Cloud Data Quality Monitoring Market Outlook

The global cloud data quality monitoring market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 4.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.6% during the forecast period. The robust market growth can be attributed primarily to the increasing adoption of cloud-based solutions across various industry verticals, coupled with the growing need to maintain high data quality standards in an era of big data and analytics.

One of the significant growth factors driving the cloud data quality monitoring market is the exponential rise in data generation. As more businesses move their operations online and leverage digital tools, the volume of data generated has skyrocketed. This massive influx of data has necessitated the deployment of sophisticated data quality monitoring tools to ensure data accuracy, consistency, and reliability. Furthermore, the increasing reliance on data-driven decision-making processes has further underscored the importance of maintaining high data quality standards, thereby fueling market growth.

Another key driver is the rapid digital transformation witnessed across various industry verticals. Companies in sectors such as healthcare, BFSI, and retail are increasingly investing in cloud-based data quality monitoring solutions to enhance their operational efficiency and customer experience. For instance, in the healthcare sector, maintaining high data quality is crucial for accurate patient diagnosis and treatment planning. Similarly, in the BFSI sector, data quality monitoring helps in reducing risks associated with financial transactions and compliance reporting.

Additionally, the increasing adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in data quality monitoring is significantly contributing to market growth. These technologies enable more efficient and accurate identification of data anomalies and inconsistencies, thus enhancing the overall data quality. Furthermore, the integration of AI and ML with cloud data quality monitoring solutions helps in automating various data management processes, thereby reducing manual intervention and operational costs.

From a regional perspective, North America holds a significant share of the global cloud data quality monitoring market, primarily due to the early adoption of advanced technologies and the presence of major market players in the region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This can be attributed to the rapid digitalization and increasing investments in cloud infrastructure across countries such as China, India, and Japan. Additionally, the growing awareness about the importance of data quality in driving business success is further propelling market growth in this region.

Component Analysis

The cloud data quality monitoring market is segmented by component into software and services. The software segment holds the largest market share and is expected to continue dominating the market throughout the forecast period. The increasing demand for advanced data quality monitoring tools and platforms that offer real-time analytics and reporting capabilities is driving the growth of this segment. Additionally, the integration of AI and ML technologies with data quality monitoring software is further enhancing its effectiveness and efficiency, thus boosting its adoption across various industry verticals.

The services segment, on the other hand, is projected to witness significant growth during the forecast period. This can be attributed to the increasing demand for professional and managed services to support the implementation and maintenance of cloud data quality monitoring solutions. Professional services include consulting, training, and support services, which help organizations in effectively deploying and utilizing these solutions. Managed services, on the other hand, offer ongoing monitoring and maintenance of data quality, thus ensuring continuous data accuracy and consistency.

Furthermore, the growing trend of outsourcing data quality monitoring services to specialized service providers is also contributing to the growth of the services segment. Organizations are increasingly leveraging the expertise of these service providers to achieve high data quality standards without investing heavily in in-house capabilities. This trend is particularly prominent among small and medium enterprises (SMEs) that often lack the resou

Abstract

The Global Data Regulation Diagnostic provides a comprehensive assessment of the quality of the data governance environment. Diagnostic results show that countries have put in greater effort in adopting enabler regulatory practices than in safeguard regulatory practices. However, for public intent data, enablers for private intent data, safeguards for personal and nonpersonal data, cybersecurity and cybercrime, as well as cross-border data flows. Across all these dimensions, no income group demonstrates advanced regulatory frameworks across all dimensions, indicating significant room for the regulatory development of both enablers and safeguards remains at an intermediate stage: 47 percent of enabler good practices and 41 percent of good safeguard practices are adopted across countries. Under the enabler and safeguard pillars, the diagnostic covers dimensions of e-commerce/e-transactions, enablers further improvement on data governance environment.

The Global Data Regulation Diagnostic is the first comprehensive assessment of laws and regulations on data governance. It covers enabler and safeguard regulatory practices in 80 countries providing indicators to assess and compare their performance. This Global Data Regulation Diagnostic develops objective and standardized indicators to measure the regulatory environment for the data economy across countries. The indicators aim to serve as a diagnostic tool so countries can assess and compare their performance vis-á-vis other countries. Understanding the gap with global regulatory good practices is a necessary first step for governments when identifying and prioritizing reforms.

Geographic coverage

80 countries

Analysis unit

Country

Kind of data

Observation data/ratings [obs]

Sampling procedure

The diagnostic is based on a detailed assessment of domestic laws, regulations, and administrative requirements in 80 countries selected to ensure a balanced coverage across income groups, regions, and different levels of digital technology development. Data are further verified through a detailed desk research of legal texts, reflecting the regulatory status of each country as of June 1, 2020.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The questionnaire comprises 37 questions designed to determine if a country has adopted good regulatory practice on data governance. The responses are then scored and assigned a normative interpretation. Related questions fall into seven clusters so that when the scores are averaged, each cluster provides an overall sense of how it performs in its corresponding regulatory and legal dimensions. These seven dimensions are: (1) E-commerce/e-transaction; (2) Enablers for public intent data; (3) Enablers for private intent data; (4) Safeguards for personal data; (5) Safeguards for nonpersonal data; (6) Cybersecurity and cybercrime; (7) Cross-border data transfers.

Response rate

100%

Objective: Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.

Materials and Methods: We used the clinical documentation of 34 UK General Practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs. consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician's final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.

Results: Supported documentation contained significantly more codes (IRR=5.76 [4.31, 7.70] P<0.001) and less free text (IRR = 0.32 [0.27, 0.40] P<0.001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b=-0.08 [-0.11, -0.05] P<0.001) in the supported consultations, and this was the case for both codes and free text.

Conclusions: We provide evidence that data entry in the EHR is incomplete and reflects physicians' cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.

Automated data transmission from diagnostic instrument networks to a central database at the Ministries of Health has the potential of providing real-time quality data not only on diagnostic instrument performance, but also continuous disease surveillance and patient care. We aimed at sharing how a locally developed novel diagnostic connectivity solution channels actionable data from diagnostic instruments to the national dashboards for disease control in Uganda between May 2022 and May 2023. The diagnostic connectivity solution was successfully configured on a selected network of multiplexing diagnostic instruments at 260 sites in Uganda, providing a layered access of data. Of these, 909,674 test results were automatically collected from 269 “GeneXpert” machines, 5597 test results from 28 “Truenat” and >12,000 were from 3 digital x-ray devices to different stakeholder levels to ensure optimal use of data for their intended purpose. The government and relevant stakeholders are empowered with usable and actionable data from the diagnostic instruments. The successful implementation of the diagnostic connectivity solution depended on some key operational strategies namely; sustained internet connectivity and short message services, stakeholder engagement, a strong in-country laboratory coordination network, human resource capacity building, establishing a network for the diagnostic instruments, and integration with existing health data collection tools. Poor bandwidth at some locations was a major hindrance for the successful implementation of the connectivity solution. Maintaining stakeholder engagement at the clinical level is key for sustaining diagnostic data connectivity. The locally developed diagnostic connectivity solution as a digital health technology offers the chance to collect high-quality data on a number of parameters for disease control, including error analysis, thereby strengthening the quality of data from the networked diagnostic sites to relevant stakeholders.

Automated data transmission from diagnostic instrument networks to a central database at the Ministries of Health has the potential of providing real-time quality data not only on diagnostic instrument performance, but also continuous disease surveillance and patient care. We aimed at sharing how a locally developed novel diagnostic connectivity solution channels actionable data from diagnostic instruments to the national dashboards for disease control in Uganda between May 2022 and May 2023. The diagnostic connectivity solution was successfully configured on a selected network of multiplexing diagnostic instruments at 260 sites in Uganda, providing a layered access of data. Of these, 909,674 test results were automatically collected from 269 “GeneXpert” machines, 5597 test results from 28 “Truenat” and >12,000 were from 3 digital x-ray devices to different stakeholder levels to ensure optimal use of data for their intended purpose. The government and relevant stakeholders are empowered with usable and actionable data from the diagnostic instruments. The successful implementation of the diagnostic connectivity solution depended on some key operational strategies namely; sustained internet connectivity and short message services, stakeholder engagement, a strong in-country laboratory coordination network, human resource capacity building, establishing a network for the diagnostic instruments, and integration with existing health data collection tools. Poor bandwidth at some locations was a major hindrance for the successful implementation of the connectivity solution. Maintaining stakeholder engagement at the clinical level is key for sustaining diagnostic data connectivity. The locally developed diagnostic connectivity solution as a digital health technology offers the chance to collect high-quality data on a number of parameters for disease control, including error analysis, thereby strengthening the quality of data from the networked diagnostic sites to relevant stakeholders.

This dataset contains the volume of California statewide DxCath procedures by year, age group, health insurance payment source, race/ethnicity, sex, and the percent decreases from 2019 to 2023.

This dataset documents the simulations demonstrating the capabilities of the new DESID module, a part of CMAQ that allows for adjustment of emissions and expanded diagnostic output. There are three figures, each with four subpanels, that are provided here. They are all present in the supporting information of the manuscript. There are no figures with data in the main manuscript. This dataset is not publicly accessible because: See explanation above. It can be accessed through the following means: See explanation above. Format: This research paper is somewhat unique in that there is no data presented in the main manuscript. There are three figures presented in the supporting information which are generated from input and output data from CMAQ. The figures are for tutorial purposes only and do not directly contribute to any analysis or conclusions of any scientific hypothesis or policy recommendation. The CMAQ code, data and figure scripts used to generate the supporting information figures may be found on ASM in the folder: /asm/MOD3DEV/bmurphy/ScienceHub/DESID. This dataset is associated with the following publication: Murphy, B., C. Nolte, F. Sidi, J. Bash, K.W. Appel, C. Jang, D. Kang, J. Kelly, R. Mathur, S. Napelenok, G. Pouliot, and H. Pye. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) Modeling System version 5.3.2. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 14(6): 3407-3420, (2021).

The In Vitro Diagnostics (IVD) Quality Control market is experiencing robust growth, driven by increasing demand for accurate and reliable diagnostic tests, stringent regulatory requirements for quality assurance, and a rising prevalence of chronic diseases globally. The market, estimated at [Let's assume a 2025 market size of $5 Billion based on a plausible estimation given the CAGR and typical market sizes for related sectors] in 2025, is projected to grow at a compound annual growth rate (CAGR) of 3.50% from 2025 to 2033. This growth is fueled by several key factors. Technological advancements, such as the development of automated quality control systems and improved data management solutions, are enhancing efficiency and accuracy in diagnostic testing. The expanding molecular diagnostics segment, particularly in areas like immunochemistry and hematology, contributes significantly to market expansion. Furthermore, the growing adoption of quality control measures in clinical laboratories and the increasing outsourcing of quality control services to Contract Research Organizations (CROs) are further driving market expansion. Hospitals, and clinical laboratories continue to be the largest end-users. However, the market faces certain restraints. High costs associated with advanced quality control products and services, especially in developing economies, can limit adoption. The complexity of regulatory compliance and the need for continuous investment in training and infrastructure for proper implementation pose challenges. Despite these restraints, the long-term outlook for the IVD quality control market remains positive. The continuous evolution of diagnostic technologies, coupled with rising healthcare expenditure and the increasing focus on improving diagnostic accuracy, will likely outweigh these challenges and contribute to sustained market growth throughout the forecast period. The competitive landscape is characterized by a mix of large multinational corporations and specialized smaller companies, indicating a dynamic and innovative market. Recent developments include: In July 2022, BrightSight Inc. launched a digital Connected Diagnostics Platform at the 2022 AACC Annual Scientific Meeting and Clinical Lab Expo. This diagnostic platform is developed for in vitro diagnostics manufacturers that streamline workflows. It includes a Proxy Agent, Analytics Dashboards, Integration Middleware, and Workflow Portals., In June 2022, EKF Diagnostics launched its new EKF Link digital connectivity solution for the secure management of point-of-care (POC) analyzers and associated data on one centralized platform.. Key drivers for this market are: Increased Demand for Advanced Diagnostics for Sensitive Reports and Accurate Diagnosis, Rise in Global Incidence of Infectious Diseases, Cancers and Genetic Disorders; Rise in the Volume of Accredited Clinical Laboratories and Adoption of Third-Party Quality Controls. Potential restraints include: Increased Demand for Advanced Diagnostics for Sensitive Reports and Accurate Diagnosis, Rise in Global Incidence of Infectious Diseases, Cancers and Genetic Disorders; Rise in the Volume of Accredited Clinical Laboratories and Adoption of Third-Party Quality Controls. Notable trends are: Molecular Diagnostics Segment is Expected to Register a Significant CAGR in the In-Vitro Diagnostics Quality Control Market Over the Forecast Period.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Global In Vitro Diagnostics (IVD) Quality Control Product Market valued at US$ 1789.5 Mn in 2023, projected to grow 7.8% CAGR to US$ 3776.49 Mn by 2032.

The In Vitro Diagnostics (IVD) Quality Control market is experiencing robust growth, driven by the increasing demand for accurate and reliable diagnostic testing across various applications. The market, valued at approximately $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of around 7% from 2025 to 2033, reaching an estimated $8.5 billion by 2033. This expansion is fueled by several key factors, including the rising prevalence of chronic diseases necessitating frequent diagnostic testing, technological advancements leading to improved accuracy and efficiency of IVD quality controls, and stringent regulatory requirements demanding high-quality diagnostic results. The market's segmentation reveals strong growth across all application areas, with clinical chemistry, immunochemistry, and molecular diagnostics leading the charge, reflecting the growing sophistication and complexity of modern diagnostic procedures. Furthermore, the increasing adoption of advanced data management and quality assurance services contributes to the market's overall growth trajectory. The key players in this market are continuously investing in R&D to develop innovative products and expand their global presence, intensifying competition and driving further market expansion. Significant regional variations exist, with North America and Europe currently dominating the market due to advanced healthcare infrastructure and higher per capita healthcare expenditure. However, emerging economies in Asia Pacific and the Middle East & Africa are showing significant growth potential, driven by increasing healthcare investment and rising awareness of disease prevention and early diagnosis. While restraints such as high costs associated with advanced quality control technologies and the need for skilled personnel pose certain challenges, the overall market outlook remains overwhelmingly positive. The continuous innovation in IVD technologies and the rising demand for improved diagnostic accuracy are expected to offset these challenges and propel the market towards sustainable growth in the coming years.

This study aimed to evaluate the quality and transparency of reporting in studies using machine learning (ML) in oncology, focusing on adherence to the Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS), TRIPOD-AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis), and PROBAST (Prediction Model Risk of Bias Assessment Tool). The literature search included primary studies published between February 1, 2024, and January 31, 2025, that developed or tested ML models for cancer diagnosis, treatment, or prognosis. To reflect the current state of the rapidly evolving landscape of ML applications in oncology, fifteen most recent articles in each category were selected for evaluation. Two independent reviewers screened studies and extracted data on study characteristics, reporting quality (CREMLS and TRIPOD+AI), risk of bias (PROBAST), and ML performance metrics. The most frequently studied cancer types were breast cancer (n=7/45; 15.6%), lung cancer (n=7/45; 15.6%), and liver cancer (n=5/45; 11.1%). The findings indicate several deficiencies in reporting quality, as assessed by CREMLS and TRIPOD+AI. These deficiencies primarily relate to sample size calculation, reporting on data quality, strategies for handling outliers, documentation of ML model predictors, access to training or validation data, and reporting on model performance heterogeneity. The methodological quality assessment using PROBAST revealed that 89% of the included studies exhibited a low overall risk of bias, and all studies have shown a low risk of bias in terms of applicability. Regarding the specific AI models identified as the best-performing, Random Forest (RF) and XGBoost were the most frequently reported, each used in 17.8% of the studies (n = 8). Additionally, our study outlines the specific areas where reporting is deficient, providing researchers with guidance to improve reporting quality in these sections and, consequently, reduce the risk of bias in their studies.

In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project. One of the major goals of the NAWQA project was to determine how river water quality has changed over time. To support that goal, long-term consistent and comparable monitoring has been conducted by the USGS on streams and rivers throughout the Nation. Outside of the NAWQA project, the USGS and other Federal, State, and local agencies also have collected long-term water-quality data to support their own assessments of changing water quality. In 2017, data from these multiple sources were combined to support one of the most comprehensive assessments to date of water-quality trends in the United States (Oelsner and others, 2017; De Cicco and others, 2017). This data release updates these water quality trends, which ended in 2012, with 5 more years of data and now end in 2017. This USGS data release contains all the input and output files necessary to reproduce the results from the Weighted Regressions on Time, Discharge, and Season (WRTDS) models, using data preparation methods described in Oelsner and others, 2017. Models were calibrated for each combination of site and parameter using the screened input data. Models were run on Yeti, the USGS supercomputer, in 3 separate runs, using the scripts in the "Script.zip" folder. See readMe.txt for details on how the files in this data release are related and the modeling process. "SiteTable.csv" gives information on sites used in this analysis. Once calibrated, the WRTDS models were initially evaluated using a logistic regression equation that estimated a probability of acceptance for each model (e.g., "a good fit") based on a set of diagnostic metrics derived from the observed, estimated, and residual values from each model and data set. Each WRTDS model was assigned to one of three categories: “auto-accept,” “auto-reject,” or “manual evaluation". Models assigned to the latter category were visually evaluated for appropriate model fit using residual and diagnostic plots. Models assigned to the first two categories were automatically included or rejected from the final results, respectively. Twenty-two water-quality parameters were assessed, including nutrients (ammonia, nitrate, filtered orthophosphate, total nitrogen, total phosphorus, and unfiltered orthophosphate), major ions (calcium, bromide, fluoride, chloride, magnesium, potassium, sodium, and sulfate), salinity indicators (total dissolved solids and specific conductance), sediment (total suspended solids and suspended sediment concentration), carbon (dissolved organic carbon, total organic carbon, and particulate organic carbon), and alkalinity. Trends are reported for six periods: 1972-2017, 1982-2017, 1987-2017, 1992-2017, 2002-2017, and 2007-2017.

This table presents the number of beneficiaries with a delivery, the number of beneficiaries with any SMM condition, and the rate of SMM conditions per 10,000 deliveries, 2017 - 2021. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues, making the data unusable for identifying this population. Data for a state are considered unusable based on DQ Atlas thresholds for the following topics: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Claims Volume - IP, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Data from Maryland, Tennessee, and Utah are omitted from the tables due to data quality concerns. Maryland was excluded in 2017 due to unusable diagnosis codes in the IP file and the OT file. Tennessee was excluded due to unusable diagnosis codes in the IP file in 2017 - 2019. Utah was excluded due to unusable procedure codes on OT professional claims in 2017 - 2020. In addition, states with a high data quality concern on one or more measures are noted in the table in the "Data Quality" column. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

The In Vitro Diagnostics Quality Control Market report segments the industry into Products and Services (Quality Control Products, Data Management Solutions, Quality Assurance Services), Application (Immunochemistry, Hematology, Molecular Diagnostics, and more), End Users (Hospitals, Clinical Laboratories, IVD Manufacturers & CROs, and more), and Geography (North America, Europe, Asia-Pacific, and more).

Production Quality Control of In Vitro Diagnostic Reagents Market Outlook 2032

The production quality control of in vitro diagnostic reagents market was valued at USD XX Billion in 2023 and is expected to reach USD XX Billion by 2032, expanding at a CAGR of XX% during the forecast period 2024-2032.



he trend toward automation in laboratories to handle higher test volumes efficiently and reduce human error has significantly boosted the demand for advanced diagnostic instruments. Moreover, the integration of IT and connectivity solutions in instruments allows for better data management and remote diagnostics, which are increasingly important in a globally connected healthcare environment.

This has led to a surge in demand for automated and integrated diagnostic systems that can handle large batches of samples with minimal manual intervention. Clinical laboratories not only demand high-quality reagents for accurate diagnostics but also influence the development and refinement of new diagnostic reagents, driving the growth of the segment.

Production Quality Control of In Vitro Diagnostic Reagents Market Dynamics

Major Drivers

The increasing prevalence of chronic and infectious diseases worldwide drives the market. For instance,

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In Vitro Diagnostics (IVD) Quality Control Market size was valued at USD 686.78 Million in 2024 and is projected to reach USD 1150.36 Million by 2031, growing at a CAGR of 6.66% from 2024 to 2031.

Global In Vitro Diagnostics (IVD) Quality Control Market Key Market Drivers

Increasing Demand for Accurate Diagnostic Tests: The rising prevalence of chronic diseases and infectious conditions worldwide fuels the demand for accurate and reliable diagnostic tests. Robust quality control measures are essential to ensure the accuracy and precision of these tests, driving the growth of the IVD quality control market.

Technological Advancements in Diagnostics: Advancements in diagnostic technologies like molecular diagnostics, next-generation sequencing, and point-of-care testing necessitate advanced quality control solutions, especially with the rise of automation, robotics, and AI.

Upsurge in Adoption of Point-of-Care Testing (POCT): The increasing use of point-of-care testing (POCT) in healthcare settings necessitates the development of quality control solutions to ensure accurate, reliable, and reproducible results in non-traditional settings.

Expansion of Diagnostic Laboratories: The global expansion of diagnostic laboratory networks, particularly in emerging markets, is driving a growing demand for quality control products and services to standardize procedures and maintain high-quality results.

Rising Focus on Personalized Medicine: The shift towards personalized medicine and precision diagnostics necessitates accurate, reliable tests tailored to individual patient characteristics, driving the adoption of quality control solutions in the IVD market.

These indicators are designed to accompany the SHMI publication. Information on the main condition the patient is in hospital for (the primary diagnosis) is used to calculate the expected number of deaths used in the calculation of the SHMI. A high percentage of records with an invalid primary diagnosis may indicate a data quality problem. A high percentage of records with a primary diagnosis which is a symptom or sign may indicate problems with data quality or timely diagnosis of patients, but may also reflect the case-mix of patients or the service model of the trust (e.g. a high level of admissions to acute admissions wards for assessment and stabilisation). Contextual indicators on the percentage of provider spells with an invalid primary diagnosis and the percentage of provider spells with a primary diagnosis which is a symptom or sign are produced to support the interpretation of the SHMI. Notes: 1. On 1st January 2025, North Middlesex University Hospital NHS Trust (trust code RAP) was acquired by Royal Free London NHS Foundation Trust (trust code RAL). This new organisation structure is reflected from this publication onwards. 2. There is a shortfall in the number of records for Northumbria Healthcare NHS Foundation Trust (trust code RTF), The Rotherham NHS Foundation Trust (trust code RFR), The Shrewsbury and Telford Hospital NHS Trust (trust code RXW), and Wirral University Teaching Hospital NHS Foundation Trust (trust code RBL). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 3. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 4. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

The In Vitro Diagnostics (IVD) Quality Control Product market is a significant sector within the broader healthcare industry, exhibiting steady growth. With a market size of $798.5 million in 2025 and a Compound Annual Growth Rate (CAGR) of 1.9% from 2019-2033, the market demonstrates consistent expansion, albeit at a moderate pace. This sustained growth is fueled by several key factors. The increasing prevalence of chronic diseases globally necessitates more frequent diagnostic testing, driving demand for reliable quality control products to ensure accurate and precise results. Advancements in IVD technologies, such as automation and molecular diagnostics, further contribute to market expansion. Stringent regulatory requirements for diagnostic accuracy and reliability also stimulate the adoption of quality control solutions, ensuring patient safety and treatment efficacy. The market is segmented by product type (Quality Control Products, Quality Assurance Services, Data Management Solutions) and application (Clinical Chemistry, Immunochemistry, Hematology, Molecular Diagnostics, Others), reflecting the diverse needs of various diagnostic testing environments. Leading companies like Bio-Rad Laboratories, Thermo Fisher Scientific, and Roche Diagnostics are key players, shaping market dynamics through innovation and product diversification. Geographic distribution shows a significant presence across North America, Europe, and Asia Pacific, highlighting the global nature of the IVD quality control market. The moderate CAGR indicates a stable, maturing market rather than explosive growth. However, emerging markets in Asia Pacific and other developing regions offer significant growth potential. The increasing focus on personalized medicine and point-of-care diagnostics will likely influence future growth, demanding sophisticated quality control mechanisms. Challenges for the market include the high cost of some advanced quality control solutions and the need for continuous training and education to ensure effective utilization. Nevertheless, the ongoing expansion of the global healthcare infrastructure and the ever-increasing need for accurate diagnostic testing underpin the long-term outlook for this market segment. The continued development of novel diagnostic assays and technologies will continue to create new opportunities for quality control product manufacturers and service providers.

This data product consists of measurements from rate counters. The rate count data are diagnostic fields, have uncalibrated energy ranges, and wrap near or above 16384 counts/s. The rate count values are stored as 4 s accumulations of counts.The BARREL Mission was a multiple-balloon investigation designed to study electron losses from Earth's Radiation Belts. Selected as a NASA Living with a Star Mission of Opportunity, BARREL was designed to augment the Radiation Belt Storm Probes, RBSP, mission by providing measurements of the spatial and temporal variations of electron precipitation from the radiation belts. The RBSP mission has since been renamed the Van Allen Probes mission. Each BARREL balloon carried an X-ray spectrometer to measure the bremsstrahlung X-rays produced by precipitating relativistic electrons as they collide with neutrals in the atmosphere, and a DC magnetometer to measure ULF-timescale variations of the magnetic field. BARREL observations collected near latitudes close to either the antarctic and arctic circles at stratospheric altitudes at about 30 km. The BARREL instrumentation provided the first balloon measurements of relativistic electron precipitation while comprehensive in situ measurements of both plasma waves and energetic particles were available. Also, the BARREL data has been used to characterize the spatial scale of precipitation at relativistic energies.The initial pair of balloon campaigns that were conducted initially during the Austral summer months of January and February of 2013 and 2014 with launches from two stations located in Antarctica: the British base located at Halley Bay on the Brunt Ice Shelf and the South African SANAE IV base (SANAE stand for South African National Antarctic Expedition) located in Vesleskarvet, Queen Maud Land. For the 2013 and 2014 the balloon campaigns, the launch plan was designed to maintain an array with about five payloads spread across about six hours of magnetic local time, MLT, in the region that magnetically maps to the radiation belts. Thus, the BARREL balloon constellation constituted an evolving and slowly moving array able to study relativistic electron precipitation from the radiation belts.Later campaigns were undertaken in 2015 and 2016 from the Esrange Space Center located in Kiruna, Sweden. The 2015 and 2016 campaigns were undertaken in coordination with the Van Allen Probes mission, the European Incoherent Scatter Scientific Association, EISCAT, incoherent scatter radar system, and other ground and space based instruments. Seven balloon launches occurred during the August 2015 BARREL campaign. A total of eight flights occurred during August 2016.Summing over the four BARREL campaigns, over 50 small, approximately 20 kg, stratospheric balloons were successively launched. The website creeated and hosted by A.J. Halford (see Information URL below) reports that: "By the end of the campaigns, there were over 90 researchers coordinating on a daily basis with the BARREL team working on 7 different satellite missions, 1 other balloon mission, and way too many ground based instruments to count." Although the BARREL mission launched only balloons during the years from 2013 to 2016, research using data collected on these flights is ongoing, so stay tuned for updates! All data and analysis software are freely available to the scientific community.The information listed above in this resource description was compiled by referencing several BARREL related resources including primarily the Millan et al. (2013) Space Science Reviews publication, the BARREL at Dartmouth mission web site, and the website maintained by A.J. Halford.The current release of all BARREL CDF data products are Version 10 files.BARREL will make all its scientific data products quickly and publicly available but all users are expected to read and follow the BARREL Data Usage Policy listed below.BARREL Data Usage PolicyBARREL data products are made freely available to the public and every effort is made to ensure that these products are of the highest quality. However, there may occasionally be issues with either the instruments or data processing that affect the accuracy of data. When possible, a quality flag is included in higher level data products, and known issues are posted in the BARREL data repository. You are also strongly encouraged to follow the guidelines below if you are planning a publication or presentation in which BARREL data are used. This will help you ensure that your science results are valid. Users should always use the highest version numbers of data and analysis tools. Browse/quick-look plots are not intended for science analysis or publication and should not be used for those purposes without consent of the principal investigator, PI. Users should notify the BARREL PI of the data use and investigation objectives. This will ensure that you are using the data appropriately and have the most recent version of the data or analysis routines. Additionally, if a BARREL team member is already working on a similar or related topic, they may be able to contribute intellectually. If BARREL team members are not part of the author list, then users should Credit/Acknowledge the BARREL team as follows: We acknowledge the BARREL team (PI: Robyn Millan) for use of BARREL data. Users are also requested to provide the PI with a copy of each manuscript that uses BARREL data upon submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI.The BARREL PI can be contacted at: Robyn.Millan@dartmouth.edu.An online copy of the BARREL Data Usage Policy document can be found at: https://barrel.rmillan.host.dartmouth.edu/documents/data.use.policy.pdf.