The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

United Healthcare Transparency in Coverage Dataset

Unlock the power of healthcare pricing transparency with our comprehensive United Healthcare Transparency in Coverage dataset. This invaluable resource provides unparalleled insights into healthcare costs, enabling data-driven decision-making for insurers, employers, researchers, and policymakers.

Key Features:

• Extensive Coverage: Access detailed pricing information for a wide range of medical procedures and services across the United States, covering approximately 76,000 employers.
• Granular Data: Analyze costs at the provider, plan, and employer levels, allowing for in-depth comparisons and trend analysis.
• Massive Scale: Over 400TB of data generated monthly, providing a wealth of information for comprehensive analysis.
• Historical Perspective: Track pricing changes over time to identify patterns and forecast future trends.
• Regular Updates: Stay current with the latest pricing information, ensuring your analyses are always based on the most recent data.

Detailed Data Points:

For each of the 76,000 employers, the dataset includes: 1. In-network negotiated rates for covered items and services 2. Historical out-of-network allowed amounts and billed charges 3. Cost-sharing information for specific items and services 4. Pricing data for medical procedures and services across providers, plans, and employers

Use Cases

For Insurers: - Benchmark your rates against competitors - Optimize network design and provider contracting - Develop more competitive and cost-effective insurance products

For Employers: - Make informed decisions about health plan offerings - Negotiate better rates with insurers and providers - Implement cost-saving strategies for employee healthcare

For Researchers: - Conduct in-depth studies on healthcare pricing variations - Analyze the impact of policy changes on healthcare costs - Investigate regional differences in healthcare pricing

For Policymakers: - Develop evidence-based healthcare policies - Monitor the effectiveness of price transparency initiatives - Identify areas for potential cost-saving interventions

Data Delivery

Our flexible data delivery options ensure you receive the information you need in the most convenient format:

• Custom Extracts: We can provide targeted datasets focusing on specific regions, procedures, or time periods.
• Regular Reports: Receive scheduled updates tailored to your specific requirements.

Why Choose Our Dataset?

1. Expertise: Our team has extensive experience in healthcare data retrieval and analysis, ensuring high-quality, reliable data.
2. Customization: We can tailor the dataset to meet your specific needs, whether you're interested in particular companies, regions, or procedures.
3. Scalability: Our infrastructure is designed to handle the massive scale of this dataset (400TB+ monthly), allowing us to provide comprehensive coverage without compromise.
4. Support: Our dedicated team is available to assist with data interpretation and technical support.

Harness the power of healthcare pricing transparency to drive your business forward. Contact us today to discuss how our United Healthcare Transparency in Coverage dataset can meet your specific needs and unlock valuable insights for your organization.

United States Health Insurance: Premium Per Member Per Month data was reported at 364.000 USD in Sep 2024. This stayed constant from the previous number of 364.000 USD for Jun 2024. United States Health Insurance: Premium Per Member Per Month data is updated quarterly, averaging 262.000 USD from Mar 2012 (Median) to Sep 2024, with 51 observations. The data reached an all-time high of 364.000 USD in Sep 2024 and a record low of 178.000 USD in Sep 2013. United States Health Insurance: Premium Per Member Per Month data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG017: Health Insurance: Industry Financial Snapshots.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

Analysis of ‘US Health Insurance Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/teertha/ushealthinsurancedataset on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The venerable insurance industry is no stranger to data driven decision making. Yet in today's rapidly transforming digital landscape, Insurance is struggling to adapt and benefit from new technologies compared to other industries, even within the BFSI sphere (compared to the Banking sector for example.) Extremely complex underwriting rule-sets that are radically different in different product lines, many non-KYC environments with a lack of centralized customer information base, complex relationship with consumers in traditional risk underwriting where sometimes customer centricity runs reverse to business profit, inertia of regulatory compliance - are some of the unique challenges faced by Insurance Business.

Despite this, emergent technologies like AI and Block Chain have brought a radical change in Insurance, and Data Analytics sits at the core of this transformation. We can identify 4 key factors behind the emergence of Analytics as a crucial part of InsurTech:

• Big Data: The explosion of unstructured data in the form of images, videos, text, emails, social media
• AI: The recent advances in Machine Learning and Deep Learning that can enable businesses to gain insight, do predictive analytics and build cost and time - efficient innovative solutions
• Real time Processing: Ability of real time information processing through various data feeds (for ex. social media, news)
• Increased Computing Power: a complex ecosystem of new analytics vendors and solutions that enable carriers to combine data sources, external insights, and advanced modeling techniques in order to glean insights that were not possible before.

This dataset can be helpful in a simple yet illuminating study in understanding the risk underwriting in Health Insurance, the interplay of various attributes of the insured and see how they affect the insurance premium.

Content

This dataset contains 1338 rows of insured data, where the Insurance charges are given against the following attributes of the insured: Age, Sex, BMI, Number of Children, Smoker and Region. There are no missing or undefined values in the dataset.

Inspiration

This relatively simple dataset should be an excellent starting point for EDA, Statistical Analysis and Hypothesis testing and training Linear Regression models for predicting Insurance Premium Charges.

Proposed Tasks: - Exploratory Data Analytics - Statistical hypothesis testing - Statistical Modeling - Linear Regression

--- Original source retains full ownership of the source dataset ---

This dataset contains estimates of health insured and uninsured population for 2020 at county and state level based on US Census Bureau program, The Small Area Health Insurance Estimates (SAHIE) program. For every state and county for each demographic group, defined by age, gender, race/ethnicity and income relative to poverty, the estimated number of persons insured and uninsured is given along with the margin of error.

Health Insurance Marketplaces

Rates, Benefits, Coverage and Networks

By Data Society [source]

About this dataset

Do you want to explore the complexities of Health Insurance Marketplace and uncover insights into plan rates, benefits, and networks? Look no further! With this dataset from the Centers for Medicare & Medicaid Services (CMS), you can investigate trends in plan rates, access coverage across states and zip codes, compare metal level plans (across years), as well as analyze benefit information all in one place.

We’ve provided six CSV files containing combined data from across all years: BenefitsCostSharing.csv provides details on benefits, BusinessRules.csv provides details about premium payment requirements for a plan or set of plans, Network.csv offers details about health plans’ networks of providers who offer services at different cost levels to members enrolled in a given plan or set of plans; PlanAttributes.csv gives attributes like age off dates for various plans; Rate.csv delivers information on rate changes; ServiceArea.csv reveals demographic characteristics related to each service area associated with a specific issuer and two CSV files that join data across years (Crosswalk2015 & Crosswalk2016).

So come on board and use your creativity to unlock the mysteries behind changes in benefits in relation to costs while exploring network providers within different regions!!!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains information about the health insurance plans offered in the US Health Insurance Marketplace. It includes data on plan benefits, cost-sharing, networks, rates and service areas for different states. The data can be used to compare and analyze plan characteristics across different states and ages which will help guide users decision making when purchasing a health insurance plan.

To begin using the dataset, you should start by looking at the columns available. These include State, Dental Plan, Multistate Plan (2015 & 2016), Metal Level (2015 & 2016), Child/Adult Only (2015 & 2016), FIPS Code, Zip Code Crosswalk Level, Reason for Crosswalk, Multistate Plan Ageoff (2016 & 2015) and MetalLevel Ageoff (2016 & 2015). These columns provide important information on each plan that can be used to compare them across states or between years.

Using this data you can explore several interesting questions such as: How do benefit levels vary among states? Are there any differences in network providers between states? What factors influence plan rates?

In order to answer these questions you should join together relevant tables from across years using Crosswalk 2015/2016 CSV files then organize your data accordingly so that it is easier to visualize differences in features between plans sold across different states or years. Once the information is organized it might be helpful to use visualizations such as line graphs or bar charts to view comparison between feature values of two plans versus one another more clearly in order differentiate variations of plans among Consumers.

By doing this you can gain a better understanding of how certain factors may affect rate changes over time or how certain benefit levels might differ by state which will allow Consumers make an informed choice when selecting their next health insurance plan

Research Ideas

• Analyzing the effectiveness of different plan benefits and how they affect premiums to determine a fair price point for different types of healthcare plans.
• Examining the variation in rates, benefits and coverage by state or zip code to identify potential trends or disparities in access to quality health care services across regions.
• Developing an algorithm that can predict premium prices based on certain factors such as age groups, type of plan (metal levels), multistate coverage, etc., to help consumers more easily understand the true cost of their health insurance plans before committing to purchase them

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit -...

This survey was sponsored by Harvard school of Public Health & Robert Wood Johnson Foundation and was conducted from July 26-September 2, 2001 among a nationally representative sample of 1206 repsondents 18 years of age and older. Topics dealt with health and health care issues in America and primarily included questions dealing with health insurance.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at the Roper Center for Public Opinion Research at https://doi.org/10.25940/ROPER-31092256. We highly recommend using the Roper Center version as they may make this dataset available in multiple data formats in the future.

Note: Due to the COVID-19 pandemic, in-person health insurance enrollment services will be suspended until further notice. During this period, our enrollment staff will continue to help New Yorkers sign up for low- and no-cost health insurance by phone. Health insurance enrollment and assistance with SNAP benefits (Food Stamps) Data collected to promote health insurance enrollment among uninsured New Yorkers. Data collected manually. Each record represents a health center location where health insurance enrollment and assistance with SNAP benefits (Food Stamps) are offered. Data can be used by general public seeking assistance with signing up for health insurance or SNAP. Data may change as program needs are changed, e.g., opening of new site or change in hours of operation at a particular site.

Analysis of ‘Health Insurance Coverage’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hhs/health-insurance on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Affordable Care Act (ACA) is the name for the comprehensive health care reform law and its amendments which addresses health insurance coverage, health care costs, and preventive care. The law was enacted in two parts: The Patient Protection and Affordable Care Act was signed into law on March 23, 2010 by President Barack Obama and was amended by the Health Care and Education Reconciliation Act on March 30, 2010.

Content

This dataset provides health insurance coverage data for each state and the nation as a whole, including variables such as the uninsured rates before and after Obamacare, estimates of individuals covered by employer and marketplace healthcare plans, and enrollment in Medicare and Medicaid programs.

Acknowledgements

The health insurance coverage data was compiled from the US Department of Health and Human Services and US Census Bureau.

Inspiration

How has the Affordable Care Act changed the rate of citizens with health insurance coverage? Which states observed the greatest decline in their uninsured rate? Did those states expand Medicaid program coverage and/or implement a health insurance marketplace? What do you predict will happen to the nationwide uninsured rate in the next five years?

--- Original source retains full ownership of the source dataset ---

This dataset provides an estimate of the percent of Detroit residents who reported having health insurance at the time they completed the American Community Survey (ACS). The data is averaged over 5 years. This data can be also be accessed in Table S2701 on the American FactFinder website.Note that the data is provided by ZIP Code Tabulation Area (ZCTA), which may not exactly match USPS ZIP Code service areas. For more information: https://web.archive.org/web/20130617034846/http://www.census.gov/geo/reference/zctas.html

United States Health Insurance: Enrollment: Dental data was reported at 47.000 USD mn in 2023. This records an increase from the previous number of 46.000 USD mn for 2022. United States Health Insurance: Enrollment: Dental data is updated yearly, averaging 41.000 USD mn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 47.000 USD mn in 2023 and a record low of 28.000 USD mn in 2007. United States Health Insurance: Enrollment: Dental data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

This layer shows Health Insurance Coverage. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2017-2021ACS Table(s): B27010, DP03Data downloaded from: Census Bureau's API for American Community SurveyDate of API call: February 16, 2023National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

This file includes data for states that are implementing their own Marketplaces, also known as State-Based Marketplaces or SBMs, and states with Marketplaces that are supported by or fully run by the federal government, including those run in partnership with states, also known as the Federally-Facilitated Marketplace or FFM. Includes demographic characteristics, and plan selected (by metal level). Please refer to the full report listed under Resources.

Data on Medicaid coverage among persons under age 65 by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Health Interview Survey, health insurance supplements (1984, 1989, 1994-1996). Starting with 1997, data are from the family core and the sample adult questionnaires. Data for level of difficulty are from the 2010 Quality of Life, 2011-2017 Functioning and Disability, and 2018 Sample Adult questionnaires. For more information on the National Health Interview Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

This map service shows the locations of healthcare facilities (hospitals, medical centers, federally qualified health centers, home health services, and nursing homes) in the United States. The data was provided by the U.S. Department of Health Human Services and is current as of 2012.The data is symbolized by facility type:Hospital: an institution providing medical and surgical treatment and nursing care for sick or injured people.Medical Center: a health care facility staffed and equipped to care for many patients and for a large number of various kinds of diseases and dysfunctions, using sophisticated technology.Federally Qualified Health Center: a community-based organization that provides comprehensive primary care and preventative care, including health, oral, and mental health/substance abuse services to persons of all ages, regardless of their ability to pay or health insurance status.Home Health Service: health care or supportive care provided in the patient's home by health care professionals (often referred to as home health care or formal care).Nursing Home: provides a type of residential care. They are a place of residence for people who require constant nursing care and have significant deficiencies with activities of daily living.Other data sources include: Data.gov_Other Health Datapalooza focused content that may interest you: Health Datapalooza Health Datapalooza

Health insurance coverage rates, from the American Community Survey, 2014 5-year Average, by Zip. For the Detroit Tri-County region. Data Driven Detroit calculated the rates by dividing the total number of insured by the total number of people in each age group.

The Medical Expenditure Panel Survey Insurance Component (MEPS-IC) is an annual survey of private employers and State and local governments. The MEPS-IC produces national and State level estimates of employer-sponsored insurance, including offered plans, costs, employee eligibility, and number of enrollees. With the MEPS-IC Data Tools, users can interactively explore maps, trends, and cross-sectional bar charts for topics related to national and state-level employer-based health insurance for employer characteristics/offerings; employee take-up; premiums; contributions; and cost-sharing. The MEPS-IC is sponsored by the Agency for Healthcare Research and Quality and is fielded by the U.S. Census Bureau.

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 data completeness information on data submitted by hospitals for the previous week, from Friday to Thursday. The U.S. Department of Health and Human Services requires all hospitals licensed to provide 24-hour care to report certain data necessary to the all-of-America COVID-19 response. The report includes the following information for each hospital:

• The percentage of mandatory fields reported.
• The number of days in the preceding week where 100% of the fields were completed.
• Whether a hospital is required to report on Wednesdays only.
• A cell for each required field with the number of days that specific field was reported for the week.