Optimization, Simulation and IT for Healthcare Processes and Services
Permanent URI for this collection
Browse
Recent Submissions
1 - 5 of 5
-
ItemSeeing the Signs of Workarounds: A Mixed-Methods Approach to the Detection of Nurses’ Process Deviations( 2021-01-05)Workarounds are intentional deviations from prescribed processes. They are most commonly studied in healthcare settings, where nurses are known for frequently deviating from the intended way of using health information systems. However, workarounds in healthcare have only been studied using qualitative methods, such as observations and interviews. We conduct a case study in a Dutch hospital and use a mixed-methods approach that draws not only on interviews and observations, but also on process mining, to detect and analyse eight workarounds that occur in a clinical care process. We contribute to theory by demonstrating that it is possible to use data to determine the occurrence of a rich variety of workarounds found using qualitative methods. Practically, this implies that workarounds that are identified qualitatively can be further analysed and monitored using quantitative methods. Once identified, workarounds also provide an attractive starting point for organisational learning and improvement.
-
ItemPatient Pathways for Comprehensive Care Networks - A Development Method and Lessons from its Application in Oncology Care( 2021-01-05)Patient pathways are recognized as a valuable tool to support standardization, comparability, quality, and transparency of care processes in comprehensive care networks. Still, existing development approaches lack real practical guidance as well as an integration of the network and patient perspectives. Therefore, a user-centered and requirements-based approach was chosen to design a patient pathway development method. It defines a role model and procedural steps. The method’s innovative character lies in the development of generic patient pathway templates to be adapted to national, regional, and local conditions of specific comprehensive care networks. The method was positively assessed in terms of demonstrating its applicability and the fulfilment of user requirements with a use case from oncology care – the development of a colorectal cancer patient pathway template. This work drives the standardization of patient pathway development and their large-scale implementation in comprehensive care networks, supporting the analysis, design, and optimization of healthcare processes.
-
ItemNext Frontiers in Emergency Medical Services in Germany: Identifying Gaps between Academia and Practice( 2021-01-05)In recent years, an increase in data availability and computation power led to the "rise of Artificial Intelligence (AI)". In many different domains, AI-based methods and more specifically intelligent decision support systems (DSS) are studied in research and already implemented in practice, but not yet so in emergency medical services (EMS). This is especially true for the German EMS system that falls short in terms of digitization in general and the use of well-grounded methods for managing and planning their logistics and processes. As the actual need for intelligent DSS in the German EMS are unclear, we have performed interviews with German EMS experts. Referring to the qualitative data, we compare the decision problems and desired DSS with existing research and identify gaps between academia and practice.
-
ItemMinimizing the usage of SARS-CoV-2 lab test resources through test pooling enhanced by classification techniques( 2021-01-05)Testing is an effective practice to limit the spread of the SARS-CoV-2. PCR is an accurate method to detect SARS-CoV-2 infected individuals, but PCR lab test kits are scarce and expensive resources. Therefore, their usage should be optimized. Testing in batch (pooling) is a procedure that merges individuals’ swabs, allowing group diagnosis without affecting the accuracy of the results. Savings on test kits depend on the prevalence of the disease, pool composition, and size. We propose a novel approach for optimizing pooling to minimize the usage of lab test kits. We show that estimating the probability of an individual being infected by means of a binary classifier leads to improvements in the efficiency of pooling strategies. We use simulation to select the components of a new pooling strategy based on a classifier and evaluate our approach using a real dataset.
-
Item