Factors Influencing Data Quality Research Articles

Factors influencing data quality in EHR systems in 50 health facilities in Rwanda and the role of clinical alerts: observational study (Preprint)

Factors influencing data quality in EHR systems in 50 health facilities in Rwanda and the role of clinical alerts: observational study (Preprint)

Healthcare providers’ views of factors influencing family planning data quality in Tshwane District, South Africa

The family planning service requires a routine health information system (RHIS) that can produce quality data that will be used for making decisions. However, the quality of data generated is not always of a good standard. Its usefulness in making data-driven decisions in family planning service is questionable. The study intended to assess the quality of family planning data and determine healthcare providers' (HCPs') views on factors influencing data quality in Tshwane district. The study was carried out in 13 healthcare institutions in Tshwane district, Gauteng province, South Africa. This paper reports on the quantitative strand of a mixed methods study. A sample of 111 HCPs was selected through a stratified random sampling technique, and six months of monthly reports from 13 institutions were reviewed for data quality. A self-administered questionnaire and a tick sheet were utilised to collect data from the HCPs and to review monthly reports for data quality, respectively. The Statistical Package for the Social Sciences (SPSS) programme for Windows version 24.0.0 was used to analyse the data. Data quality, mainly accuracy and timeliness, was a challenge. Many HCPs were not trained in the RHIS. They viewed several issues relating to organisational, behavioural and technical factors as barriers to data quality. The low quality of family planning data has a negative impact on policy development and decision-making.Contribution: There is a need for capacity building through training and supportive supervision, provision of adequate human and technical resources to enhance data quality and use of information for decision-making.

Assessment of quality of routine health information system data and associated factors among departments in public health facilities of Harari region, Ethiopia

BackgroundDespite the improvements in the knowledge and understanding of the role of health information in the global health system, the quality of data generated by a routine health information system is still very poor in low and middle-income countries. There is a paucity of studies as to what determines data quality in health facilities in the study area. Therefore, this study was aimed to assess the quality of routine health information system data and associated factors in public health facilities of Harari region, Ethiopia.MethodsA cross-sectional study was conducted in all public health facilities in the Harari region of Ethiopia. The department-level data were collected from respective department heads through document reviews, interviews, and observation checklists. Descriptive statistics were used to data quality and multivariate logistic regression was run to identify factors influencing data quality. The level of significance was declared at P value < 0.05.ResultThe study found good quality data in 51.35% (95% CI 44.6–58.1) of the departments in public health facilities in the Harari Region. Departments found in the health centers were 2.5 times more likely to have good quality data as compared to those found in the health posts. The presence of trained staffs able to fill reporting formats (AOR = 2.474; 95% CI 1.124–5.445) and provisions of feedbacks (AOR = 3.083; 95% CI 1.549–6.135) were also significantly associated with data quality.ConclusionThe level of good data quality in the public health facilities was less than the expected national level. Lack of trained personnel able to fill the reporting format and feedback were the factors that are found to be affecting data quality. Therefore, training should be provided to increase the knowledge and skills of the health workers. Regular supportive supervision and feedback should also be maintained.

Going virtual: adapting in-person interactive focus groups to the online environment

Restrictions on social interaction and travel due to the COVID-19 pandemic have affected how researchers approach fieldwork and data collection. Whilst online focus groups have received attention since the 2000s as a method for qualitative data collection, relatively little of the relevant literature appears to have made use of now ubiquitous video calling software and synchronous, interactive discussion tools. Our own experiences in organising fieldwork aimed at understanding the impact of different “future-proofing” strategies for the European agri-food system during this period resulted in several methodological changes being made at short notice. We present an approach to converting in-person focus group to a virtual methodology and provide a checklist for researchers planning their own online focus groups. Our findings suggest data are comparable to in-person focus groups and factors influencing data quality during online focus groups can be safeguarded. There are several key steps, both before and during the focus groups, which can be taken to ensure the smooth running of such events. We share our reflections on this approach and provide a resource for other researchers moving to online-only data collection.

Going virtual: adapting in-person interactive focus groups to the online environment

Restrictions on social interaction and travel due to the COVID-19 pandemic have affected how researchers approach fieldwork and data collection. Whilst online focus groups have received attention since the 2000s as a method for qualitative data collection, relatively little of the relevant literature appears to have made use of now ubiquitous video calling software and synchronous, interactive discussion tools. Our own experiences in organising fieldwork aimed at understanding the impact of different ‘future-proofing’ strategies for the European agri-food system during this period resulted in several methodological changes being made at short notice. We present an approach to converting in-person focus group to a virtual methodology and provide a checklist for researchers planning their own online focus groups. Our findings suggest data are comparable to in-person focus groups and factors influencing data quality during online focus groups can be safeguarded. There are several key steps, both before and during the focus groups, which can be taken to ensure the smooth running of such events. We share our reflections on this approach and provide a resource for other researchers moving to online-only data collection.

Going virtual: adapting in-person interactive focus groups to the online environment

Restrictions on social interaction and travel due to the COVID-19 pandemic have affected how researchers approach fieldwork and data collection. Whilst online focus groups have received attention since the 2000s as a method for qualitative data collection, relatively little of the relevant literature appears to have made use of now ubiquitous video calling software and synchronous, interactive discussion tools. Our own experiences in organising fieldwork aimed at understanding the impact of different ‘future-proofing’ strategies for the European agri-food system during this period resulted in several methodological changes being made at short notice. We present an approach to converting in-person focus group to a virtual methodology and provide a checklist for researchers planning their own online focus groups. Our findings suggest data are comparable to in-person focus groups and factors influencing data quality during online focus groups can be safeguarded. There are several key steps, both before and during the focus groups, which can be taken to ensure the smooth running of such events. We share our reflections on this approach and provide a resource for other researchers moving to online-only data collection.

Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method.

Inactivity, lack of sleep, and poor nutrition predispose individuals to health risks. Patient-Reported Outcomes (PROs) assess physical behaviours and psychological states but are subject of self-reporting biases. Conversely, wearables are an increasingly accurate source of behavioural Technology-Reported Outcomes (TechROs). However, the extent to which PROs and TechROs provide convergent information is unknown. We propose the coQoL PRO-TechRO co-calibration method and report its feasibility, reliability, and human factors influencing data quality. Thirty-nine seniors provided 7.4 ± 4.4 PROs for physical activity (IPAQ), social support (MSPSS), anxiety/depression (GADS), nutrition (PREDIMED, SelfMNA), memory (MFE), sleep (PSQI), Quality of Life (EQ-5D-3L), and 295 ± 238 days of TechROs (Fitbit Charge 2) along two years. We co-calibrated PROs and TechROs by Spearman rank and reported human factors guiding coQoL use. We report high PRO—TechRO correlations ( ≥ 0.8) for physical activity (moderate domestic activity—light+fair active duration), social support (family help—fair activity), anxiety/depression (numeric score—sleep duration), or sleep (duration to sleep—sleep duration) at various durations (7–120 days). coQoL feasibly co-calibrates constructs within physical behaviours and psychological states in seniors. Our results can inform designs of longitudinal observations and, whenever appropriate, personalized behavioural interventions.

Towards quantification of incompleteness in the pairwise comparisons methods

Apart from consistency, the completeness of information is one of the key factors influencing data quality. In the case of the pairwise comparisons (PC) method, much space in the literature is devoted to the quantitative analysis of this first idea, while the second issue has not been properly studied. The presented article is an attempt to bridge this gap. The aim of the article is to examine how the incompleteness of a set of paired comparisons influences the sensitivity of the PC method.During the research, two important factors related to the incompleteness of PC matrices have been identified, namely the number of missing pairwise comparisons and their arrangements. Accordingly, an easy-to-calculate incompleteness index have been developed. It takes into account both the total number of missing data and their distribution in the PC matrix. During the series of Montecarlo experiments, the properties of this index have been examined. It demonstrated that both the incompleteness and inconsistency of data almost equally contribute to the sensitivity of the PC matrix. The relative simplicity of the proposed index may help decision makers to quickly estimate the impact of missing comparisons on the quality of final results.

Barriers to Clinical Trial Efficiency: The Heart Failure Collaboratory Industry Experience

Objectives Clinical trial efficiency is a barrier to success in heart failure research. The Heart Failure Collaboratory (HFC) is a consortium developed to address this barrier. Methods HFC performed a cross-sectional survey of sponsors active in the heart failure field. From August to September 2018, self-administered survey data were electronically collected from industry partners, using a 10 item survey comprising 3 domains and 10 sub-domains ranging from ‘improving efficiency’ to ‘improving patient access.’ The scale response utilized two Likert type scale of agreement. Item responses were aggregated into binary responses to create ‘most efficient’ or ‘largest impediment’ responses. Group comparisons were made via Fisher's Exact or chi-square test, where appropriate. All statistical analyses were conducted using SAS (ver. 9.3, Cary NC). Results 125 potential participants were emailed a link to the online survey, with 43 participants (57.3%) completing the survey for an overall response rate of 34.4%. Respondents rated the US FDA (44.2%), Health Canada (39.5%) and European Medicines Agency (EMA) (32.6%) as agencies most efficient and agencies currently available with opportunities for the most improvement in efficiency (US FDA [46.5%], EMA [20.9%], and Health Canada [4.7%]). Participants rated the top 3 items of concern as ‘assuming more information will be required later by payers’ (58.1%), ‘variability across global agencies’ (53.5%), and ‘assuming more information will be required later by regulatory agencies’ (48.8%) (Figure). Respondents rated items of ‘excessive clinical staff workload’ (55.8%), ‘CRF complexity; difficulty entering data’ (51.2%) and ‘deficiencies in site training’ (39.5%) as the top 3 factors influencing data quality. Respondents rated items of ‘excessively onerous prior authorization’ (51.2%), ‘availability of decision-making rationale’ (46.5%) and ‘rationality of access barriers’ (44.2%) as the top 3 impediments to improving patient access to approved therapies. Conclusions This survey of industry respondents suggests substantial barriers to clinical trial efficiency within the HF landscape.

An empirical study of the antecedents of data completeness in electronic medical records

There is a body of research that highlights the role of data management to improve the quality of data, which in return improves organizational performance. The literature in data management has indicated the five theoretical constructs used to understand the factors influencing data quality, including top management support, capability on the regulation and process management, business-IT alignment, staff participation, and integration of information systems. However, it is unclear how these theoretical constructs can be utilized to understand the antecedents of data completeness as a dimension of data quality. Following that stream of research, the current paper examines the factors influencing data completeness in electronic medical records (EMR). The scope of this study is by only surveying medical professionals at healthcare settings in northern Nevada. The empirical results reveal that resources should be added as one of the antecedents of data completeness in EMR.

Hoping for optimality or designing for inclusion: Persistence, learning, and the social network of citizen science

The explosive growth in citizen science combined with a recalcitrance on the part of mainstream science to fully embrace this data collection technique demands a rigorous examination of the factors influencing data quality and project efficacy. Patterns of contributor effort and task performance have been well reviewed in online projects; however, studies of hands-on citizen science are lacking. We used a single hands-on, out-of-doors project—the Coastal Observation and Seabird Survey Team (COASST)—to quantitatively explore the relationships among participant effort, task performance, and social connectedness as a function of the demographic characteristics and interests of participants, placing these results in the context of a meta-analysis of 54 citizen science projects. Although online projects were typified by high (>90%) rates of one-off participation and low retention (<10%) past 1 y, regular COASST participants were highly likely to continue past their first survey (86%), with 54% active 1 y later. Project-wide, task performance was high (88% correct species identifications over the 31,450 carcasses and 163 species found). However, there were distinct demographic differences. Age, birding expertise, and previous citizen science experience had the greatest impact on participant persistence and performance, albeit occasionally in opposite directions. Gender and sociality were relatively inconsequential, although highly gregarious social types, i.e., “nexus people,” were extremely influential at recruiting others. Our findings suggest that hands-on citizen science can produce high-quality data especially if participants persist, and that understanding the demographic data of participation could be used to maximize data quality and breadth of participation across the larger societal landscape.

Correlative Atomic Force and Single-Molecule Fluorescence Microscopy of Nucleoprotein Complexes.

Correlative imaging by fluorescence and atomic force microscopy provides a versatile tool to extract orthogonal information on structurally heterogeneous biomolecular assemblies. In this chapter, we describe an integrated setup for correlative fluorescence and force microscopy. We present factors influencing data quality, as well as step-by-step protocols for sample preparation, data acquisition, and data processing that yield nanoscale topographic resolution, high image registration accuracy, and single-fluorophore sensitivity. We demonstrate the capabilities of the approach through simultaneous characterization of mesoscale geometry and composition in a multipart nucleoprotein complex.

Factors Influencing Data Quality in Citizen Science Roadkill Projects

Factors Influencing Data Quality in Citizen Science Roadkill Projects

Challenges in collecting pharmacokinetic and pharmacodynamic information in an intensive care setting: PK/PD modelling of clazosentan in patients with aneurysmal subarachnoid haemorrhage

This paper describes the pharmacokinetic/pharmacodynamic modelling of clazosentan in patients with aneurysmal subarachnoid haemorrhage (aSAH), and the impact of collecting data in an intensive care unit (ICU) setting. Factors influencing data quality, analysis, and interpretation are provided with recommendations for future clinical studies in ICU settings. CONSCIOUS-2 was a phase III study involving 1,157 patients with aSAH. Secured by surgical clipping, patients were infused with clazosentan or placebo for up to 14 days post-aSAH. Clazosentan exposure relationships with vital signs, QT intervals, and AST/ALT values as well as efficacy and safety endpoints were characterised using population PK/PD and logistic regression models. Clazosentan clearance was influenced by age, sex, Asian origin, and disease status at baseline, and increased with time. Volume of distribution showed a sex difference. Exposure had no relationship with any efficacy endpoint or ALT/AST values, but was related to the increasing probability of lung complications. Blood pressure decreased proportionally to clazosentan concentrations, and the presence of clazosentan was associated with QT interval increases. Implausible values in the concentration data reflect the specific ICU challenges, possibly arising from PK sampling from the infusion arm or haemodilution. Population PK/PD modelling of CONCIOUS-2 data provided clinically relevant knowledge about various effects of clazosentan in the aSAH patient population in a real clinical setting. The quality of data and analyses could be improved by the collection of additional data and stricter training of study personnel. Differences in clinical practice between sites and geographical regions are more challenging to overcome.

Evaluating the quality of entity relationship models

Entity Relationship (E-R) models are at the core of logical database design. This paper describes the development of a model, associated metrics and methodology for assessing the quality of an E-R model. The model was developed by investigating into the causal relationships between ontological and behavioural factors influencing data quality. The methodology describes aggregation of the scores on various metrics to calculate an overall quality score for an E-R model, and use of the model to identify problem areas if the individual quality scores on different factors do not meet organizational standards. Further possible improvement of the model and future research issues are also discussed.