Challenges in Inpatient Care Coordinators’ Clinical Workflow and Opportunities in Designing a Health IT Solution (Preprint)

BACKGROUND Inpatient care coordinators (ICCs) play a critical role in case management and care transition because they address patient needs by referring them to available services and facilities prior to discharge. ICCs tend to spend a significant amount of time reviewing patient charts and documenting the cases using Electronic Health Record (EHR) systems. However, significant knowledge gaps exist regarding their clinical workflow and potential use of health information technology to improve work efficiency and job satisfaction. OBJECTIVE We aimed to address the gap by answering the research questions: 1) what is a typical day of an inpatient care coordinator? 2) what challenges exist in terms of their care delivery and documentation activities? and 3) what patterns in the EHR event logs reinforce our findings from the qualitative interviews? In addition, we aimed to demonstrate the feasibility of our novel mixed-method approach to study clinical workflow. METHODS A mixed-methods approach was developed and employed to understand ICCs workflow patterns and identify existing barriers to workflow. This approach involved data collection from semi-structured interviews and EHR event logs to construct a generalizable picture of all ICC workflow at the University of Cincinnati Medical Center (UCMC). The study consisted of 12 qualitative interviews with ICCs at UCMC, and their EHR event logs for one month. The qualitative interviews were analyzed using thematic analysis and the event logs were analyzed using statistical and pattern analysis. RESULTS We identified three major workflow barriers faced by ICCs: long travel time, heavy documentation load, and suboptimal communication. The event logs provided empirical evidence to support the workflow barriers identified during the semi-structured interviews, especially in travel time and documentation load. CONCLUSIONS ICC workflow has several inefficiencies. We recommend a mobile-based informatics solution with streamlined, intelligent, and EHR-linked documentation support. Our mixed-methods approach can be applied to other clinical settings and healthcare institutions. CLINICALTRIAL NA </sec

Effects of Differential Shading on Summer Tea Quality and Tea Garden Microenvironment

Shading is an effective agronomic technique to protect tea plants from intense sunlight. However, there are currently very few studies on more effective shading methods to improve the quality of summer tea. In this study, &lsquo;Longjing43&rsquo; plants were grown under four different shading treatments for 14 days, with no shading as the control. Among the four shading treatments, double-layer-net shadings had the most positive impact on the tea quality, resulting in higher levels of amino acids but lower levels of tea polyphenols. Additionally, double-layer-net shadings provided more suitable microenvironments for tea plants. The tea leaves in T4 (double nets 50 cm above the plant canopy) contained 16.13 mg&#8729;g&minus;1 of umami and sweet amino acids, which was significantly higher than in other treatments. T4 had the lowest air temperature and the most suitable and stable soil water content. Interestingly, the ratio of red light to far-red light in T4 was only 1.65, much lower than other treatments, which warrants further study. In conclusion, the microenvironment induced by shading can greatly affect the tea quality, and double-layer-net shading is better for improving the quality of summer tea

A machine learning approach for predicting descending thoracic aortic diameter

BackgroundTo establish models for predicting descending thoracic aortic diameters and provide evidence for selecting the size of the stent graft for TBAD patients.MethodsA total of 200 candidates without severe deformation of aorta were included. CTA information was collected and 3D reconstructed. In the reconstructed CTA, a total of 12 cross-sections of peripheral vessels were made perpendicular to the axis of flow of the aorta. Parameters of the cross sections and basic clinical characteristics were used for prediction. The data was randomly split into the training set and the test set in an 8:2 ratio. To fully describe diameters of descending thoracic aorta, three predicted points were set based quadrisection, and a total of 12 models at three predicted points were established using four algorithms included linear regression (LR), support vector machine (SVM), Extra-Tree regression (ETR) and random forest regression (RFR). The performance of models was evaluated by mean square error (MSE) of the prediction value, and the ranking of feature importance was given by Shapley value. After modeling, prognosis of five TEVAR cases and stent oversizing were compared.ResultsWe identified a series of parameters which affect the diameter of descending thoracic aorta, including age, hypertension, the area of proximal edge of superior mesenteric artery, etc. Among four predictive models, all the MSEs of SVM models at three different predicted position were less than 2 mm2, with approximately 90% predicted diameters error less than 2 mm in the test sets. In patients with dSINE, stent oversizing was about 3 mm, while only 1 mm in patients without complications.ConclusionThe predictive models established by machine learning revealed the relationship between basic characteristics and diameters of different segment of descending aorta, which help to provide evidence for selecting the matching distal size of the stent for TBAD patients, thereby reducing the incidence of TEVAR complications.</jats:sec

Table_4_A machine learning approach for predicting descending thoracic aortic diameter.XLSX

BackgroundTo establish models for predicting descending thoracic aortic diameters and provide evidence for selecting the size of the stent graft for TBAD patients.MethodsA total of 200 candidates without severe deformation of aorta were included. CTA information was collected and 3D reconstructed. In the reconstructed CTA, a total of 12 cross-sections of peripheral vessels were made perpendicular to the axis of flow of the aorta. Parameters of the cross sections and basic clinical characteristics were used for prediction. The data was randomly split into the training set and the test set in an 8:2 ratio. To fully describe diameters of descending thoracic aorta, three predicted points were set based quadrisection, and a total of 12 models at three predicted points were established using four algorithms included linear regression (LR), support vector machine (SVM), Extra-Tree regression (ETR) and random forest regression (RFR). The performance of models was evaluated by mean square error (MSE) of the prediction value, and the ranking of feature importance was given by Shapley value. After modeling, prognosis of five TEVAR cases and stent oversizing were compared.ResultsWe identified a series of parameters which affect the diameter of descending thoracic aorta, including age, hypertension, the area of proximal edge of superior mesenteric artery, etc. Among four predictive models, all the MSEs of SVM models at three different predicted position were less than 2 mm2, with approximately 90% predicted diameters error less than 2 mm in the test sets. In patients with dSINE, stent oversizing was about 3 mm, while only 1 mm in patients without complications.ConclusionThe predictive models established by machine learning revealed the relationship between basic characteristics and diameters of different segment of descending aorta, which help to provide evidence for selecting the matching distal size of the stent for TBAD patients, thereby reducing the incidence of TEVAR complications.</p

Image_3_A machine learning approach for predicting descending thoracic aortic diameter.TIF

BackgroundTo establish models for predicting descending thoracic aortic diameters and provide evidence for selecting the size of the stent graft for TBAD patients.MethodsA total of 200 candidates without severe deformation of aorta were included. CTA information was collected and 3D reconstructed. In the reconstructed CTA, a total of 12 cross-sections of peripheral vessels were made perpendicular to the axis of flow of the aorta. Parameters of the cross sections and basic clinical characteristics were used for prediction. The data was randomly split into the training set and the test set in an 8:2 ratio. To fully describe diameters of descending thoracic aorta, three predicted points were set based quadrisection, and a total of 12 models at three predicted points were established using four algorithms included linear regression (LR), support vector machine (SVM), Extra-Tree regression (ETR) and random forest regression (RFR). The performance of models was evaluated by mean square error (MSE) of the prediction value, and the ranking of feature importance was given by Shapley value. After modeling, prognosis of five TEVAR cases and stent oversizing were compared.ResultsWe identified a series of parameters which affect the diameter of descending thoracic aorta, including age, hypertension, the area of proximal edge of superior mesenteric artery, etc. Among four predictive models, all the MSEs of SVM models at three different predicted position were less than 2 mm2, with approximately 90% predicted diameters error less than 2 mm in the test sets. In patients with dSINE, stent oversizing was about 3 mm, while only 1 mm in patients without complications.ConclusionThe predictive models established by machine learning revealed the relationship between basic characteristics and diameters of different segment of descending aorta, which help to provide evidence for selecting the matching distal size of the stent for TBAD patients, thereby reducing the incidence of TEVAR complications.</p