Data Quality-Driven Improvement in Health Care: Systematic Literature Review.

BACKGROUND: The promise of real-world evidence and the learning health care system primarily depends on access to high-quality data. Despite widespread awareness of the prevalence and potential impacts of poor data quality (DQ), best practices for its assessment and improvement are unknown. OBJECTIVE: This review aims to investigate how existing research studies define, assess, and improve the quality of structured real-world health care data. METHODS: A systematic literature search of studies in the English language was implemented in the Embase and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023. We standardized DQ concepts according to the Data Management Association (DAMA) DQ framework to enable comparison between studies. After screening and filtering by 2 independent authors, we identified 39 relevant articles reporting DQ improvement initiatives. RESULTS: The studies were characterized by considerable heterogeneity in settings and approaches to DQ assessment and improvement. Affiliated institutions were from 18 different countries and 18 different health domains. DQ assessment methods were largely manual and targeted completeness and 1 other DQ dimension. Use of DQ frameworks was limited to the Weiskopf and Weng (3/6, 50%) or Kahn harmonized model (3/6, 50%). Use of standardized methodologies to design and implement quality improvement was lacking, but mainly included plan-do-study-act (PDSA) or define-measure-analyze-improve-control (DMAIC) cycles. Most studies reported DQ improvements using multiple interventions, which included either DQ reporting and personalized feedback (24/39, 61%), IT-related solutions (21/39, 54%), training (17/39, 44%), improvements in workflows (5/39, 13%), or data cleaning (3/39, 8%). Most studies reported improvements in DQ through a combination of these interventions. Statistical methods were used to determine significance of treatment effect (22/39, 56% times), but only 1 study implemented a randomized controlled study design. Variability in study designs, approaches to delivering interventions, and reporting DQ changes hindered a robust meta-analysis of treatment effects. CONCLUSIONS: There is an urgent need for standardized guidelines in DQ improvement research to enable comparison and effective synthesis of lessons learned. Frameworks such as PDSA learning cycles and the DAMA DQ framework can facilitate this unmet need. In addition, DQ improvement studies can also benefit from prioritizing root cause analysis of DQ issues to ensure the most appropriate intervention is implemented, thereby ensuring long-term, sustainable improvement. Despite the rise in DQ improvement studies in the last decade, significant heterogeneity in methodologies and reporting remains a challenge. Adopting standardized frameworks for DQ assessment, analysis, and improvement can enhance the effectiveness, comparability, and generalizability of DQ improvement initiatives.

Challenges in the evaluation of suicide prevention measures and quality of suicide data in Germany, Austria, and Switzerland: findings from qualitative expert interviews.

BACKGROUND: Suicide prevention requires diverse, integrated, and evidence-based measures. Comprehensive evaluation of interventions and reliable suicide data are crucial for guiding policy-making and advancing suicide prevention efforts. This study aimed to analyze current issues and gaps in the evaluation of suicide prevention measures and the quality of suicide data in Germany, Austria, and Switzerland to derive specific recommendations for improvement. METHODS: Online, semi-structured interviews were conducted with 36 experts in suicide prevention from Germany, Austria, and Switzerland, covering insights from policy, science, and practice. The interviews took place between September 2022 and February 2023, were audio-recorded, transcribed verbatim, and analyzed using the Framework method. RESULTS: While solid evidence supports the effectiveness of some suicide prevention interventions, experts indicated that the evaluation of many other measures is weak. Conducting effectiveness studies in suicide prevention presents a range of methodological and practical challenges, including recruitment difficulties, choosing adequate outcome criteria, ethical considerations, and trade-offs in allocating resources to evaluation efforts. Many interviewees rated the quality of national suicide statistics in Germany, Austria, and Switzerland as comparatively high. However, they noted limitations in the scope, timeliness, and reliability of these data, prompting some regions to implement their own suicide monitoring systems. None of the three countries has national routine data on suicide attempts. CONCLUSION: While some challenges in evaluating suicide prevention measures are inevitable, others can potentially be mitigated. Evaluations could be enhanced by combining traditional and innovative research designs, including intermediate outcomes and factors concerning the implementation process, and employing participatory and transdisciplinary research to engage different stakeholders. Reliable suicide data are essential for identifying trends, supporting research, and designing targeted prevention measures. To improve the quality of suicide data, a standardized monitoring approach, including uniform definitions, trained professionals, and cross-sector agreement on leadership and financing, should be pursued. This study provides actionable recommendations and highlights existing good practice approaches, thereby supporting decision-makers and providing guidance for advancing suicide prevention on a broader scale.

Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality.

Ordinary differential equation (ODE) models are powerful tools for studying the dynamics of metabolic pathways. However, key challenges lie in constructing ODE models for metabolic pathways, specifically in our limited knowledge about which metabolite levels control which reaction rates. Identification of these regulatory networks is further complicated by the limited availability of relevant data. Here, we assess the conditions under which it is feasible to accurately identify regulatory networks in metabolic pathways by computationally fitting candidate network models with biochemical systems theory (BST) kinetics to data of varying quality. We use network motifs commonly found in metabolic pathways as a simplified testbed. Key features correlated with the level of difficulty in identifying the correct regulatory network were identified, highlighting the impact of sampling rate, data noise, and data incompleteness on structural uncertainty. We found that for a simple branched network motif with an equal number of metabolites and fluxes, identification of the correct regulatory network can be largely achieved and is robust to missing one of the metabolite profiles. However, with a bi-substrate bi-product reaction or more fluxes than metabolites in the network motif, the identification becomes more challenging. Stronger regulatory interactions and higher metabolite concentrations were found to be correlated with less structural uncertainty. These results could aid efforts to predict whether the true metabolic regulatory network can be computationally identified for a given stoichiometric network topology and dataset quality, thus helping to identify optimal measures to mitigate such identifiability issues in kinetic model development.

How to Accurately Detect Renal Replacement Therapy Weaning in Intensive Care: Data Quality and Standardization Considerations for the OMOP Common Data Model.

This study assesses the effectiveness of the Observational Medical Outcomes Partnership common data model (OMOP CDM) in standardising Continuous Renal Replacement Therapy (CRRT) data from intensive care units (ICU) of two French university hospitals. Our objective was to extract and standardise data from various sources, enabling the development of predictive models for CRRT weaning that are agnostic to the data's origin. Data for 1,696 ICU stays from the two data sources were extracted, transformed, and loaded into the OMOP format after semantic alignment of 46 CRRT standard concepts. Although the OMOP CDM demonstrated potential in harmonising CRRT data, we encountered challenges related to data variability and the lack of standard concepts. Despite these challenges, our study supports the promise of the OMOP CDM for ICU data standardization, suggesting that further refinement and adaptation could significantly improve clinical decision making and patient outcomes in critical care settings.

Quality Assessment for Secondary Use of Imaging Trials.

Secondary use of health data has become an emerging topic in medical informatics. Many initiatives focus on clinical routine data, but clinical trial data has complementary strengths regarding highly structured documentation and mandatory data quality (DQ) reviews during the implementation. Clinical imaging trials investigate new imaging methods and procedures. Recently, DQ frameworks for structured data were proposed for harmonized quality assessments (QA). In this article, we investigate the application of these concepts to imaging trials and how a DQ framework could be defined for secondary use scenarios. We conclude that image quality can be assessed through both pixel data and metadata, and the latter can mostly be handled like structured study documentation in QA. For pixel data, typical quality indicators can be mapped to existing frameworks, but require additional image processing. Specific attention needs to be drawn to complete de-identification of imaging data, both on pixel data and metadata level.

Comparing the Accuracy of Traditional vs. FHIR®-Based Extraction of Electronic Health Record Data for Two Clinical Trials.

While pilots and production use of software based on the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard are increasing in clinical research, we lack consistent evaluative data on important outcomes, such as data accuracy. We compared the accuracy of EHR collected, FHIR® extracted data (called EHR-to-eCRF data collection) to traditional clinical trial data collection. The accuracy rate for EHR-collected data was significantly higher than for the same data collected through traditional methods. It is possible that EHR-collected (FHIR® extracted) data can substantially improve data quality in clinical studies while decreasing the burden on study sites.

Data Quality in Medical Real-World Data - An Oncological Use Case.

Data quality deficiencies significantly limit the applicability of real-world data in data-driven medical research. In this study, using an oncological use case, we report and discuss common quality deficiencies in real-world medical datasets, such as missing data, class imbalances, and timeliness issues. We compiled a multi-departmental real-world dataset comprising 13861 cancer cases diagnosed at University Hospital Cologne and examined data quality throughout the data integration process.

Quality Assessment Framework of Clinical Routine Data for Secondary Use.

To systematically and comprehensively identify data issues in large clinical datasets, we adopted a harmonized data quality assessment framework with Python scripts before integrating the data into FHIR® for secondary use. We also added a preliminary step of categorizing data fields within the database scheme to facilitate the implementation of the data quality framework. As a result, we demonstrated the efficiency and comprehensiveness of detecting data issues using the framework. In future steps, we plan to continually utilize the framework to identify data issues and develop strategies for improving our data quality.

Development and Evaluation of a Data Quality Management System for the Korea Biobank Network.

As the reliance on clinical epidemiological information from human specimens grows, so does the need for effective clinical information management systems, particularly for biobanks. Our study focuses on enhancing the Korea Biobank Network's (KBN) system with data quality verification features. By comparing the quality of data collected before and after these enhancements, we observed a notable improvement in data accuracy, with the error rate decreasing from 0.1198% to 0.0492%. This advancement underscores the importance of robust data quality management in supporting high-quality clinical research and sets a precedent for the development of clinical information management systems.

Improving Health Information Systems Data Quality in Sub-Saharan Africa.

Data quality in health information systems (HIS) is essential for informed decision-making in the health sector, particularly in sub-Saharan Africa (SSA) where these systems face many challenges like resource limitations and weak infrastructure. This systematic review assessed the quality of HIS data in the region, focusing on the dimensions, and factors influencing this quality. It highlights the importance of systematic evaluation, ongoing training for data collectors in the analysis and use of data for decision-making, and the adoption of information and communication technologies in the healthcare system to improve data quality. These findings point the way to better use of health data and the need for a more integrated approach to digital health in SSA.

Improving the Quality of Unstructured Cancer Data Using Large Language Models: A German Oncological Case Study.

With cancer being a leading cause of death globally, epidemiological and clinical cancer registration is paramount for enhancing oncological care and facilitating scientific research. However, the heterogeneous landscape of medical data presents significant challenges to the current manual process of tumor documentation. This paper explores the potential of Large Language Models (LLMs) for transforming unstructured medical reports into the structured format mandated by the German Basic Oncology Dataset. Our findings indicate that integrating LLMs into existing hospital data management systems or cancer registries can significantly enhance the quality and completeness of cancer data collection - a vital component for diagnosing and treating cancer and improving the effectiveness and benefits of therapies. This work contributes to the broader discussion on the potential of artificial intelligence or LLMs to revolutionize medical data processing and reporting in general and cancer care in particular.

Assessing the accuracy of self-reported health expenditure data: Evidence from two public surveys in China.

This paper utilizes Benford's law, the distribution that the first significant digit of numbers in certain datasets should follow, to assess the accuracy of self-reported health expenditure data known for measurement errors. We provide both simulation and real data evidence supporting the validity assumption that genuine health expenditure data conform to Benford's law. We then conduct a Benford analysis of health expenditure variables from two widely utilized public datasets, the China Health and Nutrition Survey and the China Family Panel Studies. Our findings show that health expenditure data in both datasets exhibit inconsistencies with Benford's law, with the former dataset tending to be less prone to reporting errors. These results remain robust while accounting for variations in survey design, recall periods, and sample sizes. Moreover, we demonstrate that data accuracy improves with a shorter time interval between hospitalization and interviews, when the data is self-reported as opposed to proxy responses, and at the household level. We find no compelling evidence that enumerators' assessments of respondents' credibility or urgency to end interviews are indicative of data accuracy. This paper contributes to literature by introducing an easy-to-implement analytical framework for scrutinizing and comparing the reporting accuracy of health expenditure data.

Race adjustments in clinical algorithms can help correct for racial disparities in data quality.

Despite ethical and historical arguments for removing race from clinical algorithms, the consequences of removal remain unclear. Here, we highlight a largely undiscussed consideration in this debate: varying data quality of input features across race groups. For example, family history of cancer is an essential predictor in cancer risk prediction algorithms but is less reliably documented for Black participants and may therefore be less predictive of cancer outcomes. Using data from the Southern Community Cohort Study, we assessed whether race adjustments could allow risk prediction models to capture varying data quality by race, focusing on colorectal cancer risk prediction. We analyzed 77,836 adults with no history of colorectal cancer at baseline. The predictive value of self-reported family history was greater for White participants than for Black participants. We compared two cancer risk prediction algorithms-a race-blind algorithm which included standard colorectal cancer risk factors but not race, and a race-adjusted algorithm which additionally included race. Relative to the race-blind algorithm, the race-adjusted algorithm improved predictive performance, as measured by goodness of fit in a likelihood ratio test (P-value: <0.001) and area under the receiving operating characteristic curve among Black participants (P-value: 0.006). Because the race-blind algorithm underpredicted risk for Black participants, the race-adjusted algorithm increased the fraction of Black participants among the predicted high-risk group, potentially increasing access to screening. More broadly, this study shows that race adjustments may be beneficial when the data quality of key predictors in clinical algorithms differs by race group.

Data quality in a survey of registered medical cannabis users with cancer: nonresponse and measurement error.

Cannabis use among individuals with cancer is best understood using survey self-report. As cannabis remains federally illegal, surveys could be subject to nonresponse and measurement issues impacting data quality. We surveyed individuals using medical cannabis for a cancer-related condition in the Minnesota Medical Cannabis Program (MCP). Although survey responders are older, there are no differences by race and ethnicity, gender, or receipt of reduced cannabis registry enrollment fee. Responders made a more recent purchase and more recently completed an independent symptom assessment for the registry than nonresponders, suggesting some opportunity for nonresponse error. Among responders, self-report and MCP administrative data with respect to age, race, gender, registry certification, and cannabis purchase history were similar. Responders were less likely to report receipt of Medicaid than would be expected based on registry low-income enrollment eligibility. Although attention should be paid to potential for nonresponse error, surveys are a reliable tool to ascertain cannabis behavior patterns in this population.

[The quality of the anthropometric data on children under five years of age in information systems in the State of São Paulo, Brazil]. / Qualidade dos dados antropométricos de crianças menores de cinco anos em sistemas de informação no estado de São Paulo, Brasil.

This paper involves the analysis of the quality of anthropometric data on children under five years of age in two information systems in the State of São Paulo. The sample included 2,117,108 children from the Food and Nutrition Surveillance System (SISVAN), and 748,551 from the State Milk Project (VIVALEITE). Initially, we evaluated the frequency of missing values and others outside the equipment spectrum and calculated the digit-to-weight preference index. After calculating height-for-age (HAZ), weight-for-age (WAZ), and body mass index-for-age (BAZ), we flagged the biologically implausible values (BIV) and calculated the standard deviation (SD). For each municipality, we calculated the mean and the SD of HAZ, WAZ, and BAZ; and plotted the SD values as a function of the mean. The digit-to-weight preference index was greater among children aged between 24 and 59 months in SISVAN. The frequency of BIV for HAZ (SISVAN 2.56%; VIVALEITE 0.98%) was higher than for WAZ (SISVAN 2.10%; VIVALEITE 0.18%). For HAZ, variations among municipalities were more pronounced in VIVALEITE than in SISVAN. The height variable presents low reliability in both systems. The weight variable reveals satisfactory quality in VIVALEITE and unsatisfactory quality in SISVAN.

O objetivo foi analisar a qualidade dos dados antropométricos de crianças menores de cinco anos em dois sistemas de informação no estado de São Paulo. A amostra compreendeu 2.117.108 crianças do Sistema de Vigilância Alimentar e Nutricional (Sisvan) e 748.551 do Projeto Estadual do Leite (Vivaleite). Inicialmente, avaliamos a frequência de valores faltantes e fora do espectro do equipamento, e calculamos o índice de preferência de dígito para peso. Após calcular os índices de altura para idade (A-I), peso para idade (P-I) e índice de massa corporal para idade (IMC-I), identificamos os valores biologicamente implausíveis (VBI) e calculamos o desvio-padrão (DP). Para cada município, calculamos a média e o DP de A-I, P-I e IMC-I; e plotamos os valores de DP em função da média. A preferência de dígito no peso foi maior em crianças de 24 a 59 meses no Sisvan. A frequência de VBI para A-I (SISVAN 2,56%; Vivaleite 0,98%) foi maior do que para P-I (Sisvan 2,10%; Vivaleite 0,18%). Para o índice A-I as variações entre os municípios foram mais acentuadas no Vivaleite do que no Sisvan. A variável altura apresentou baixa confiabilidade nos dois sistemas. A variável peso apresentou qualidade satisfatória no Vivaleite e insatisfatória no Sisvan.

A standardized image processing and data quality platform for rodent fMRI.

Functional magnetic resonance imaging in rodents holds great potential for advancing our understanding of brain networks. Unlike the human community, there remains no standardized resource in rodents for image processing, analysis and quality control, posing significant reproducibility limitations. Our software platform, Rodent Automated Bold Improvement of EPI Sequences, is a pipeline designed to address these limitations for preprocessing, quality control, and confound correction, along with best practices for reproducibility and transparency. We demonstrate the robustness of the preprocessing workflow by validating performance across multiple acquisition sites and both mouse and rat data. Building upon a thorough investigation into data quality metrics across acquisition sites, we introduce guidelines for the quality control of network analysis and offer recommendations for addressing issues. Taken together, this software platform will allow the emerging community to adopt reproducible practices and foster progress in translational neuroscience.

Assessment of expanded programme on immunization routine data quality in the upper east region of Ghana.

BACKGROUND: Data quality is a major challenge for most health institutions and organizations across the globe. The Ghana Health Service, supported by other non-governmental organizations, has instituted various strategies to address and improve data quality issues in regional and district health facilities in Ghana. This study sought to assess routine data quality of Expanded Programme on Immunization, specifically for Penta 1 and Penta 3 vaccines. METHODS: A descriptive cross-sectional study design was used for the study. A simple random sampling method was used to select thirty-four health facilities across seven sub-municipalities. Records from the Expanded Programme on Immunization (EPI) Tally Books and Monthly Vaccination Summary Report were reviewed and compared with data entered into the District Health Information Management System 2 (DHIMS2) software for the period of January to December 2020. The World Health Organization Data quality self-assessment (DQS) tool was used to compare data recorded in the EPI tally books with monthly data from summary reports and DHIMS2. Data accuracy ratio was determined by the data quality assessment tools and STATA version 14.2 was used to run additional analysis. A data discrepancy is when two corresponding data sets don't match. RESULTS: The results showed discrepancies between recounted tallies in EPI tally books and summary reports submitted as well as DHIMS2. Verification factor of 97.4% and 99.3% and a discrepancy rate of 2.6 and 0.7 for Penta 1 and Penta 3 respectively were recorded for tallied data and summary reports. A verification factor of 100.5% and 99.9% and a discrepancy of -0.5 and 0.1 respectively for the same antigens were obtained for the summary reports and DHIMS2. Data timeliness was 90.7% and completeness was 100% for both antigens. CONCLUSION: The accuracy of Penta 1 and Penta 3 data on EPI in the Upper East Region of Ghana was high. The data availability, timeliness and completeness were also high.

Characterizing physician directory data quality: variation by specialty, state, and insurer.

BACKGROUND: As U.S. legislators are urged to combat ghost networks in behavioral health and address the provider data quality issue, it becomes important to better characterize the variation in data quality of provider directories to understand root causes and devise solutions. Therefore, this manuscript examines consistency of address, phone number, and specialty information for physician entries from 5 national health plan provider directories by insurer, physician specialty, and state. METHODS: We included all physicians in the Medicare Provider Enrollment, Chain, and Ownership System (PECOS) found in ≥ 2 health insurer physician directories across 5 large national U.S. health insurers. We examined variation in consistency of address, phone number, and specialty information among physicians by insurer, physician specialty, and state. RESULTS: Of 634,914 unique physicians in the PECOS database, 449,282 were found in ≥ 2 directories and included in our sample. Across insurers, consistency of address information varied from 16.5 to 27.9%, consistency of phone number information varied from 16.0 to 27.4%, and consistency of specialty information varied from 64.2 to 68.0%. General practice, family medicine, plastic surgery, and dermatology physicians had the highest consistency of addresses (37-42%) and phone numbers (37-43%), whereas anesthesiology, nuclear medicine, radiology, and emergency medicine had the lowest consistency of addresses (11-21%) and phone numbers (9-14%) across health insurer directories. There was marked variation in consistency of address, phone number, and specialty information by state. CONCLUSIONS: In evaluating a large national sample of U.S. physicians, we found minimal variation in provider directory consistency by insurer, suggesting that this is a systemic problem that insurers have not solved, and considerable variation by physician specialty with higher quality data among more patient-facing specialties, suggesting that physicians may respond to incentives to improve data quality. These data highlight the importance of novel policy solutions that leverage technology targeting data quality to centralize provider directories so as not to not reinforce existing data quality issues or policy solutions to create national and state-level standards that target both insurers and physician groups to maximize quality of provider information.

Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study.

BACKGROUND: Electronic health records (EHRs) play an increasingly important role in delivering HIV care in low- and middle-income countries. The data collected are used for direct clinical care, quality improvement, program monitoring, public health interventions, and research. Despite widespread EHR use for HIV care in African countries, challenges remain, especially in collecting high-quality data. OBJECTIVE: We aimed to assess data completeness, accuracy, and timeliness compared to paper-based records, and factors influencing data quality in a large-scale EHR deployment in Rwanda. METHODS: We randomly selected 50 health facilities (HFs) using OpenMRS, an EHR system that supports HIV care in Rwanda, and performed a data quality evaluation. All HFs were part of a larger randomized controlled trial, with 25 HFs receiving an enhanced EHR with clinical decision support systems. Trained data collectors visited the 50 HFs to collect 28 variables from the paper charts and the EHR system using the Open Data Kit app. We measured data completeness, timeliness, and the degree of matching of the data in paper and EHR records, and calculated concordance scores. Factors potentially affecting data quality were drawn from a previous survey of users in the 50 HFs. RESULTS: We randomly selected 3467 patient records, reviewing both paper and EHR copies (194,152 total data items). Data completeness was >85% threshold for all data elements except viral load (VL) results, second-line, and third-line drug regimens. Matching scores for data values were close to or >85% threshold, except for dates, particularly for drug pickups and VL. The mean data concordance was 10.2 (SD 1.28) for 15 (68%) variables. HF and user factors (eg, years of EHR use, technology experience, EHR availability and uptime, and intervention status) were tested for correlation with data quality measures. EHR system availability and uptime was positively correlated with concordance, whereas users' experience with technology was negatively correlated with concordance. The alerts for missing VL results implemented at 11 intervention HFs showed clear evidence of improving timeliness and completeness of initially low matching of VL results in the EHRs and paper records (11.9%-26.7%; P<.001). Similar effects were seen on the completeness of the recording of medication pickups (18.7%-32.6%; P<.001). CONCLUSIONS: The EHR records in the 50 HFs generally had high levels of completeness except for VL results. Matching results were close to or >85% threshold for nondate variables. Higher EHR stability and uptime, and alerts for entering VL both strongly improved data quality. Most data were considered fit for purpose, but more regular data quality assessments, training, and technical improvements in EHR forms, data reports, and alerts are recommended. The application of quality improvement techniques described in this study should benefit a wide range of HFs and data uses for clinical care, public health, and disease surveillance.

Data quality assessment of the Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP), Thailand, 2015-2021.

BACKGROUND: Quality assessments of gonococcal surveillance data are critical to improve data validity and to enhance the value of surveillance findings. Detecting data errors by systematic audits identifies areas for quality improvement. We designed and implemented an internal audit process to evaluate the accuracy and completeness of surveillance data for the Thailand Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP). METHODS: We conducted a data quality audit of source records by comparison with the data stored in the EGASP database for five audit cycles from 2015-2021. Ten percent of culture-confirmed cases of Neisseria gonorrhoeae were randomly sampled along with any cases identified with elevated antimicrobial susceptibility testing results and cases with repeat infections. Incorrect and incomplete data were investigated, and corrective action and preventive actions (CAPA) were implemented. Accuracy was defined as the percentage of identical data in both the source records and the database. Completeness was defined as the percentage of non-missing data from either the source document or the database. Statistical analyses were performed using the t-test and the Fisher's exact test. RESULTS: We sampled and reviewed 70, 162, 85, 68, and 46 EGASP records during the five audit cycles. Overall accuracy and completeness in the five audit cycles ranged from 93.6% to 99.4% and 95.0% to 99.9%, respectively. Overall, completeness was significantly higher than accuracy (p = 0.017). For each laboratory and clinical data element, concordance was >85% in all audit cycles except for two laboratory data elements in two audit cycles. These elements significantly improved following identification and CAPA implementation. DISCUSSION: We found a high level of data accuracy and completeness in the five audit cycles. The implementation of the audit process identified areas for improvement. Systematic quality assessments of laboratory and clinical data ensure high quality EGASP surveillance data to monitor for antimicrobial resistant Neisseria gonorrhoeae in Thailand.