scholarly journals Findings from the Yearbook 2010 Section on Decision Support Systems

2010 ◽  
Vol 19 (01) ◽  
pp. 55-57
Author(s):  
P. Ruch ◽  

Summary Objective: To summarize current excellent research in the field of computer-based decision support systems in health and healthcare. Methods: We provide a synopsis of the articles selected for the IMIA Yearbook 2010, from which we attempt to derive a synthetic overview of the activity and new trends in the field. Results: While the state of the research in the field of medical decision support systems is illustrated by a set of fairly heterogeneous studies, it is possible to identify trends. Thus, clearly, the importance of studies related to computerized prescription order entry (CPOE) systems and guidelines management systems for both medical decision making and care providers, occupies a central role in the field, with application affecting also EHR vendors. In parallel, we observe translational interests for developing bridges with results generated by molecular biology, where the mass of data generated by high/ throughput experiments and large-scale genome analysis projects, raises specific processing challenges. Conclusions: The best paper selection of articles on decision support shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. This selected set of scientific investigations demonstrates the needs for computerized applications to transform the biomedical data overflow into more operational clinical knowledge. Altogether these papers support the idea that more elaborated computer tools, likely to combine heterogeneous contextual contents, are needed.

2012 ◽  
Vol 21 (01) ◽  
pp. 113-116
Author(s):  
P. Ruch ◽  

SummaryTo summarize current excellent research in the field of computer-based decision support systems in health and healthcare.We provide a synopsis of the articles selected for the IMIA Yearbook 2012, from which we attempt to draft a synthetic overview of the activity and new trends in the field.While the state of the research in the field of medical decision support systems is illustrated by a set of fairly heterogeneous studies, it is possible to identify fundamental aspects of the fields, e.g. Decision Support Systems for Computerized Provider Order Entry, both for physicians and pharmacists, as well as more specific developments such as instruments to improve processing of data related to Clinical Trials and applications to capture family health history.The best paper selection of articles on decision support shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. This selected set of scientific investigations clearly question the way decision support systems are deployed in clinical environments as these systems seem to have little impact on patient safety and even could harm the patient. Furthermore, while significant research efforts are invested into translational & “omics” medicine, it is interesting to observe that simple data capture applications can reasonably lead to positive changes in healthcare.


Author(s):  
Simone A. Ludwig ◽  
Stefanie Roos ◽  
Monique Frize ◽  
Nicole Yu

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.


2012 ◽  
pp. 1068-1079
Author(s):  
Simone A. Ludwig ◽  
Stefanie Roos ◽  
Monique Frize ◽  
Nicole Yu

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.


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