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. 😄

ONC uses the SK&A Office-based Provider Database to calculate the counts of medical doctors, doctors of osteopathy, nurse practitioners, and physician assistants at the state and count level from 2011 through 2013. These counts are grouped as a total, as well as segmented by each provider type and separately as counts of primary care providers.

Evaluating Health Home Care Quality

CMS Core Set and Health Home SPA Measures

By Health Data New York [source]

About this dataset

This dataset provides comprehensive measures to evaluate the quality of medical services provided to Medicaid beneficiaries by Health Homes, including the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). This allows us to gain insight into how well these health homes are performing in terms of delivering high-quality care. Our data sources include the Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse. With this data set you can explore essential indicators such as rates for indicators within scope of Core Set Measures, sub domains, domains and measure descriptions; age categories used; denominators of each measure; level of significance for each indicator; and more! By understanding more about Health Home Quality Measures from this resource you can help make informed decisions about evidence based health practices while also promoting better patient outcomes

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How to use the dataset

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS). With this dataset, you can get an overview of how a health home is performing in terms of quality. You can use this data to compare different health homes and their respective service offerings.

The data used to create this dataset was collected from Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Incentive Program (DSRIP) Data Warehouse sources.

In order to use this dataset effectively, you should start by looking at the columns provided. These include: Measurement Year; Health Home Name; Domain; Sub Domain; Measure Description; Age Category; Denominator; Rate; Level of Significance; Indicator. Each column provides valuable insight into how a particular health home is performing in various measurements of healthcare quality.

When examining this data, it is important to remember that many variables are included in any given measure and that changes may have occurred over time due to varying factors such as population or financial resources available for healthcare delivery. Furthermore, changes in policy may also affect performance over time so it is important to take these things into account when evaluating the performance of any given health home from one year to the next or when comparing different health homes on a specific measure or set of indicators over time

Research Ideas

• Using this dataset, state governments can evaluate the effectiveness of their health home programs by comparing the performance across different domains and subdomains.
• Healthcare providers and organizations can use this data to identify areas for improvement in quality of care provided by health homes and strategies to reduce disparities between individuals receiving care from health homes.
• Researchers can use this dataset to analyze how variations in cultural context, geography, demographics or other factors impact delivery of quality health home services across different locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: health-home-quality-measures-beginning-2013-1.csv | Column name | Description | |:--------------------------|:----------------------------------------------------| | Measurement Year | The year in which the data was collected. (Integer) | | Health Home Name | The name of the health home. (String) | | Domain | The domain of the measure. (String) | | Sub Domain | The sub domain of the measure. (String) | | Measure Description | A description of the measure. (String) | | Age Category | The age category of the patient. (String) | | Denominator | The denominator of the measure. (Integer) | | Rate | The rate of the measure. (Float) | | Level of Significance | The level of significance of the measure. (String) | | Indicator | The indicator of the measure. (String) |

Acknowledgements

...

US Healthcare NPI Data is a comprehensive resource offering detailed information on health providers registered in the United States.

Dataset Highlights:

• NPI Numbers: Unique identification numbers for health providers.
• Contact Details: Includes addresses and phone numbers.
• State License Numbers: State-specific licensing information.
• Additional Identifiers: Other identifiers related to the providers.
• Business Names: Names of the provider’s business entities.
• Taxonomies: Classification of provider types and specialties.

Taxonomy Data:

• Includes codes, groupings, and classifications.
• Facilitates detailed analysis and categorization of providers.

Data Updates:

• Weekly Delta Changes: Ensures the dataset is current with the latest changes.
• Monthly Full Refresh: Comprehensive update to maintain accuracy.

Use Cases:

• Market Analysis: Understand the distribution and types of healthcare providers across the US. Analyze market trends and identify potential gaps in healthcare services.
• Outreach: Create targeted marketing campaigns to reach specific types of healthcare providers. Use contact details for direct outreach and engagement with providers.
• Research: Conduct in-depth research on healthcare providers and their specialties. Analyze provider attributes to support academic or commercial research projects.
• Compliance and Verification: Verify provider credentials and compliance with state licensing requirements. Ensure accurate provider information for regulatory and compliance purposes.

Data Quality and Reliability:

• The dataset is meticulously curated to ensure high quality and reliability. Regular updates, both weekly and monthly, ensure that users have access to the most current information. The comprehensive nature of the data, combined with its regular updates, makes it a valuable tool for a wide range of applications in the healthcare sector.

Access and Integration: - CSV Format: The dataset is provided in CSV format, making it easy to integrate with various data analysis tools and platforms. - Ease of Use: The structured format of the data ensures that it can be easily imported, analyzed, and utilized for various applications without extensive preprocessing.

Ideal for:

• Healthcare Professionals: Physicians, nurses, and other healthcare providers who need to verify information about their peers.
• Analysts: Data analysts and business analysts who require detailed and accurate healthcare provider data for their projects.
• Businesses: Companies in the healthcare sector looking to understand market dynamics and reach out to providers.
• Researchers: Academic and commercial researchers conducting studies on healthcare providers and services.

Why Choose This Dataset?

• Comprehensive Coverage: Detailed information on millions of healthcare providers across the US.
• Regular Updates: Weekly and monthly updates ensure that the data remains current and reliable.
• Ease of Integration: Provided in a user-friendly CSV format for easy integration with your existing systems.
• Versatility: Suitable for a wide range of applications, from market analysis to compliance and research.

By leveraging the US Healthcare NPI & Taxonomy Data, users can gain valuable insights into the healthcare landscape, enhance their outreach efforts, and conduct detailed research with confidence in the accuracy and comprehensiveness of the data.

Summary:

• This dataset is an invaluable resource for anyone needing detailed and up-to-date information on US healthcare providers. Whether for market analysis, research, outreach, or compliance, the US Healthcare NPI & Taxonomy Data offers the detailed, reliable information needed to achieve your goals.

The Washington State Department of Health presents this information as a service to the public. True and correct copies of legal disciplinary actions taken after July 1998 are available on our Provider Credential Search site. These records are considered certified by the Department of Health.

This includes information on health care providers.

Please contact our Customer Service Center at 360-236-4700 for information about actions before July 1998. The information on this site comes directly from our database and is updated daily at 10:00 a.m.. This data is a primary source for verification of credentials and is extracted from the primary database at 2:00 a.m. daily.

News releases about disciplinary actions taken against Washington State healthcare providers, agencies or facilities are on the agency's Newsroom webpage.

Disclaimer The absence of information in the Provider Credential Search system doesn't imply any recommendation, endorsement or guarantee of competence of any healthcare professional. The presence of information in this system doesn't imply a provider isn't competent or qualified to practice. The reader is encouraged to carefully evaluate any information found in this data set.

Problem Statement

👉 Download the case studies here

Hospitals and healthcare providers faced challenges in ensuring continuous monitoring of patient vitals, especially for high-risk patients. Traditional monitoring methods often lacked real-time data processing and timely alerts, leading to delayed responses and increased hospital readmissions. The healthcare provider needed a solution to monitor patient health continuously and deliver actionable insights for improved care.

Challenge

Implementing an advanced patient monitoring system involved overcoming several challenges:

Collecting and analyzing real-time data from multiple IoT-enabled medical devices.

Ensuring accurate health insights while minimizing false alarms.

Integrating the system seamlessly with hospital workflows and electronic health records (EHR).

Solution Provided

A comprehensive patient monitoring system was developed using IoT-enabled medical devices and AI-based monitoring systems. The solution was designed to:

Continuously collect patient vital data such as heart rate, blood pressure, oxygen levels, and temperature.

Analyze data in real-time to detect anomalies and provide early warnings for potential health issues.

Send alerts to healthcare professionals and caregivers for timely interventions.

Development Steps

Data Collection

Deployed IoT-enabled devices such as wearable monitors, smart sensors, and bedside equipment to collect patient data continuously.

Preprocessing

Cleaned and standardized data streams to ensure accurate analysis and integration with hospital systems.

AI Model Development

Built machine learning models to analyze vital trends and detect abnormalities in real-time

Validation

Tested the system in controlled environments to ensure accuracy and reliability in detecting health issues.

Deployment

Implemented the solution in hospitals and care facilities, integrating it with EHR systems and alert mechanisms for seamless operation.

Continuous Monitoring & Improvement

Established a feedback loop to refine models and algorithms based on real-world data and healthcare provider feedback.

Results

Enhanced Patient Care

Real-time monitoring and proactive alerts enabled healthcare professionals to provide timely interventions, improving patient outcomes.

Early Detection of Health Issues

The system detected potential health complications early, reducing the severity of conditions and preventing critical events.

Reduced Hospital Readmissions

Continuous monitoring helped manage patient health effectively, leading to a significant decrease in readmission rates.

Improved Operational Efficiency

Automation and real-time insights reduced the burden on healthcare staff, allowing them to focus on critical cases.

Scalable Solution

The system adapted seamlessly to various healthcare settings, including hospitals, clinics, and home care environments.

The All CMS Data Feeds dataset is an expansive resource offering access to 119 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system including nursing facility owners and accountable care organization participants contact data. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis too...

Problem Statement

👉 Download the case studies here

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.

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.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

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

Big Data Analytics in Healthcare Market Outlook

The global market size for Big Data Analytics in Healthcare was valued at approximately USD 34 billion in 2023 and is anticipated to grow at a robust CAGR of 11.9%, reaching an estimated USD 90 billion by 2032. This remarkable growth is driven by the increasing adoption of data-driven decision-making processes within the healthcare sector, spurred by the mounting pressure to enhance operational efficiencies, improve patient outcomes, and reduce overall healthcare costs. The integration of big data analytics within healthcare systems is enabling organizations to leverage vast amounts of data, leading to enhanced patient care and streamlined operations.

A significant growth factor fueling the expansion of the big data analytics market in healthcare is the ever-increasing volume of data generated by healthcare systems. With the surge of electronic health records, wearable health devices, and various other digital health technologies, the volume of data being generated is unprecedented. This data, if analyzed correctly, holds the potential to transform healthcare delivery models, allowing for more precise diagnostics, personalized treatment plans, and proactive disease management strategies. Consequently, healthcare organizations are increasingly investing in big data analytics tools to harness this data for clinical and operational improvements.

Another key driver of market growth is the growing emphasis on value-based care and the need for healthcare providers to demonstrate high-quality patient outcomes. Value-based care models require providers to focus on the quality rather than the quantity of care delivered, inherently demanding the use of advanced analytics to derive actionable insights from patient data. Big data analytics facilitates the identification of patterns and trends that can lead to improved treatment effectiveness and patient satisfaction. This shift in care models is prompting healthcare organizations to integrate sophisticated analytics solutions that help in predictive modeling, trend analysis, and real-time decision-making, further propelling market expansion.

Additionally, the increasing incidence of chronic diseases worldwide is driving the need for more efficient healthcare services. Big data analytics in healthcare can play a critical role in managing chronic diseases by enabling preventive care and personalized treatment plans. By analyzing patient data, including historical health records, genetic information, and lifestyle choices, healthcare providers can predict potential health issues and intervene early, thereby improving patient outcomes and reducing healthcare costs. This capability is essential in managing the global burden of chronic diseases, thereby boosting the adoption of big data analytics solutions in the healthcare sector.

Regionally, North America dominates the market due to the presence of advanced healthcare infrastructure, the availability of technologically advanced products, and the high adoption rate of healthcare IT solutions. The region's robust regulatory environment and substantial investments in healthcare IT make it a fertile ground for the growth of big data analytics solutions. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by increasing government initiatives supporting the digitization of healthcare, burgeoning healthcare infrastructure, and a growing focus on precision medicine. The integration of big data analytics in healthcare across diverse regions is indicative of its global importance in optimizing healthcare delivery and patient care.

Component Analysis

In the realm of big data analytics in healthcare, the component segment is vitally instrumental to the market's evolution and includes software and services. Software solutions are the backbone of big data analytics, providing healthcare organizations with the necessary tools to collect, process, and analyze vast datasets. These solutions encompass data management and analytical platforms, which are indispensable for extracting actionable insights from disparate data sources. The software component is continually evolving with advancements in artificial intelligence and machine learning, which enhance data analytics capabilities. Moreover, the increasing demand for user-friendly, customizable software solutions is driving innovation and growth within this segment.

The services component, on the other hand, plays a critical role in the implementation and maintenance of big data analytics solutions. This component includes cons

The size of the Healthcare Data Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 16.20% during the forecast period. Data in healthcare signifies all the information that is created or gathered in the healthcare industry. These include patient records, electronic health records, genomic data, health insurance claims, medical images, and all other clinical trial data. All this stands at the back of modern healthcare and could support many critical applications. First and foremost, health data improves patient care. Pattern analysis for patient records is simplified by health care providers in ensuring accurate disease diagnosis and application of personalized treatment plans. Medical field images, such as X-rays and MRIs, are helpful in finding abnormalities and useful in surgical methods. Genomic data insights comprise susceptibility from a genetic view point, which therefore enables coming up with a customised treatment plan for diseases such as cancer. Then, the health information data is very crucial in conducting research and developing new medical knowledge. Researchers analyze epidemiology of diseases by adopting massive datasets, manufacture new drugs and treatments, and analyze effectiveness of health care programs by such datasets. For instance, the medical trials dataset helps in the development of evidence about the safety and efficiency of new treatment options. The health insurance claims dataset can help assess healthcare utilization patterns so as to identify areas in need of improvement. Therefore, health care data also enables administrative and operational functions of health care organizations. EHRs allow easy maintenance of the patient data, enable sound communications among healthcare providers, and minimize errors. Apart from this, analytics on health insurance claims are performed to make possible billing and reimbursement services to ensure the payment of the healthcare provider in the right amount of their rendered service. Further, analytics data could be used for optimization of resource utilization, in identifying potential cost savings, and making health care organizations efficient as a whole. Healthcare information is one of those precious assets that propel innovation, promote better patient outcomes, and support the coherent functioning of the healthcare system. Therefore, improving the quality and efficiency in which care delivery is offered can be achieved through the effective use of healthcare information by healthcare providers, researchers, and administrators for a better state of health among individuals and communities. Recent developments include: March 2022: Microsoft launched Azure Health Data Services in the United States. It is a platform as a service (PAAS) offering designed exclusively to support protected health information (PHI) in the cloud., March 2022: The government of Thailand launched a big data portal for healthcare facilities. The National Reforms Committee on Public Health recently joined hands with 12 government agencies to improve the quality of healthcare services by implementing digital technologies.. Key drivers for this market are: Increase in Demand for Analytics Solutions for Population Health Management, Rise in Need for Business Intelligence to Optimize Health Administration and Strategy; Surge in Adoption of Big Data in the Healthcare Industry. Potential restraints include: Security Concerns Related to Sensitive Patients Medical Data, High Cost of Implementation and Deployment. Notable trends are: Cloud Segment is Expected to Register a High Growth Rate Over the Forecast Period.

Context

The CMS National Plan and Provider Enumeration System (NPPES) was developed as part of the Administrative Simplification provisions in the original HIPAA act. The primary purpose of NPPES was to develop a unique identifier for each physician that billed medicare and medicaid. This identifier is now known as the National Provider Identifier Standard (NPI) which is a required 10 digit number that is unique to an individual provider at the national level.

Once an NPI record is assigned to a healthcare provider, parts of the NPI record that have public relevance, including the provider’s name, speciality, and practice address are published in a searchable website as well as downloadable file of zipped data containing all of the FOIA disclosable health care provider data in NPPES and a separate PDF file of code values which documents and lists the descriptions for all of the codes found in the data file.

Content

The dataset contains the latest NPI downloadable file in an easy to query BigQuery table, npi_raw. In addition, there is a second table, npi_optimized which harnesses the power of Big Query’s next-generation columnar storage format to provide an analytical view of the NPI data containing description fields for the codes based on the mappings in Data Dissemination Public File - Code Values documentation as well as external lookups to the healthcare provider taxonomy codes . While this generates hundreds of columns, BigQuery makes it possible to process all this data effectively and have a convenient single lookup table for all provider information.

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:nppes?_ga=2.117120578.-577194880.1523455401

https://console.cloud.google.com/marketplace/details/hhs/nppes?filter=category:science-research

Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

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Inspiration

What are the top ten most common types of physicians in Mountain View?

What are the names and phone numbers of dentists in California who studied public health?

Healthcare NLP Solution Market Outlook

The global Healthcare NLP Solution market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach around USD 7.5 billion by 2032, exhibiting a CAGR of 17.1% during the forecast period. This impressive growth trajectory is primarily driven by the increasing adoption of advanced technologies in healthcare, such as natural language processing (NLP), aimed at improving patient care and operational efficiency.

One significant growth factor for the Healthcare NLP Solution market is the rising volume of unstructured clinical data. Healthcare organizations generate massive amounts of data, including clinical notes, patient records, and research papers. Traditional data processing methods are often inadequate to handle this unstructured data efficiently. NLP solutions can process, analyze, and interpret this data to extract meaningful insights, thus supporting clinical decision-making and improving patient outcomes. Consequently, the demand for NLP solutions in healthcare is surging.

Another crucial growth driver for the market is the increasing focus on precision medicine and personalized healthcare. NLP solutions enable healthcare providers to analyze large datasets to identify patterns and trends that can help in personalized treatment plans. By leveraging NLP technologies, clinicians can tailor treatments to individual patient profiles, thus enhancing the effectiveness of medical interventions. This personalized approach not only improves patient care but also contributes to the rapid growth of the Healthcare NLP Solution market.

Moreover, the integration of NLP solutions with electronic health records (EHRs) is significantly boosting market growth. EHRs have become ubiquitous in healthcare settings, and the addition of NLP capabilities enhances their utility by enabling more effective data retrieval and analysis. This integration facilitates better patient management, reduces the likelihood of errors, and improves clinical workflows. As healthcare providers continue to adopt EHR systems, the demand for integrated NLP solutions is anticipated to grow, further propelling market expansion.

Natural Language Processing (NLP) Software is at the forefront of transforming the healthcare industry by enabling the efficient processing of unstructured data. This software leverages advanced algorithms to understand and interpret human language, making it possible to extract valuable insights from clinical notes, patient feedback, and research articles. By automating these processes, NLP software reduces the time and effort required for data analysis, allowing healthcare professionals to focus more on patient care. The integration of NLP software into healthcare systems is not only enhancing operational efficiency but also paving the way for more personalized and precise medical treatments. As the demand for data-driven decision-making grows, the role of NLP software in healthcare is becoming increasingly indispensable.

From a regional perspective, North America currently holds the largest market share in the Healthcare NLP Solution market, driven by the early adoption of advanced healthcare technologies and substantial investments in healthcare infrastructure. However, the Asia Pacific region is expected to exhibit the highest CAGR during the forecast period. Factors such as increasing healthcare expenditures, growing awareness of advanced healthcare technologies, and supportive government initiatives are driving market growth in this region. Europe and Latin America are also showing significant growth potential, driven by improving healthcare systems and increasing adoption of digital health solutions.

Component Analysis

The component segment of the Healthcare NLP Solution market is bifurcated into software and services. The software segment includes NLP tools and platforms designed to analyze unstructured clinical data, while the services segment encompasses implementation, training, and maintenance services required to deploy these solutions effectively. The software segment is currently dominating the market, driven by the increasing need for advanced analytics tools to manage and interpret vast amounts of healthcare data.

NLP software solutions are gaining traction due to their ability to streamline clinical documentation processes. These tools can automatically transcribe and structure clinical notes, significantly reducing

Healthcare Cloud Based Analytics Market Outlook

The global healthcare cloud based analytics market size was valued at approximately USD 14.8 billion in 2023, and it is anticipated to reach around USD 54.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.7% from 2024 to 2032. One of the primary growth factors influencing this market is the increasing demand for data-driven decision-making processes in healthcare settings to enhance patient outcomes and operational efficiency.

One significant growth factor for the healthcare cloud based analytics market is the rapid digital transformation within the healthcare sector. The transition from paper-based systems to electronic health records (EHRs) and the adoption of telehealth services are driving the need for sophisticated analytics solutions that can process vast amounts of healthcare data. The accessibility and scalability offered by cloud-based solutions make them particularly attractive for healthcare providers looking to leverage patient data for better diagnostic and treatment outcomes.

Moreover, the rising focus on personalized medicine and the need for population health management are propelling the demand for healthcare cloud based analytics. Personalized medicine requires the analysis of large datasets to understand individual patient profiles and predict responses to treatments. Similarly, population health management aims to improve health outcomes by analyzing data to identify trends and intervene proactively. Cloud-based analytics platforms provide the necessary computational power and flexibility to handle these complex data requirements efficiently.

The cost-efficiency of cloud based solutions compared to traditional on-premises systems is another crucial growth driver. Healthcare organizations are under constant pressure to reduce operational costs while improving patient care quality. Cloud-based analytics solutions eliminate the need for significant upfront investments in hardware and software while offering the benefits of scalable resources and reduced IT maintenance costs. This financial advantage is particularly appealing to small and medium-sized healthcare providers who may have limited budgets for technology investments.

The integration of Business Intelligence in Healthcare is transforming the way data is utilized to improve patient care and streamline operations. By employing BI tools, healthcare organizations can analyze vast datasets to uncover insights that drive better decision-making. These tools enable healthcare providers to track patient outcomes, optimize resource allocation, and enhance overall operational efficiency. The ability to visualize data through dashboards and reports allows for a deeper understanding of patient trends and organizational performance, ultimately leading to improved healthcare delivery and patient satisfaction.

From a regional perspective, North America currently holds the largest market share in the healthcare cloud based analytics market, driven by advanced healthcare infrastructure and high adoption rates of digital healthcare technologies. However, regions like Asia Pacific are expected to witness the highest growth rates during the forecast period. Factors such as increasing healthcare expenditures, growing awareness about the benefits of healthcare analytics, and supportive government initiatives are contributing to the market expansion in these regions.

Component Analysis

The healthcare cloud based analytics market can be segmented by component into software and services. The software segment includes various analytics platforms and tools designed to process and analyze healthcare data. These software solutions are essential for enabling healthcare providers to harness the power of big data and derive actionable insights. As the volume of healthcare data continues to grow exponentially, the demand for robust and scalable analytics software solutions is expected to increase significantly. Innovations in artificial intelligence and machine learning are also enhancing the capabilities of these software solutions, making them more effective in predictive analytics and decision support.

Cloud Computing in Healthcare is revolutionizing the way healthcare data is stored, accessed, and analyzed. By leveraging cloud technology, healthcar

This dataset was generated using Simio simulation software. The simulations model patient flow in healthcare settings, capturing key metrics such as queue times, length of stay (LOS) for patients, and nurse utilization rates. Each CSV file contains time-series data, with measured variables including patient waiting times, resource utilization percentages, and service durations.## File Overview**CheckBloodPressure.csv** - (9 KB): Contains blood pressure Server records of patients.**CheckPatientType.csv** - (19 KB): Identifies the type of each patient (e.g., 1 or 3).**Fill_Information.csv** - (2 KB): Fill information records for new patients.**MedicalRecord1.csv** - (10 KB): Medical record dataset for patient type 1.**MedicalRecord2.csv** - (4 KB): Medical record dataset for patient type 2.**MedicalRecord3.csv** - (2 KB): Medical record dataset for patient type 3.**MedicalRecord4.csv** - (13 KB): Medical record dataset for patient type 4.**OutPatientDepartment.csv** - (18 KB): Data related to the satisfaction and length of stay of an given patient.**Triage.csv** - (13 KB): Data related to the triage process.**README.txt** - (4 KB): Documentation of the dataset, including structure, metadata, and usage.## Common Fields Across Files**Patient ID** (Integer): Unique identifier for each patient.**Patient Type** (Integer): Classification of patient (e.g., 1, 4).**Medical Records Arrival Time** (DateTime): Timestamp of the patient's first arrival in the medical record department.**Exiting Time** (DateTime): Timestamp when the patient exits a Server.**Waiting Time (min)** (Real): Total waiting time before being attended to.**Resource Used** (String): Resource (e.g., Operator) allocated to the patient.**Utilization %** (Real): Utilization rate of the resource as a percentage.**Queue Count Before Processing** (Integer): Number of patients in the queue before processing begins.**Queue Count After Processing** (Integer): Number of patients in the queue after processing ends.**Queue Difference** (Integer): Difference between the before and after queue counts.**Length of Stay (min)** (Real): Total time spent in the simulation by the patient.**LOS without Queues (min)** (Real): Length of stay excluding any queuing time.**Satisfaction %** (Real): Patient satisfaction rating based on their experience.**New Patient?** (String): Indicates if this is a new patient or a returning one.

Synthetic dataset of emergency services comprised of several CSV files that we have generated using a simulation software. This dataset is open for public use; please cite our work if used in research or applications. File Overview CheckBloodPressure.csv** - (9 KB): Contains blood pressure Server records of patients. CheckPatientType.csv** - (19 KB): Identifies the type of each patient (e.g., 1 or 3). Fill_Information.csv - (2 KB): Fill information records for new patients. MedicalRecord1.csv - (10 KB): Medical record dataset for patient type 1. MedicalRecord2.csv - (4 KB): Medical record dataset for patient type 2. MedicalRecord3.csv - (2 KB): Medical record dataset for patient type 3. MedicalRecord4.csv - (13 KB): Medical record dataset for patient type 4. OutPatientDepartment.csv - (18 KB): Data related to the satisfaction and length of stay of an given patient. Triage.csv - (13 KB): Data related to the triage process. README.txt - (4 KB): Documentation of the dataset, including structure, metadata, and usage. Common Fields Across Files Patient ID (Integer): Unique identifier for each patient. Patient Type (Integer): Classification of patient (e.g., 1, 4). Medical Records Arrival Time (DateTime): Timestamp of the patient's first arrival in the medical record department. Exiting Time (DateTime): Timestamp when the patient exits a Server. Waiting Time (min) (Real): Total waiting time before being attended to. Resource Used (String): Resource (e.g., Operator) allocated to the patient. Utilization % (Real): Utilization rate of the resource as a percentage. Queue Count Before Processing (Integer): Number of patients in the queue before processing begins. Queue Count After Processing (Integer): Number of patients in the queue after processing ends. Queue Difference (Integer): Difference between the before and after queue counts. Length of Stay (min) (Real): Total time spent in the simulation by the patient. LOS without Queues (min) (Real): Length of stay excluding any queuing time. Satisfaction % (Real): Patient satisfaction rating based on their experience. New Patient? (String): Indicates if this is a new patient or a returning one.

State Health IT Policy Levers

300+ Examples of Advancing Interoperability and Promoting Health IT

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains over 300 examples of health IT policy levers used by states to advance interoperability, promote health IT and support delivery system reform. The U.S Government's Office of National Coordinator for Health Information Technology (ONC) has curated this catalog as part of its Health IT State Policy Levers Compendium. It provides an exhaustive directory on the policy levers being utilized, along with information on the state enacting them and their official sources. This collection seeks to act as a comprehensive guide for government officials and healthcare providers who are interested in state-based initiatives for optimizing health information technology. Explore the strategies your own state might be using to unlock improved patient outcomes!

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How to use the dataset

This dataset provides information on policy levers used by various states in the United States to promote health IT and advance interoperability. The comprehensive list includes over 300 documented examples of health IT policy levers used by these states. This catalog can be used to identify which specific policy levers are being used, as well as what activities they are associated with.

If you're interested in learning more about how states use health IT policy levers, this dataset is a great resource. It contains detailed information on each entry, including the state where it's being used, the status of that activity, a description of the activity and its purpose, and an official source for additional information about that particular entry.

Using this data set is easy - simply search for specific states or find out which kinds of activities each state is using their health IT policy levers for. You can also look up any specific application or implementation detail from each record by opening up its corresponding source URL link . With all this information at hand you can better understand how states use their health IT tools to make a difference in advancing interoperability within healthcare systems today!

Research Ideas

• It can be used to provide states with potential models of successful health IT policy levers, allowing them to learn from the experiences of other states in developing and implementing health IT legislation.
• The dataset can also be used by researchers looking to study the effectiveness of existing health care policy levers, as well as to identify any gaps that need to be filled in order for certain policies to have a greater overall impact.
• Additionally, it could be used by industry stakeholders such as hospitals or other healthcare organizations for benchmarking their own efforts related to IT implementation, such as understanding what activities are being undertaken and which sources are being used for best practices or additional resources when making decisions related to new technology implementations into an organization's operations and services

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: policy-levers-activities-catalog-csv-1.csv | Column name | Description | |:-------------------------|:----------------------------------------------------------------------------------------------| | state | The state in which the policy lever is being used. (String) | | policy_lever | Type of policy lever being used. (String) | | activity_status | Status of activity (e.g., active or inactive). (String) | | activity_description | Description of activity. (String) | | source | Source from where data is gathered from. (String) | | source_url | A link that points directly back to an original sources with additional information. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit US Open Data Portal, data.gov.

Dataset Description:

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

Key Features:

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

Potential Use Cases:

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

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

[IMPORTANT NOTE: Sample file posted on Datarade is not the complete dataset, as Datarade permits only a single CSV file. Visit https://www.careprecise.com/healthcare-provider-data-sample.htm for more complete samples.] Updated every month, CarePrecise developed the AHD to provide a comprehensive database of U.S. hospital information. Extracted from the CarePrecise master provider database with information all of the 6.3 million HIPAA-covered US healthcare providers and additional sources, the Authoritative Hospital Database (AHD) contains records for all HIPAA-covered hospitals. In this database of hospitals we include bed counts, patient satisfaction data, hospital system ownership, hospital charges and cases by Zip Code®, and more. Most records include a cabinet-level or director-level contact. A PlaceKey is provided where available.

The AHD includes bed counts for 95% of hospitals, full contact information on 85%, and fax numbers for 62%. We include detailed patient satisfaction data, employee counts, and medical procedure volumes.

The AHD integrates directly with our extended provider data product to bring you the physicians and practice groups affiliated with the hospitals. This combination of data is the only commercially available hospital dataset of this depth.

NEW: Hospital NPI to CCN Rollup A CarePrecise Exclusive. Using advanced record-linkage technology, the AHD now includes a new file that makes it possible to mine the vast hospital information available in the National Provider Identifier registry database. Hospitals may have dozens of NPI records, each with its own information about a unit, listing facility type and/or medical specialties practiced, as well as separate contact names. To wield the power of this new feature, you'll need the CarePrecise Master Bundle, which contains all of the publicly available NPI registry data. These data are available in other CarePrecise data products.

Counts are approximate due to ongoing updates. Please review the current AHD information here: https://www.careprecise.com/detail_authoritative_hospital_database.htm

The AHD is sold as-is and no warranty is offered regarding accuracy, timeliness, completeness, or fitness for any purpose.