After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

This report shows facilities currently in suspense regarding CoP requirements due to being in a work plan or other related reasons is shown if any facilities are currently in suspense. These CCNs will not be included in the tab listing all other hospitals or included in any summary counts while in suspense.
01/05/2024 – As of FAQ 6, the following optional fields have been added to this report:

Context

Part of Janatahack Hackathon in Analytics Vidhya

Content

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

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

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

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

The Process:

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

Other things to note:

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

Favorable outcome:

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

Train / Test split:

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

Acknowledgements

Credits to AV

Inspiration

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

The healthcare data and analytics services market is expanding rapidly, with a market size estimated at XXX million in 2025 and a CAGR of XX% over the forecast period 2025 to 2033. The increasing demand for data-driven insights to improve healthcare outcomes, reduce costs, and enhance operational efficiency is a major driver of market growth. The adoption of artificial intelligence (AI) and machine learning (ML) algorithms for data analysis and prediction is also fueling market development. Key trends include the increasing use of healthcare IoT devices and sensors to collect real-time patient data, the growing adoption of cloud-based healthcare data platforms for data storage and analysis, and the development of new data analytics tools and applications for personalized medicine and precision healthcare. However, data privacy and security concerns, as well as the lack of interoperability and standardization in healthcare data systems, pose challenges to market growth. North America and Europe are currently the largest regional markets, but Asia-Pacific is expected to witness significant growth over the forecast period due to rising healthcare investments and growing demand for healthcare data and analytics services in the region.

Despite the exponential transformation occurring in the healthcare industry, operational failures pose significant challenges in the delivery of safe and efficient care. Incident management plays a crucial role in mitigating these challenges; however, it encounters limitations due to organizational factors within complex and dynamic healthcare systems. Further, there are limited studies examining the interdependencies and relative importance of these factors in the context of incident management practices. To address this gap, this study utilized aggregate-level hospital data to explore the influence of organizational factors on incident management practices. Employing a Bayesian Belief Network (BBN) structural learning algorithm, Tree Augmented Naive (TAN), this study assessed the probabilistic relationships, represented graphically, between organizational factors and incident management. Significantly, the model highlighted the critical roles of morale and staff engagement in influencing incident management practices within organizations. This study enhances our understanding of the importance of organizational factors in incident management, providing valuable insights for healthcare managers to effectively prioritize and allocate resources for continuous quality improvement efforts.

The National Hospital Care Survey (NHCS) collects data on patient care in hospital-based settings to describe patterns of health care delivery and utilization in the United States. Settings currently include inpatient and emergency departments (ED). Additionally, the NHCS contributes data that may inform public health emergencies as the survey is designed to capture emerging diseases and viruses that require hospitalizations, including COVID-19 encounters. The 2020 - 2023 NHCS are not yet fully operational so it is important to note that these data are not nationally representative.

The data are from 26 hospitals submitting inpatient and 26 hospitals submitting ED Uniform Bill (UB)-04 administrative claims from March 18, 2020-December 26, 2023. Even though the data are not nationally representative, they can provide insight on the impact of COVID-19 on various types of hospitals throughout the country. This information is not available in other hospital reporting systems. The NHCS data from these hospitals can show results by a combination of indicators related to COVID-19, such as length of inpatient stay, in-hospital mortality, comorbidities, and intubation or ventilator use. NHCS data allow for reporting on patient conditions and treatments within the hospital over time.

The clinical data analytics in healthcare market is experiencing robust growth, driven by the increasing volume of healthcare data, the need for improved patient outcomes, and the rising adoption of value-based care models. The market's expansion is fueled by technological advancements such as artificial intelligence (AI), machine learning (ML), and big data analytics, which enable healthcare providers to extract actionable insights from complex datasets. This allows for more precise diagnoses, personalized treatment plans, improved operational efficiency, and reduced healthcare costs. While the exact market size for 2025 is unavailable, considering a reasonable CAGR of 15% from a 2019 base of (estimated) $10 billion, the 2025 market size would be approximately $20 billion. This substantial growth is projected to continue throughout the forecast period (2025-2033), reaching an estimated $50 Billion by 2033. Factors such as data security concerns, interoperability challenges, and the need for skilled professionals pose potential restraints to market growth. However, continuous technological innovations and increasing government support for digital health initiatives are expected to mitigate these challenges. The market is segmented by various factors, including solutions (predictive analytics, diagnostic support, etc.), deployment models (cloud, on-premise), end-users (hospitals, clinics, pharmaceutical companies), and geography. Key players like Cerner, IBM, Allscripts, and Optum are actively contributing to the market's advancement through continuous innovation and strategic partnerships. The competitive landscape is characterized by both large established players and emerging innovative companies. These companies are vying for market share through product development, strategic acquisitions, and partnerships to expand their reach and capabilities. North America is currently the largest market segment, however, regions like Europe and Asia Pacific are witnessing significant growth due to increasing healthcare spending and the rising adoption of advanced technologies. The continued focus on population health management, predictive risk scoring, and precision medicine will drive further market expansion. The market’s overall growth trajectory indicates a significant opportunity for companies to capitalize on the increasing demand for data-driven healthcare solutions. Furthermore, a strong emphasis on regulatory compliance and data privacy will continue to shape the market’s development in the coming years.

The global healthcare data analytics platform market is rapidly expanding, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in the healthcare industry. The market size is expected to reach $XXX million by 2033, growing at a CAGR of XX% from 2023 to 2033. This growth is attributed to the rising demand for solutions that can analyze vast amounts of data to improve patient outcomes, reduce costs, and optimize healthcare delivery. Key market trends include the adoption of cloud-based platforms, the integration of AI and ML algorithms, and the increasing focus on personalized medicine. On-premise and cloud-based platforms are dominating the market, with cloud-based solutions gaining significant traction due to their flexibility, scalability, and lower upfront costs. Healthcare providers and payers are increasingly adopting these platforms to gain real-time insights into patient data, improve clinical decision-making, and manage population health. Major players in the market include Microsoft, Mercury Healthcare, IBM, Apexon, Netsmart, Clarify Health, MedeAnalytics, Alteryx, and Certilytics.

The global Medical Data Integration Analysis Platform market is experiencing robust growth, projected to reach $23.43 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 23.8% from 2025 to 2033. This expansion is driven by several key factors. The increasing volume of patient data generated through Electronic Health Records (EHRs) and wearable devices necessitates sophisticated platforms for integration and analysis. Furthermore, the rising adoption of value-based care models demands more efficient data utilization for improved patient outcomes and cost reduction. Hospitals and clinics are increasingly investing in these platforms to enhance operational efficiency, improve clinical decision-making, and comply with stringent data privacy regulations. The market is segmented by application (hospitals, clinics, etc.) and type (Electronic Medical Record Systems, Health Information Exchange Platforms, and others), with Electronic Medical Record Systems currently dominating the market share due to their widespread adoption. Growth is also spurred by technological advancements in artificial intelligence (AI) and machine learning (ML), which enable more precise data analysis and predictive modeling for personalized medicine initiatives. Competitive forces among established players like Cerner, Epic Systems, and Athenahealth, along with the emergence of innovative startups, contribute to continuous market innovation and expansion. Geographical distribution shows a significant concentration in North America, driven by high healthcare spending and early adoption of advanced technologies. However, other regions like Europe and Asia-Pacific are demonstrating rapid growth, fueled by increasing healthcare investments and improving healthcare infrastructure. While data security and interoperability challenges pose some restraints, the overall market outlook remains overwhelmingly positive. The continued focus on improving patient care, enhancing research capabilities through data analysis, and strengthening healthcare regulatory compliance will all significantly contribute to the sustained growth of the Medical Data Integration Analysis Platform market in the coming years.

key Features

How to use this Dataset

1. Healthcare Provider Analysis: This dataset can be used to perform in-depth analyses of healthcare providers across various cities. You can extract insights into the distribution of different types of healthcare professionals, their practice locations, and their specialties. This information is valuable for healthcare workforce planning and resource allocation.

2. Geospatial Mapping: Utilize the city names and addresses in the dataset to create geospatial visualizations. You can map the locations of healthcare providers in each city, helping stakeholders identify areas with potential shortages or surpluses of healthcare services.

3. Provider Directory Development: The dataset provides detailed information about healthcare providers, including their names, contact details, and credentials. You can use this data to build a comprehensive healthcare provider directory or search tool, helping patients and healthcare organizations find and connect with the right providers in their area.

If you find this dataset useful, give it an upvote – it's a small gesture that goes a long way! Thanks for your support. 😄

Snapshot img

Clinical Workflow Solution Market Size 2024-2028

The clinical workflow solution market size is forecast to increase by USD 9.37 billion, at a CAGR of 13.63% between 2023 and 2028. Clinical workflow solutions are increasingly being adopted by healthcare providers in the US to streamline care delivery, improve patient outcomes, and enhance safety. These solutions offer numerous advantages, including the ability to close care gaps, facilitate visit planning and call scheduling, and support economic integration. The market is driven by the growing demand for cutting-edge clinical research and advanced care management solutions to remote patient monitoring that enable effective collaboration among nursing staff, doctors, pharmacists, and residents' relatives.

Furthermore, the trend towards connected hospitals and clinical communications is fueling the adoption of clinical workflow solutions, which enable real-time information exchange and help prevent medical errors. However, challenges such as interoperability and security issues must be addressed to ensure the successful implementation of these solutions. Overall, clinical workflow solutions are essential for effective care management and delivery in today's complex healthcare environment.

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Clinical workflow solutions are essential tools for hospitals and clinics to manage patient volume efficiently and effectively. These solutions help streamline clinical care communications, optimize patient flow metrics, and enhance safety and care delivery. Electronic Health Records (EHRs) form the backbone of clinical workflow solutions, enabling IT professionals and nurses to access patient data in real-time. However, interoperability issues between different systems and health information exchanges remain a significant challenge. Telehealth and remote patient monitoring have become crucial components of clinical workflow solutions, allowing healthcare providers to deliver care beyond traditional clinic settings. Clinical workflow solutions offer flexibility, enabling healthcare organizations to adapt to changing patient needs and healthcare spending.

Interoperability solutions address the challenge of seamless data exchange between different systems, ensuring accurate and timely patient data insights. Predictive analytics and clinical pathways are essential features of advanced clinical workflow solutions, providing healthcare teams with valuable patient data insights to improve care quality and patient journey. Surveillance units in hospitals can benefit from clinical workflow solutions to monitor patient safety and care effectively. Overall, clinical workflow solutions are vital for healthcare organizations to deliver efficient, cost-effective, and high-quality care.

Market Segmentation

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.

End-user

  Hospitals
  Long-term care facilities
  Ambulatory care centers


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China


  Rest of World (ROW)

By End-user Insights

The hospitals segment is estimated to witness significant growth during the forecast period. Clinical workflow solutions play a vital role in various healthcare departments, including radiology, laboratory, and pharmacy, by standardizing procedures, minimizing human errors, and improving interdepartmental communication. In 2023, the hospitals sector held a significant market share in The market due to the increasing number of hospitals and the need to manage the accompanying data efficiently. Furthermore, government initiatives aiming to advance the medical industry and simplify the handling of vast hospital data are expected to fuel market growth. Additionally, the expanding demand for workflow optimization and the ongoing trend toward connected hospitals are projected to boost the market throughout the forecast period. Long-term care facilities, eHealth, medical tourism, and clinics also benefit from these solutions by ensuring quality healthcare, surveillance, safety, and flexibility for physicians, surgeons, nurses, patients, and units. Reimbursement levels continue to be a crucial factor driving the adoption of clinical workflow solutions in the healthcare sector.

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The hospitals segment accounted for USD 3.49 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 45% 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.

For more i

This guidance update reflects changes made to the required data elements for reporting as well as the cadence with which these elements need to be reported to CDC’s National Healthcare Safety Network (NHSN) following the expiration of the federal COVID-19 public health emergency declaration. There are no significant changes or additions to the reporting questions as a result of this guidance update. Information on reporting to NHSN can be found here: https://www.cdc.gov/nhsn/covid19/hospital-reporting.html.

This dataset is not being updated as hospitals are no longer mandated to report COVID Hospitalizations to CDPH.

Data is from the California COVID-19 State Dashboard at https://covid19.ca.gov/state-dashboard/

Note: Hospitalization counts include all patients diagnosed with COVID-19 during their stay. This does not necessarily mean they were hospitalized because of COVID-19 complications or that they experienced COVID-19 symptoms.

Note: Cumulative totals are not available due to the fact that hospitals report the total number of patients each day (as opposed to new patients).

This dataset tracks the updates made on the dataset "COVID-19 Hospital Data" as a repository for previous versions of the data and metadata.

The global Big Data Analytics in Healthcare market is experiencing robust growth, projected to reach $10.37 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.4% from 2025 to 2033. This expansion is driven by several key factors. The increasing volume of healthcare data generated through electronic health records (EHRs), wearable devices, and medical imaging is fueling the demand for sophisticated analytics solutions to improve patient care, operational efficiency, and research outcomes. Furthermore, the rising prevalence of chronic diseases necessitates predictive analytics for early diagnosis and personalized treatment plans, further boosting market growth. Government initiatives promoting the adoption of health information technology and the increasing focus on value-based care models are also significant contributors. Key market segments include software and services, with hospitals and clinics representing the largest application area, followed by finance & insurance agencies and research organizations. Leading players like Cisco, IBM, and McKesson are leveraging their expertise to develop advanced solutions that meet the evolving needs of this dynamic market. The market's segmentation reflects the diverse applications of big data analytics in healthcare. Software solutions offer advanced capabilities for data processing, analysis, and visualization, while service offerings provide consulting, implementation, and support. North America currently holds a significant market share, driven by high technology adoption rates and a strong focus on data-driven healthcare initiatives. However, other regions, particularly Asia Pacific and Europe, are also exhibiting significant growth potential, fueled by expanding healthcare infrastructure and increasing government investments. While the market faces challenges such as data privacy concerns and the need for robust data security measures, the overall trajectory remains positive, indicating considerable opportunities for innovation and expansion in the years to come. The continued convergence of big data, artificial intelligence, and cloud computing will further accelerate market growth, leading to more sophisticated and impactful analytics solutions.

This dataset provides comprehensive, visit-level hospital data for predicting patient readmission risk, including demographics, diagnoses, treatments, medications, and outcomes. It enables advanced analytics and machine learning for care management, resource allocation, and quality improvement in healthcare settings. The dataset is ideal for developing predictive models, benchmarking hospital performance, and supporting population health initiatives.