Combined data mining techniques based patient data outlier detection for healthcare safety

2016 ◽  
Vol 9 (1) ◽  
pp. 42-68 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He ◽  
Dawit Haile
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Patient Safety ◽  
Outlier Detection ◽  
Clinical Data ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Healthcare Services ◽  
Content Type ◽  
Outliers Detection

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.

Evaluation of offending behaviour programme selection: the PNA

2019 ◽  
Vol 21 (4) ◽  
pp. 264-277
Author(s):  
Laura Ramsay ◽  
Jamie S. Walton ◽  
Gavin Frost ◽  
Chloe Rewaj ◽  
Gemma Westley ◽  
...  
Keyword(s):  
Decision Making ◽  
High Intensity ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Policy Makers ◽  
Content Type ◽  
Maximum Security ◽  
Research Findings ◽  
Probation Service ◽  
Offending Behaviour

Purpose The purpose of this paper is to outline the qualitative research findings of the effectiveness of Her Majesty’s Prison and Probation Service Programme Needs Assessment (PNA) in supporting decision making regarding selection onto high-intensity offending behaviour programmes. Design/methodology/approach Qualitative data analysis was used through the application of thematic analysis. Results were pooled using principles from meta-synthesis in order to draw conclusions as to whether the PNA was operating as designed. Findings Four overarching themes were identified, which have meaning in guiding decision making into, or out of high-intensity programmes. These were risk, need and responsivity, the importance of attitudes, motivation and formulation and planning. Research limitations/implications The majority of data were collected from category C prisons. Generalisability of findings to high-intensity programmes delivered in maximum security prisons and prisons for younger people aged 18–21 years is limited. The research team had prior knowledge of the PNA, whether through design or application. Procedures were put in place to minimise researcher biases. Practical implications Findings suggest that the PNA is effective in guiding clinical decision making. Practitioners and policy makers can be assured that the processes in place to select into high-intensity programmes are effective, and aligned with the What Works in reducing re-offending. Originality/value This is the first evaluation into the effectiveness of the PNA designed to support clinical decision making regarding participant selection onto accredited offending behaviour programmes. Implications for practice have been discussed.

Understanding critical care nurses’ autonomy in Jordan

2017 ◽  
Vol 30 (4) ◽  
pp. 432-442 ◽  
Author(s):  
Mahmoud Maharmeh
Keyword(s):  
Decision Making ◽  
Critical Care ◽  
Clinical Practice ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Critical Care Nurses ◽  
Cross Sectional Survey ◽  
Eligibility Criteria ◽  
Cross Sectional ◽  
Content Type

Purpose The aim of this study was to describe Jordanian critical care nurses’ experiences of autonomy in their clinical practice. Design/methodology/approach A descriptive correlational design was applied using a self-reported cross-sectional survey. A total of 110 registered nurses who met the eligibility criteria participated in this study. The data were collected by a structured questionnaire. Findings A majority of critical care nurses were autonomous in their decision-making and participation in decisions to take action in their clinical settings. Also, they were independent to develop their own knowledge. The study identified that their autonomy in action and acquired knowledge were influenced by a number of factors such as gender and area of practice. Practical implications Nurse’s autonomy could be increased if nurses are made aware of the current level of autonomy and explore new ways to increase empowerment. This could be offered through classroom lectures that concentrate on the concept of autonomy and its implication in practice. Nurses should demonstrate autonomous nursing care at the same time in the clinical practice. This could be done through collaboration between educators and clinical practice to help merge theory to practice. Originality/value Critical care nurses were more autonomous in action and knowledge base. This may negatively affect the quality of patient care and nurses’ job satisfaction. Therefore, improving nurses’ clinical decision-making autonomy could be done by the support of both hospital administrators and nurses themselves.

scholarly journals Health record systems that meet clinical needs

2012 ◽  
Author(s):  
Gabriella Negrini
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Patient Safety ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Clinical Information ◽  
Clinical Decision ◽  
Health Record ◽  
Record System ◽  
Health Records

Introduction Increased attention has recently been focused on health record systems as a result of accreditation programs, a growing emphasis on patient safety, and the increase in lawsuits involving allegations of malpractice. Health-care professionals frequently express dissatisfaction with the health record systems and complain that the data included are neither informative nor useful for clinical decision making. This article reviews the main objectives of a hospital health record system, with emphasis on its roles in communication and exchange among clinicians, patient safety, and continuity of care, and asks whether current systems have responded to the recent changes in the Italian health-care system.Discussion If health records are to meet the expectations of all health professionals, the overall information need must be carefully analyzed, a common data set must be created, and essential specialist contributions must be defined. Working with health-care professionals, the hospital management should define how clinical information is to be displayed and organized, identify a functionally optimal layout, define the characteristics of ongoing patient assessment in terms of who will be responsible for these activities and how often they will be performed. Internet technology can facilitate data retrieval and meet the general requirements of a paper-based health record system, but it must also ensure focus on clinical information, business continuity, integrity, security, and privacy.Conclusions The current health records system needs to be thoroughly revised to increase its accessibility, streamline the work of health-care professionals who consult it, and render it more useful for clinical decision making—a challenging task that will require the active involvement of the many professional classes involved.

scholarly journals Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance

2020 ◽  
Vol 17 (21) ◽  
pp. 7923
Author(s):  
Davide Barbieri ◽  
Nitesh Chawla ◽  
Luciana Zaccagni ◽  
Tonći Grgurinović ◽  
Jelena Šarac ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cardiovascular Risk ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Classification Performance ◽  
Biomedical Data ◽  
Sport Practice

Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine sport medical examinations, which included electrocardiography at rest. Subjects were involved in competitive sport practice, for which medical clearance was needed. Outcomes were negative for the largest majority, as expected in an active population. Resampling was applied to balance positive/negative class ratio. A decision tree and logistic regression were used to classify individuals as either at risk or not. The receiver operating characteristic curve was used to assess classification performances. Data mining and resampling improved cardiovascular risk assessment in terms of increased area under the curve. The proposed methodology can be effectively applied to biomedical data in order to optimize clinical decision making, and—at the same time—minimize the amount of unnecessary examinations.

scholarly journals Emotionally evocative patients in the emergency department: a mixed methods investigation of providers’ reported emotions and implications for patient safety

2020 ◽  
Vol 29 (10) ◽  
pp. 1.3-2 ◽  
Author(s):  
Linda M Isbell ◽  
Julia Tager ◽  
Kendall Beals ◽  
Guanyu Liu
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Emergency Department ◽  
Patient Safety ◽  
Qualitative Data ◽  
Clinical Decision Making ◽  
Quality Care ◽  
Clinical Decision ◽  
Safety Risks ◽  
Recent Patient

BackgroundEmergency department (ED) physicians and nurses frequently interact with emotionally evocative patients, which can impact clinical decision-making and behaviour. This study introduces well-established methods from social psychology to investigate ED providers’ reported emotional experiences and engagement in their own recent patient encounters, as well as perceived effects of emotion on patient care.MethodsNinety-four experienced ED providers (50 physicians and 44 nurses) vividly recalled and wrote about three recent patient encounters (qualitative data): one that elicited anger/frustration/irritation (angry encounter), one that elicited happiness/satisfaction/appreciation (positive encounter), and one with a patient with a mental health condition (mental health encounter). Providers rated their emotions and engagement in each encounter (quantitative data), and reported their perception of whether and how their emotions impacted their clinical decision-making and behaviour (qualitative data).ResultsProviders generated 282 encounter descriptions. Emotions reported in angry and mental health encounters were remarkably similar, highly negative, and associated with reports of low provider engagement compared with positive encounters. Providers reported their emotions influenced their clinical decision-making and behaviour most frequently in angry encounters, followed by mental health and then positive encounters. Emotions in angry and mental health encounters were associated with increased perceptions of patient safety risks; emotions in positive encounters were associated with perceptions of higher quality care.ConclusionsPositive and negative emotions can influence clinical decision-making and impact patient safety. Findings underscore the need for (1) education and training initiatives to promote awareness of emotional influences and to consider strategies for managing these influences, and (2) a comprehensive research agenda to facilitate discovery of evidence-based interventions to mitigate emotion-induced patient safety risks. The current work lays the foundation for testing novel interventions.

scholarly journals A generic concept for the development of model-guided clinical decision support systems

2015 ◽  
Vol 1 (1) ◽  
pp. 322-326
Author(s):  
Kerstin Denecke ◽  
Claire Chalopin
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Data ◽  
Clinical Decision Making ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Patient Data ◽  
Biological Knowledge ◽  
Biological Processes ◽  
Data Types

AbstractDisease development and progression are very complex processes which make clinical decision making non-trivial. On the one hand, examination results that are stored in multiple formats and data types in clinical information systems need to be considered. Beyond, biological or molecular-biological processes can influence clinical decision making. So far, biological knowledge and patient data is separated from each other. This complicates inclusion of all relevant knowledge and information into the decision making. In this paper, we describe a concept of model-based decision support that links knowledge about biological processes, treatment decisions and clinical data. It consists of three models: 1) a biological model, 2) a decision model encompassing medical knowledge about the treatment workflow and decision parameters, and 3) a patient data model generated from clinical data. Requirements and future steps for realizing the concept will be presented and it will be shown how the concept can support the clinical decision making.

Evaluating the effect of distractions in the operating room on clinical decision-making and patient safety

2016 ◽  
Vol 30 (10) ◽  
pp. 4499-4504 ◽  
Author(s):  
Ally Murji ◽  
Lea Luketic ◽  
Mara L. Sobel ◽  
Kulamakan Mahan Kulasegaram ◽  
Nicholas Leyland ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Safety ◽  
Operating Room ◽  
Clinical Decision Making ◽  
Clinical Decision
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