Single-site studies have demonstrated inadequate quality of discharge summaries in timeliness, transmission, and content, potentially contributing to adverse outcomes. However, degree of ...hospital-level variation in discharge summary quality for patients hospitalized with heart failure (HF) is uncertain.
We analyzed discharge summaries of patients enrolled in the Telemonitoring to Improve Heart Failure Outcomes (Tele-HF) study. We assessed hospital-level performance on timeliness (fraction of summaries completed on the day of discharge), documented transmission to the follow-up physician, and content (presence of components suggested by the Transitions of Care Consensus Conference). We obtained 1501 discharge summaries from 1640 (91.5%) patients discharged alive from 46 hospitals. Among hospitals contributing ≥ 10 summaries, the median hospital dictated 69.2% of discharge summaries on the day of discharge (range, 0.0%-98.0%; P<0.001); documented transmission of 33.3% of summaries to the follow-up physician (range, 0.0%-75.7%; P<0.001); and included 3.6 of 7 Transitions of Care Consensus Conference elements (range, 2.9-4.5; P<0.001). Hospital course was typically included (97.2%), but summaries were less likely to include discharge condition (30.7%), discharge volume status (16.0%), or discharge weight (15.7%). No discharge summary included all 7 Transitions of Care Consensus Conference-endorsed content elements, was dictated on the day of discharge, and was sent to a follow-up physician.
Even at the highest performing hospital, discharge summary quality is insufficient in terms of timeliness, transmission, and content. Improvements in all aspects of discharge summary quality are necessary to enable the discharge summary to serve as an effective transitional care tool.
Objectives: The aim of this study is to assess the risk of venous thromboembolism (VTE) in surgical patients. Study Design: An Observational Study. Setting: 4 Tertiary Hospitals in Faisalabad city. ...Period: 6 months from July 2015 to December 2015. Material & Methods: Clinical data sheets of surgical in patients of 4 teaching hospitals in Faisalabad city were retrospectively reviewed. Caprini assessment model (CAM) was used for VTE risk assessment and the patients were classified into very high risk (VHR), high risk (HR), moderate risk (MR) and low risk (LR) groups. The data was collected on an Excel spread sheet and statistical analysis was done using SPSS version 17. Chi square test was carried out to assess the association of VTE risk with age, gender, BMI and surgical units. Results: We identified a total of 256 patients from July 2015 to December 2015. The median age was 42 years. 118 (46%) patients were male and 138 (54%) were female. 106 (41.4%) patients were VHR and 124 (48.4%) patients were HR for VTE according to CAM. Nineteen (7.4%) patients were MR and only 7 (2.7%) patients were LR. Higher age and male gender were found to be significantly associated with the high risk for VTE (p<0.001). Conclusion: Our study shows that the postoperative surgical patients are at higher risk of developing VTE. There is statistically significant association between increasing age and male gender with risk of VTE in this group of patients.
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IZUM, KILJ, NUK, ODKLJ, PILJ, PNG, SAZU, UL, UM, UPUK
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of Global Positioning System (GPS)–equipped mobile devices and other inexpensive ...location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated a significant impact in various domains, including traffic management, urban planning, and health sciences. In this article, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state-of-the-art, and describe open challenges for the research community in the coming years.
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, ...mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.
This report documents the program and the outcomes of Dagstuhl Seminar 22021 "Mobility Data Science". This seminar was held January 9-14, 2022, including 47 participants from industry and academia. ...The goal of this Dagstuhl Seminar was to create a new research community of mobility data science in which the whole is greater than the sum of its parts by bringing together established leaders as well as promising young researchers from all fields related to mobility data science.
Specifically, this report summarizes the main results of the seminar by (1) defining Mobility Data Science as a research domain, (2) by sketching its agenda in the coming years, and by (3) building a mobility data science community. (1) Mobility data science is defined as spatiotemporal data that additionally captures the behavior of moving entities (human, vehicle, animal, etc.). To understand, explain, and predict behavior, we note that a strong collaboration with research in behavioral and social sciences is needed. (2) Future research directions for mobility data science described in this report include a) mobility data acquisition and privacy, b) mobility data management and analysis, and c) applications of mobility data science. (3) We identify opportunities towards building a mobility data science community, towards collaborations between academic and industry, and towards a mobility data science curriculum.
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