Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study

Exploring the factors influencing evidence-based approaches to advanced chronic kidney disease: a qualitative study involving nurses and physicians

BACKGROUND

Advanced chronic kidney disease (ACKD) is associated with a high risk of adverse cardiovascular and renal events and has a significant impact on quality of life and life expectancy. Several studies have identified areas for improvement in their management in primary care. Some professional and environmental factors can act as key barriers to appropriate care.

OBJECTIVE

To analyse attitudes, subjective norms, and perceived behavioural control among primary care professionals related to the implementation of an evidence-based approach for individuals with ACKD in primary care.

METHODOLOGY

This was a qualitative study using an interpretative phenomenological approach based on the theory of planned behaviour. Two aspects of the evidence-based approach were explored: the implementation of clinical practice guidelines and the utilisation of electronic kidney disease records within the scope of this study. Primary care nurses and physicians participated in a previous pilot interview and five focus groups. Subsequently, a thematic analysis of the gathered data was conducted.

FINDINGS

Thirty-three primary care professionals participated. The emerging themes included: experiences in the management of ACKD (highlighting a distinct profile of older, frail patients with comorbidities masking CKD and a CKD follow-up primarily focused on analytical monitoring and drug adjustment); factors in the professional environment influencing the use of scientific evidence (such as time constraints, excessive electronic health records, and unfamiliar reference guidelines); attitudes towards the application of recommendations on ACKD (recognising limitations of computer systems despite considering them as guidance); and capacities to implement evidence-based recommendations (acknowledging formative needs and challenges in coordinating care with nephrology services).

CONCLUSIONS

Several psychological elements identified through the TBP hinder the adequate implementation of an evidence-based approach for individuals with CKD. Attitudes have been identified as factors modulating the use of standardised electronic records. Instead, subjective norms (influences from the professional environment) and perceived behavioral control (perception of capabilities) acted as barriers to the proper application of clinical practice guidelines and standardised records.

IMPLICATIONS FOR PRACTICE

Strategies aimed at optimising the management of people with ACKD should focus not only on training but also on improving attitudes, organisational structures, IT systems and coordination between primary care and nephrology.

Factors impacting clinical data and documentation quality in Australian aged care and disability services: a user-centred perspective

BACKGROUND

Research has highlighted a need to improve the quality of clinical documentation and data within aged care and disability services in Australia to support improved regulatory reporting and ensure quality and safety of services. However, the specific causes of data quality issues within aged care and disability services and solutions for optimisation are not well understood.

OBJECTIVES

This study explored aged care and disability workforce (referred to as 'data-users') experiences and perceived root causes of clinical data quality issues at a large aged care and disability services provider in Western Australia, to inform optimisation solutions.

METHODS

A purposive sample of n = 135 aged care and disability staff (including community-based and residential-based) in clinical, care, administrative and/or management roles participated in semi-structured interviews and web-based surveys. Data were analysed using an inductive thematic analysis method, where themes and subthemes were derived.

RESULTS

Eight overarching causes of data and documentation quality issues were identified: (1) staff-related challenges, (2) education and training, (3) external barriers, (4) operational guidelines and procedures, (5) organisational practices and culture, (6) technological infrastructure, (7) systems design limitations, and (8) systems configuration-related challenges.

CONCLUSION

The quality of clinical data and documentation within aged care and disability services is influenced by a complex interplay of internal and external factors. Coordinated and collaborative effort is required between service providers and the wider sector to identify behavioural and technical optimisation solutions to support safe and high-quality care and improved regulatory reporting.

Training pediatric physicians and staff to obtain data from the electronic health record

Electronic health records (EHRs) have provided physicians with user-friendly self-service reporting tools to extract patient data from the EHR. Despite such benefits, physician training on how to use these tools has been limited. At our institution, physicians were faced with prolonged wait time for EHR data extraction requests and were unaware of self-service reporting tool availability in the EHR. Our goal was to develop an EHR data reporting curriculum for physicians and staff and examine the effectiveness of such training. In 2019, physician informaticists developed two interactive sessions to train physicians and staff on self-service reporting tools (Epic® SlicerDicer and Reporting Workbench (RWB)) available in our tertiary children's hospital EHR. We assessed participants' knowledge, confidence, and tool utilization before, after, and 3-months post training via survey. Training sessions occurred between April and August 2021. Thirty-six participants completed the study, with 25 surveys collected immediately post and 22 surveys collected at 3-months post training. Data literacy knowledge pre-test average score improved from 62% to 93% (p < 0.05) immediately post-session and 74% at 3-months post assessment (p = 0.05). Regular tool utilization increased from 29% (RWB) and 34% (SlicerDicer) pre-session to 56% and 44% at 3-months post, respectively. Participants reported increased confidence in performing SlicerDicer model selection, criteria selection, and data visualization as well as RWB report navigation, report creation, report visualization, and describing report's benefits/limitations. Ultimately, physician and staff self-service reporting tools training were effective in increasing data literacy knowledge, tool utilization, and confidence.

Machine learning models to predict surgical case duration compared to current industry standards: scoping review

BACKGROUND

Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres by optimal scheduling could help mitigate this, but this requires accurate case duration predictions. Current standards for predicting the duration of surgery are inaccurate. Artificial intelligence (AI) offers the potential for greater accuracy in predicting surgical case duration. This study aimed to investigate whether there is evidence to support that AI is more accurate than current industry standards at predicting surgical case duration, with a secondary aim of analysing whether the implementation of the models used produced efficiency savings.

METHOD

PubMed, Embase, and MEDLINE libraries were searched through to July 2023 to identify appropriate articles. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Study quality was assessed using a modified version of the reporting guidelines for surgical AI papers by Farrow et al. Algorithm performance was reported using evaluation metrics.

RESULTS

The search identified 2593 articles: 14 were suitable for inclusion and 13 reported on the accuracy of AI algorithms against industry standards, with seven demonstrating a statistically significant improvement in prediction accuracy (P < 0.05). The larger studies demonstrated the superiority of neural networks over other machine learning techniques. Efficiency savings were identified in a RCT. Significant methodological limitations were identified across most studies.

CONCLUSION

The studies suggest that machine learning and deep learning models are more accurate at predicting the duration of surgery; however, further research is required to determine the best way to implement this technology.

Data Quality in Health Research: Integrative Literature Review

BACKGROUND

Decision-making and strategies to improve service delivery must be supported by reliable health data to generate consistent evidence on health status. The data quality management process must ensure the reliability of collected data. Consequently, various methodologies to improve the quality of services are applied in the health field. At the same time, scientific research is constantly evolving to improve data quality through better reproducibility and empowerment of researchers and offers patient groups tools for secured data sharing and privacy compliance.

OBJECTIVE

Through an integrative literature review, the aim of this work was to identify and evaluate digital health technology interventions designed to support the conducting of health research based on data quality.

METHODS

A search was conducted in 6 electronic scientific databases in January 2022: PubMed, SCOPUS, Web of Science, Institute of Electrical and Electronics Engineers Digital Library, Cumulative Index of Nursing and Allied Health Literature, and Latin American and Caribbean Health Sciences Literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and flowchart were used to visualize the search strategy results in the databases.

RESULTS

After analyzing and extracting the outcomes of interest, 33 papers were included in the review. The studies covered the period of 2017-2021 and were conducted in 22 countries. Key findings revealed variability and a lack of consensus in assessing data quality domains and metrics. Data quality factors included the research environment, application time, and development steps. Strategies for improving data quality involved using business intelligence models, statistical analyses, data mining techniques, and qualitative approaches.

CONCLUSIONS

The main barriers to health data quality are technical, motivational, economical, political, legal, ethical, organizational, human resources, and methodological. The data quality process and techniques, from precollection to gathering, postcollection, and analysis, are critical for the final result of a study or the quality of processes and decision-making in a health care organization. The findings highlight the need for standardized practices and collaborative efforts to enhance data quality in health research. Finally, context guides decisions regarding data quality strategies and techniques.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1101/2022.05.31.22275804.

The data quality and applicability of a Danish prehospital electronic health record: A mixed-methods study

BACKGROUND

Without accurate documentation, it can be difficult to assess the quality of care and the impact of quality improvement initiatives. Prehospital lack of documentation of the basic measurements is associated with a twofold risk of mortality. The aim of this study was to investigate data quality in the electronic prehospital patient record (ePPR) system in the Region of Southern Denmark. In addition, we investigated ambulance professionals' attitudes toward the use of ePPR and identified barriers and facilitators to its use.

METHOD

We used an explanatory sequential mixed-methods design. Phase one consisted of a retrospective assessment of the data quality of ePPR information, and phase two included semi-structured interviews with ambulance professionals combined with observations. We included patients who were acutely transported to an emergency department by ambulance in the Region of Southern Denmark from 2016 to 2020. Data completeness was calculated for each vital sign using a two-way table of frequency. Vital signs were summarised to calculate data correctness. Interviews and observations were analysed using thematic analysis.

RESULTS

Overall, an improvement in data completeness and correctness was observed from 2016-2020. When stratified by age group, children (<12 years) accounted for the majority of missing vital sign registrations. In the thematic analysis, we identified four themes; ambulance professionals' attitudes, emergency setting, training and guidelines, and tablet and software.

CONCLUSION

We found high data quality, but there is room for improvement. The ambulance professionals' attitudes toward the ePPR, working in an emergency setting, a notion of insufficient training in completing the ePPR, and challenges related to the tablet and software could be barriers to data completeness and correctness. It would be beneficial to include the end-user when developing an ePPR system and to consider that the tablet should be used in emergency situations.

Effective and feasible interventions to improve structured EHR data registration and exchange: A concept mapping approach and exploration of practical examples in the Netherlands

BACKGROUND

Data in Electronic Health Records (EHRs) is often poorly structured and standardized, which hampers data reuse. Research described some examples of interventions to increase and improve structured and standardized data, such as guidelines and policies, training and user friendly EHR interfaces. However, little is known about the translation of this knowledge into practical solutions. Our study aimed to specify the most effective and feasible interventions that enable better structured and standardized EHR data registration and described practical examples of successfully implemented interventions.

METHODS

A concept mapping approach was used to determine feasible interventions that were considered to be effective or have been successfully implemented in Dutch hospitals. A focus group was held with Chief Medical Information Officers and Chief Nursing Information Officers. After interventions were determined, multidimensional scaling and cluster analysis were performed to categorize sorted interventions using Groupwisdom™, an online tool for concept mapping. Results are presented as Go-Zone plots and cluster maps. Following, semi-structured interviews were conducted to describe practical examples of successful interventions.

RESULTS

Interventions were classified into seven clusters ranked from highest to lowest perceived effectiveness: (1) education on usefulness and need; (2) strategic and (3) tactical organizational policies; (4) national policy; (5) monitoring and adjusting data (6) structure of and support from the EHR and (7) support in the registration process (EHR independent). Interviewees emphasized the following interventions proven successful in their practice: an enthusiastic ambassador per specialty who is responsible for educating peers by increasing awareness of the direct benefit of structured and standardized data registration; dashboards for continuous feedback on data quality; and EHR functionalities that support (automating) the registration process.

CONCLUSIONS

Our study provided a list of effective and feasible interventions including practical examples of interventions that have been successful. Organizations should continue to share their best practices to learn from and attempted interventions to prevent implementation of ineffective interventions.

Digital Health Data Quality Issues: Systematic Review

BACKGROUND

The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact.

OBJECTIVE

The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ?

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework.

RESULTS

The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes.

CONCLUSIONS

The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

Quality of records in clinical forms of childbirth in the Maternity Hospital of Lubango, Angola

OBJECTIVE

To describe the quality of clinical records of deliveries and births by quantitative assessing the unfilled variables in birth data collection forms and their implications at Maternity Hospital, in the municipality of Lubango, Angola.

METHOD

The study was conducted from January to August 2018. It adopted a quantitative research design, analysed variables not filled in a total of 202 birth record forms collected for 3 months (secondary data).

RESULTS

The findings revealed that 80% of the sections of the entire set of information about obstetrical history were not filled in. This occurred with a relatively high frequency resulting in some of the relevant variables being left blank, such as antenatal diagnosis (94%) and the number of last menstruation (91%).

CONCLUSIONS

The rate of missing fundamental information from the clinical birth record are high. This result has important implications in evaluating the quality of data and may, consequently, jeopardize: 1) the evaluation of the prenatal assistance, 2) the clinical assistance at delivery, and 3) decision-making for preventive and intervening procedures.

A narrative review on the validity of electronic health record-based research in epidemiology

Electronic health records (EHRs) are widely used in epidemiological research, but the validity of the results is dependent upon the assumptions made about the healthcare system, the patient, and the provider. In this review, we identify four overarching challenges in using EHR-based data for epidemiological analysis, with a particular emphasis on threats to validity. These challenges include representativeness of the EHR to a target population, the availability and interpretability of clinical and non-clinical data, and missing data at both the variable and observation levels. Each challenge reveals layers of assumptions that the epidemiologist is required to make, from the point of patient entry into the healthcare system, to the provider documenting the results of the clinical exam and follow-up of the patient longitudinally; all with the potential to bias the results of analysis of these data. Understanding the extent of as well as remediating potential biases requires a variety of methodological approaches, from traditional sensitivity analyses and validation studies, to newer techniques such as natural language processing. Beyond methods to address these challenges, it will remain crucial for epidemiologists to engage with clinicians and informaticians at their institutions to ensure data quality and accessibility by forming multidisciplinary teams around specific research projects.

Clinical researchers' lived experiences with data quality monitoring in clinical trials: a qualitative study

BACKGROUND

Fundamental to the success of clinical research that involves human participants is the quality of the data that is generated. To ensure data quality, clinical trials must comply with the Good Clinical Practice guideline which recommends data monitoring. To date, the guideline is broad, requires technology for enforcement, follows strict industry standards, mostly designed for drug-registration trials and based on informal consensus. It is also unknown what challenges clinical trials and researchers face in implementing data monitoring procedures. Thus, this study aimed to describe researcher experiences with data quality monitoring in clinical trials.

METHODS

We conducted semi-structured telephone interviews following a guided-phenomenological approach. Participants were recruited from the Australian and New Zealand Clinical Trials Registry and were researchers affiliated with a listed clinical study. Each transcript was analysed with inductive thematic analysis before thematic categorisation of themes from all transcripts. Primary, secondary and subthemes were categorised according to the emerging relationships.

RESULTS

Data saturation were reached after interviewing seven participants. Five primary themes, two secondary themes and 21 subthemes in relation to data quality monitoring emerged from the data. The five primary themes included: education and training, ways of working, working with technology, working with data, and working within regulatory requirements. The primary theme 'education and training' influenced the other four primary themes. While 'working with technology' influenced the 'way of working'. All other themes had reciprocal relationships. There was no relationship reported between 'working within regulatory requirements' and 'working with technology'. The researchers experienced challenges in meeting regulatory requirements, using technology and fostering working relationships for data quality monitoring.

CONCLUSION

Clinical trials implemented a variety of data quality monitoring procedures tailored to their situation and study context. Standardised frameworks that are accessible to all types of clinical trials are needed with an emphasis on education and training.

A retrospective single-site data-linkage study comparing manual to electronic data abstraction for routine post-operative nausea and vomiting audit

BACKGROUND

Post-operative nausea and vomiting (PONV) is a common cause of patient dissatisfaction following anaesthesia. Audit of adherence to PONV prevention guidelines is resource intensive when performed by manual chart extraction. Electronic audit can require costly anaesthetic and medical records.

OBJECTIVE

In our single-site study we sought to compare manual and electronic PONV audits by utilizing existing non-anaesthetic electronic medical records to avoid expensive additional software.

METHODS

The audits were performed from 13 January 2020 to 1 February 2020 for surgical inpatients. Two PONV periods were captured-the post-anaesthetic recovery unit and on the ward (to 24 h). Electronic PONV was defined as the administration of an anti-emetic medication. A 6-month electronic PONV rate was also calculated.

RESULTS

Manual audit captured 142 patients and electronic audit captured 294 patients, over the same time period. The manual PONV rate was 10% (95% confidence interval (CI) 5-16%) in the post-anaesthetic recovery unit and 20% (95% CI 14-28%) the next day. The electronic rate was 5% (95% CI 3-8%) in the post-anaesthetic recovery unit and 15% (11-19%) in a 24-h period. The 6-month electronic audit found 3510 patients, with a post-anaesthetic recovery unit and 24-h PONV rates of 5% (4-6%) and 14% (13-16%), respectively. Electronic audit did not identify 5.8% of PONV patients in the manual audit.

CONCLUSION

Electronic audit enrolled more patients and identified a lower PONV rate than manual audit, likely from less enrolment bias. Electronic audit was easily repeated over a 6-month period. While electronic PONV audit is possible without additional software, an electronic anaesthetic chart would greatly improve audit quality.

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study

BACKGROUND

With the proliferation of electronic medical record (EMR) systems, there is an increasing interest in utilizing EMR data for medical research; yet, there is no quantitative research on EMR data utilization for medical research purposes in China.

OBJECTIVE

This study aimed to understand how and to what extent EMR data are utilized for medical research purposes in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 hospital in Beijing, China. Obstacles and issues in the utilization of EMR data were also explored to provide a foundation for the improved utilization of such data.

METHODS

For this descriptive cross-sectional study, cluster sampling from Xuanwu Hospital, one of two Stage 7 hospitals in Beijing, was conducted from 2016 to 2019. The utilization of EMR data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. Comparisons by year, professional title, and age were conducted by double-sided chi-square tests.

RESULTS

From 2016 to 2019, EMR data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times per person per year. The frequency per capita gradually slowed and older senior-level staff more frequently used EMR data compared with younger staff.

CONCLUSIONS

The value of using EMR data for research purposes is not well studied in China. More research is needed to quantify to what extent EMR data are utilized across all hospitals in Beijing and how these systems can enhance future studies. The results of this study also suggest that young doctors may be less exposed or have less reason to access such research methods.

Clinical effect of application of nursing concept of rehabilitation surgery for improvement of quality of postoperative recovery in orthopedics

Objective

To analyze the application of concept nursing of accelerated rehabilitation surgery in orthopedic postoperative recovery.

Methods

A total of 120 patients who received orthopedic surgery were divided into the control group undergoing routine orthopedic nursing and the observation group undergoing the concept of accelerated rehabilitation surgery nursing.

Results

Patients in the observation group had shorter in-bed activity time and out-of-bed activity time, average time of hospital stay, and lower total treatment costs. The incidence of incision infection, respiratory system infection, digestive tract infection, urinary tract infection, deep vein thrombosis, and other complications in the observation group was much lower. The recovery scores of joint function in the observation group at 1, 3, 6, and 12 months after the operation were all better, and the recovery rate of joint function within 1 year after the operation was higher.

Conclusion

Following the concept of accelerated rehabilitation surgery nursing during the perioperative period can improve the quality of postoperative orthopedic recovery.

Application of a four-dimensional framework to evaluate the quality of the HIV/AIDS data collection process in China

OBJECTIVE

To qualitatively evaluate the quality of the data collection process used by the Chinese national HIV/AIDS data repository (CRIMS), using a four-dimensional (4D) framework. The process is vital for the acquisition of high-quality data for ending the HIV/AIDS epidemic in China.

METHODS

The study was carried out in China from September 2014 to April 2015. Stratified convenient sampling was conducted to recruit 28 study participants including health administrators, public health professionals and clinicians. Data were collected through semi-structured interviews with the participants and from field observations in six hospitals. Content analysis was conducted following the 4D Framework.

RESULTS

61 percent of the facilitators and 74 percent of the barriers of the 4D Framework were confirmed in the CRIMS data collection process. The CRIMS achieved better-quality data collection management. The perceived gaps primarily included: impractical data collection protocol and invalid quality assessment mechanism for data collection management; weak leadership and unsupportive organizational policy for data collection environment; poor communication and job fatigue for data collection personnel; and inflexibility and inaccessibility of data collection system. Areas for improvement included: engaging frontline staff in the design of data collection protocol, standardizing quality assurance procedures, strengthening leadership, recognizing data collector's contributions, and meeting end-users' needs for the CRIMS.

CONCLUSION

The findings generated knowledge about the quality of the CRIMS data collection process. The 4D Framework has potential as an evaluation tool for decision-makers on the improvement of the public health data collection process.