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.

Patients' preferences when comparing analogue implant impressions using a polyether impression material versus digital impressions (Intraoral Scan) of dental implants.

OBJECTIVES: The primary objective of this clinical study was to assess the patients' perception of the difference between an analogue impression approach on the one hand and an intra-oral scan (IO scan) on the other when restoring implants in the non-aesthetic zone. A second objective was to analyse the difference in time needed to perform these two procedures. MATERIALS AND METHODS: Thirty consecutive patients who had received 41 implants (Straumann tissue level) in the non-aesthetic zone in an implant-based referral practice setting in the Netherlands. As they were to receive crown and or bridge work on the implants, in one session, the final impressions were taken with both an analogue technique and with an intraoral scan. Patients were also asked if, directly after the treatment was carried out, they would be prepared to fill out a questionnaire on their perception of both techniques. The time involved following these two procedures was also recorded. RESULTS: The preparatory activities of the treatment, the taste of the impression material and the overall preference of the patients were significantly in favour of the IO scan. The bite registration, the scan head and gag reflex positively tended to the IO scan, but none of these effects were significant. The overall time involved with the IO scan was more negatively perceived than the analogue impression. Overall less time was involved when following the analogue impression technique than with the IO scan. CONCLUSIONS: The overall preference of the patients in our sample is significantly in favour of the approach using the IO scan. This preference relates mainly to the differences between the compared approaches with respect to taste effects and their preparatory activities. The patients did perceive the duration of IO scan more negatively than the analogue impression approach.

Interpretable Identification of Comorbidities Associated with Recurrent ED and Inpatient Visits.

In the hospital setting, a small percentage of recurrent frequent patients contribute to a disproportional amount of healthcare resource utilization. Moreover, in many of these cases, patient outcomes can be greatly improved by reducing re-occurring visits, especially when they are associated with substance abuse, mental health, and medical factors that could be improved by social-behavioral interventions, outpatient or preventative care. Additionally, health care costs can be reduced significantly with fewer preventable recurrent visits. To address this, we developed a novel, interpretable framework that both identifies recurrent patients with high utilization and determines which comorbidities contribute most to their recurrent visits. Specifically, we present a novel algorithm, called the minimum similarity association rules (MSAR), which balances the confidence-support trade-off, to determine the conditions most associated with re-occurring Emergency department and inpatient visits. We validate MSAR on a large Electronic Health Record dataset, demonstrating the effectiveness and consistency in ability to find low-support comorbidities with high likelihood of being associated with recurrent visits, which is challenging for other algorithms such as XGBoost. Clinical relevance- In the era of value-based care and population health management, the proposal could be used for decision making to help reduce future recurrent admissions, improve patient outcomes and reduce the cost of healthcare.

Process mining techniques: an application to stroke care.

In a competitive health-care market, hospitals have to focus on ways to streamline their processes in order to deliver high quality care while at the same time reducing costs. To accomplish this goal, hospital managers need a thorough understanding of the actual processes. Diffusion of Information and Communication Technology tools within hospitals, such as electronic clinical charts, computerized guidelines and, more generally, decision support systems, make huge collections of data available, not only for data analysis, but also for process analysis. Process mining can be used to extract process related information (e.g., process models) from data, i.e., process mining describes a family of a-posteriori analysis techniques exploiting the information recorded in the event logs. This process information can be used to understand and redesign processes to become efficient high quality processes. In this paper, we apply process mining on two datasets for stroke patients and present the most interesting results. Above all, the paper demonstrates the applicability of process mining in the health-care domain.