Data Accuracy

The Pneumonia Etiology Research for Child Health study was conducted across 7 diverse research sites and relied on standardized clinical and laboratory methods for the accurate and meaningful interpretation of pneumonia etiology data. Blood, respiratory specimens, and urine were collected from children aged 1-59 months hospitalized with severe or very severe pneumonia and community controls of the same age without severe pneumonia and were tested with an extensive array of laboratory diagnostic tests. A standardized testing algorithm and standard operating procedures were applied across all study sites. Site laboratories received uniform training, equipment, and reagents for core testing methods. Standardization was further assured by routine teleconferences, in-person meetings, site monitoring visits, and internal and external quality assurance testing. Targeted confirmatory testing and testing by specialized assays were done at a central reference laboratory.

Objetivo: Diagnosticar a situação de saúde bucal do município. Metodologia: Baseada no "Projeto SBBrasil 2002/2003", investigaram-se, entre outras condições de saúde, as mesmas condições do SBBrasil. Após aprovação pelo Comitê de Ética da Unimontes foram conduzidas entrevistas e exames intrabucais realizados nos domicílios. Utilizou-se programa de computador para coleta de dados de saúde (PCDS) e construção simultânea do banco de dados de amostra probabilística por conglomerados com poder de inferências para quatro faixas etárias e duas idades-índice. Avaliaram-se cárie dentária, necessidades de tratamento, doença periodontal, fluorose, máoclusão, edentulismo, lesões fundamentais em tecidos moles, dados sociodemográficos, uso/avaliação dos serviços odontológicos, autopercepção da condição de saúde geral e bucal, capacidade cognitiva dos idosos, impacto da saúde bucal e geral na qualidade de vida, saúde geral e estilo de vida. Nas análises estatísticas foi proposta a correção pelo efeito de desenho. Considerações finais: O Projeto foi pioneiro e desafiador, algumas de suas características foram inéditas: o desenvolvimento do PCDS e a correção pelo efeito de desenho. O SBMOC promoveu a integração serviço, ensino e comunidade, gerou resultados importantes a partir do uso da epidemiologia e promoveu a formação de recursos humanos do serviço público de saúde e da Unimontes.

Objective: Electronic health record (EHR) data are used to exchange information among health care providers. For this purpose, the quality of the data is essential. We developed a data quality feedback tool that evaluates differences in EHR data quality among practices and software packages as part of a larger intervention. Methods: The tool was applied in 92 practices in the Netherlands using different software packages. Practices received data quality feedback in 2010 and 2012. Results: We observed large differences in the quality of recording. For example, the percentage of episodes of care that had a meaningful diagnostic code ranged from 30% to 100%. Differences were highly related to the software package. A year after the first measurement, the quality of recording had improved significantly and differences decreased, with 67% of the physicians indicating that they had actively changed their recording habits based on the results of the first measurement. About 80% found the fe...

Background: Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. In this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data. Methods: This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. Conclusions: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor-and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

To investigate the reproducibility of the updated Standards for the Reporting of Diagnostic Accuracy Studies tool (STARD 2015) in a set of 106 studies included in a Cochrane diagnostic test accuracy (DTA) systematic review of imaging tests for diagnosing manifest glaucoma. One senior rater with DTA methodological and clinical expertise used STARD 2015 on all studies, and each of three raters with different training profiles assessed about a third of the studies. Raw agreement was very good or almost perfect between the senior rater and an ophthalmology resident with DTA methods training, acceptable with a clinical rater with little DTA methods training, and only moderate with a pharmacology researcher with general, but not DTA, systematic review training and no clinical expertise. The relationship between adherence with STARD 2015 and methodological quality with QUADAS 2 was only partial and difficult to investigate, suggesting that raters used substantial context knowledge in risk ...

Pollination syndromes and their predictive power regarding actual plant-animal interactions have been controversially discussed in the past. We investigate pollination syndromes in Balsaminaceae, utilizing quantitative respectively categorical data sets of flower morphometry, signal and reward traits for 86 species to test for the effect of different types of data on the test patterns retrieved. Cluster Analyses of the floral traits are used in combination with independent pollinator observations. Based on quantitative data we retrieve seven clusters, six of them corresponding to plausible pollination syndromes and one additional, well-supported cluster comprising highly divergent floral architectures. This latter cluster represents a non-syndrome of flowers not segregated by the specific data set here used. Conversely, using categorical data we obtained only a rudimentary resolution of pollination syndromes, in line with several earlier studies. The results underscore that the use ...

Organizations struggle to attain scalable, secure, and unified data intelligence in the exponential data proliferation age. Conventional data structures are likely to face the problem of data fragmentation, lack of scalability, little or no capability to govern data, and hence, the inability to draw contextualized real-time actions out of this data. This paper examines the strategy of deploying Azure Databricks, Delta Lake, and Unity Catalogue as a complete solution that will help tackle these looming challenges. Azure Databricks can help an enterprise provide a familiar, unified analytics environment running in the cloud to perform high-performance distributed data processing on any scale. Delta Lake reliably and consistently manages data with ACID transactions and applied schema. Unity Catalogue also boosts data governance and security by offering centralized control over access and lineage observation across cross-cloud situations.

Today, data, as one of the valuable assets of various organizations and industries, plays an important role in the development and progress of businesses. In fact, every organization uses different sources to collect its data, one of which is the web platform, where a lot of data is produced and published by different users or even robots all over the world every day. Examining, researching, studying and analyzing such data can provide useful information and knowledge for the organization. For this purpose, during the past decades, various tools have been developed that have greatly helped in extracting information from the web platform, among which we can mention Request, Selenium, Scrapy, Beautiful Soup, etc. libraries in the Python programming language. However, each of these libraries faces challenges. In this article, by studying the Selenium library and considering the existence of many challenges in it, we have presented a solution for time management and improving the challenge of its Asynchronous. Our experiments show that the use of the proposed solution increases the accuracy of the information retrieved from the

Prevalence of pediatric HIV disclosure is low in resource-limited settings. Innovative, culturally sensitive, and patientcentered disclosure approaches are needed. Conducting such studies in resource-limited settings is not trivial considering the challenges of capturing, cleaning, and storing clinical research data. To overcome some of these challenges, the Sankofa pediatric disclosure intervention adopted an interactive cyber infrastructure for data capture and analysis. The Sankofa Project database system is built on the HUBzero cyber infrastructure (), an open source software platform. The hub database components support: (1) data managementthe "databases" component creates, configures, and manages database access, backup, repositories, applications, and access control; (2) data collectionthe "forms" component is used to build customized web case report forms that incorporate common data elements and include tailored form submit processing to handle error checking, data validation, and data linkage as the data are stored to the database; and (3) data explorationthe "dataviewer" component provides powerful methods for users to view, search, sort, navigate, explore, map, graph, visualize, aggregate, drill-down, compute, and export data from the database. The Sankofa cyber data management tool supports a user-friendly, secure, and systematic collection of all data. We have screened more than 400 child-caregiver dyads and enrolled nearly 300 dyads, with tens of thousands of data elements. The dataviews have successfully supported all data exploration and analysis needs of the Sankofa Project. Moreover, the ability of the sites to query and view data summaries has proven to be an incentive for collecting complete and accurate data. The data system has all the desirable attributes of an electronic data capture tool. It also provides an added advantage of building data management capacity in resource-limited settings due to its innovative data query and summary views and availability of real-time support by the data management team.

AimTo assess the ability of two‐way interactions between baseline stage, grade and extent to predict tooth loss due to periodontitis (TLP) over a long‐term follow‐up period.Materials and MethodsPatients treated for periodontal disease with a complete medical history, baseline periodontal chart, full mouth radiographs and a minimum of ≥10 years follow‐up were recruited. Supportive periodontal therapy (SPT) visits were recorded during the entire follow‐up period. Patients were categorized according to their stage, grade and extent. The absolute survival at 10‐, 20‐, and 30‐year follow‐up was calculated for TLP. Kaplan–Meier survival curves were plotted at the tooth‐level and multilevel Cox regression frailty models were constructed in order to assess the association among predictive variables and TLP by taking into account the hierarchical patient‐teeth structure.Results442 patients (11,125 teeth) with a mean follow‐up of 23 years met the inclusion criteria and were included in this s...

Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria. To develop and evaluate automated methods for converting textual clinical diagnostic criteria in a structured format using QDM. We used a clinical Natural Language Processing (NLP) tool known as cTAKES to detect sentences and annotate events in diagnostic criteria. We developed a rule-based approach for assigning the QDM datatype(s) to an individual criterion, whereas we invoked a machine learning algorithm based on the Conditional Random Fields (CRFs) for annotating attributes belonging to each particular QDM datatype. We manually developed an annotated corpus as the gold standard and used standard measures (precision, recall and f-measure) for the performance evaluation. We harvested 267 individual criteria with...

Today, data, as one of the valuable assets of various organizations and industries, plays an important role in the development and progress of businesses. In fact, every organization uses different sources to collect its data, one of which is the web platform, where a lot of data is produced and published by different users or even robots all over the world every day. Examining, researching, studying and analyzing such data can provide useful information and knowledge for the organization. For this purpose, during the past decades, various tools have been developed that have greatly helped in extracting information from the web platform, among which we can mention Request, Selenium, Scrapy, Beautiful Soup, etc. libraries in the Python programming language. However, each of these libraries faces challenges. In this article, by studying the Selenium library and considering the existence of many challenges in it, we have presented a solution for time management and improving the challenge of its Asynchronous. Our experiments show that the use of the proposed solution increases the accuracy of the information retrieved from the

Gestational age estimation by last menstrual period (LMP) vs. ultrasound (or best obstetric estimate in the US) may result in discrepant classification of preterm vs. term birth. We investigated whether such discrepancies are associated with adverse infant outcomes. We studied singleton livebirths in the Medical Birth Registries of Norway, Sweden and Finland and US live birth certificates from 1999 to the most recent year available. Risk ratios (RR) with 95% confidence intervals (CI) by discordant and concordant gestational age estimation for infant, neonatal and post-neonatal mortality, Apgar score <4 and <7 at 5 min, and neonatal intensive care unit (NICU) admission were estimated using generalised linear models, adjusting for maternal age, education, parity, year of birth, and infant sex. Results were presented stratified by country. Compared to infants born at term by both methods, infants born preterm by ultrasound/best obstetric estimate but term by LMP had higher infant...

Background: The Delirium Observation Screening Scale (DOS) was developed to facilitate early recognition of delirium by nurses during routine clinical care. It has shown good validity in a variety of patient populations, but has not yet been validated in hospitalized patients with advanced cancer, although the DOS is commonly used in this setting in daily practice. The aim of this study was to evaluate the accuracy of the DOS in hospitalized patients with advanced cancer using the revised version of the Delirium Rating Scale (DRS-R -98 ) as the gold standard. Methods: Patients with advanced cancer admitted to the medical oncology ward were screened for delirium with the DOS and DRS-R-98. Sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) of the DOS were calculated, using a DOS score ≥ 3 as a cut-off for delirium. Results: Ninety-five DOS negative and 98 DOS positive patients were identified. Sensitivity of the DOS, was > 99.9% (95%-CI, 95.8-100.0%), specificity was 99.5% (95%-CI 95.5-99.96%), PPV was 94.6% (95% CI 88.0-97.7), and NPV was > 99.9% (95% CI 96.1-100.0). The DOS is an accurate screening tool for delirium in patients with advanced cancer. Since it has the benefit of being easily implicated in daily practice, we recommend to educate caregivers to screen patients with advanced cancer by DOS analysis. By early recognition and adequate treatment of this distressing delirium syndrome the quality of life of patients with advanced cancer can be improved. Trial registration: ClinicalTrials.gov Identifier NCT01539733 (Feb 27, 2012 -retrospectively registered), Netherlands Trial Register NTR2559 (Oct 7, 2010).

BackgroundAccurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments.MethodsWe describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garb...

Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya. RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs. 27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records review...

So far, several prognostic models have been effectively developed and validated using RCT data (table ). Data generated by an RCT can confer specific benefits over data from alternative sources, such as from predesigned observational studies, electronic health records, disease specific registers, or administrative medical databases. We outline the key opportunities that RCT data might provide when developing or externally validating a prognostic model.

Evidence based medicine tells us that we should not accept published research at face value. Even research from established teams published in the highest impact journals can have methodological flaws, biases and limited generalisability. The critical appraisal of research studies can seem daunting, but tools are available to make the process easier for the non-specialist. Understanding the language and process of quality assessment is essential when considering or conducting research, and is also valuable for all clinicians who use published research to inform their clinical practice. We present a review written specifically for the practising geriatrician. This considers how quality is defined in relation to the methodological conduct and reporting of research. Having established why quality assessment is important, we present and critique tools which are available to standardise quality assessment. We consider five study designs: RCTs, nonrandomised studies, observational studies, systematic reviews and diagnostic test accuracy studies. Quality assessment for each of these study designs is illustrated with an example of published cognitive research. The practical applications of the tools are highlighted, with guidance on their strengths and limitations. We signpost educational resources and offer specific advice for use of these tools. We hope that all geriatricians become comfortable with critical appraisal of published research and that use of the tools described in this reviewalong with awareness of their strengths and limitationsbecome a part of teaching, journal clubs and practice.

Background: Arthroplasty registries are a relevant source of information for research and quality improvement in patient care and its value depends on the quality of the recorded data. The purpose of this study is to describe a model of validation and present the findings of validation of an Institutional Arthroplasty Registry (IAR). Methods: Information from 209 primary arthroplasties and revision surgeries of the hip, knee and shoulder recorded in the IAR between March and September 2015 were analyzed in the following domains. Adherence defined as the proportion of patients included in the registry; completeness defined as the proportion of data effectively recorded, and accuracy defined as the proportion of data consistent with medical records. A random sample of 53 patients (25.4%) was selected to assess the latest two domains. A direct comparison between the registry's database and medical records was performed. Results: 324 variables containing information on demographic data, surgical procedure, clinical outcomes and key performance indicators were analyzed. 209 out of 212 patients who underwent surgery during the study period were included in the registry, accounting for an adherence of 98.6%. Completeness was 91.7% and accuracy was 85.8%. Most errors were found in the preoperative range of motion and timely administration of prophylactic antibiotics and thromboprophylaxis.

Self-monitoring of blood glucose (SMBG) is an important technique for assessing glycemic control in people with diabetes. 1 SMBG can be useful for guiding therapy as it allows people with diabetes to evaluate their individual responses to treatment. 1 When used for self-management of diabetes, SMBG has been correlated with lower glycated hemoglobin (HbA1c) levels, 1 and is associated with positive effects on overall outcomes. Furthermore, SMBG has been associated with better glycemic control regardless of the type of diabetes or the therapy used. Emerging technologies, such as mobile apps, are helping people to manage their diabetes by providing personalized, real-time, data-driven support. Currently, there are many apps available to directly assist with diabetes management, as well as other apps that help track additional relevant information such as exercise, stress, diet, and medications. Many available diabetes apps involve manual logging of blood glucose and insulin data, which may be inconvenient for users and prone to errors. It is important that results obtained from SMBG are accurate because they are used to make critical decisions about diabetes management, and inaccurate results could lead to nutritional and drug dosing errors.

Self-monitoring of blood glucose (SMBG) is an important technique for assessing glycemic control in people with diabetes. 1 SMBG can be useful for guiding therapy as it allows people with diabetes to evaluate their individual responses to treatment. 1 When used for self-management of diabetes, SMBG has been correlated with lower glycated hemoglobin (HbA1c) levels, 1 and is associated with positive effects on overall outcomes. Furthermore, SMBG has been associated with better glycemic control regardless of the type of diabetes or the therapy used. Emerging technologies, such as mobile apps, are helping people to manage their diabetes by providing personalized, real-time, data-driven support. Currently, there are many apps available to directly assist with diabetes management, as well as other apps that help track additional relevant information such as exercise, stress, diet, and medications. Many available diabetes apps involve manual logging of blood glucose and insulin data, which may be inconvenient for users and prone to errors. It is important that results obtained from SMBG are accurate because they are used to make critical decisions about diabetes management, and inaccurate results could lead to nutritional and drug dosing errors.

Self-monitoring of blood glucose (SMBG) is an important technique for assessing glycemic control in people with diabetes. 1 SMBG can be useful for guiding therapy as it allows people with diabetes to evaluate their individual responses to treatment. 1 When used for self-management of diabetes, SMBG has been correlated with lower glycated hemoglobin (HbA1c) levels, 1 and is associated with positive effects on overall outcomes. Furthermore, SMBG has been associated with better glycemic control regardless of the type of diabetes or the therapy used. Emerging technologies, such as mobile apps, are helping people to manage their diabetes by providing personalized, real-time, data-driven support. Currently, there are many apps available to directly assist with diabetes management, as well as other apps that help track additional relevant information such as exercise, stress, diet, and medications. Many available diabetes apps involve manual logging of blood glucose and insulin data, which may be inconvenient for users and prone to errors. It is important that results obtained from SMBG are accurate because they are used to make critical decisions about diabetes management, and inaccurate results could lead to nutritional and drug dosing errors.

Spinal cord injury (SCI) is one of the most disabling consequences of trauma with unparalleled economic, social, and personal burden. Any attempt aimed at improving quality of care should be based on comprehensive and reliable data. This pilot investigation studied the feasibility of implementing the National Spinal Cord and Column Injury Registry of Iran (NSCIR-IR) and scrutinized the quality of the registered data. From October 2015 to May 2016, over an 8-month period, 65 eligible trauma patients who were admitted to hospitals in three academic centers in mainland Iran were included in this pilot study. Certified registered nurses and neurosurgeons were in charge of data collection, quality verification, and registration. Sixty-five patients with vertebral column fracture dislocations were registered in the study, of whom 14 (21.5%) patients had evidence of SCI. Mechanisms of injury included mechanical falls in 30 patients (46.2%) and motor vehicle accidents in 29 (44.6%). The cas...

Null hypothesis significance testing (NHST) dominates quantitative data analysis, but its use is controversial and has been heavily criticized. The American Psychological Association has advocated the reporting of effect sizes (ES), confidence intervals (CIs), and statistical power analysis to complement NHST results to provide a more comprehensive understanding of research findings. The aim of this paper is to carry out a sample survey of statistical reporting practices in two journals with the highest h5-index scores in the areas of developmental disability and rehabilitation. Using a checklist that includes critical recommendations by American Psychological Association, we examined 100 randomly selected articles out of 456 articles reporting inferential statistics in the year 2013 in the Journal of Autism and Developmental Disorders (JADD) and Research in Developmental Disabilities (RDD). The results showed that for both journals, ES were reported only half the time (JADD 59.3%; RDD 55.87%). These findings are similar to psychology journals, but are in stark contrast to ES reporting in educational journals (73%). Furthermore, a priori power and sample size determination (JADD 10%; RDD 6%), along with reporting and interpreting precision measures (CI: JADD 13.33%; RDD 16.67%), were the least reported metrics in these journals, but not dissimilar to journals in other disciplines. To advance the science in developmental disability and rehabilitation and to bridge the research-to-practice divide, reforms in statistical reporting, such as providing supplemental measures to NHST, are clearly needed.

Parametric mixed-effects models are useful in longitudinal data analysis when the sampling frequencies of a response variable and the associated covariates are the same. We propose a threestep estimation procedure using local polynomial smoothing and demonstrate with data where the variables to be assessed are repeatedly sampled with different frequencies within the same time frame. We first insert pseudo data for the less frequently sampled variable based on the observed measurements to create a new dataset. Then standard simple linear regressions are fitted at each time point to obtain raw estimates of the association between dependent and independent variables. Lastly, local polynomial smoothing is applied to smooth the raw estimates. Rather than use a kernel function to assign weights, only analytical weights that reflect the importance of each raw estimate are used. The standard errors of the raw estimates and the distance between the pseudo data and the observed data are considered as the measure of the importance of the raw estimates. We applied the proposed method to a weight loss clinical trial, and it efficiently estimated the correlation between the inconsistently sampled longitudinal data. Our approach was also evaluated via simulations. The results showed that the proposed method works better when the residual variances of the standard linear regressions are small and the within-subjects correlations are high. Also, using analytic weights instead of kernel function during local polynomial smoothing is important when raw estimates have extreme values, or the association between the dependent and independent variable is nonlinear.

The extensive genetic diversity of HIV-1 represents a major challenge to public health interventions, treatment, and successful vaccine design. This challenge is particularly pronounced in the Middle East and North Africa (MENA) region, where limited data among other barriers preclude the accurate characterization of HIV-1 genetic diversity. The objective of this review was to analyze studies conducted in the MENA region to delineate possible barriers that would hinder the accurate depiction of HIV-1 genetic diversity in this region. A systematic search of PubMed/MEDLINE and Google Scholar was conducted for published records on HIV-1 genetic diversity in the English language up until 1 October 2024 across 18 MENA countries. The pre-defined themes of challenges/barriers included limited sampling, data gaps, resource and infrastructure constraints, HIV-1-specific factors, and socio-cultural barriers. A total of 38 records were included in the final review, comprising original articles (55.3%), reviews (21.1%), and sequence notes (10.5%). Libya (15.8%), Morocco (13.2%), Saudi Arabia, and MENA as a whole (10.5% for each) were the primary sources of the included records. Of the 23 records with original MENA HIV-1 sequences, the median number of sequences was 46 (range: 6–193). The identified barriers included the following: (1) low sampling density; (2) limited clinical data (21.7% with no data, 60.9% partial data, and 17.4% with full data); (3) reliance solely on population sequencing and insufficient use of advanced sequencing technologies; (4) lack of comprehensive recombination analysis; and (5) socio-cultural barriers, including stigma with subsequent under-reporting among at-risk groups. The barriers identified in this review can hinder the ability to map the genetic diversity of HIV-1 in the MENA. Poor characterization of HIV-1’s genetic diversity in the MENA would hinder efforts to optimize prevention strategies, monitor drug resistance, and develop MENA-specific treatment protocols. To overcome these challenges, investment in public health/research infrastructure, policy reforms to reduce stigma, and strengthened regional collaboration are recommended.

Collect Once - Use Many Times can possibly increase the research potential of clinical data from electronic healthcare records (EHR). The objective of this study was to achieve an increased understanding of the research potential of low back pain patients' municipal EHR by assessing the data quality. This explanatory sequentially mixed-methods case study consists of descriptive- and content analysis. The descriptive analysis was based on data extracted from the municipal EHR. The indicators chosen were pain and physical function. Included subjects were low back pain patients in the Northern Denmark. For the content analysis, clinical notes from the municipal EHR was used. The descriptive analysis (n=172) showed that the outcome measure for pain was documented in 50% of the municipal EHR and the outcome measure for physical function was documented in 48% of the municipal EHR. The content analysis (n=5) revealed imprecise, inconsistent, and nonsystematic use of outcome measures. I...

Patients today have ample opportunities to inform themselves about their disease and possible treatments using the Internet. While this type of patient empowerment is widely regarded as having a positive influence on the treatment, there exists the problem that the quality of information that can be found on online is very diverse. This paper presents a platform which empowers patients by allowing searching in a high quality document repository. In addition, it automatically provides intelligent and personalized recommendations according to the individual preferences and medical conditions.

Objective: The current paper describes Diet In Nutrients Out (DINO), an integrated dietary assessment system incorporating dietary data entry and nutritional analysis within one platform for use in dietary assessment in small-scale intervention studies to national surveys. Design: DINO contains > 6000 food items, mostly aggregated composites of branded foods, across thirty-one main food groups divided into 151 subsidiary groups for detailed reporting requirements, with fifty-three core nutrient fields.

Cancer registries (CRs) are fundamental for estimating cancer burden, evaluating screening and monitoring health service performance. Stage at diagnosis-an essential information item collected by CRs-has been made available, for the first time, by CRs participating in EUROCARE-5. We analysed the quality of this information and estimated stage-specific survival across Europe for CRs with good data quality. Sixty-two CRs sent stage (as TNM, condensed TNM or extent of disease) for 15 cancers diagnosed in 2000-2007. We assessed the quality, partly by comparing stage according to the three systems. We also developed procedures to reconstruct stage (categories: local, regional, metastatic and unknown) using information from all three systems, thus minimising the amount of missing information. Moderate-to-excellent stage concordance was found for practically all 24 CRs, for which it was possible to compare at least two staging systems. However, since stage was often incorrectly assigned, a...

Completeness of notifications of violence perpetrated against adolescents in the State of Pernambuco, Brazil Resumo O objetivo deste trabalho foi analisar a tendência da completitude dos dados de violência perpetrada contra adolescentes registrados em Pernambuco, em 2009-2012. Estudo transversal, com 5.259 adolescentes vítimas de violência notificadas no SINAN-VIVA da Secretaria Estadual de Saúde de Pernambuco. Utilizou regressão linear simples para investigar a tendência de completitude das variáveis. Os percentuais de completitude foram considerados como variáveis dependentes (Y) e os anos da série, como independentes (X). Os resultados mostram um incremento significativo de 204% no número de notificações. Porém, das 34 variáveis analisadas, 27 (79,4%) apresentaram tendência Estacionária, 6 (17,6%) Decrescente e apenas uma (2,9%) Crescente. A completitude foi considerada 'Muito Ruim' para as variáveis: Escolaridade (47,3%), Complemento (21,3%), Hora da Ocorrência (38,0%) e Uso de Álcool Pelo Agressor (47,0%). Portanto, apesar do grande incremento no numero de notificações, a qualidade dos dados permaneceu comprometida, dificultando uma análise mais realista neste grupo. Palavras-chave Adolescente,

Resumo O objetivo deste trabalho foi analisar a tendência da completitude dos dados de violência perpetrada contra adolescentes registrados em Pernambuco, em 2009-2012. Estudo transversal, com 5.259 adolescentes vítimas de violência notificadas no SINAN-VIVA da Secretaria Estadual de Saúde de Pernambuco. Utilizou regressão linear simples para investigar a tendência de completitude das variáveis. Os percentuais de completitude foram considerados como variáveis dependentes (Y) e os anos da série, como independentes (X). Os resultados mostram um incremento significativo de 204% no número de notificações. Porém, das 34 variáveis analisadas, 27 (79,4%) apresentaram tendência Estacionária, 6 (17,6%) Decrescente e apenas uma (2,9%) Crescente. A completitude foi considerada ‘Muito Ruim’ para as variáveis: Escolaridade (47,3%), Complemento (21,3%), Hora da Ocorrência (38,0%) e Uso de Álcool Pelo Agressor (47,0%). Portanto, apesar do grande incremento no numero de notificações, a qualidade do...

In today's data-driven world, organizations rely on multiple platforms for data collection, storage, processing, and reporting. However, these platforms often generate inconsistent reports, leading to data quality issues, misaligned metrics, and challenges in decision-making. Standardizing data reporting across multiple platforms is essential for ensuring consistency, accuracy, and reliability of data, which is crucial for informed decision-making, regulatory compliance, and operational efficiency. This white paper explores the importance of standardizing data reporting, highlights the key challenges organizations face, and presents effective methodologies for implementing reporting standardization. By defining uniform reporting formats, establishing a data governance framework, and leveraging automation tools, organizations can streamline data reporting, improve data quality, and enhance the integration of data across different systems. Ultimately, standardizing data reporting fosters greater organizational alignment, improves strategic decision-making, and ensures compliance with industry regulations.

Healthcare data governance has emerged as a critical priority in the digital age, driven by the proliferation of electronic health records (EHRs), wearable devices, and data-intensive healthcare solutions. This study delves into the intricacies of healthcare data governance, identifying key challenges such as data silos, privacy concerns, and regulatory compliance. It proposes a framework combining robust strategies like role-based access control, data lineage tracking, and advanced encryption techniques to ensure data integrity, security, and accessibility. The research underscores the importance of stakeholder collaboration and the integration of emerging technologies, including blockchain and AI, to address governance complexities. By focusing on strategies tailored to the healthcare industry, the study offers actionable insights for improving decision-making, fostering innovation, and safeguarding patient trust in a rapidly evolving digital landscape.

To describe the completeness of routinely collected primary care data that could be used by computer models to predict clinical outcomes among patients with Type 2 Diabetes (T2D). Data on blood pressure, weight, total cholesterol, HDL-cholesterol and glycated haemoglobin levels for regular patients were electronically extracted from the medical record software of 12 primary care practices in Australia for the period 2000-2012. The data was analysed for temporal trends and for associations between patient characteristics and completeness. General practitioners were surveyed to identify barriers to recording data and strategies to improve its completeness. Over the study period data completeness improved up to around 80% complete although the recording of weight remained poorer at 55%. T2D patients with Ischaemic Heart Disease were more likely to have their blood pressure recorded (OR 1.6, p=0.02). Practitioners reported not experiencing any major barriers to using their computer medi...

The mixed research design is a progressive methodological discourse that combines the advantages of quantitative and qualitative methods. Its possibilities of application are, however, dependent on the efficiency with which the particular research techniques are used and combined. The aim of the paper is to introduce the possible combination of Hypothesis with EyeTribe tracker. The Hypothesis is intended for quantitative data acquisition and the EyeTribe is intended for qualitative (eye-tracking) data recording. In the first part of the paper, Hypothesis software is described. The Hypothesis platform provides an environment for web-based computerized experiment design and mass data collection. Then, evaluation of the accuracy of data recorded by EyeTribe tracker was performed with the use of concurrent recording together with the SMI RED 250 eye-tracker. Both qualitative and quantitative results showed that data accuracy is sufficient for cartographic research. In the third part of the paper, a system for connecting EyeTribe tracker and Hypothesis software is presented. The interconnection was performed with the help of developed web application HypOgama. The created system uses open-source software OGAMA for recording the eye-movements of participants together with quantitative data from Hypothesis. The final part of the paper describes the integrated research system combining Hypothesis and EyeTribe.

The agricultural sector is undergoing a significant transformation with the adoption of the agricultural internet of things (IoT), yet it faces persistent challenges in optimizing resource efficiency and data-driven performance due to limitations in current optimization algorithms. This research assesses the effectiveness of four prominent algorithms such as ant colony optimization (ACO), genetic algorithms (GA), particle swarm optimization (PSO), and artificial bee colony (ABC) in addressing these challenges within agricultural IoT (AIoT). Introducing a novel hybrid optimization algorithm (HOA), we aim to overcome these limitations by prioritizing both resource efficiency and data-driven performance. Through a thorough evaluation, HOA demonstrates its superiority in enhancing both aspects, thereby establishing itself as a compelling solution for AIoT applications. The introduction of HOA sets the stage for sustainable, cost-effective, and data-driven precision agriculture, significantly enhancing resource efficiency and data accuracy within the IoT network.

Introduction: Administrative healthcare databases are useful to investigate the epidemiology, health outcomes, quality indicators and healthcare utilisation concerning peptic ulcers and gastrointestinal bleeding, but the databases need to be validated in order to be a reliable source for research. The aim of this protocol is to perform the first systematic review of studies reporting the validation of International Classification of Diseases, 9th Revision and 10th version (ICD-9 and ICD-10) codes for peptic ulcer and upper gastrointestinal bleeding diagnoses. Methods and analysis: MEDLINE, EMBASE, Web of Science and the Cochrane Library databases will be searched, using appropriate search strategies. We will include validation studies that used administrative data to identify peptic ulcer disease and upper gastrointestinal bleeding diagnoses or studies that evaluated the validity of peptic ulcer and upper gastrointestinal bleeding codes in administrative data. The following inclusion criteria will be used: (a) the presence of a reference standard case definition for the diseases of interest; (b) the presence of at least one test measure (eg, sensitivity, etc) and (c) the use of an administrative database as a source of data. Pairs of reviewers will independently abstract data using standardised forms and will evaluate quality using the checklist of the Standards for Reporting of Diagnostic Accuracy (STARD) criteria. This systematic review protocol has been produced in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocol (PRISMA-P) 2015 statement. Trial registration number: CRD42015029216.

Objective: To assess analytic approaches randomized controlled trial (RCT) authors use to address missing participant data (MPD) for patient-important continuous outcomes. Study Design and Setting: We conducted a systematic survey of RCTs published in 2014 in the core clinical journals that reported at least one patient-important outcome analyzed as a continuous variable. Results: Among 200 studies, 187 (93.5%) trials explicitly reported whether MPD occurred. In the 163 (81.5%) trials that reported the occurrence of MPD, the median and interquartile ranges of the percentage of participants with MPD were 11.4% (2.5%e22.6%).Among the 147 trials in which authors made clear their analytical approach to MPD, the approaches chosen included available data only (109, 67%); mixed-effect models (10, 6.1%); multiple imputation (9, 4.5%); and last observation carried forward (9, 4.5). Of the 163 studies reporting MPD, 16 (9.8%) conducted sensitivity analyses examining the impact of the MPD and (18, 11.1%) discussed the risk of bias associated with MPD. Conclusion: RCTs reporting continuous outcomes typically have over 10% of participant data missing. Most RCTs failed to use optimal analytic methods, and very few conducted sensitivity analyses addressing the possible impact of MPD or commented on how MPD might influence risk of bias.

Using current climate models, regional-scale changes for Florida over the next 100 years are predicted to include warming over terrestrial areas and very likely increases in the number of high temperature extremes. No uniform definition of a heat wave exists. Most past research on heat waves has focused on evaluating the aftermath of known heat waves, with minimal consideration of missing exposure information. To identify and discuss methods of handling and imputing missing weather data and how those methods can affect identified periods of extreme heat in Florida. In addition to ignoring missing data, temporal, spatial, and spatio-temporal models are described and utilized to impute missing historical weather data from 1973 to 2012 from 43 Florida weather monitors. Calculated thresholds are used to define periods of extreme heat across Florida. Modeling of missing data and imputing missing values can affect the identified periods of extreme heat, through the missing data itself or through the computed thresholds. The differences observed are related to the amount of missingness during June, July, and August, the warmest months of the warm season (April through September).

Birth weight is essential information in fetal programming studies and is generally obtained retrospectively. In Brazil's Information System on Live Births (SINASC), birth weight is valid information but is not always accessible. The study aimed to establish an algorithm for the selection of the most reliable data source for birth weight in the absence of information in the SINASC database. In a cross-sectional study of 6-14-year-old schoolchildren in Niterói, Rio de Janeiro State, Brazil, in 2010, birth weight was collected through a self-completed questionnaire, interview, medical records from the Family Physician Program, and the SINASC database. We calculated intra-class correlation coefficients (ICCs) and differences in mean birth weight. ICCs varied from 0.90 to 0.99. All the other sources showed higher mean birth weight than SINASC, allowing differences up to 100g. Birth weight is recorded at birth (SINASC) or close to it (Family Physician Program), and in the absence of ...

Assessment of clinical outcomes such as 30-day mortality following coronary revascularization procedures has historically been used to spur quality improvement programs. Public reporting of risk-adjusted outcomes is already mandated in several states, and proposals to further expand public reporting have been put forward as a means of increasing transparency and potentially incentivizing high quality care. However, for public reporting of outcomes to be considered a useful surrogate of procedural quality of care, several prerequisites must be met. First, the reporting measure must be truly representative of the quality of the procedure itself, rather than be dominated by other underlying factors, such as the overall level of illness of a patient. Second, to foster comparisons among physicians and institutions, the metric requires accurate ascertainment of and adjustment for differences in patient risk profiles. This is particularly relevant for high-risk clinical patient scenarios. Finally, the potential deleterious consequences of public reporting of a quality metric should be considered prior to expanding the use of public reporting more broadly. In this viewpoint, the authors review in particular the characterization of high-risk patients currently treated by percutaneous coronary interventional procedures, assessing the adequacy of clinical risk models used in this population. They then expand upon the limitations of 30-day mortality as a quality metric for percutaneous coronary intervention, addressing the strengths and limitations of this metric, as well as offering suggestions to enhance its future use in public reporting. (J Am Coll Cardiol Intv 2016;9: 2077-85) © 2016 by the American College of Cardiology Foundation. C alls for public reporting of cardiovascular outcomes have been growing, with transparency touted as a fundamental component of quality improvement in an emerging era of valuebased health care (1). Public reporting of outcomes following coronary revascularization procedures is already available in several states, with a number of proposals for its expansion looming (2,3). As an example, the American College of Cardiology has announced plans for voluntary public reporting of process-related institutional data for limited metrics as a first step toward public reporting within the National Cardiovascular Data Registry (4). Increasingly, regulators are legislating public reporting, while payers may use these data to rank physicians (5). These efforts stem from a desire for transparency in From the

The healthcare sector is witnessing an unprecedented surge in data volume and complexity, driven by the adoption of electronic health records (EHRs), telemedicine, and precision medicine. However, the lack of a cohesive master data architecture hampers the efficient utilization of this data for informed decision-making and improved patient outcomes. This study introduces a novel master data architecture tailored for the healthcare industry, emphasizing interoperability, scalability, and data governance. By leveraging cutting-edge technologies such as AI, blockchain, and cloud computing, the proposed architecture enables seamless data integration across diverse systems and stakeholders. Real-world implementation scenarios demonstrate its ability to streamline operations, enhance data quality, and foster a patient-centric approach to healthcare delivery. This research underscores the transformative potential of advanced data management frameworks in driving efficiency, compliance, and innovation within the digital healthcare landscape.

PurposeThe purpose of this study was to develop a national program for Canadian diagnostic laboratories to compare DNA-variant interpretations and resolve discordant-variant classifications using the BRCA1 and BRCA2 genes as a case study.MethodsBRCA1 and BRCA2 variant data were uploaded and shared through the Canadian Open Genetics Repository (COGR; http://www.opengenetics.ca). A total of 5,554 variant observations were submitted; classification differences were identified and comparison reports were sent to participating laboratories. Each site had the opportunity to reclassify variants. The data were analyzed before and after the comparison report process to track concordant- or discordant-variant classifications by three different models.ResultsVariant-discordance rates varied by classification model: 38.9% of variants were discordant when using a five-tier model, 26.7% with a three-tier model, and 5.0% with a two-tier model. After the comparison report process, the proportion of...

Accurate electronic health records are important for clinical care and research as well as ensuring patient safety. It is crucial for misspelled words to be corrected in order to ensure that medical records are interpreted correctly. This paper describes the development of a spelling correction system for medical text. Our spell checker is based on Shannon's noisy channel model, and uses an extensive dictionary compiled from many sources. We also use named entity recognition, so that names are not wrongly corrected as misspellings. We apply our spell checker to three different types of free-text data: clinical notes, allergy entries, and medication orders; and evaluate its performance on both misspelling detection and correction. Our spell checker achieves detection performance of up to 94.4% and correction accuracy of up to 88.2%. We show that high-performance spelling correction is possible on a variety of clinical documents.

Cloud Computing (CC) and Artificial intelligence (simulated intelligence) are emerging as transformative technologies for businesses, enabling them to deliver services in a brighter, more efficient, and economical way that best fulfils their clients. This study analyzes the utilization of CC and simulated intelligence in the main training, guard, and examination areas. Implementing these twin technologies has increased operational efficiency, enhanced product quality, and more customer-satisfying services for intelligent companies and government institutions. The research suggest that cloud computing and artificial intelligence represent significant business opportunities for huge confidential undertakings and government organizations with a significant client base and various exchanges happening consistently. Subsequently, it is suggested that the instruction, protection, and research areas remain innovatively dynamic and cutting-edge.

Some studies have reported a ceiling effect in EQ-5D-3L, especially in healthy and/or young individuals. Recently, two further levels have been included in its measurement model (EQ-5D-5L). The purposes of this study were (1) to assess the properties of the EQ-5D-5L in comparison with the standard EQ-5D-3L in a sample of young adults, (2) to foreground the importance of collecting qualitative data to confirm, validate or refine the EQ-5D questionnaire items and (3) to raise questions pertaining to the wording in these questionnaire items. The data used came from a sample of respondents aged 30 or under (n = 624). They completed both versions of the EQ-5D, which were compared in terms of feasibility, level of inconsistency and ceiling effect. Agreement between the instruments was assessed using correlation coefficients and Bland-Altman plots. Known-groups validity of the EQ-5D-5L was also assessed using non-parametric tests. The discriminative properties were compared using receiver ...

Machine learning (ML) has become a transformative technology across industries, significantly enhancing
automation, decision-making, and predictive modeling. However, biases present in data can unintentionally be reinforced or
even amplified by ML algorithms, leading to unfair and potentially harmful outcomes. This study presents a comprehensive
framework to address bias identification and mitigation within ML data pipelines, ensuring fairness and accuracy. We explore
strategies for detecting and correcting bias across different stages of the ML pipeline, including pre-processing, in-processing,
and post-processing methods. Each stage offers distinct opportunities for intervention to minimize bias effectively. Case
examples illustrate the practical application of these strategies in real-world scenarios, providing a tangible view of how bias
mitigation can be implemented across diverse applications. Validation results on datasets with known bias issues demonstrate
the framework’s ability to reduce bias without compromising model performance. This approach emphasizes the importance
of proactive bias management within ML development, encouraging ethical and equitable model outcomes across various
industries.

Medical countermeasures (MCMs) are medical products used during public health emergencies. This study, conducted within the Mini-Sentinel Initiative, sought to develop the patient identification and matching processes necessary to assess safety outcomes for MCMs. A handheld device was used to collect identifying information (e.g., name, birthdate, and sex) from the driver's licenses of 421 individuals presenting for routine care at their primary care medical office. Overall, 374 individuals (88.8%) could be linked to their electronic health data using driver's license information. The device was also pilot-tested at a seasonal influenza immunization clinic: detailed vaccine information (e.g., lot number and manufacturer) was captured with a high degree of accuracy. This investigation demonstrated that a handheld device is a feasible means of collecting patient identity and medical product receipt data. This capacity should be useful for safety surveillance of MCMs, particula...