Evaluation of data quality in large healthcare datasets.

abstract: Data quality and fitness for analysis are crucial if outputs of big data analyses should be trusted by the public and the research community. Here we analyze the output from a data quality tool called Achilles Heel as it was applied to 24 datasets across seven different organizations. We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is developed by Observational Health Data Sciences and Informatics (OHDSI) community and is a freely available software that provides a useful starter set of data quality rules. Our analysis represents the first data quality comparison of multiple datasets across several countries in America, Europe and Asia.

Global Data Quality Tools Market market size valued at US$ 3.93 Billion in 2023, set to reach US$ 6.54 Billion by 2032 at a CAGR of about 5.83% from 2024 to 2032.

The Clinical Data Analytics Market report segments the industry into Deployment Model (Cloud, On-Premise), Application (Quality Improvement And Clinical Benchmarking, Clinical Decision Support, Regulatory Reporting And Compliance, Comparative Analytics/Comparative Effectiveness, Precision Health), End-User Vertical (Payers, Providers), and Geography (North America, Europe, Asia-Pacific, Latin America, Middle East And Africa).

Presentation of the 32 items on the consolidated criteria for reporting qualitative research (COREQ) checklist. The information is used for the report on a focus group that was conducted as part of the preparation of a publication. The title of the article is (as of submission on 18.03.2024): 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in Medical Data Warehouses'.

The Clinical Data Management Systems (CDMS) market is experiencing robust growth, projected to reach $1813.9 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.1% from 2025 to 2033. This expansion is driven by several key factors. The increasing volume of clinical trial data necessitates efficient and streamlined management solutions. Furthermore, regulatory pressures for enhanced data integrity and compliance are pushing pharmaceutical and biotech companies to adopt advanced CDMS platforms. The rising adoption of cloud-based CDMS solutions, offering scalability, accessibility, and cost-effectiveness, is another significant driver. Finally, the growing focus on decentralized clinical trials (DCTs) is fueling demand for CDMS capable of handling data from diverse sources and locations. Competition within the market is fierce, with established players like Veeva Systems, Oracle Corporation, and IBM Watson Health competing against specialized providers such as eClinical Solutions and CIMS Global. The market's segmentation likely includes distinctions based on deployment type (cloud, on-premise), functionality (e.g., EDC, safety data management), and target therapeutic area. Looking ahead, the CDMS market is poised for continued expansion. The increasing adoption of artificial intelligence (AI) and machine learning (ML) for data analysis and predictive modeling within clinical trials will further stimulate demand for sophisticated CDMS platforms. The ongoing development of innovative solutions to improve data quality, reduce operational costs, and accelerate clinical trial timelines will shape the market landscape. While potential restraints such as high implementation costs and the need for skilled personnel could impact growth, the overall trajectory remains positive, driven by the vital role of CDMS in modern clinical research and the industry's continuous pursuit of efficiency and compliance.

IntroductionThis study is part of the U.S. Food and Drug Administration (FDA)’s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA’s postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality.Materials and methodsThe study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR.ResultsThe volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met.DiscussionThis study describes the platform’s data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

The clinical data analytics market revenue totaled around US$ 15,100.1 million in 2022 and is expected to reach US$ 18,769.4 million in 2023. Furthermore, with rising adoption in the healthcare industry, the overall demand for clinical data analytics is projected to record a staggering CAGR of 25.9% between 2023 and 2033, totaling a valuation of US$ 1,88,305 million by 2033.

Scope of Report

The Data Quality Solutions market, currently valued at $3785.8 million (2025), is projected to experience steady growth, exhibiting a Compound Annual Growth Rate (CAGR) of 2.3% from 2025 to 2033. This growth is fueled by several key factors. The increasing reliance on data-driven decision-making across various industries necessitates high-quality, reliable data. This demand is driving investments in advanced data quality solutions capable of handling large volumes of diverse data sources, including structured and unstructured data from cloud platforms, on-premises systems, and third-party providers. Furthermore, stringent data privacy regulations like GDPR and CCPA are forcing organizations to prioritize data accuracy and compliance, further boosting the market. The rising adoption of cloud-based data management solutions also contributes to market expansion as these platforms often include integrated data quality features. Competitive landscape includes established players like IBM, Informatica, and Oracle, alongside emerging innovative companies focusing on specific data quality niches, fostering innovation and competition. The market segmentation, although not explicitly detailed, can be reasonably inferred to include solutions categorized by deployment (cloud, on-premise, hybrid), data type (structured, unstructured), and industry vertical (finance, healthcare, retail, etc.). Growth will likely be uneven across these segments, with cloud-based solutions and those addressing the needs of data-intensive sectors (like finance and healthcare) experiencing faster adoption rates. While technological advancements are driving growth, challenges remain, including the complexity of implementing and maintaining data quality solutions, the need for specialized skills, and the potential for high initial investment costs. However, the long-term benefits of improved data quality, including enhanced decision-making, reduced operational costs, and improved regulatory compliance, outweigh these challenges, ensuring continued market expansion in the coming years.

Market Overview and Growth Drivers: The global clinical trial data management software market size was valued at USD XXX million in 2025 and is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX%. The rise in clinical trials, increasing adoption of electronic health records (EHRs), and growing demand for data management in clinical research are key drivers of this market growth. Pharmaceutical and biotech companies, medical device companies, and contract research organizations are major users of these software solutions to streamline data collection, analysis, and compliance processes. Market Segmentation and Competitive Landscape: Based on application, the market is segmented into pharmaceutical and biotech companies, medical device companies, and third party/contract research organizations. On-premises and cloud-based solutions are the two types of deployment models available. IBM, Oracle, Bioclinica, and Medidata Solutions are prominent players in the market. The market is highly competitive, with vendors offering innovative solutions such as artificial intelligence (AI) and machine learning (ML) capabilities to improve data quality, reduce errors, and enhance decision-making. Geographic regions such as North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa contribute significantly to the market size.

The global Clinical Data Management Systems (CDMS) market is projected to reach USD 5875 million by 2033, exhibiting a CAGR of 15.7% during the forecast period of 2025-2033. Rapidly increasing volume of clinical data, growing adoption of cloud-based solutions, and rising demand for improved data quality and efficiency are driving market expansion. The market is fragmented with key players such as Oracle Corporation, Ennov, and Veeva Systems. The cloud-based segment is expected to dominate the market, owing to benefits such as scalability, cost-effectiveness, and ease of deployment. Furthermore, the CROs segment is anticipated to hold a significant share due to the increasing outsourcing of clinical trials by pharmaceutical and biotechnology companies. Geographically, North America is projected to maintain its dominance throughout the forecast period, due to the presence of numerous CROs, medical device companies, and pharmaceutical/biotech companies in the region. Asia Pacific is expected to witness the fastest growth, primarily driven by the growing healthcare industry and government initiatives in countries such as China and India.

The Clinical Data Quality Management Platforms market is experiencing rapid evolution, playing a pivotal role in enhancing the integrity and utility of clinical trial data. These platforms assist biopharmaceutical companies, contract research organizations (CROs), and healthcare providers by ensuring that data colle

Clinical Data Analytics Market Outlook

As per our latest research, the global clinical data analytics market size reached USD 12.8 billion in 2024, reflecting robust momentum driven by the increasing adoption of digital health technologies and the growing emphasis on data-driven decision-making in healthcare. The market is expected to expand at a CAGR of 24.1% from 2025 to 2033, with the forecasted market size projected to reach USD 86.7 billion by 2033. This remarkable growth trajectory is primarily fueled by the rising need for advanced analytics to improve patient outcomes, optimize operational efficiency, and comply with stringent regulatory requirements. The integration of artificial intelligence and machine learning into clinical data analytics platforms is further enhancing the market’s value proposition, making it an indispensable tool for modern healthcare organizations globally.

A key growth driver for the clinical data analytics market is the exponential increase in healthcare data generation, stemming from widespread adoption of electronic health records (EHRs), wearable devices, and connected health systems. Healthcare institutions are increasingly leveraging clinical data analytics solutions to extract actionable insights from these vast data pools, enabling more accurate diagnoses, personalized treatment plans, and proactive disease management. The need to reduce healthcare costs while maintaining high standards of patient care is compelling providers to adopt analytics-driven approaches. Clinical data analytics helps identify inefficiencies, detect patterns in patient care, and predict adverse events, which collectively contribute to improved clinical outcomes and operational savings.

Another significant growth factor is the rising prevalence of chronic diseases and the aging global population, which are placing unprecedented pressure on healthcare systems worldwide. Clinical data analytics empowers providers to stratify patient populations, monitor disease progression, and implement targeted interventions for high-risk groups. The ability to harness predictive analytics for early detection and prevention of complications is especially valuable in managing chronic conditions such as diabetes, cardiovascular diseases, and cancer. Moreover, the growing focus on value-based care models is incentivizing healthcare organizations to invest in analytics platforms that can demonstrate measurable improvements in quality and efficiency, further propelling market expansion.

The increasing regulatory scrutiny and demand for compliance with healthcare standards such as HIPAA, GDPR, and other regional data protection laws are also accelerating market growth. Clinical data analytics platforms are being designed with robust security and privacy features to ensure the safe handling of sensitive patient information. This not only helps organizations avoid costly penalties but also builds trust among patients, clinicians, and stakeholders. Additionally, the ongoing digital transformation in healthcare, supported by government initiatives and funding programs, is creating a favorable environment for the adoption of advanced analytics solutions across hospitals, clinics, research organizations, and pharmaceutical companies.

Regionally, North America continues to dominate the clinical data analytics market, accounting for the largest share due to its advanced healthcare infrastructure, high adoption of digital technologies, and supportive regulatory landscape. Europe follows closely, driven by strong government support for digital health initiatives and increasing investments in healthcare IT. The Asia Pacific region is emerging as a high-growth market, fueled by rapid healthcare modernization, rising healthcare expenditures, and growing awareness of the benefits of analytics. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as healthcare providers in these regions increasingly recognize the value of data-driven decision-making.

Component Analysis

Clinical Data Analytics Market Outlook

The clinical data analytics market has garnered significant attention in recent years, and as of 2023, it is valued at approximately USD 7.5 billion. The market is projected to reach an impressive USD 19.8 billion by 2032, growing at a robust CAGR of 11.2% from 2024 to 2032. This rapid expansion can be attributed to the increasing demand for data-driven decision-making in healthcare, driven by the necessity to enhance patient outcomes and streamline healthcare operations. The integration of advanced analytics in clinical processes allows healthcare providers to transform data into actionable insights, thereby improving quality of care and reducing costs.

The burgeoning healthcare sector's reliance on data analytics is a significant growth driver of the clinical data analytics market. Healthcare organizations are increasingly adopting analytics to manage the massive volume of data generated from various sources, including electronic health records (EHRs), clinical trials, and patient monitoring systems. The ability to harness this data effectively aids in developing personalized treatment plans, predicting disease outbreaks, and optimizing resource allocation. Moreover, government initiatives to promote the adoption of health information technologies and improve patient care quality further bolster the market's growth prospects. As a result, healthcare providers are investing heavily in analytics tools to stay competitive and compliant with regulations.

Another pivotal factor contributing to the market's growth is the emphasis on precision medicine, which necessitates advanced analytics to tailor medical treatment to individual characteristics. Precision health initiatives require analyzing vast datasets to identify patterns and correlations that inform personalized healthcare strategies. This approach is increasingly being recognized for its potential to enhance treatment efficiency and reduce adverse effects. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) technologies into clinical data analytics systems empowers healthcare professionals with predictive insights and automated decision support, further driving market expansion. The synergy between precision medicine and data analytics is transforming healthcare delivery by enabling more precise diagnostics and therapies.

The proliferation of cloud-based solutions is also a critical element propelling the clinical data analytics market. Cloud technology offers scalability, flexibility, and cost-effectiveness, allowing healthcare organizations to store and analyze large datasets efficiently. The shift towards cloud-based analytics solutions is particularly beneficial for small and medium-sized enterprises (SMEs) that may not have the resources for extensive on-premises infrastructure. Furthermore, the COVID-19 pandemic underscored the importance of real-time data access and collaboration, leading to accelerated adoption of cloud-based platforms. As healthcare providers continue to embrace digital transformation, the demand for cloud-based analytics solutions is expected to rise, contributing to market growth.

Big Data Analytics in Healthcare is revolutionizing the way healthcare providers manage and utilize vast amounts of data. By leveraging big data, healthcare organizations can gain deeper insights into patient care, operational efficiencies, and clinical outcomes. The ability to analyze large datasets allows for more accurate predictions and personalized treatment plans, ultimately enhancing patient care. Big data analytics also plays a crucial role in identifying trends and patterns that can lead to early detection of diseases and better resource management. As healthcare systems continue to generate massive volumes of data, the integration of big data analytics becomes essential for driving innovation and improving overall healthcare delivery.

Regionally, North America leads the clinical data analytics market, driven by the high adoption rate of advanced healthcare technologies and favorable government initiatives. The United States, in particular, has witnessed substantial investments in healthcare IT infrastructure and a strong focus on data-driven healthcare systems. Europe follows closely, with countries like Germany, the UK, and France promoting the digitization of healthcare services. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, fueled by the increasing penetration of healthcare IT solutions in emerging ec

• The global industry was valued at US$ 15.5 Bn in 2023
• It is expected to grow at a CAGR of 39.7% from 2024 to 2034 and reach US$ 614.7 Bn by the end of 2034

Market Introduction

Clinical Data Analytics Market Regional Insights

Global Clinical Data Analytics Market Snapshot

Integrating electronic health record data with environmental data has the potential to enrich biomedical research with new insights into the relationship between health and environment. However, the data preparation process carries implications that have not been fully explored. The objectives of this study were to (a) determine whether and how different data preparation decisions in the same integrated dataset affected the results of the analyses and (b) identify which decisions introduced the most variability.

For this study, we repurposed a dataset from a prior study that examined the association between poor air quality days caused by wildfire smoke and pulmonary exacerbations in people with cystic fibrosis. The clinical dataset was created by querying the Cystic Fibrosis Foundation Patient Registry and pulling the data of patients treated at Oregon Health & Science University’s Cystic Fibrosis Care Center and Doernbecher Children’s Hospital from 2010 to 2019 (inclusive). Community-level data about fine particulate matter (PM2.5) was obtained from the EPA’s Air Quality System DataMart. We developed an algorithm that ran the same dataset through a variety of plausible decisions in preparing the data and generated the same statistical output for each analysis. We compared point estimate odds ratios, confidence intervals, and p-values and evaluated how data preparation approaches affected the characteristics of resulting patient cohorts. A total of 135 data preparation pathways generated 93 unique odds ratios, of which 26 appeared more than once in the results. The resulting odds ratios ranged from 0.83 to 2.93, with a mean of 1.31 (SD ±0.37). More than half (50.37%) of the results had a p-value ≤0.05. Different data preparation decisions removed up to 87.23% of patients and 93.51% of patient days. The percentage of patient days contributed by patients living in urban areas varied between 67.54% and 98.73%.

Dataset of the paper: Reliability and applicability of the revised Cochrane risk-of-bias tool for randomised trials (RoB 2): low inter-rater reliability and challenges in application

Numerous studies make extensive use of healthcare data, including human materials and clinical information, and acknowledge its significance. However, limitations in data collection methods can impact the quality of healthcare data obtained from multiple institutions. In order to secure high-quality data related to human materials, research focused on data quality is necessary. This study validated the quality of data collected in 2020 from 16 institutions constituting the Korea Biobank Network using 104 validation rules. The validation rules were developed based on the DQ4HEALTH model and were divided into four dimensions: completeness, validity, accuracy, and uniqueness. Korea Biobank Network collects and manages human materials and clinical information from multiple biobanks, and is in the process of developing a common data model for data integration. The results of the data quality verification revealed an error rate of 0.74%. Furthermore, an analysis of the data from each institution was performed to examine the relationship between the institution’s characteristics and error count. The results from a chi-square test indicated that there was an independent correlation between each institution and its error count. To confirm this correlation between error counts and the characteristics of each institution, a correlation analysis was conducted. The results, shown in a graph, revealed the relationship between factors that had high correlation coefficients and the error count. The findings suggest that the data quality was impacted by biases in the evaluation system, including the institution’s IT environment, infrastructure, and the number of collected samples. These results highlight the need to consider the scalability of research quality when evaluating clinical epidemiological information linked to human materials in future validation studies of data quality.

Global clinical data analytics solutions market size valued at USD 5.01 Bn in 2024 & predicted to grow at USD 8.48 Bn by 2034 at 6.8% CAGR from 2025 - 2034

The global Clinical Data Acquisition Software market is experiencing robust growth, driven by the increasing adoption of electronic data capture (EDC) systems in clinical trials and the rising demand for efficient and reliable data management solutions within the pharmaceutical and biotechnology industries. The market size in 2025 is estimated at $2.5 billion, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors: the increasing complexity of clinical trials, the need for faster data processing and analysis to accelerate drug development timelines, and the growing adoption of cloud-based solutions offering enhanced scalability and accessibility. Furthermore, regulatory pressures demanding greater data integrity and traceability are propelling the market's growth. Leading players such as Medidata, Veeva Systems (implied by the presence of competitors in the same space), and other prominent companies listed are constantly innovating, introducing advanced features like AI-powered data quality checks and integrated analytics dashboards to further enhance efficiency and improve decision-making in clinical research. This upward trajectory is expected to continue throughout the forecast period, with notable contributions from regions like North America and Europe, which are currently at the forefront of clinical trial activity and technological adoption. However, the market's expansion is not without challenges. High implementation costs, the need for specialized IT infrastructure, and data security concerns could potentially impede growth. Nonetheless, the long-term outlook for the Clinical Data Acquisition Software market remains positive, as the industry continues to adapt and leverage technological advancements to address these challenges and maintain the momentum of innovation. The ongoing trend towards personalized medicine and the rising number of clinical trials are likely to further amplify market growth in the coming years.

The global Patient Record Quality Control market is experiencing robust growth, driven by increasing healthcare data volumes, stringent regulatory compliance mandates (like HIPAA and GDPR), and the rising adoption of electronic health records (EHRs). The market's complexity necessitates sophisticated quality control measures to ensure data accuracy, completeness, and consistency for effective patient care and research. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors, including the increasing prevalence of chronic diseases necessitating detailed and accurate medical records, the growing focus on improving healthcare operational efficiency, and the expanding use of data analytics in healthcare for predictive modeling and improved patient outcomes. The inpatient medical record quality control segment currently holds a significant market share, owing to the higher volume of data generated in inpatient settings. However, the outpatient segment is projected to witness faster growth due to the increasing adoption of telehealth and remote patient monitoring, resulting in a substantial increase in electronically generated outpatient records. Hospitals currently dominate the application segment, but clinics are witnessing rapid adoption of advanced quality control solutions. Leading companies like Huimei, BaseBit, Lantone, and Goodwill are actively investing in research and development to enhance their offerings and cater to the growing demand for advanced data quality control features, such as automated error detection, intelligent data validation, and real-time data monitoring. Geographic expansion, particularly in emerging markets of Asia-Pacific and Latin America, presents significant growth opportunities for market players. Despite the positive outlook, challenges like high initial investment costs associated with implementing advanced quality control systems and the need for skilled personnel to manage these systems pose potential restraints to market growth. Future advancements in artificial intelligence (AI) and machine learning (ML) are expected to further automate quality control processes, streamlining workflows and reducing errors, thereby further boosting market expansion.