Background: The National Health Service (NHS) Talking Therapies program treats people with common mental health problems in England according to "stepped care," in which lower-intensity interventions are offered in the first instance, where clinically appropriate. Limited resources and pressure to achieve service standards mean that program providers are exploring all opportunities to evaluate and improve the flow of patients through their service. Existing research has found variation in clinical performance and stepped care implementation across sites and has identified associations between service delivery and patient outcomes. Process mining offers a data-driven approach to analyzing and evaluating health care processes and systems, enabling comparison of presumed models of service delivery and their actual implementation in practice. The value and utility of applying process mining to NHS Talking Therapies data for the analysis of care pathways have not been studied.
Objective: A better understanding of systems of service delivery will support improvements and planned program expansion. Therefore, this study aims to demonstrate the value and utility of applying process mining to NHS Talking Therapies care pathways using electronic health records.
Methods: Routine collection of a wide variety of data regarding activity and patient outcomes underpins the Talking Therapies program. In our study, anonymized individual patient referral records from two sites over a 2-year period were analyzed using process mining to visualize the care pathway process by mapping the care pathway and identifying common pathway routes.
Results: Process mining enabled the identification and visualization of patient flows directly from routinely collected data. These visualizations illustrated waiting periods and identified potential bottlenecks, such as the wait for higher-intensity cognitive behavioral therapy (CBT) at site 1. Furthermore, we observed that patients discharged from treatment waiting lists appeared to experience longer wait durations than those who started treatment. Process mining allowed analysis of treatment pathways, showing that patients commonly experienced treatment routes that involved either low- or high-intensity interventions alone. Of the most common routes, >5 times as many patients experienced direct access to high-intensity treatment rather than stepped care. Overall, 3.32% (site 1: 1507/45,401) and 4.19% (site 2: 527/12,590) of all patients experienced stepped care.
Conclusions: Our findings demonstrate how process mining can be applied to Talking Therapies care pathways to evaluate pathway performance, explore relationships among performance issues, and highlight systemic issues, such as stepped care being relatively uncommon within a stepped care system. Integration of process mining capability into routine monitoring will enable NHS Talking Therapies service stakeholders to explore such issues from a process perspective. These insights will provide value to services by identifying areas for service improvement, providing evidence for capacity planning decisions, and facilitating better quality analysis into how health systems can affect patient outcomes.
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http://dx.doi.org/10.2196/53894 | DOI Listing |
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State Key Laboratory of Marine Resources Utilization in South China Sea, School of Marine Science and Engineering, Hainan University, Haikou 570228, China.
In response to the 2023 "Action Plan for Methane Emission Control" in China, which mandates precise methane (CH) emission accounting, we developed a dynamic model to estimate CH emissions from fossil-fuel and food systems in China for the period 1990-2020. We also analyzed their socioeconomic drivers through the Logarithmic Mean Divisia Index (LMDI) model. Our analysis revealed an accelerated emission increase (850.
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January 2025
The Fourth Engineering Co., LTD, China Railway Fourth Bureau, Hefei, 230012, People's Republic of China.
Research investigating the complex mechanical properties and energy evolution mechanisms of frozen calcareous clay under the influence of multiple factors is crucial for optimizing the artificial ground freezing method in shaft sinking, thereby enhancing construction quality and safety. In this study, a four-factor, four-level orthogonal test was devised, taking into account temperature, confining pressure, dry density, and water content. The complex nonlinear curvilinear relationship between deviatoric stress, volume strain, and axial strain of frozen calcareous clay under different interaction levels was analyzed.
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January 2025
Department of Mining and Geological Engineering, University of Arizona, Tucson, AZ, 85721, USA.
The thermodynamic properties of frozen soil depend on its temperature state and ice content. Additionally, the permeability coefficient significantly affects both the temperature distribution and water movement. In this study, the dynamic variation of soil permeability coefficient with temperature is considered, the permeability coefficient is defined as a piecewise function with temperature as independent variable, and the hydrothermal coupling equation is established.
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January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFJ Mol Model
January 2025
School of Chemical and Environmental Engineering, China University of Mining and Technology-Beijing, Haidian District, Ding No.11 Xueyuan Road, Beijing, 100083, People's Republic of China.
Context: Understanding the structural characteristics of coal at the molecular level is fundamental for its effective utilization. To explore the molecular structure characteristic, the long-flame coal from Daliuta (DLT), coking coal from Yaoqiao (YQ), and anthracite from Taixi (TX) were investigated using various techniques such as elemental analysis, Fourier transform infrared spectroscopy, solid-state C nuclear magnetic resonance spectroscopy, and X-ray photoelectron spectroscopy. Based on the structural parameters, the coal molecular model was constructed and optimized.
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