students' performance regarding the sequence of steps followed.

Performance Analysis of Emergency Room Episodes Through Process Mining.

The performance analysis of Emergency Room episodes is aimed at providing decision makers with knowledge that allows them to decrease waiting times, reduce patient congestion, and improve the quality of care provided. In this case study, Process Mining is used to determine which activities, sub-processes, interactions, and characteristics of episodes explain why some episodes have a longer duration. The employed method and the results obtained are described in detail to serve as a guide for future performance analysis in this domain. It was discovered that the main cause of the increment in the episode duration is the occurrence of a loop between the Examination and Treatment sub-processes. It was also found out that as the episode severity increases, the number of repetitions of the Examination-Treatment loop increases as well. Moreover, the episodes in which this loop is more common are those that lead to Hospitalization as discharge destination. These findings might help to reduce the occurrence of this loop, in turn lowering the episode duration and, consequently, providing faster attention to more patients.