The Uniform Clinical Data Set: An Evaluation of the Proposed National Database for Medicare's Quality Review Program

1993 ◽  
Vol 119 (12) ◽  
pp. 1209 ◽  
Author(s):  
Anne-Marie Audet
Keyword(s):  
Clinical Data ◽  
National Database ◽  
Data Set ◽  
Quality Review

scholarly journals On self-neglect and safeguarding adult reviews: diminishing returns or adding value?

2017 ◽  
Vol 19 (2) ◽  
pp. 53-66 ◽  
Author(s):  
Michael Preston-Shoot
Keyword(s):  
Thematic Analysis ◽  
Annual Reports ◽  
National Database ◽  
Service Improvement ◽  
Policy And Practice ◽  
Data Set ◽  
Content Type ◽  
Core Data ◽  
The Core

Purpose The purpose of this paper is twofold: first, to update the core data set of self-neglect serious case reviews (SCRs) and safeguarding adult reviews (SARs), and accompanying thematic analysis; second, to respond to the critique in the Wood Report of SCRs commissioned by Local Safeguarding Children Boards (LSCBs) by exploring the degree to which the reviews scrutinised here can transform and improve the quality of adult safeguarding practice. Design/methodology/approach Further published reviews are added to the core data set from the websites of Safeguarding Adults Boards (SABs) and from contacts with SAB independent chairs and business managers. Thematic analysis is updated using the four domains employed previously. The findings are then further used to respond to the critique in the Wood Report of SCRs commissioned by LSCBs, with implications discussed for Safeguarding Adult Boards. Findings Thematic analysis within and recommendations from reviews have tended to focus on the micro context, namely, what takes place between individual practitioners, their teams and adults who self-neglect. This level of analysis enables an understanding of local geography. However, there are other wider systems that impact on and influence this work. If review findings and recommendations are to fully answer the question “why”, systemic analysis should appreciate the influence of national geography. Review findings and recommendations may also be used to contest the critique of reviews, namely, that they fail to engage practitioners, are insufficiently systemic and of variable quality, and generate repetitive findings from which lessons are not learned. Research limitations/implications There is still no national database of reviews commissioned by SABs so the data set reported here might be incomplete. The Care Act 2014 does not require publication of reports but only a summary of findings and recommendations in SAB annual reports. This makes learning for service improvement challenging. Reading the reviews reported here against the strands in the critique of SCRs enables conclusions to be reached about their potential to transform adult safeguarding policy and practice. Practical implications Answering the question “why” is a significant challenge for SARs. Different approaches have been recommended, some rooted in systems theory. The critique of SCRs challenges those now engaged in SARs to reflect on how transformational change can be achieved to improve the quality of adult safeguarding policy and practice. Originality/value The paper extends the thematic analysis of available reviews that focus on work with adults who self-neglect, further building on the evidence base for practice. The paper also contributes new perspectives to the process of conducting SARs by using the analysis of themes and recommendations within this data set to evaluate the critique that reviews are insufficiently systemic, fail to engage those involved in reviewed cases and in their repetitive conclusions demonstrate that lessons are not being learned.

scholarly journals Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

2020 ◽  
Vol 9 (3) ◽  
pp. 2758-2762
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Clinical Data ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Information Storage ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

scholarly journals Nutrigenomic Information in the openEHR Data Set

2018 ◽  
Vol 09 (01) ◽  
pp. 221-231 ◽  
Author(s):  
Priscila Maranhão ◽  
Gustavo Bacelar-Silva ◽  
Duarte Ferreira ◽  
Conceição Calhau ◽  
Pedro Vieira-Marques ◽  
...  
Keyword(s):  
Human Genome ◽  
Clinical Data ◽  
Literature Search ◽  
Systemic Review ◽  
Personalized Nutrition ◽  
Clinical Practices ◽  
Modeling Tools ◽  
Data Set ◽  
Traditional Concept ◽  
The Future

Background The traditional concept of personalized nutrition is based on adapting diets according to individual needs and preferences. Discussions about personalized nutrition have been on since the Human Genome Project, which has sequenced the human genome. Thenceforth, topics such as nutrigenomics have been assessed to help in better understanding the genetic variation influence on the dietary response and association between nutrients and gene expression. Hence, some challenges impaired the understanding about the nowadays important clinical data and about clinical data assumed to be important in the future. Objective Finding the main clinical statements in the personalized nutrition field (nutrigenomics) to create the future-proof health information system to the openEHR server based on archetypes, as well as a specific nutrigenomic template. Methods A systematic literature search was conducted in electronic databases such as PubMed. The aim of this systemic review was to list the chief clinical statements and create archetype and templates for openEHR modeling tools, namely, Ocean Archetype Editor and Ocean Template Design. Results The literature search led to 51 articles; however, just 26 articles were analyzed after all the herein adopted inclusion criteria were assessed. Of these total, 117 clinical statements were identified, as well as 27 archetype-friendly concepts. Our group modeled four new archetypes (waist-to-height ratio, genetic test results, genetic summary, and diet plan) and finally created the specific nutrigenomic template for nutrition care. Conclusion The archetypes and the specific openEHR template developed in this study gave dieticians and other health professionals an important tool to their nutrigenomic clinical practices, besides a set of nutrigenomic data to clinical research.

scholarly journals Deep learning and feature based medication classifications from EEG in a large clinical data set

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
David O. Nahmias ◽  
Eugene F. Civillico ◽  
Kimberly L. Kontson
Keyword(s):  
Deep Learning ◽  
Clinical Data ◽  
Data Set ◽  
Feature Based

scholarly journals 1048. Beta-Hemolytic Streptococcal Infective Endocarditis: Characteristics and Outcomes From a Large, Multi-National Cohort

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S313-S313
Author(s):  
Nuria Fernandez-Hidalgo ◽  
Amal Gharamti ◽  
María Luisa Aznar ◽  
Vivian H Chu ◽  
Hussein Rizk ◽  
...  
Keyword(s):  
Infective Endocarditis ◽  
Hospital Mortality ◽  
High Rate ◽  
National Database ◽  
Case Report Form ◽  
Viridans Streptococci ◽  
Cardiac Device ◽  
Data Set ◽  
Standard Case ◽  
National Cohort

Abstract Background β-Hemolytic streptococci (BHS) are an uncommon cause of infective endocarditis (IE). The aim of this study was to describe the clinical features and outcomes of patients with β-hemolytic streptococcal infective endocarditis in a large multi-national cohort, and compare them to patients with oral Viridans IE, a more common cause of IE. Methods The International Collaboration on Endocarditis Prospective Cohort Study (ICE-PCS) is a large multi-national database that recruited patients with IE prospectively using a standardized data set. Sixty-four sites in 28 countries reported patients prospectively using a standard case report form (CRF) developed by ICE collaborators. Patients with BHS IE were compared with patients with IE due to Oral Viridans Streptococci (OVS). Results Among 1336 cases of streptococcal IE, 823 (62%) were caused by OVS and 147 (11%) by BHS. The majority of patients in both groups belonged to the male gender and had similar median age. Among the predisposing conditions, congenital heart disease and native valve predisposition were more commonly associated with OVS IE than with BHS IE (P < 0.005). The presence of endocavitary cardiac device is associated more with BHS IE than with OVS IE (P = 0.026). BHS were more likely to be penicillin-susceptible than OVS (P = 0.001). Clinically, patients with BHS IE are more likely to present acutely (P < 0.005) and with fever (P = 0.024). BHS IE is more likely to be complicated by stroke (P < 0.005) and other systemic embolism (P < 0.005). The overall in-hospital mortality of BHS IE was significantly higher than that of OVS IE (P = 0.001). The independent factors associated with in-hospital mortality for β-hemolytic streptococcal IE were age, per 1-year increment (OR 1.044; CI 1.014–1.075; P = 0.004) and prosthetic valve IE (OR 3.029; CI 1.171–7.837; P = 0.022). The complications associated with a higher in-hospital mortality were CHF (OR 2.513; CI 1.074–5.879; P = 0.034), especially CHF NYHA III or IV (OR 4.136; CI 1.707–10.025; P = 0.002), and stroke (OR 3.198; CI 1.343–7.619; P = 0.009). Conclusion Our findings suggest that BHS IE is an aggressive disease characterized by an acute presentation. It is associated with a significant rate of complications and a high rate of in-hospital mortality. This underlines the importance of early surgery to prevent the progression of disease. Disclosures All authors: No reported disclosures.

scholarly journals A Multiple Hypothesis Approach to Estimating Meal Times in Individuals With Type 1 Diabetes

2019 ◽  
Vol 15 (1) ◽  
pp. 141-146
Author(s):  
John P. Corbett ◽  
Marc D. Breton ◽  
Stephen D. Patek
Keyword(s):  
Type 1 Diabetes ◽  
Clinical Data ◽  
Estimation Error ◽  
Average Error ◽  
Accurate Information ◽  
Data Set ◽  
Multiple Hypothesis ◽  
Meal Time ◽  
Normally Distributed

Introduction: It is important to have accurate information regarding when individuals with type 1 diabetes have eaten and taken insulin to reconcile those events with their blood glucose levels throughout the day. Insulin pumps and connected insulin pens provide records of when the user injected insulin and how many carbohydrates were recorded, but it is often unclear when meals occurred. This project demonstrates a method to estimate meal times using a multiple hypothesis approach. Methods: When an insulin dose is recorded, multiple hypotheses were generated describing variations of when the meal in question occurred. As postprandial glucose values informed the model, the posterior probability of the truth of each hypothesis was evaluated, and from these posterior probabilities, an expected meal time was found. This method was tested using simulation and a clinical data set ( n = 11) and with either uniform or normally distributed ( μ = 0, σ = 10 or 20 minutes) prior probabilities for the hypothesis set. Results: For the simulation data set, meals were estimated with an average error of −0.77 (±7.94) minutes when uniform priors were used and −0.99 (±8.55) and −0.88 (±7.84) for normally distributed priors ( σ = 10 and 20 minutes). For the clinical data set, the average estimation error was 0.02 (±30.87), 1.38 (±21.58), and 0.04 (±27.52) for the uniform priors and normal priors ( σ = 10 and 20 minutes). Conclusion: This technique could be used to help advise physicians about the meal time insulin dosing behaviors of their patients and potentially influence changes in their treatment strategy.

ASSESSMENT OF WEIGHT CHANGES AND SUGGESTION OF NUTRITIONAL FORMULAS FOR PREMATURELY BORN BABIES WITH CLOSEST REASONABLE CENTROIDS

2014 ◽  
Vol 26 (01) ◽  
pp. 1450001
Author(s):  
Chao-Yi Huang ◽  
Jong-Chen Chen
Keyword(s):  
Clinical Data ◽  
Back Propagation ◽  
Health Conditions ◽  
Experimental Result ◽  
Weight Changes ◽  
Data Set ◽  
Innovative Method ◽  
Nutritional Needs ◽  
Effect Relation ◽  
Better Than

Recently, many models of applying artificial intelligence (AI) techniques into the analysis of clinical data have been proposed. Unfortunately, most models provide little help when specific "cause–effect" relation of data is not available, or even known. In this paper, an innovative method, called closest reasonable centroids (CRC), is directed to address this issue. Our present application domain was a clinical data set of the weight changes of 274 prematurely born babies who had nutritional deficiency problem and were given TPN treatments to improve their nutritional needs. Experimental result shows that the CRC's differentiability is comparable to that of the back-propagation neural networks (BPN) and better than that of statistical method. Also, from the health conditions of babies and their nutritional treatments, the proposed method can roughly predict their weight changes and provide some suggested feasible formula. All of the above results have been double confirmed by the clinicians, implicating that CRC could be used as assistant tool.

High Resolution Screening of Genomic Aberrations in Follicular Lymphoma Using Microarray Based Comparative Genomic Hybridization (MATRIX-CGH).

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2271-2271
Author(s):  
Carsten Schwaenen ◽  
Swen Wessendorf ◽  
Andreas Viardot ◽  
Sandra Ruf ◽  
Martina Enz ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
Clinical Data ◽  
Array Cgh ◽  
Screening Method ◽  
Comparative Genomic ◽  
Rapid Progression ◽  
Human Beings ◽  
Data Set ◽  
Term Survival ◽  
Genomic Aberrations

Abstract Follicular Lymphoma (FL), one of the most frequent lymphoma entities in the western world, is characterized by a highly variable clinical course reaching from rapid progression with fatal outcome to cases with long term survival. In a recent study applying chromosomal comparative hybridization (CGH) to FL, in 70% of the cases genomic aberrations were detectable and a loss of genomic material on chromosomal bands 6q25-q27 was the strongest predictor for short overall survival. However, limitations of CGH as a screening method are a restricted genomic resolution to 3–10 Mbp and demanding non-automated evaluation procedures. Thus, high throughput analysis of genomic alterations for risk adapted patient stratification and monitoring within treatment trials should rely on efficient and automated diagnostic techniques. In this study, we used array CGH to a novel generation of DNA Chips containing 2800 genomic DNA probes. Target clones comprised i) contigs mapping to genomic regions of possible pathogenetic relevance in lymphoma (n=610 target clones mapping to e.g. 1p, 2p, 3q, 7q, 9p, 11q, 12q, 13q, 17p, 18q, X); ii) selected oncogenes and tumor suppressor genes (n=686) potentially relevant in hematologic neoplasms; and iii) a large genome-wide cluster of 1502 target DNA clones covering the genome at a distance of app. 2 Mbp (part of the golden path clone set). This chip represents a median genomic resolution of app. 1.5 Mbp. In total, DNAs from 70 FL samples were analyzed and results were compared to data from chromosomal CGH experiments and clinical data sets. The sensitivity of array CGH was considerably higher compared to chromosomal CGH (aberrations in 95% of cases vs 70% of cases). Most frequent aberrations were gain mapping to chromosome arms 2p (21%), 7p (24%), 7q (30%), 12p (17%), 12q (21%), 18p (21%) and 18q (34%) as well as losses mapping to chromosome arms 1p (19%), 6q (23%), 7p (20%), 11q (26%) and 17p (20%). In addition, several genomic aberrations were identified which have not been described before in FL. Currently, these aberrations are characterized in more detail and results will be correlated with the clinical data set. Moreover, three recurrent sites of genomic polymorphisms in human beings affecting chromosomes 5q, 14q and 15q were identified. In conclusion, these data underline the potential of array CGH for the sensitive detection of genomic imbalances in FL.

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