Clinical Data Analytics in Healthcare Market Outlook

The Clinical Data Analytics in Healthcare Market is experiencing a significant surge in demand, with a market size valued at $12 billion in 2023 and projected to reach approximately $35 billion by 2032, expanding at an impressive compound annual growth rate (CAGR) of 12.5%. The driving force behind this robust growth is the increasing need for data-driven decision-making processes in healthcare that enhance operational efficiency and improve patient outcomes. This demand is further fueled by the global shift towards value-based healthcare, which emphasizes the quality of care provided and patient satisfaction over the quantity of services rendered.

A primary growth factor propelling this market is the technological advancements in data processing and storage capacities, allowing healthcare providers to manage and analyze vast amounts of clinical data efficiently. The integration of technologies such as artificial intelligence and machine learning into healthcare data analytics has revolutionized the way data is interpreted, enabling predictive analytics and personalized medicine. These technologies aid in early disease detection and facilitate the creation of tailored treatment plans, which are proving to be more effective than traditional approaches in managing chronic diseases and improving patient care outcomes.

Another significant growth factor is the increasing adoption of electronic health records (EHRs) across healthcare facilities worldwide. EHRs play a crucial role in data collection, providing a comprehensive view of patient histories that is essential for effective data analytics. The widespread implementation of EHRs improves data accuracy and accessibility, which are critical for successful clinical data analytics. Furthermore, healthcare regulations globally are increasingly mandating the digital recording and sharing of patient data, further accelerating the adoption of EHRs and subsequently driving the demand for data analytics solutions.

The growing emphasis on population health management is also a strong catalyst for market growth. As healthcare systems shift towards a more holistic approach to patient care, there is a heightened focus on understanding and managing the health of entire populations. Clinical data analytics provides the tools necessary for identifying health trends and risk factors within populations, allowing healthcare providers to develop targeted interventions and preventive measures. This trend is especially pertinent amid the increasing prevalence of lifestyle-related diseases, which require ongoing monitoring and management to mitigate their impact on healthcare systems.

In the realm of healthcare, operational analytics plays a pivotal role in streamlining processes and enhancing the efficiency of healthcare delivery systems. By leveraging Healthcare Operational Analytics, healthcare organizations can optimize resource allocation, reduce operational costs, and improve patient flow management. This approach enables healthcare providers to identify bottlenecks and inefficiencies within their operations, allowing for data-driven decisions that enhance overall service delivery. As healthcare systems continue to face increasing demands and financial pressures, the adoption of operational analytics becomes essential in maintaining high standards of care while ensuring sustainability and cost-effectiveness.

Regionally, North America dominates the Clinical Data Analytics in Healthcare Market, accounting for the largest market share due to advanced healthcare infrastructure and significant investments in R&D. The region's well-established EHR systems and the presence of major market players spearheading technological innovations further bolster this dominance. However, Asia Pacific is expected to witness the highest growth rate, driven by the rapid adoption of healthcare IT solutions, increasing government initiatives towards digital health transformation, and the growing burden of chronic diseases. Europe follows closely, benefiting from stringent healthcare regulations and a strong focus on improving healthcare outcomes through data analytics.

Component Analysis

The component segment of the Clinical Data Analytics in Healthcare Market is bifurcated into software and services, both integral to the effective deployment of data analytics solutions. Software, the larger of the two segments, encompasses a range of applications designed to

The size and share of the market is categorized based on Type (Audio, Image) and Application (Biotech, Dentistry, Diagnostic Centers, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This statistic displays information on the healthcare professionals who use or intend to use health data recorded via their patients' smartphones in the United Kingdom (UK) in 2015. As of January 2015, 10.2 percent of healthcare professionals used health data collected via smartphones.

In 2021, 42 percent of consumers surveyed in Canada reported they would not be comfortable with their personal health information being collected by mainstream devices to track their health. On the other hand, 39 percent stated they would be comfortable with devices collecting their data if they knew what it was being used for and who had access to it.

Maternal health data is of great importance to the understanding and improvement of maternal healthcare services. However, the availability and quality of this data remain scarce for most researchers around the world. In many regions, obtaining detailed and reliable maternal health data is very challenging due to the lack of comprehensive data collection and inconsistent records. This has created critical gaps in data availability to support and enhance maternal health research and services. To bridge this gap, we created a curated dataset of maternal healthcare questions and answers with the oversight of medical experts. The dataset, titled "MOTHER: Maternal Online Technology for Health Care," provides time-relevant information for maternal health questions. The data was collected in 2023 and includes questions and answers related to various aspects of maternal health. This dataset aims to provide informative responses to common questions that pregnant women or those using family planning health services might have regarding their health. The questions and answers are designed to address various concerns, symptoms, and conditions associated with pregnancy. This work was funded by the Makerere University Research and Innovation Fund, RIF

Healthcare Data Collection and Labeling Market Analysis

The Healthcare Data Collection and Labeling Market is exp...

The NIHR is one of the main funders of public health research in the UK. Public health research falls within the remit of a range of NIHR Research Programmes, NIHR Centres of Excellence and Facilities, plus the NIHR Academy. NIHR awards from all NIHR Research Programmes and the NIHR Academy that were funded between January 2006 and the present extraction date are eligible for inclusion in this dataset. An agreed inclusion/exclusion criteria is used to categorise awards as public health awards (see below). Following inclusion in the dataset, public health awards are second level coded to one of the four Public Health Outcomes Framework domains. These domains are: (1) wider determinants (2) health improvement (3) health protection (4) healthcare and premature mortality.More information on the Public Health Outcomes Framework domains can be found here.This dataset is updated quarterly to include new NIHR awards categorised as public health awards. Please note that for those Public Health Research Programme projects showing an Award Budget of £0.00, the project is undertaken by an on-call team for example, PHIRST, Public Health Review Team, or Knowledge Mobilisation Team, as part of an ongoing programme of work.Inclusion criteriaNIHR awards are categorised as public health awards if they are determined to be ‘investigations of interventions in, or studies of, populations that are anticipated to have an effect on health or on health inequity at a population level.’ This definition of public health is intentionally broad to capture the wide range of NIHR public health awards across prevention, health improvement, health protection, and healthcare services (both within and outside of NHS settings). This dataset does not reflect the NIHR’s total investment in public health research. The intention is to showcase a subset of the wider NIHR public health portfolio. This dataset includes NIHR awards categorised as public health awards from NIHR Research Programmes and the NIHR Academy. This dataset does not currently include public health awards or projects funded by any of the three NIHR Research Schools or any of the NIHR Centres of Excellence and Facilities. Therefore, awards from the NIHR Schools for Public Health, Primary Care and Social Care, NIHR Public Health Policy Research Unit and the NIHR Health Protection Research Units do not feature in this curated portfolio.DisclaimersUsers of this dataset should acknowledge the broad definition of public health that has been used to develop the inclusion criteria for this dataset. This caveat applies to all data within the dataset irrespective of the funding NIHR Research Programme or NIHR Academy award.Please note that this dataset is currently subject to a limited data quality review. We are working to improve our data collection methodologies. Please also note that some awards may also appear in other NIHR curated datasets. Further informationFurther information on the individual awards shown in the dataset can be found on the NIHR’s Funding & Awards website here. Further information on individual NIHR Research Programme’s decision making processes for funding health and social care research can be found here.Further information on NIHR’s investment in public health research can be found as follows: NIHR School for Public Health here. NIHR Public Health Policy Research Unit here. NIHR Health Protection Research Units here. NIHR Public Health Research Programme Health Determinants Research Collaborations (HDRC) here. NIHR Public Health Research Programme Public Health Intervention Responsive Studies Teams (PHIRST) here.

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

Problem Statement

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Healthcare providers often rely on generalized treatment protocols that may not address the unique needs of individual patients. This approach led to variability in treatment outcomes, reduced efficacy, and limited patient satisfaction. A leading hospital sought a solution to develop personalized treatment plans tailored to each patient’s medical history, genetic profile, and current health status.

Challenge

Implementing a personalized healthcare treatment system involved overcoming the following challenges:

Integrating diverse patient data, including medical history, lab results, genetic information, and lifestyle factors.

Developing predictive models capable of identifying optimal treatment plans for individual patients.

Ensuring compliance with privacy regulations and maintaining data security throughout the process.

Solution Provided

An advanced healthcare treatment recommendation system was developed using machine learning models and predictive analytics. The solution was designed to:

Analyze patient data to identify patterns and predict treatment outcomes.

Recommend individualized treatment plans optimized for efficacy and patient preferences.

Continuously learn and adapt to improve recommendations based on new medical insights and patient feedback.

Development Steps

Data Collection

Aggregated data from electronic health records (EHR), genetic testing reports, and patient-provided health information.

Preprocessing

Standardized and anonymized data to ensure accuracy, consistency, and compliance with healthcare privacy regulations.

Model Development

Trained machine learning models to identify correlations between patient characteristics and treatment outcomes. Developed predictive algorithms to recommend personalized treatment plans for conditions like chronic diseases, cancer, and rare disorders.

Validation

Tested the system on historical patient data to evaluate its accuracy in predicting successful treatment outcomes.

Deployment

Integrated the solution into the hospital’s clinical decision support systems, enabling healthcare providers to access personalized treatment recommendations during consultations.

Continuous Monitoring & Improvement

Established a feedback mechanism to refine models using real-world treatment outcomes and patient satisfaction data.

Results

Improved Patient Outcomes

The system delivered personalized treatment recommendations that significantly improved recovery rates and health outcomes.

Increased Treatment Efficacy

Optimized treatment plans reduced trial-and-error approaches, leading to more effective interventions and fewer side effects.

Personalized Healthcare Experiences

Patients reported higher satisfaction levels due to treatment plans tailored to their individual needs and preferences.

Enhanced Decision-Making

Healthcare providers benefited from data-driven insights, enabling more informed and confident decisions.

Scalable and Future-Ready Solution

The system scaled seamlessly to support diverse medical specialties and adapted to incorporate emerging medical research.

Objectives: We sought to work collaboratively with public health stakeholders who use evidence in their work to identify practical ways that cross-sectoral data sharing and linkage could be used to best effect to improve health and reduce health inequalities.

Methods: We undertook three sequential stakeholder workshops with participants from local and central government, public health teams, Health and Social Care Partnerships, the third sector, organisations which support data-intensive research, and public representatives from across Scotland. The workshops were informed by a scoping review on use of evidence in public health policy and practice, searching Medline, Scopus, SSCI, and key institutional websites, and by three case studies of existing cross-sectoral linkage projects.

Details of data collection: The data collection comprises de-identified transcripts of stakeholder workshops and a copy of the visual map produced as part of the workshops. Stakeholders comprised people We held workshops to bring together people working in public health practice; in policy sectors potentially relevant to health; and in information governance, infrastructure and/or support for data and research; as well as a number of public representatives. Potential attendees were identified through a stakeholder mapping exercise with the project advisory group, followed by review of relevant organisational websites and advice from gatekeeper organisations such as Administrative Data Scotland.

Background Secondary data from different sectors can provide unique insights into the social, environmental, economic, and political determinants of health. This is especially pertinent in the context of whole-systems approaches to public health, which typically combine cross-sectoral collaboration with the application of theoretical insights from systems science. However, sharing and linkage of data between different sectors to inform healthy public policy is still relatively rare. Previous research has documented the perspectives of researchers and members of the public on data sharing, especially healthcare data, but has not engaged with decision-makers working in public health practice and public policy. Objective(s) We sought to work collaboratively with public health stakeholders who use evidence in their work to identify practical ways that cross-sectoral data sharing and linkage could be used to best effect to improve health and reduce health inequalities. Methods We undertook three sequential stakeholder workshops with participants from local and central government, public health teams, Health & Social Care Partnerships, the third sector, organisations which support data-intensive research, and public representatives from across Scotland. The workshops were informed by a scoping review on use of evidence in public health policy and practice, searching Medline, Scopus, SSCI, and key institutional websites, and by three case studies of existing cross-sectoral linkage projects. Findings were synthesised using thematic analysis. Setting and scope Scotland; public and third sector data.

This survey charted Finnish citizens' as well as social and healthcare service professionals' attitudes and views concerning secondary use of health and social care data in research and development of services. The study contained two target groups: (1) persons who suffered or had a close relative or acquaintance who suffered from one or more chronic conditions, diseases or disorders, and (2) social and healthcare service professionals. First, the respondents' opinions on the reliability of a variety of authorities and organisations were examined (e.g. the police, Kela, register and statistics authorities, universities) as well as trust in appropriate handling of personal data. They were also asked which type of information they deemed personal or not (e.g. bank account number and balance, purchase history at a grocery store, web browsing history, patient records, genetic information, social security number, phone number). They were asked to evaluate which principles they considered important in handling personal health data (e.g. being able to access one's personal data and to have inaccurate data rectified, and being able to restrict data processing), and the study also surveyed how interested the respondents were in keeping track of the use of their health data, and how willing they would be to permit the use of anonymous health data and genetic information for a variety of purposes (e.g. medicine and treatment development, development of equipment and services, and operations of insurance companies). Next, it was examined whether the respondents kept track of their physical activity with a smartphone or a fitness tracker, for instance, and if they would be willing to permit the use of anonymous data concerning physical activity for a variety of purposes. In addition, the respondents' attitudes were charted with regard to developing medicine research by combining anonymous health data and patient records with other data on, for instance, physical activity, alcohol use, grocery store purchase history, web browsing history, and social media use. The study also examined the willingness to permit access to personal health data for social and healthcare service professionals in a service situation, as well as for social and healthcare authorities and other authorities outside of a service situation. Finally, it was charted how important the respondents deemed different factors relating to data collection (e.g. being able to decide for which purposes personal data, or even anonymous data, can be used, and increasing awareness on how health data can be utilised in scientific research). The reliability of a variety of authorities and organisations, such as social welfare/healthcare organisations, academic researchers and pharmaceutical companies, was also examined in terms of data security and purposes for using data. Background variables included, among others, mother tongue, marital status, household composition, housing tenure, socioeconomic class, political party preference, left-right political self-placement, gross income, economic activity and occupational status, and respondent group (citizen/healthcare service professional/social service professional).

The Nuclear Medicine National HQ System database is a series of MS Excel spreadsheets and Access Database Tables by fiscal year. They consist of information from all Veterans Affairs Medical Centers (VAMCs) performing or contracting nuclear medicine services in Veterans Affairs medical facilities. The medical centers are required to complete questionnaires annually (RCS 10-0010-Nuclear Medicine Service Annual Report). The information is then manually entered into the Access Tables, which includes: * Distribution and cost of in-house VA - Contract Physician Services, whether contracted services are made via sharing agreement (with another VA medical facility or other government medical providers) or with private providers. * Workload data for the performance and/or purchase of PET/CT studies. * Organizational structure of services. * Updated changes in key imaging service personnel (chiefs, chief technicians, radiation safety officers). * Workload data on the number and type of studies (scans) performed, including Medicare Relative Value Units (RVUs), also referred to as Weighted Work Units (WWUs). WWUs are a workload measure calculated as the product of a study's Current Procedural Terminology (CPT) code, which consists of total work costs (the cost of physician medical expertise and time), and total practice costs (the costs of running a practice, such as equipment, supplies, salaries, utilities etc). Medicare combines WWUs together with one other parameter to derive RVUs, a workload measure widely used in the health care industry. WWUs allow Nuclear Medicine to account for the complexity of each study in assessing workload, that some studies are more time consuming and require higher levels of expertise. This gives a more accurate picture of workload; productivity etc than using just 'total studies' would yield. * A detailed Full-Time Equivalent Employee (FTEE) grid, and staffing distributions of FTEEs across nuclear medicine services. * Information on Radiation Safety Committees and Radiation Safety Officers (RSOs). Beginning in 2011 this will include data collection on part-time and non VA (contract) RSOs; other affiliations they may have and if so to whom they report (supervision) at their VA medical center.Collection of data on nuclear medicine services' progress in meeting the special needs of our female veterans. Revolving documentation of all major VA-owned gamma cameras (by type) and computer systems, their specifications and ages. * Revolving data collection for PET/CT cameras owned or leased by VA; and the numbers and types of PET/CT studies performed on VA patients whether produced on-site, via mobile PET/CT contract or from non-VA providers in the community. Types of educational training/certification programs available at VA sites * Ongoing funded research projects by Nuclear Medicine (NM) staff, identified by source of funding and research purpose. * Data on physician-specific quality indicators at each nuclear medicine service. Academic achievements by NM staff, including published books/chapters, journals and abstracts. * Information from polling field sites re: relevant issues and programs Headquarters needs to address. * Results of a Congressionally mandated contracted quality assessment exercise, also known as a Proficiency study. Study results are analyzed for comparison within VA facilities (for example by mission or size), and against participating private sector health care groups. * Information collected on current issues in nuclear medicine as they arise. Radiation Safety Committee structures and membership, Radiation Safety Officer information and information on how nuclear medicine services provided for female Veterans are examples of current issues.The database is now stored completely within MS Access Database Tables with output still presented in the form of Excel graphs and tables.

Minimum Data Set (MDS) enables integration in data collection, uniform data reporting, and data exchange across clinical and research information systems. The current study was conducted to determine a comprehensive national MDS for the Epidermolysis Bullosa (EB) information management system in Iran. This cross-sectional descriptive study consists of three steps: systematic review, focus group discussion, and the Delphi technique. A systematic review was conducted using relevant databases. Then, a focus group discussion was held to determine the extracted data elements with the help of contributing multidisciplinary experts. Finally, MDSs were selected through the Delphi technique in two rounds. The collected data were analyzed using Microsoft Excel 2019. In total, 103 data elements were included in the Delphi survey. The data elements, based on the experts’ opinions, were classified into two main categories: administrative data and clinical data. The final categories of data elements consisted of 11 administrative items and 92 clinical items. The national MDS, as the core of the EB surveillance program, is essential for enabling appropriate and informed decisions by healthcare policymakers, physicians, and healthcare providers. In this study, a MDS was developed and internally validated for EB. This research generated new knowledge to enable healthcare professionals to collect relevant and meaningful data for use. The use of this standardized approach can help benchmark clinical practice and target improvements worldwide.

These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center.

The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.

The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.

Study approval and data collection

Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.

This statistic depicts the results of a survey about Artificial Intelligence (AI) collecting large amounts of personal data for healthcare purposes in Italy in 2018. As the graph shows, 39 percent of respondents stated that knowing how the data were used was very important, whereas 30 percent of them thought that it was fine having their data collected for health purposes.

MHADC data to be released includes: a) Number of consumers per month by geographical location b) Number of service contacts per month by geographical location c) Number of consumers by facility and …Show full descriptionMHADC data to be released includes: a) Number of consumers per month by geographical location b) Number of service contacts per month by geographical location c) Number of consumers by facility and target population d) Number of service contacts by facility and target population

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Between January and September 2024, healthcare organizations in the United States saw 491 large-scale data breaches, resulting in the loss of over 500 records. This figure has increased significantly in the last decade. To date, the highest number of large-scale data breaches in the U.S. healthcare sector was recorded in 2023, with a reported 745 cases.

The "https://addhealth.cpc.unc.edu/" Target="_blank">National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents' social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The fifth wave of data collection is planned to begin in 2016.

Initiated in 1994 and supported by three program project grants from the "https://www.nichd.nih.gov/" Target="_blank">Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) with co-funding from 23 other federal agencies and foundations, Add Health is the largest, most comprehensive longitudinal survey of adolescents ever undertaken. Beginning with an in-school questionnaire administered to a nationally representative sample of students in grades 7-12, the study followed up with a series of in-home interviews conducted in 1995, 1996, 2001-02, and 2008. Other sources of data include questionnaires for parents, siblings, fellow students, and school administrators and interviews with romantic partners. Preexisting databases provide information about neighborhoods and communities.

Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health, and Waves I and II focus on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants have aged into adulthood, however, the scientific goals of the study have expanded and evolved. Wave III, conducted when respondents were between 18 and 26** years old, focuses on how adolescent experiences and behaviors are related to decisions, behavior, and health outcomes in the transition to adulthood. At Wave IV, respondents were ages 24-32* and assuming adult roles and responsibilities. Follow up at Wave IV has enabled researchers to study developmental and health trajectories across the life course of adolescence into adulthood using an integrative approach that combines the social, behavioral, and biomedical sciences in its research objectives, design, data collection, and analysis.

* 52 respondents were 33-34 years old at the time of the Wave IV interview.
** 24 respondents were 27-28 years old at the time of the Wave III interview.

The Wave III public-use data are helpful in analyzing the transition between adolescence and young adulthood. Included in this dataset are graduation data, including high school exit status.

There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.