From Clinical Data to Knowledge Mining for Decision Making within Hospitals

Author(s):  
Michael Steyn
2021 ◽  
Author(s):  
Alysha Taxter ◽  
Lisa Johnson ◽  
Doreen Tabussi ◽  
Yukiko Kimura ◽  
Brittany Donaldson ◽  
...  

BACKGROUND Coproduction of care involves patients and families partnering with their clinicians and care teams, with the premise that each brings their own perspective, knowledge, and expertise, as well as their own values, goals, and preferences to the partnership. Dashboards can display meaningful patient and clinical data to assess how a patient is doing and inform shared decision making. Increasing communication between patients and care teams is particularly important for children with chronic conditions, such as juvenile idiopathic arthritis (JIA), which is the most common, chronic rheumatic condition of childhood, and is associated with increased pain, decreased function, and decreased quality of life. OBJECTIVE We aimed to design a dashboard prototype for use in coproducing care for JIA patients. We evaluated the context use and needs of end users, obtained consensus on the necessary dashboard data elements, and constructed display prototypes to inform meaningful discussions for coproduction. METHODS A human-centered design approach involving parents, patients, clinicians, and care team members was used to develop a dashboard to support coproduction of care in four diverse ambulatory pediatric rheumatology clinics across the United States. We engaged a multidisciplinary team (n=18) of patients/parents, clinicians, nurses, and staff during an in-person kick-off meeting, followed by bi-weekly meetings. We also leveraged advisory panels. Teams mapped workflows and patient journeys, created personas, and developed dashboard sketches. Final necessary dashboard components were determined using Delphi consensus voting. Low-tech dashboard testing was completed during clinic visits, and visual display prototypes were iterated using PDSA methodology. Patients and providers were surveyed about their experiences. RESULTS Teams achieved consensus on what data matters most at point-of-care to support JIA patients, families, and clinicians partnering together to make the best possible decisions for better health. Notable themes included: the right data, in the right place, at the right time; data in once for multiple purposes; patient and family self-management components; and opportunity for education and increased transparency. A final set of 11 dashboard data elements were identified which include patient-reported outcomes, clinical data, and medications. Important design considerations include incorporation of real-time data, clearly labeled graphs, and vertical orientation to facilitate review and discussion. Prototype paper testing with 36 patients/families yielded positive feedback about the dashboard’s usefulness during clinic discussions, helped to talk about what mattered most, and informed healthcare decision making. CONCLUSIONS Our study developed a dashboard prototype that displays patient-reported and clinical data over time, along with medications, that can be used during a clinic visit to support meaningful conversations and shared decision making between JIA patients/families and their clinicians and care teams. CLINICALTRIAL N/A


2006 ◽  
Vol 05 (04) ◽  
pp. 729-738 ◽  
Author(s):  
P. L. YU

We usually use a set of ideas, thinking paradigms and judgment rules, including alternatives, criteria, outcomes, preferences, to make decision. This set, known as actual domain (working knowledge) of habitual domain, will be stabilized over time unless extraordinary events occur. As such, our working knowledge cannot be broad and deep. Inevitably, we could get into decision traps, which lead us to making wrong decision or solving wrong problems. The actual domain is only a small part of our potential domain, the collection of all thoughts, ideas, thinking paradigms, etc. that have ever been encoded in our brain. In this paper, we will describe nine principles for deep knowledge, so that, we could expand and enrich our working knowledge by utilizing the potential domains of ourselves and other participants in the decision making. As a consequence, good ideas for solving challenging decision problems can be obtained or created. These principles are: The deep and down principle, the alternating principle, the contrasting and complementing principle, the revolving and cycling principle, the inner connection principle, the changing and transforming principle, the contradiction principle, the cracking and ripping principle, the void principle.


Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He ◽  
Dawit Haile

Purpose – Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular. Design/methodology/approach – It is a combined DM (clustering and the nearest neighbor) technique for outliers’ detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients’ events and situations prove that play a significant role in the process of patient care safety and medications. Findings – The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers’ satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works. Research limitations/implications – This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems. Originality/value – DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.


2013 ◽  
Vol 448-453 ◽  
pp. 2259-2265
Author(s):  
Sheng Chun Yang ◽  
Bi Qiang Tang ◽  
Jian Guo Yao ◽  
Feng Li ◽  
Yi Jun Yu ◽  
...  

With the construction of UHV power grid, integration of large-scale renewable clean energy, and large-scale energy base putting into operation, the power grid dispatching faced with more and more complex challenges. On the basis of existing research results, architecture of intelligent dispatching based on situation awareness is proposed, so as to accurately achieve prevention and control of the power system. The shortcomings of traditional dispatching mode are analyzed firstly, and the concepts and characterization approaches of grid situational awareness and operation state trajectory of power grid are then introduced. The overall objective of intelligent dispatching is presented, including data processing and integrated knowledge mining, predictive perception of grid operation, risk analysis and comprehensive early warning, so as to achieve "automatic cruise under normal operating conditions, automatic navigation under abnormal operating conditions ". The functional framework of intelligent dispatching is also proposed in details, including four major aspects of the perception and forecasts, risk analysis, decision-making support, and automatic control, as well as three supporting functions such as post-assessment of dispatching, trajectory index calculation, and human-computer interaction (HCI).Technical innovations to support automatic intelligent dispatching are discussed and organised in three levels, i.e. perception, comprehension and projection. The breakthroughs are: construction of index system, trajectory recognition based on massive information and knowledge mining, trajectory projection taking into accounts the uncertainties, online risk assessment and early warning, power grid intelligent decision-making support, automatic coordination of grid operation control, online assessment, natural human-computer interaction mode, and etc... These are the future research areas of automatic intelligent dispatching.


2014 ◽  
Vol 25 (4) ◽  
pp. 612-625 ◽  
Author(s):  
Robert H. Anderson ◽  
Diane E. Spicer ◽  
G. William Henry ◽  
Cynthia Rigsby ◽  
Anthony M. Hlavacek ◽  
...  

AbstractBackground: Disagreement currently exists regarding the definition of aortic dextroposition. It is suggested that the term be used interchangeably with aortic overriding, along with suggestions that the aortic valve overrides in the normal heart. The dextroposed aorta, however, does not always override the crest of the muscular ventricular septum. It is incorrect to argue that the normal aortic valve overrides. It is the cavity of the right aortic valvar sinus, rather than the valvar orifice, that sits above the muscular septum when the septum itself is intact. Therefore, to circumvent these difficulties, those using the term “dextroposition” find it necessary to distinguish “true” as opposed to “false” categories. The problems arise because “dextroposition” is remarkably ill-suited as an alternative term for aortic valvar overriding.Methods and Results:In this review, combining developmental, morphologic, and clinical data, we show how aortic overriding is best considered on the basis of biventricular connection of the aortic root in the setting of deficient ventricular septation. When analysed in this manner, it becomes an easy matter to distinguish between one-to-one and double outlet ventriculo-arterial connections. Appreciation of these features emphasises the different spatial alignments of interventricular communications as opposed to the plane of deficient ventricular septation. The concept of overriding is applicable not only to biventricular connection of the aortic root, but also the pulmonary and common arterial roots.Conclusions:The diagnostic techniques now available to the paediatric cardiologist illustrate the features of arterial valvar overriding with exquisite accuracy, informing the discussions now required for optimal decision making.


2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Taavy Miller ◽  
Shane Wurdeman

The goal of health economics and outcomes research is to improve healthcare decision making. In the absence of high-value clinical data, the availability and quality of administrative healthcare data could be vital in the generation of evidence for orthotics and prosthetics services. The purpose of this article is to provide a stronger understanding of administrative healthcare data analysis, an area that has been scarcely examined within prosthetics and orthotics despite the wealth of information available within such data. Examples of common datasets in this arena currently available are provided, as well as an overview of the limitations and advantages of studies utilizing such datasets. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/35958/28315 How To Cite: Miller TA, Wurdeman S. Value and applicability of large administrative healthcare databases in prosthetics and orthotics outcomes research. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.4. https://doi.org/10.33137/cpoj.v4i2.35958 Corresponding Author: Taavy A Miller, PhD, CPODepartment of Clinical and Scientific Affairs, Hanger Clinic, Austin, Texas, USA.E-Mail: [email protected] ID: https://orcid.org/0000-0001-7117-6124


Author(s):  
Samer Kais Jameel ◽  
Sezgin Aydin ◽  
Nebras H. Ghaeb

Machine learning techniques become more related to medical researches by using medical images as a dataset. It is categorized and analyzed for ultimate effectiveness in diagnosis or decision-making for diseases. Machine learning techniques have been exploited in numerous researches related to corneal diseases, contribution to ophthalmologists for diagnosing the diseases and comprehending the way automated learning techniques act. Nevertheless, confusion still exists in the type of data used, whether it is images, data extracted from images or clinical data, the course reliant on the type of device for obtaining them. In this study, the researches that used machine learning were reviewed and classified in terms of the kind of utilized machine for capturing data, along with the latest updates in sophisticated approaches for corneal disease diagnostic techniques.


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