The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, clinical researchers struggle to find real-world examples of Open Data sharing.

The aim of this 1 hr virtual workshop is to provide real-world examples of Open Data sharing for both qualitative and quantitative data.

Specifically, participants will learn:
1. Primary challenges and successes when sharing quantitative and qualitative clinical research data.

2. Platforms available for open data sharing.

3. Ways to troubleshoot data sharing and publish from open data.

Workshop Agenda:

1. “Data sharing during the COVID-19 pandemic” - Speaker: Srinivas Murthy, Clinical Associate Professor, Department of Pediatrics, Faculty of Medicine, University of British Columbia. Investigator, BC Children's Hospital

2. “Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project.” - Speaker: Maggie Woo Kinshella, Global Health Research Coordinator, Department of Obstetrics and Gynaecology, BC Children’s and Women’s Hospital and University of British Columbia

This workshop draws on work supported by the Digital Research Alliance of Canada.

Data Description: Presentation slides, Workshop Video, and Workshop Communication

Srinivas Murthy: Data sharing during the COVID-19 pandemic presentation and accompanying PowerPoint slides.

Maggie Woo Kinshella: Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project presentation and accompanying Powerpoint slides.

This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the https://alliancecan.ca/en">Digital Research Alliance of Canada.

The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of reduced access to healthcare for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included questions about unmet care in the last 2 months during the coronavirus pandemic. Unmet needs for health care are often the result of cost-related barriers. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor cost-related health care access problems in the United States. For example, in 2018, 7.3% of persons of all ages reported delaying medical care due to cost and 4.8% reported needing medical care but not getting it due to cost in the past year. However, cost is not the only reason someone might delay or not receive needed medical care. As a result of the coronavirus pandemic, people also may not get needed medical care due to cancelled appointments, cutbacks in transportation options, fear of going to the emergency room, or an altruistic desire to not be a burden on the health care system, among other reasons. The Household Pulse Survey (https://www.cdc.gov/nchs/covid19/pulse/reduced-access-to-care.htm), an online survey conducted in response to the COVID-19 pandemic by the Census Bureau in partnership with other federal agencies including NCHS, also reports estimates of reduced access to care during the pandemic (beginning in Phase 1, which started on April 23, 2020). The Household Pulse Survey reports the percentage of adults who delayed medical care in the last 4 weeks or who needed medical care at any time in the last 4 weeks for something other than coronavirus but did not get it because of the pandemic. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who were unable to receive medical care (including urgent care, surgery, screening tests, ongoing treatment, regular checkups, prescriptions, dental care, vision care, and hearing care) in the last 2 months. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/reduced-access-to-care.htm#limitations

Healthcare Data Storage Market size was valued at USD 3.97 Billion in 2024 and is projected to reach USD 10.27 Billion by 2032, growing at a CAGR of 13.90% during the forecast period 2026-2032.Global Healthcare Data Storage Market DriversThe market drivers for the Healthcare Data Storage Market can be influenced by various factors. These may include:Growing volume of healthcare data: The amount of data produced by healthcare providers has increased dramatically as a result of the digitalization of medical records. This covers genomic information, medical imaging, electronic health records (EHRs), and more. To handle this data, healthcare institutions need effective and safe storage options.Severe laws and compliance requirements: HIPAA (Health Insurance Portability and Accountability Act) in the US and GDPR (General Data Protection Regulation) in Europe are two examples of the severe laws that apply to healthcare data. In order to protect patient information, these requirements mandate that healthcare organisations employ secure data storage solutions.Cloud storage is becoming more and more popular since it is affordable, flexible, and scalable, which appeals to healthcare institutions. Adoption is accelerated by cloud storage companies' provision of specialised healthcare cloud solutions that meet legal and regulatory standards.Technological developments: Artificial intelligence (AI), machine learning (ML), and big data analytics are some of the technologies that are revolutionising healthcare. To handle the massive volumes of data collected and analysed, these technologies need reliable data storage systems.Growing need for data interoperability: In order to enhance patient care coordination and results, healthcare providers are placing a greater emphasis on interoperability. This calls for the smooth transfer of medical data between various systems, which calls for trustworthy data storage options.Escalating healthcare expenses: There is pressure on healthcare institutions to save expenses without sacrificing care quality. Healthcare data management and storage operations can be made more cost-effective with the use of efficient data storage solutions.Growing comprehension of data security's significance Healthcare data breaches may result in severe repercussions, such as monetary losses and reputational harm. To safeguard patient data from online dangers, healthcare institutions are investing in secure data storage solutions.

The dataset contains clinical records of patients, including their reported symptoms, diagnosed conditions, and prescribed medications.

Columns: 1. patient_id (Integer) - Description: A unique identifier assigned to each patient. - Example Values: 1, 2, 3, ..., 17 2. clinical_notes (String/Text) - Description: A text field containing detailed clinical information about the patient. It includes three main components: Reported Symptoms: The symptoms the patient reported during the medical consultation. Diagnosed Conditions: The medical conditions diagnosed by the healthcare professional. Prescribed Medications: The medications prescribed for treatment. - Example Values: "Patient reports headache, shortness of breath, fatigue. Diagnosed with Bronchitis. Prescribed Amoxicillin." "Patient reports dizziness, fever. Diagnosed with Asthma, Anemia. Prescribed Aspirin." "Patient reports chest pain, cough, shortness of breath. Diagnosed with Obesity, Asthma. Prescribed Metoprolol."

Snapshot img

Healthcare Analytics Market Size 2025-2029

The healthcare analytics market size is forecast to increase by USD 81.28 billion, at a CAGR of 25% between 2024 and 2029.

The market is experiencing significant growth due to several key trends. The integration of big data with healthcare analytics is a major growth factor, enabling healthcare providers to make data-driven decisions and improve patient outcomes.
Another trend is the increasing use of Internet-enabled mobile devices in healthcare services, allowing for remote monitoring and real-time data access. However, data security and privacy concerns remain a challenge, with the need for strong security measures to protect sensitive patient information. These trends are shaping the future of patient engagement and driving growth in the global healthcare analytics market as well.

What will be the Size of the Healthcare Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing adoption of digital solutions for improving patient care and reducing treatment costs. Healthcare organizations are leveraging descriptive analytics to gain insights from clinical data, while predictive and prescriptive analytics enable the development of personalized treatment plans and optimal therapeutic strategies. Financial analytics help manage healthcare expenses, ensuring cost-effective patient care. The National Institutes of Health (NIH) and other research institutions are driving innovation in health data analytics, leading to advancements in areas such as patient compliance, medication selection, and disease management. Industry leaders are utilizing artificial intelligence and machine learning to enhance clinical care, outreach, and disease management, ultimately leading to better treatment consistency and optimal outcomes for patients.

How is this Healthcare Analytics Industry segmented and which is the largest segment?

The healthcare analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Services
  Software
  Hardware


Deployment

  On-premise
  Cloud-based


Type

  Descriptive Analysis
  Predictive Analysis
  Prescriptive and Diagnostics


Application

  Financial Analytics
  Clinical Analytics
  Operations and Administrative Analytics
  Population Health Analytics


End-User

  Insurance Company
  Government Agencies
  Healthcare Providers
  Pharmaceutical and Medical Device Companies


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America



  Middle East and Africa

By Component Insights

The services segment is estimated to witness significant growth during the forecast period. Healthcare analytics services encompass consulting, learning and training, development and integration, hardware maintenance and support, IT management, process management, and software support. The consulting and software support segments are experiencing significant growth due to the increasing demand for advanced healthcare delivery systems and cost-effective models. The healthcare sector's ongoing transition from on-premises to cloud-based software and IT infrastructure deployment is another growth driver. This shift is expected to increase the demand for IT education and training services. End-users of these services range from individual doctor offices to full-service hospitals and multi-location clinics, including large hospitals and tissue and blood processing organizations.

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The services segment was valued at USD 6.7 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market is driven by the increasing demand for secure data access and effective patient information management. The US and Canada are the primary contributors to this market due to their early adoption of advanced technologies, such as machine learning, predictive analytics, and quantum computing, across various industries. These technologies enable the healthcare sector to optimize patient compliance, medication selection, and therapeutic strategies and, ultimately, achieve optimal outcomes. Major companies in this market provide solutions to help healthcare organizations manage and

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old. End User Licence versions of MCS studies:
The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).


How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Health Administrative Datasets (SAIL) for Wales held under SN 9310
  
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application.

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

The Millennium Cohort Study: Linked Health Administrative Data (Scottish Medical Records), Prescribing Information System, 2009-2015: Secure Access includes data files from the NHS Digital Hospital Episode Statistics database for those cohort members who provided consent to health data linkage in the Age 50 sweep, and had ever lived in Scotland. The Scottish Medical Records database contains information about all hospital admissions in Scotland. This study concerns the Prescribing Information System.

Other datasets are available from the Scottish Medical Records database, these include:

• Child Health Reviews (CHR) held under SN 8709
• Scottish Immunisation and Recall System (SIRS) held under SN 8711
• Scottish Birth Records (SMR11) held under SN 8712
• Inpatient and Day Care Attendance (SMR01) held under SN 8713
• Outpatient Attendance (SMR00) held under SN 8714

Users should note that linkage to

The dataset contains multi-modal data from over 75,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.

Almost 100,000 patients and almost 400,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.

Refer to the examples showcased in this GitHub repository to understand how to optimize the use of this dataset.

For a detailed insight about the contents of this dataset, please refer to this data article published in Data In Brief.

Introduction: Today, many initiatives and papers are devoted to clinical trial data (and to a lesser extent sample) sharing. Journal editors, pharmaceutical companies, funding agencies, governmental organizations, regulators, and clinical investigators have been debating the legal, ethical, and social implications of clinical data and sample sharing for several years. However, only little research has been conducted to unveil the patient perspective.Aim: To substantiate the current debate, we aimed to explore the attitudes of patients toward the re-use of clinical trial samples and data and to determine how they would prefer to be involved in this process.Materials and Methods: Sixteen in-depth interviews were conducted with cancer patients currently participating in a clinical trial.Results: This study indicates a general willingness of cancer patients participating in a clinical trial to allow re-use of their clinical trial data and/or samples by the original research team, and a generally open approach to share data and/or samples with other research teams, but some would like to be informed in this case. Despite divergent opinions about how patients prefer to be engaged, ranging from passive donors up to those explicitly wanting more control, participants expressed positive opinions toward technical solutions that allow indicating their preferences.Conclusion: Patients were open to sharing and re-use of data and samples to advance medical research but opinions varied on the level of patient involvement and the need for re-consent. A stratified approach for consent that allows individualization of data and sample sharing preferences may be useful, yet the implementation of such an approach warrants further research.

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Clinical metadata of all samples included in the study "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E..

File reports clinical data of 27 primary breast cancers and their associated ipsilateral breast tumor recurrences (samples marked with S). Additionally, a cohort of 21 primary breast tumors with no recurrence is reported (samples marked with V). Data includes age at diagnosis of primary tumor, time to recurrence (S samples) or follow-up (V samples), Estrogen receptor status (positive/negative), progesterone receptor status (positive/negative), ERBB2 status (normal/amplified), proliferation marker Ki-67 (low/high), tumor grade (1/2/3), and adjuvant therapies (yes/no).

This dataset was used for Figure 1-6 in the following manuscript: "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. accepted for publication

Snapshot img

Healthcare Information Systems Market Size 2024-2028

The healthcare information systems market size is forecast to increase by USD 126.2 billion at a CAGR of 9.5% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for efficient medical care and disease management. Key features of HIS, such as medical device integration and ease of use, are driving this growth. Remote patient monitoring and disease management are becoming increasingly important, enabling healthcare providers to deliver better patient care and financial savings through improved efficiency. However, technical considerations, including data security and privacy, remain challenges that must be addressed to ensure the successful implementation and adoption of HIS. The market is witnessing a high demand for electronic health record (EHR) solutions and an increasing number of mergers and acquisitions. Despite these opportunities, it is crucial for providers to carefully consider the technical aspects of HIS implementation to ensure seamless integration and optimal performance.

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The healthcare industry is undergoing a significant transformation, driven by advancements in technology and the increasing demand for efficient, patient-centric care. The market is witnessing substantial growth as healthcare organizations seek to optimize their operations, improve patient outcomes, and reduce costs. Healthcare data management is a critical component of this transformation. The ability to collect, store, and analyze large volumes of patient data is essential for delivering personalized and precise medical care. Healthcare data analytics is playing an increasingly important role in this regard, enabling healthcare providers to gain valuable insights from patient data and make informed decisions.
In addition, another key trend in the market is healthcare data security. With the increasing digitization of healthcare data, ensuring its security and privacy is a top priority. Healthcare organizations are investing in advanced cybersecurity solutions to protect sensitive patient information from cyber threats. Mobile technology is also transforming the healthcare landscape. Mobile health apps, telehealth platforms, and wearable technology are enabling remote patient monitoring, teleconsultations, and other innovative healthcare services. These technologies are improving patient engagement, enhancing the patient experience, and reducing the need for in-person visits. Cloud-based healthcare systems are another area of growth in the market.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Revenue cycle management
  Hospital information system
  Medical imaging information system
  Pharmacy information systems
  Laboratory information systems


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  Asia

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The revenue cycle management segment is estimated to witness significant growth during the forecast period.

The healthcare industry's shift towards digitalization is driving the adoption of Healthcare Information Systems (HCIS), particularly in patient engagement and managing patient-related data. Chronic diseases, which account for a significant portion of healthcare expenditures, necessitate effective data management and analysis. HCIS product lines, including hardware and healthcare IT solutions, enable healthcare facilities to streamline operations, reduce costs, and enhance patient care. As the US population ages and the prevalence of chronic diseases increases, the need for advanced healthcare data analytics becomes more critical. HCIS solutions help manage complex billing processes, ensuring accuracy and compliance with regulations such as HIPAA and FDCPA.

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The revenue cycle management segment was valued at USD 81.10 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 47% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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In North America, the market is among the most advanced, driven by substantial investments in healthcare and government initiativ

Real World Evidence Solutions Market size was valued at USD 1.30 Billion in 2024 and is projected to reach USD 3.71 Billion by 2031, growing at a CAGR of 13.92% during the forecast period 2024-2031.

Global Real World Evidence Solutions Market Drivers

The market drivers for the Real World Evidence Solutions Market can be influenced by various factors. These may include:

Growing Need for Evidence-Based Healthcare: Real-world evidence (RWE) is becoming more and more important in healthcare decision-making, according to stakeholders such as payers, providers, and regulators. In addition to traditional clinical trial data, RWE solutions offer important insights into the efficacy, safety, and value of healthcare interventions in real-world situations. Growing Use of RWE by Pharmaceutical Companies: RWE solutions are being used by pharmaceutical companies to assist with market entry, post-marketing surveillance, and drug development initiatives. Pharmaceutical businesses can find new indications for their current medications, improve clinical trial designs, and convince payers and providers of the worth of their products with the use of RWE. Increasing Priority for Value-Based Healthcare: The emphasis on proving the cost- and benefit-effectiveness of healthcare interventions in real-world settings is growing as value-based healthcare models gain traction. To assist value-based decision-making, RWE solutions are essential in evaluating the economic effect and real-world consequences of healthcare interventions. Technological and Data Analytics Advancements: RWE solutions are becoming more capable due to advances in machine learning, artificial intelligence, and big data analytics. With the use of these technologies, healthcare stakeholders can obtain actionable insights from the analysis of vast and varied datasets, including patient-generated data, claims data, and electronic health records. Regulatory Support for RWE Integration: RWE is being progressively integrated into regulatory decision-making processes by regulatory organisations including the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA). The FDA's Real-World Evidence Programme and the EMA's Adaptive Pathways and PRIority MEdicines (PRIME) programme are two examples of initiatives that are making it easier to incorporate RWE into regulatory submissions and drug development. Increasing Emphasis on Patient-Centric Healthcare: The value of patient-reported outcomes and real-world experiences in healthcare decision-making is becoming more widely acknowledged. RWE technologies facilitate the collection and examination of patient-centered data, offering valuable insights into treatment efficacy, patient inclinations, and quality of life consequences. Extension of RWE Use Cases: RWE solutions are being used in medication development, post-market surveillance, health economics and outcomes research (HEOR), comparative effectiveness research, and market access, among other healthcare fields. The necessity for a variety of RWE solutions catered to the needs of different stakeholders is being driven by the expansion of RWE use cases.

These files relate to data extracted from ClinicalTrials.gov. In the database file, data for individual clinical trials are included, and attributes include study identifier, study type, trial dates, interventions, sample size, countries in which the study was conducted, etc. The Edges file contains geographic data derived from the clinical trials data set that can be used to generate networks to illustrate geographic connectivity through clinical research, using open access software such as Gephi. The Gephi file includes networks for all countries worldwide, as well as regional networks for each major grographic region. The figures are network diagrams generated by Gephi showing geographic connectivity among individual countries through common participation in multinational clinical trials. The thickness of the connecting lines (edges) reflects the strength of a connection.

The objective of UK Biobank is to create a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants, which will contribute to the advancement of modern medicine, treatment and scientific discoveries that improve human health.

Lifestyle and environmental information, medical history, physical measurements, and biological samples are being collected from about 500,000 people aged 40-69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples are stored so that they can be used for a wide range of biochemical and genetic analyses in the future.

Electronic Health Records (EHR) Software Market Outlook

The global Electronic Health Records (EHR) Software market size is poised for substantial growth, projected to expand from USD 32 billion in 2023 to USD 52 billion by 2032, reflecting a CAGR of approximately 5.2% during the forecast period. Growth in this market is primarily driven by increased adoption of healthcare IT solutions, the necessity for coordinated care, and the rising demand for an efficient healthcare system that allows for seamless information sharing across various medical platforms. EHR software plays a pivotal role in modernizing and streamlining clinical operations, significantly reducing the burden of paperwork while enhancing patient care quality and safety.

One of the major growth factors influencing the EHR software market is the increasing shift towards digitization in the healthcare sector. As governments and healthcare providers recognize the need for streamlined, efficient record-keeping processes, investments in EHR systems have grown exponentially. This shift is driven not only by the need to reduce administrative burdens but also by the push to deliver more personalized patient care. The implementation of EHR systems allows for improved data accuracy, real-time patient data access, and the facilitation of informed clinical decisions, all of which are crucial in enhancing the overall quality of healthcare services.

Another significant growth driver is the growing emphasis on regulatory compliance and government initiatives pushing for electronic health record adoption. In regions such as North America and Europe, legislation and policies like the Health Information Technology for Economic and Clinical Health (HITECH) Act and the General Data Protection Regulation (GDPR) have been pivotal. These regulations mandate and encourage healthcare facilities to adopt digital record-keeping practices, providing financial incentives and frameworks that further fuel the adoption of EHR systems. Such governmental support is critical as it not only ensures compliance but also inspires confidence among healthcare providers to transition from traditional paper-based records to advanced electronic systems.

The rising prevalence of chronic diseases and the subsequent increase in patient data generation are also significant contributors to market growth. Chronic conditions require continuous monitoring and long-term management, necessitating detailed and accurate patient records. EHR systems are invaluable in managing such vast amounts of data, enabling healthcare providers to efficiently track patient history, medication, and treatment plans. This capability is particularly important in enhancing patient outcomes and optimizing healthcare delivery, making EHR software indispensable in modern medical practices.

Community Health Systems EHR is a notable example of how electronic health records are being leveraged to enhance healthcare delivery. By integrating advanced EHR solutions, Community Health Systems has been able to streamline patient data management, improve clinical workflows, and facilitate better communication among healthcare providers. This integration not only enhances the quality of care but also supports the organization's commitment to patient safety and regulatory compliance. The adoption of such comprehensive EHR systems is crucial in addressing the challenges of modern healthcare, where the efficient handling of vast amounts of patient data is essential for optimal outcomes. As more healthcare organizations follow suit, the role of EHR systems in transforming healthcare delivery continues to expand.

Regionally, North America dominates the EHR software market due to its advanced healthcare infrastructure and early adoption of digital health solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is attributed to rapidly developing healthcare infrastructures, increasing government initiatives to promote healthcare digitization, and an expanding geriatric population, which collectively drive the demand for efficient healthcare solutions. The increasing investment in healthcare IT infrastructure and the growing awareness of the benefits of EHRs among healthcare providers in the region are also key factors contributing to market expansion.

Product Type Analysis

The EHR software market is broadly segmented by product type into Cloud-Based and On-Premises solutions, each offering di

This fileset provides supporting data and corpora for the empirical study described in: Laura Miron, Rafael S. Goncalves and Mark A. Musen. Obstacles to the Reuse of Metadata in ClinicalTrials.govDescription of filesOriginal data files:- AllPublicXml.zip contains the set of all public XML records in ClinicalTrials.gov (protocols and summary results information), on which all remaining analyses are based. Set contains 302,091 records downloaded on April 3, 2019.- public.xsd is the XML schema downloaded from ClinicalTrials.gov on April 3, 2019, used to validate records in AllPublicXML.BioPortal API Query Results- condition_matches.csv contains the results of querying the BioPortal API for all ontology terms that are an 'exact match' to each condition string scraped from the ClinicalTrials.gov XML. Columns={filename, condition, url, bioportal term, cuis, tuis}. - intervention_matches.csv contains BioPortal API query results for all interventions scraped from the ClinicalTrials.gov XML. Columns={filename, intervention, url, bioportal term, cuis, tuis}.Data Element Definitions- supplementary_table_1.xlsx Mapping of element names, element types, and whether elements are required in ClinicalTrials.gov data dictionaries, the ClinicalTrials.gov XML schema declaration for records (public.XSD), the Protocol Registration System (PRS), FDAAA801, and the WHO required data elements for clinical trial registrations.Column and value definitions: - CT.gov Data Dictionary Section: Section heading for a group of data elements in the ClinicalTrials.gov data dictionary (https://prsinfo.clinicaltrials.gov/definitions.html) - CT.gov Data Dictionary Element Name: Name of an element/field according to the ClinicalTrials.gov data dictionaries (https://prsinfo.clinicaltrials.gov/definitions.html) and (https://prsinfo.clinicaltrials.gov/expanded_access_definitions.html) - CT.gov Data Dictionary Element Type: "Data" if the element is a field for which the user provides a value, "Group Heading" if the element is a group heading for several sub-fields, but is not in itself associated with a user-provided value. - Required for CT.gov for Interventional Records: "Required" if the element is required for interventional records according to the data dictionary, "CR" if the element is conditionally required, "Jan 2017" if the element is required for studies starting on or after January 18, 2017, the effective date of the FDAAA801 Final Rule, "-" indicates if this element is not applicable to interventional records (only observational or expanded access) - Required for CT.gov for Observational Records: "Required" if the element is required for interventional records according to the data dictionary, "CR" if the element is conditionally required, "Jan 2017" if the element is required for studies starting on or after January 18, 2017, the effective date of the FDAAA801 Final Rule, "-" indicates if this element is not applicable to observational records (only interventional or expanded access) - Required in CT.gov for Expanded Access Records?: "Required" if the element is required for interventional records according to the data dictionary, "CR" if the element is conditionally required, "Jan 2017" if the element is required for studies starting on or after January 18, 2017, the effective date of the FDAAA801 Final Rule, "-" indicates if this element is not applicable to expanded access records (only interventional or observational) - CT.gov XSD Element Definition: abbreviated xpath to the corresponding element in the ClinicalTrials.gov XSD (public.XSD). The full xpath includes 'clinical_study/' as a prefix to every element. (There is a single top-level element called "clinical_study" for all other elements.) - Required in XSD? : "Yes" if the element is required according to public.XSD, "No" if the element is optional, "-" if the element is not made public or included in the XSD - Type in XSD: "text" if the XSD type was "xs:string" or "textblock", name of enum given if type was enum, "integer" if type was "xs:integer" or "xs:integer" extended with the "type" attribute, "struct" if the type was a struct defined in the XSD - PRS Element Name: Name of the corresponding entry field in the PRS system - PRS Entry Type: Entry type in the PRS system. This column contains some free text explanations/observations - FDAAA801 Final Rule FIeld Name: Name of the corresponding required field in the FDAAA801 Final Rule (https://www.federalregister.gov/documents/2016/09/21/2016-22129/clinical-trials-registration-and-results-information-submission). This column contains many empty values where elements in ClinicalTrials.gov do not correspond to a field required by the FDA - WHO Field Name: Name of the corresponding field required by the WHO Trial Registration Data Set (v 1.3.1) (https://prsinfo.clinicaltrials.gov/trainTrainer/WHO-ICMJE-ClinTrialsgov-Cross-Ref.pdf)Analytical Results:- EC_human_review.csv contains the results of a manual review of random sample eligibility criteria from 400 CT.gov records. Table gives filename, criteria, and whether manual review determined the criteria to contain criteria for "multiple subgroups" of participants.- completeness.xlsx contains counts and percentages of interventional records missing fields required by FDAAA801 and its Final Rule.- industry_completeness.xlsx contains percentages of interventional records missing required fields, broken up by agency class of trial's lead sponsor ("NIH", "US Fed", "Industry", or "Other"), and before and after the effective date of the Final Rule- location_completeness.xlsx contains percentages of interventional records missing required fields, broken up by whether record listed at least one location in the United States and records with only international location (excluding trials with no listed location), and before and after the effective date of the Final RuleIntermediate Results:- cache.zip contains pickle and csv files of pandas dataframes with values scraped from the XML records in AllPublicXML. Downloading these files greatly speeds up running analysis steps from jupyter notebooks in our github repository.

Objective: To determine whether two specific criteria in Uniform Requirements for Manuscripts (URM) created by International Committee of Medical Journal Editors (ICMJE) of including the trial ID registration within manuscripts and timely registration of trials are being followed. Materials and Methods: Observational study using computerized analysis of publicly available MEDLINE article data and clinical trial registry data. We analyzed a purposive set of five ICMJE founding journals looking at all trial articles published in those journals during 2010-2011, and data from the ClinicalTrials.gov trial registry. We measured adherence to trial ID inclusion policy as the percentage of trial journal articles that contained a valid trial ID within the article (journal-based sample). Adherence to timely registration was measured as percentage of trials that registered the trial prior enrolling the first participant within a 60-day grace period. We also examined timely registration rates by year of all phase II and higher interventional trials in ClinicalTrials.gov (registry-based sample). Results: To determine trial ID inclusion, we analyzed 698 clinical trial articles in five journals. A total of 95.8% of trial journal articles included the trial ID. In 88.3% the trial-article link is stored within a structured MEDLINE field. To evaluate timely registration, we analyzed trials referenced by 451 articles from the selected five journals. A total of 60% of articles were registered in a timely manner with an improving trend for trials initiated in later years (e.g., 89% of trials that initiated in 2008 were registered in a timely manner). In the registry-based sample, the timely registration rates ranged from 56% for trials registered in 2006 to 72% for trials registered in 2011. Discussion: Adherence to URM requirements for registration and trial ID inclusion increases the utility of PubMed and links it in an important way to clinical trial repositories. This new integrated knowledge source can facilitate research prioritization, clinical guidelines creation and precision medicine. Conclusions: The five selected journals adhere well to the policy of mandatory trial registration and also outperform the registry in adherence to timely registration. ICMJE’s URM policy represents a unique international mandate that may be providing a powerful incentive for sponsors and investigators to document clinical trials and trial result publications and thus fulfill important obligations to trial participants and society.

The information flow of the Hospital Discharge database (SDO flow) is the tool for collecting information relating to all hospitalization episodes provided in public and private hospitals throughout the national territory.

Born for purely administrative purposes of the hospital setting, the SDO, thanks to the wealth of information contained, not only of an administrative but also of a clinical nature, has become an indispensable tool for a wide range of analyzes and elaborations, ranging from areas to support of health planning activities for monitoring the provision of hospital assistance and the Essential Levels of Assistance, for use for proxy analyzes of other levels of assistance as well as for more strictly clinical-epidemiological and outcome analyzes. In this regard, the SDO database is a fundamental element of the National Outcomes Program (PNE).

The information collected includes the patient's personal characteristics (including age, sex, residence, level of education), characteristics of the hospitalization (for example institution and discharge discipline, hospitalization regime, method of discharge, booking date, priority class of hospitalization) and clinical features (e.g. main diagnosis, concomitant diagnoses, diagnostic or therapeutic procedures)

Information relating to drugs administered during hospitalization or adverse reactions to them (subject to other specific information flows) is excluded from the discharge form.