Studies in health technology and informatics
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Stud Health Technol Inform · Jan 2017
Randomized Controlled TrialCognitive Performance of Users Is Affected by Electronic Handovers Depending on Role, Task and Human Factors.
Patient handovers are cognitively demanding, crucial for information continuity and patient safety, but error prone. This study investigated the effect of an electronic handover tool, i.e. the handoverEHR, on the memory and care planning performance of nurse students (n=32) in a randomised, controlled cross-over design with the factors handover task and handover role. ⋯ Without handover experience and with low fluency to word problems, givers performed badly in the most demanding of the handover tasks. Final recommendations, however, can only be made after replicating this study in a clinical setting with mixed groups.
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Stud Health Technol Inform · Jan 2017
The Journey to Become a Health Literate Organization: A Snapshot of Health System Improvement.
A health literate health care organization is one that makes it easy for people to navigate, understand, and use information and services to take care of their health. This chapter explores the journey that a growing number of organizations are taking to become health literate. Health literacy improvement has increasingly been viewed as a systems issue, one that moves beyond siloed efforts by recognizing that action is required on multiple levels. ⋯ Nonetheless, discernable progress has been made. While committed to transformation, organizations seeking to be health literate recognize that it is not a destination you can ever reach. A health literate organization is constantly striving, always knowing that further improvement can be made.
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Stud Health Technol Inform · Jan 2017
Development of a Quick SOFA-Based Sepsis Clinical Decision Support System in a Tertiary Hospital Emergency Department.
New definition of sepsis introduced quick sequential organ failure assessment (qSOFA) score. A qSOFA-based sepsis decision support system was designed and developed in an emergency departement. The system has functions of autmatically retrieving qSOFA score, 3 and 6 hours of sepsis and septic shock treatment bundle, semi-automatic calculation of SOFA score, etc. Early usage and user requests are needed to find aspects of improvement.
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Much has been written on the benefits of big data for healthcare such as improving patient outcomes, public health surveillance, and healthcare policy decisions. Over the past five years, Big Data, and the data sciences field in general, has been hyped as the "Holy Grail" for the healthcare industry promising a more efficient healthcare system with the promise of improved healthcare outcomes. ⋯ Our study results show that although Big Data is built up to be as a the "Holy Grail" for healthcare, small data techniques using traditional statistical methods are, in many cases, more accurate and can lead to more improved healthcare outcomes than Big Data methods. In sum, Big Data for healthcare may cause more problems for the healthcare industry than solutions, and in short, when it comes to the use of data in healthcare, "size isn't everything."
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Stud Health Technol Inform · Jan 2017
Machine Learning Models of Post-Intubation Hypoxia During General Anesthesia.
Fine-meshed perioperative measurements are offering enormous potential for automatically investigating clinical complications during general anesthesia. In this study, we employed multiple machine learning methods to model perioperative hypoxia and compare their respective capabilities. After exporting and visualizing 620 series of perioperative vital signs, we had ten anesthesiologists annotate the subjective presence and severity of temporary post-intubation oxygen desaturation. ⋯ Furthermore, we deployed our classification methods for processing unlabeled inputs to estimate the incidence of hypoxic episodes in another sizeable patient cohort, which attests to the feasibility of using the approach on a larger scale. We interpret that our machine learning models could be instrumental for computerized observational studies of the clinical determinants of post-intubation oxygen deficiency. Future research might also investigate potential benefits of more advanced preprocessing approaches such as automated feature learning.