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Presented in this paper is the Patient Assessment-Data Quality Model (PA-DQM). It is designed to assess how patient datasets which are poor in composition can impact on the decision processes following patient assessment. The PA-DQM in particular examines four key Data Quality (DQ) dimensions: timeliness, accuracy, consistency and completeness. Thi...
Context in source publication
Context 1
... DQ from a number of perspectives. The role or importance of IQ/DQ frameworks vary from application to application. For example, a timeliness IQ/DQ dimension may have a higher level of importance within a medical environment than with a data warehouse reporting system. A short summary of well known IQ/DQ frameworks is presented in section 2 (cf . table 1). An explicit distinction between IQ and DQ is not applied in this paper since our findings are general and suitable for both concepts. Therefore, both terms are used in this article ...
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Citations
... Author found that the most frequently mentioned data quality dimensions in the scientific literature are accuracy, completeness, consistency and timeliness [15]. Despite these dimensions are the most frequently applicable, in the scientific literature are still topical discussions on interpretation and evaluation of these data quality dimensions. ...
... 3 Although data quality can be evaluated according to a multifactorial paradigm, missing data is a basic parameter that can be used to easily assess the quality of data collection and effectiveness of a given data collection tool. [3][4][5][6] We aim to establish a clinical and research partnership between HSR and MUHC for pediatric injury surveillance. This initiative entails the development and deployment of a PTR that can be used for the purposes of epidemiologic analysis, research, quality improvement, outcomes benchmarking and decision-making for resource allocation. ...
... We included 351 patients, of which 234 (66%) were female. Median age was 5 (2)(3)(4)(5)(6)(7)(8)(9). Approximately 48% of the patients were seen by pediatric general surgery, 28% were seen by neurosurgery and 24% were seen by orthopedics. ...
Background
Trauma registries contribute to improving trauma care, but their impact is highly dependent on the quality of the data. A simplified point of care pediatric trauma registry (PTR) was developed at the Centre for Global Surgery from the McGill University Health Centre (MUHC) for implementation in Low-middle income countries (LMICs). Pilot deployment was launched at a large urban trauma center in May 2016 in Santiago, Chile. Prior to deployment, we sought to identify missing data in existing trauma records in order to optimize PTR practicality and user benefit.
Materials and methods
The project was approved by the local Institutional Review Board. Retrospective chart review was conducted on trauma patients below the age of 15 who were evaluated at the emergency room (ER) of Hospital Dr. Sotero del Rio (HSR) between January 1st and June 30th 2015. Data missingness was evaluated for each component of the PTR (demographics, mechanism, injury and outcomes). Potential independent predictors of data missingness were evaluated using multiple linear regression.
Results
A total of 351 patients were included. Demographic data missingness ranged from 0% (age) to 95% (mode of arrival). Mechanism data missingness ranged from 6% (cause of injury) to 42% (site of injury). Injury physiology data missingness ranged from 37% (oxygen saturation) to 99% (respiratory rate). Interestingly, mean injury anatomy data missingness was significantly inferior to physiology data (0.6% vs. 78.6%, p < 0.05). Outcome data missingness reached 54% at 2 weeks.
Conclusion
In resource-limited settings, high quality data is essential to guide responsible resource allocation. We believe implementation of a simplified trauma registry has the potential to reduce data gaps for pediatric trauma patients by streamlining trauma data collection at point of care. This should include streamlined data collection with a short per-patient completion time, and should forego attempts to collect data at 2 weeks, which has proven unsuccessful.
How to cite this article
St-Louis E, Roizblatt D, Deckelbaum DL, Baird R, Millán CV, Ebensperger A, Razek T. Identifying Pediatric Trauma Data Gaps at a Large Urban Trauma Referral Center in Santiago, Chile. Panam J Trauma Crit Care Emerg Surg 2017;6(3):169-176.
... Failure to measure process errors could mean a PEWS model is disregarded as poorly performing, when poor compliance and human factor errors are confounding that analysis. [27][28][29] There is progression toward electronic documentation of vital signs and clinical assessments worldwide. This has the potential for improved accuracy in documentation of a patient's physiological state 30 with obvious benefits to clinical care. ...
Pediatric Early Warning Scores are advocated to assist health professionals to identify early signs of serious illness or deterioration in hospitalized children. Scores are derived from the weighting applied to recorded vital signs and clinical observations reflecting deviation from a predetermined "norm." Higher aggregate scores trigger an escalation in care aimed at preventing critical deterioration. Process errors made while recording these data, including plotting or calculation errors, have the potential to impede the reliability of the score. To test this hypothesis, we conducted a controlled study of documentation using five clinical vignettes. We measured the accuracy of vital sign recording, score calculation, and time taken to complete documentation using a handheld electronic physiological surveillance system, VitalPAC Pediatric, compared with traditional paper-based charts. We explored the user acceptability of both methods using a Web-based survey. Twenty-three staff participated in the controlled study. The electronic physiological surveillance system improved the accuracy of vital sign recording, 98.5% versus 85.6%, P < .02, Pediatric Early Warning Score calculation, 94.6% versus 55.7%, P < .02, and saved time, 68 versus 98 seconds, compared with paper-based documentation, P < .002. Twenty-nine staff completed the Web-based survey. They perceived that the electronic physiological surveillance system offered safety benefits by reducing human error while providing instant visibility of recorded data to the entire clinical team.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
... 78 Without doubt, EWS charts are useful in aiding staff to identify at-risk patients but they cannot replace good clinical judgement and clinical skills. 31,[79][80][81][82] An overreliance on EWS can lead to poor decision making and overshadow accountability. 31,83 Although EWS have been shown to reduce in-hospital SAE, 32,34,36,40,68,84 many studies have found that EWS have not conclusively improved patient outcomes. ...
Introduction:
Early warning systems (EWS) were developed as a means of alerting medical staff to patient clinical decline. Since 85% of severe adverse events are preceded by abnormal physiological signs, the patient bed-side vital signs observation chart has emerged as an EWS tool to help staff identify and quantify deteriorating patients. There are three broad categories of patient observation chart EWS: single or multiple parameter systems; aggregated weighted scoring systems; or combinations of single or multiple parameter and aggregated weighted scoring systems.
Objective:
This scoping review is an overview of quantitative studies and systematic reviews examining the efficiency of the adult EWS charts in the recognition of in-hospital patient deterioration.
Method:
A broad search was undertaken of peer-reviewed publications, official government websites and databases housing research theses, using combinations of keywords and phrases.
Data sources:
CINAHL with full text; MedLine, PsycINFO, MasterFILE Premier, GreenFILE and ScienceDirect. Also, the Cochrane Library database, Department of Health government websites and Ethos, ProQuest and Trove databases were searched.
Exclusions:
Paediatric, obstetric and intensive care studies, studies undertaken at the point of hospital admission or pre-admission, non-English publications and editorials.
Results:
Five hundred and sixty five publications, government documents, reports and theses were located of which 91 were considered and 21 were included in the scoping review. Of the 21 publications eight studies compared the efficacy of various EWS and 13 publications validated specific EWS.
Conclusions:
There is low level quantitative evidence that EWS improve patient outcomes and strong anecdotal evidence that they augment the ability of the clinical staff to recognise and respond to patient decline, thus reducing the incidence of severe adverse events. Although aggregated weighted scoring systems are most frequently used, the efficiency of the specific EWS appears to be dependent on the patient cohort, facilities available and staff training and attitude. While the review demonstrates support for EWS, researchers caution that given the contribution of human factors to the EWS decision-making process, patient EWS charts alone cannot replace good clinical judgment.
... Failure to measure process errors could mean a PEWS model is disregarded as poorly performing, when poor compliance and human factor errors are confounding that analysis. [27][28][29] There is progression toward electronic documentation of vital signs and clinical assessments worldwide. This has the potential for improved accuracy in documentation of a patient's physiological state 30 with obvious benefits to clinical care. ...
Aim To determine the impact of electronic vital sign data recording and paediatric early warning (PEW) scoring, 2 months after the implementation of MEDITECH 6 electronic health records, and to explore how electronic systems are used both by doctors on ward rounds and nurses recording observations.
Methods This was a prospective observational study on a cardiac ward, in a specialist children’s hospital. An independent observer undertook 104 h of observations over 4 weeks, shadowing medical ward rounds (WR) and nursing staff undertaking and recording vital sign observations. The method of documentation of vital signs and the length of time taken for documentation were noted. How often doctors reviewed the patients’ vital sign observations and PEW score trends during ward rounds were noted. Nurses were asked for their perceptions of recording vital signs on the MEDITECH 6 system compared to the previous paper system.
Results 135 episodes of vital signs recording by nursing staff were observed and 123 vital signs and PEWs score reviews during the morning WR were observed. Two methods were used by nursing staff for recording vital signs. In most cases vital signs were recorded electronically at the patient’s bedside, immediately after they were obtained (58.5%, n = 79). A second method used was recording vital sign observations on any available surface and documenting electronically at a later stage (41.5%, n = 56). The available surfaces used included scrap paper, paper towels and discarded packaging. The mean time taken to record and document bedside observations was 3 min 41 s (Figure 1). The mean time between recording vital signs on an available surface and documenting was 8min 46 s (the shortest was 30 s and the longest was >1 hr) (Figure 2). On the morning WR, vital signs and PEWs trends were looked at when reviewing the patient in three quarters of cases (74.0%), but in 26% of cases they were not. Subjectively, most nurses felt documenting on the electronic system was slower than the original paper system.
Conclusions There were some delays in recording vital signs and thus PEW calculation using the electronic system and doctors did not review observations and PEW score trends for all patients during ward rounds.
... 11Y13 The effectiveness of trauma registries in improving patient outcomes depends on data quality (DQ). 1,14Y16 It has been shown that quality problems in clinical registries significantly impact patient decision processes 16 and quality-of-care evaluations. 1,14,15 Like diagnostic tests, studies based on low DQ may miss real quality problems (low sensitivity) and identify problems that are not real (low specificity). ...
BACKGROUND: Trauma registries are clinical databases designed for quality improvement activities and research, and have made important contributions to improvements in trauma care over the last few decades. The effectiveness of trauma registries in improving patient outcomes depends on data quality (DQ). However, our understanding of DQ in trauma registries is limited. The objective of this study is to review evidence of the completeness, accuracy, precision, correctness, consistency and timeliness of data in trauma registries. METHODS: A systematic review using MEDLINE, EMBASE, Web of Science, CINAHL and The Cochrane Library was performed including studies evaluating trauma registry data quality based on completeness, accuracy, precision, correctness consistency, or timeliness. We also searched MEDLINE to identify regional, national and international trauma registries whose data was used >=10 times in original studies in the last 10 years; administrators of those registries were contacted to obtain their latest data quality report. Two authors abstracted the data independently. RESULTS: The search retrieved 7495 distinct published articles of which 10 were eligible for inclusion. We also reviewed data quality reports from 5 provincial and international trauma registries. Evaluation was mostly based on completeness with values between 46.8% (mechanism of injury) and 100% (age and gender). Accuracy was between 81.0% (operating room time) and 99.8% (gender). No evidence of data precision or timeliness was available. Correctness varied from 47.6% (injury severity score) to 83.2% (Glasgow coma score) and consistency between variables from 87.5% (ICD9-CM/AIS) to 99.6% (procedure time). CONCLUSIONS: In the few studies we identified, DQ evaluation in trauma registries was mostly based on completeness. There is a need to develop a standardized and reproducible method to evaluate DQ in trauma registries. Determinants of DQ and the impact of DQ on trauma registry analyses such as benchmarking with quality indicators should also be explored. Level of evidence: Systematic review, level I evidence
... Several authors have introduced and discussed dimensions for data quality. These dimensions can have quality categories such as accuracy, timeliness, completeness, consistency, change frequency, and many others (Ballou and Pazer, 1985;Wang and Strong, 1996;Van Zeist and Hendriks, 1996;Leung, 2001;Kahn et al., 2002;O'Donoghue et al., 2011;Hüner et al., 2011;Rocha, 2012;Alpar and Winkelsträter, 2014). For example, Kahn et al. (2002) present 16 dimensions for data quality, including: accessibility, appropriate amount, believability, completeness, concise representation, consistent representation, ease of manipulation, free-of-error, interpretability, objectivity, relevancy, reputation, security, timeliness, understandability, and value added. ...
Data quality has significance to companies, but is an issue that can be challenging to approach and operationalise. This study focuses on data quality from the perspective of operationalisation by analysing the practices of a company that is a world leader in its business. A model is proposed for managing data quality to enable evaluation and operationalisation. The results indicate that data quality is best ensured when organisation specific aspects are taken into account. The model acknowledges the needs of different data domains, particularly those that have master data characteristics. The proposed model can provide a starting point for operationalising data quality assessment and improvement. The consequent appreciation of data quality improves data maintenance processes, IT solutions, data quality and relevant expertise, all of which form the basis for handling the origins of products.
... In medical images, texture can be thought of as the local characteristic pattern of image intensity that identifies a tissue. It also determines local spectral or frequency content in an image; changes in local texture should cause changes in the local spatial frequency [23,25]. Texture analysis is of interest in neuro-imaging because, as biological tissues become abnormal during a disease process, their underlying texture may also change. ...
... Backpropagation algorithm is the best algorithm to be used in the multi-layer perceptron [22] and it gives high accuracy. [23] Row completeness is measured in this paper by applying neural network model. A multilayer feed-forward neural network is used as a classifier for the rows' completeness. ...
... Step-5: Return Generation($) SA [16] 565828 BCGA [22] 567367 GA [13] 603423 ICGA [22] 566404 EPL [12] 563977 ACSA [23] 564049 DP [17] 565825 ...
This Issue (DOI: 10.13140/RG.2.1.1976.0086) includes the following papers; P1121046354, Maha El Meseery, Mahmoud Fakhr El Din, Tarek abouldahab, "Sketch Recognition using Enhanced Particle Swarm Optimization" P1121102458, T.R.Sivapriya and V.Saravanan, "Dementia Diagnosis relying on Texture based features and SVM classification" P1121053406, Eslam Omara, Taha El Said, Mervat Mousa, "Employing Neural Networks for Assessment of Data Quality with Emphasis on Data Completeness" P1121051397, N. D. Salih and Rosli Besar and F. S. Abas, "Sketch-based Shape Retrieval relying on BSR Technique" P1121117560, N. Malla Reddy and N.V.Ramana and K.Ramesh Reddy, "Implementation of Agglomerative and Divisive Cluster Algorithm for Unit Commitment Solution based on Genetic Algorithm" P1121129662, Eslam Omara, Taha El Sayed, Mervat Mousa, "Neuro Assessment of Data Quality based on Data Timeliness Criterion"
... This rate is significant given the fact that the study was carried out in a relaxed environment. "Accuracy" as one dimension of data and information quality, is of importance and requires further investigation in regards to its impact on decision making [13]. ...
Paper-based Modified Early Warning Scorecards (MEWS) have been developed to help nursing staff detect hospital in-patient deterioration at an early stage. MEWS is based on patient vital signs where these values are transformed into a MEWS score. An electronic Modified Early Warning Scorecard (eMEWS) prototype has been designed and developed to fulfill the role of a computerized Clinical Decision Support System (CDSS) and to assist healthcare professionals in their decision making activities. A review of the existing electronic Early Warning Scorecards (eEWS) revealed they lack certain features that assist in capturing a holistic view of the patient health status for example color codes and vital sign trends. The proposed eMEWS prototype employs these features with the aim of assisting healthcare professionals to obtain a clear understanding of the patient status. A survey was conducted to evaluate the impact of paper-based MEWS and eMEWS as part of the decision making process. The advantages and disadvantages of eMEWS over the paper-based MEWS are presented.
... [17] DQ was stated in software engineering to assess the techniques used to manage quality within software datasets [20,21] and problems in the use of e-business system were analyzed using the lens of data quality. [19] Health data quality was assessed according to the different DQ dimensions [22,23]. ...
Data quality (DQ) is a success key of all information systems form those used to support different management levels (data warehouses, BI solutions …etc.) to systems applied to execute customer operations and mange customer relations. Quality of data produced from these systems has serious consequences on the processes depend on that data. Data quality involves all required characteristics of a data product and the concept was discussed in many fields as computer science, statistics, management …etc. Data may be available but quality defects make it useless, it also may be accurate, complete and consistent but it doesn't represent the current real world state, it represents an outdated view. Outdated or expired data is worth than no data, it may lead to delayed business actions, wrong decisions, inapplicable business plans. Evaluating the time at which the data product will expire can be used as a base line to plane update or recollection actions before reaching the expiration state. Data expiration age is calculated in this paper based on the equations used to measure the data quality dimension timeliness stated in the literature and a generalized regression neural network (GRNN) model is applied to measure a dataset expiration age.
