Health Check Software Market Outlook

The global Health Check Software market size is projected to experience a robust growth with a Compound Annual Growth Rate (CAGR) of 12.5% from 2024 to 2032. The market size was valued at approximately USD 1.2 billion in 2023 and is anticipated to reach around USD 3.2 billion by 2032. Key growth factors driving this market include the increasing emphasis on preventative healthcare, advancements in digital technology, and the rising demand for efficient health management solutions.

A significant growth factor for the Health Check Software market is the increasing global focus on preventative healthcare. Governments and healthcare providers are recognizing the benefits of early detection and intervention, which not only improve patient outcomes but also reduce healthcare costs in the long run. Health check software solutions enable continuous monitoring and early diagnosis of diseases, which is crucial in managing chronic conditions and preventing severe health complications.

Advancements in digital technology and artificial intelligence are also accelerating the growth of the Health Check Software market. Developments in AI and machine learning algorithms have enhanced the capabilities of health check software, making it possible to provide more accurate and personalized health assessments. These technologies enable the analysis of large datasets to identify patterns and predict potential health risks, thereby offering proactive healthcare solutions.

The rising demand for efficient health management solutions among corporate enterprises is another key driver of market growth. Many organizations are investing in health check software to monitor and improve the health and wellness of their employees. This not only helps in reducing absenteeism and boosting productivity but also demonstrates the companyÂ’s commitment to employee well-being, which can enhance corporate reputation and employee satisfaction.

The integration of Healthcare Compliance Software into the health check ecosystem is becoming increasingly vital as regulatory requirements continue to evolve. This type of software ensures that healthcare providers adhere to the necessary legal and ethical standards, safeguarding patient data and maintaining the integrity of healthcare services. By automating compliance processes, healthcare organizations can focus more on patient care while minimizing the risk of legal issues. Furthermore, Healthcare Compliance Software helps in streamlining audits and reporting, making it easier for organizations to demonstrate their adherence to regulations. As the healthcare landscape becomes more complex, the role of compliance software in ensuring smooth operations cannot be overstated.

Regionally, North America is expected to dominate the Health Check Software market during the forecast period. The regionÂ’s growth can be attributed to the presence of advanced healthcare infrastructure, high adoption of digital health technologies, and a strong emphasis on preventative healthcare. Additionally, supportive government policies and significant investments in healthcare IT are further propelling the market growth in North America.

Component Analysis

The Health Check Software market is segmented by components into software and services. The software segment is the primary driver of market growth, driven by the increasing adoption of digital health solutions. Health check software includes various applications that facilitate the monitoring, diagnosing, and management of health conditions. These applications are designed to integrate with existing healthcare systems, making it easier for healthcare providers and patients to access and utilize health data efficiently.

The services segment, which includes implementation, training, and maintenance services, is also crucial for the market. As more organizations and healthcare providers adopt health check software, the demand for services that ensure smooth implementation and operation of these software solutions is rising. Maintenance services are particularly important to ensure that the software is up-to-date and functioning correctly, preventing any disruptions in health monitoring and management processes.

The integration of advanced technologies such as AI and machine learning in health check software is also enhancing the capabilities of these solutions. AI-driven health

For full documentation, please see our Github repository. We are releasing under the CC-BY licence a new large-scale dataset for Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the medical domain. The dataset contains patients synthesized using a proprietary medical knowledge base and a commercial rule-based AD system. Patients in the dataset are characterized by their socio-demographic data, a pathology they are suffering from, a set of symptoms and antecedents related to this pathology, and a differential diagnosis. The symptoms and antecedents can be binary, categorical and multi-choice, with the potential of leading to more efficient and natural interactions between ASD/AD systems and patients. To the best of our knowledge, this is the first large-scale dataset that includes the differential diagnosis, and non-binary symptoms and antecedents. For more information, please check our paper.

Owing to increasing medical expenses, researchers have attempted to detect clinical signs and preventive measures of diseases using electronic health record (EHR). In particular, time-series EHRs collected by periodic medical check-up enable us to clarify the relevance among check-up results and individual environmental factors such as lifestyle. However, usually such time-series data have many missing observations and some results are strongly correlated to each other. These problems make the analysis difficult and there exists strong demand to detect clinical findings beyond them. We focus on blood test values in medical check-up results and apply a time-series analysis methodology using a state space model. It can infer the internal medical states emerged in blood test values and handle missing observations. The estimated models enable us to predict one’s blood test values under specified condition and predict the effect of intervention, such as changes of body composition and lifestyle. We use time-series data of EHRs periodically collected in the Hirosaki cohort study in Japan and elucidate the effect of 17 environmental factors to 38 blood test values in elderly people. Using the estimated model, we then simulate and compare time-transitions of participant’s blood test values under several lifestyle scenarios. It visualizes the impact of lifestyle changes for the prevention of diseases. Finally, we exemplify that prediction errors under participant’s actual lifestyle can be partially explained by genetic variations, and some of their effects have not been investigated by traditional association studies.

The researcher tests the QA capability of ChatGPT in the medical field from the following aspects:1. Test their reserve capacity for medical knowledge2. Check their ability to read literature and understand medical literature3. Test their ability of auxiliary diagnosis after reading case data4. Test its error correction ability for case data5. Test its ability to standardize medical terms6. Test their evaluation ability to experts7. Check their ability to evaluate medical institutionsThe conclusion is:ChatGPT has great potential in the application of medical and health care, and may directly replace human beings or even professionals at a certain level in some fields;The researcher preliminarily believe that ChatGPT has basic medical knowledge and the ability of multiple rounds of dialogue, and its ability to understand Chinese is not weak;ChatGPT has the ability to read, understand and correct cases;ChatGPT has the ability of information extraction and terminology standardization, and is quite excellent;ChatGPT has the reasoning ability of medical knowledge;ChatGPT has the ability of continuous learning. After continuous training, its level has improved significantly;ChatGPT does not have the academic evaluation ability of Chinese medical talents, and the results are not ideal;ChatGPT does not have the academic evaluation ability of Chinese medical institutions, and the results are not ideal;ChatGPT is an epoch-making product, which can become a useful assistant for medical diagnosis and treatment, knowledge service, literature reading, review and paper writing.

The online health assessment market is experiencing robust growth, driven by increasing smartphone penetration, rising healthcare costs, and a growing preference for convenient, accessible healthcare solutions. The market, valued at approximately $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the increasing adoption of telehealth and remote patient monitoring technologies is creating a surge in demand for online health assessments as a cost-effective and efficient preliminary diagnostic tool. Secondly, the convenience and accessibility offered by online assessments are particularly appealing to younger demographics (teenagers and adults) and those in geographically remote areas with limited access to traditional healthcare facilities. Finally, the integration of artificial intelligence (AI) and machine learning (ML) is enhancing the accuracy and efficiency of these assessments, further propelling market growth. The market is segmented by assessment type (condition-specific questionnaires, symptom checkers, eligibility checkers) and target demographic (teenagers, adults, elderly), offering diverse opportunities for market players. While data privacy concerns and the need for regulatory compliance represent potential restraints, the overall market outlook remains highly positive. The competitive landscape is characterized by a mix of established healthcare providers (Inova, CHRISTUS, Cigna, OSF Healthcare, Northwell Health, HCA, Kaiser, Beaumont, MyMichigan Health), technology companies (WebMD), and specialized health data providers (Global Health Metrics). These companies are actively investing in developing sophisticated online assessment tools and integrating them into their existing healthcare platforms. Regional growth is expected to be geographically diverse, with North America and Europe currently leading the market due to high levels of technology adoption and healthcare infrastructure. However, Asia-Pacific is poised for rapid growth in the coming years driven by increasing internet penetration and a burgeoning middle class with rising disposable incomes. Strategic partnerships, technological advancements, and expanding regulatory approvals are key factors that will influence the competitive dynamics and overall growth trajectory of the online health assessment market over the forecast period.

The global health check software market is experiencing robust growth, driven by increasing demand for preventive healthcare, the rising adoption of telehealth technologies, and a growing emphasis on personalized medicine. The market's expansion is fueled by several key factors. Firstly, the increasing prevalence of chronic diseases necessitates proactive health monitoring and management, making health check software an indispensable tool for individuals and healthcare providers. Secondly, the integration of advanced technologies like artificial intelligence (AI) and machine learning (ML) is enhancing the diagnostic capabilities and predictive analytics of these platforms, enabling earlier detection and intervention. Thirdly, the rising adoption of mobile health (mHealth) applications is contributing to improved accessibility and convenience for users. While the market faces challenges such as data security concerns and the need for robust regulatory frameworks, the overall trajectory suggests a sustained period of significant growth. We estimate the 2025 market size to be $5 billion, growing at a CAGR of 15% through 2033, driven by the aforementioned factors. This growth will be further propelled by the continued expansion of telehealth services and the increasing investment in digital health infrastructure.
The market is segmented by various factors including deployment mode (cloud-based, on-premise), application (personal health management, disease management, risk assessment), and end-user (hospitals, clinics, individuals). Key players such as Intuit, Epic Systems, and Healthdirect are actively driving innovation and market penetration through strategic partnerships, acquisitions, and the development of advanced software features. The competitive landscape is characterized by both established players and emerging technology companies, resulting in a dynamic and rapidly evolving market. Regional variations exist, with North America and Europe currently holding significant market share, but growth in Asia-Pacific and other emerging markets is expected to accelerate in the coming years. Continued focus on user experience, data privacy, and interoperability will be crucial for sustained success in this dynamic sector.

IT Health Check Service Market Outlook

The global market size for IT Health Check Services was valued at approximately USD 5.2 billion in 2023 and is forecasted to reach around USD 12.8 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 10.5%. This growth is driven by the increasing need for organizations to secure their IT infrastructure, comply with regulatory standards, and ensure optimal performance of their technological assets.

One of the primary growth factors contributing to the IT health check service market is the rising incidence of cyber threats and data breaches. Organizations across various sectors are increasingly recognizing the importance of regularly assessing their IT systems to identify vulnerabilities and mitigate risks. Consequently, the demand for comprehensive IT health check services, which encompass network, security, application, and infrastructure assessments, is on the rise. The growing focus on cybersecurity has made it imperative for businesses to adopt proactive measures, further propelling the market's growth.

Another significant growth driver is the rapid technological advancements and the subsequent adoption of new technologies by businesses. With the emergence of cloud computing, the Internet of Things (IoT), and artificial intelligence (AI), organizations are continuously evolving their IT landscapes. This evolution necessitates regular health checks to ensure that these new technologies are seamlessly integrated into existing systems and are functioning optimally. Additionally, regulatory requirements and standards, such as GDPR and HIPAA, mandate regular IT assessments, further fueling the market's expansion.

The increasing trend of digital transformation across industries is also a crucial factor contributing to the market's growth. As businesses strive to enhance operational efficiencies and customer experiences through digital initiatives, the need for robust and reliable IT infrastructure becomes paramount. IT health check services play a vital role in identifying potential issues and optimizing the performance of digital systems, thereby ensuring successful digital transformation initiatives. The growing reliance on digital platforms and solutions underscores the importance of regular IT assessments, driving the demand for health check services.

Geographically, North America holds a significant share of the IT health check service market, driven by the high adoption rate of advanced technologies and stringent regulatory requirements. The region's well-established IT infrastructure and the presence of major technology companies further contribute to market growth. Europe follows closely, with increasing investments in cybersecurity and IT infrastructure. The Asia Pacific region is expected to witness the highest growth rate, owing to the rapid digitalization and technological advancements in emerging economies. The increasing awareness of cybersecurity and the need for regulatory compliance are key factors driving market growth in Latin America and the Middle East & Africa.

Service Type Analysis

The IT health check service market can be segmented by service type, including Network Health Check, Security Health Check, Application Health Check, Infrastructure Health Check, Compliance Health Check, and Others. Each of these service types addresses specific aspects of an organization's IT environment, offering tailored solutions to ensure optimal performance and security.

Network Health Check services focus on assessing the performance and security of an organization's network infrastructure. This involves evaluating network configurations, identifying potential bottlenecks, and ensuring that the network is secure from cyber threats. With the increasing reliance on digital communication and data transfer, the demand for network health check services is on the rise. Organizations are keen to ensure that their network infrastructure is robust and can handle the growing volume of data traffic without compromising security.

Security Health Check services are crucial for identifying vulnerabilities and potential threats within an organization's IT environment. These services involve a comprehensive assessment of security protocols, firewall configurations, and access controls. Given the rising incidence of cyberattacks and data breaches, businesses are increasingly investing in security health check services to safeguard their sensitive information and maintain customer trust. The growing emphasis on cybersecurity across indust

Context

Part of Janatahack Hackathon in Analytics Vidhya

Content

The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data, health camps and records, and the treatment of chronic diseases.

MedCamp organizes health camps in several cities with low work life balance. They reach out to working people and ask them to register for these health camps. For those who attend, MedCamp provides them facility to undergo health checks or increase awareness by visiting various stalls (depending on the format of camp).

MedCamp has conducted 65 such events over a period of 4 years and they see a high drop off between “Registration” and number of people taking tests at the Camps. In last 4 years, they have stored data of ~110,000 registrations they have done.

One of the huge costs in arranging these camps is the amount of inventory you need to carry. If you carry more than required inventory, you incur unnecessarily high costs. On the other hand, if you carry less than required inventory for conducting these medical checks, people end up having bad experience.

The Process:

MedCamp employees / volunteers reach out to people and drive registrations.
During the camp, People who “ShowUp” either undergo the medical tests or visit stalls depending on the format of health camp.

Other things to note:

Since this is a completely voluntary activity for the working professionals, MedCamp usually has little profile information about these people.
For a few camps, there was hardware failure, so some information about date and time of registration is lost.
MedCamp runs 3 formats of these camps. The first and second format provides people with an instantaneous health score. The third format provides  
information about several health issues through various awareness stalls.

Favorable outcome:

For the first 2 formats, a favourable outcome is defined as getting a health_score, while in the third format it is defined as visiting at least a stall.
You need to predict the chances (probability) of having a favourable outcome.

Train / Test split:

Camps started on or before 31st March 2006 are considered in Train
Test data is for all camps conducted on or after 1st April 2006.

Acknowledgements

Credits to AV

Inspiration

To share with the data science community to jump start their journey in Healthcare Analytics

This is a dataset for testing purposes of the Health Open Data Platform

Dataset Description:

This dataset comprises transcriptions of conversations between doctors and patients, providing valuable insights into the dynamics of medical consultations. It includes a wide range of interactions, covering various medical conditions, patient concerns, and treatment discussions. The data is structured to capture both the questions and concerns raised by patients, as well as the medical advice, diagnoses, and explanations provided by doctors.

Key Features:

• Doctor and Patient Roles: Each conversation is annotated with the role of the speaker (doctor or patient), making it easy to analyze communication patterns.
• Medical Context: The dataset includes diverse scenarios, from routine check-ups to more complex medical discussions, offering a broad spectrum of healthcare dialogues.
• Natural Language: The conversations are presented in natural language, allowing for the development and testing of NLP models focused on healthcare communication.
• Applications: This dataset can be used for various applications, such as building dialogue systems, analyzing communication efficacy, developing medical NLP models, and enhancing patient care through better understanding of doctor-patient interactions.

Potential Use Cases:

• NLP Model Training: Train models to understand and generate medical dialogues.
• Healthcare Communication Studies: Analyze communication strategies between doctors and patients to improve healthcare delivery.
• Medical Chatbots: Develop intelligent medical chatbots that can simulate doctor-patient conversations.
• Patient Experience Enhancement: Identify common patient concerns and doctor responses to enhance patient care strategies.

This dataset is a valuable resource for researchers, data scientists, and healthcare professionals interested in the intersection of technology and medicine, aiming to improve healthcare communication through data-driven approaches.

This dataset contains the estimated percentages of eligible England resident persons (age 40-74 years old) who were invited and received the National Health Service (NHS) Health Check, by type of liver disease England regions, counties and unitary authorities, and the level of multiple deprivations. Comparisons to England and region level, are also available in the dataset.

This is the de-identified data set used to conduct the analyses in the preprint submitted to JMIR Human Factors under the title "Determinants of Laypersons’ Trust in Medical Decision Aids: Randomized Controlled Trial" (https://doi.org/10.2196/35219).

This dataset contains 494 respondents' appraisals of a fictitious case vignette. They received support from a decision aid (that always disagreed with participants' first appraisal) showing a mock symptom checker logo, a decision aid framed as anthropomorphic or as an AI. Their second appraisal - taking into account the symptom checker advice - was collected again.

Additionally, the data contains participants'

• age
• gender
• education
• medical training
• propensity to trust
• eHealth Literacy
• certainty in their appraisals
• trust in the decision aid

This dataset is historical only and ends at 5/7/2021. For more information, please see http://dev.cityofchicago.org/open%20data/data%20portal/2021/05/04/covid-19-testing-by-person.html. The recommended alternative dataset for similar data beyond that date is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Testing-By-Test/gkdw-2tgv. This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html. For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19. This dataset contains counts of people tested for COVID-19 and their results. This dataset differs from https://data.cityofchicago.org/d/gkdw-2tgv in that each person is in this dataset only once, even if tested multiple times. In the other dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in that dataset more than once on the same day unless he/she had both a positive and not-positive test. Only Chicago residents are included based on the home address as provided by the medical provider. Molecular (PCR) and antigen tests are included, and only one test is counted for each individual. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table. Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results. Demographic data are more complete for those who test positive; care should be taken when calculating percentage positivity among demographic groups. All data are provisional and subject to change. Information is updated as additional details are received. Data Source: Illinois National Electronic Disease Surveillance System

These two data sets present the accuracy of triage (disposition) and diagnostic advice of symptom checker apps sampled in 2020. The sample consists of 22 commonly used symptom checker apps, of which 14 also provide diagnostic advice. The apps were tested on 45 case vignettes, i.e. fictitious descriptions of patients. As not every app was able to appraise every vignette our study yielded a total of 796 unique triage evaluations and 520 unique diagnostic evaluations. The data sets are a supplement to the paper "Triage Accuracy of Symptom Checker Apps: A Five-year Follow-up Evaluation" (doi: 10.2196/31810).

The was collected by Anna Dames as partial requirement for her MSc degree in Human Factors in the Department of Psychology and Ergonomics (IPA) at Technische Universität Berlin.

The clinical vignettes were originally compiled and modified by Semigran et al. in 2015 (https://doi.org/10.1136/bmj.h3480), and further adapted by Hill et al. (2020) (doi: 10.5694/mja2.50600) and in the study these data sets are supplement to (doi: 10.2196/31810).

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

The global third-party health check-up center market size was valued at USD 34.2 billion in 2025 and is projected to expand at a CAGR of 7.3% during the forecast period (2025-2033), reaching USD 62.3 billion by 2033. The market growth is primarily driven by the increasing prevalence of chronic diseases, rising healthcare costs, and growing awareness about preventive healthcare. The availability of advanced diagnostic tools and technologies has further fueled the demand for third-party health check-up services. The market is segmented by application (male and female) and type (specialized check-up and general check-up). The specialized check-up segment accounted for a larger market share in 2025 due to the growing prevalence of chronic diseases and the need for specialized diagnosis and treatment plans. The general check-up segment is expected to witness a significant growth rate during the forecast period as preventive healthcare measures gain traction. Regionally, North America held the largest market share in 2025, followed by Europe and Asia Pacific. The presence of well-established healthcare systems and high healthcare expenditure in North America has contributed to the region's dominance in the market. The Asia Pacific region is expected to grow at a faster rate during the forecast period due to the increasing healthcare spending and the rising awareness about preventive healthcare in emerging economies. This report provides comprehensive insights into the global third-party health check-up center market, with a particular focus on market concentration, trends, key segments, competitive landscape, and growth drivers. The market is valued at USD 300 million and is projected to reach USD 1.2 billion by 2028, exhibiting a CAGR of 15.2%.

Defines a core set of patient level (non-identifiable) information to be returned to the Department of Health to enable monitoring against Key Performance Indicators identified for the NHS Health Check programme.

Dataset

This dataset was created by boma store

Contents

This web map contains fictitious neighborhood health data to be used for software testing and demonstration purposes only.The purpose of this web map is to show how predominant conditions can be mapped using the new Predominant Smart Symbology styling setting in ArcGIS Online.