According to a survey conducted in 2022, ** percent of respondents from healthcare organizations at a mature stage of AI adoption stated that natural language text was used in their AI applications. Structured data was the most common data type on which AI models were applied by healthcare organizations in early-stage AI adoption.

Healthcare Insurance Report Type Codes is a dataset that defines the type of report being described in an insurance claim and are transmitted in 005010X306, loop 2300, REF03. This dataset also contains information on the different report type codes and their descriptions, start and modified dates, and the status of each code whether active, to be deactivated or deactivated.

According to a survey conducted in the U.S. in 2020, ** percent of clinical leaders surveyed were currently offering their staff training related to privacy/ Health Insurance Portability and Accountability Act (HIPAA), and ensuring that patient information is protected on virtual platforms, while ** percent of respondents mentioned the training was in development. On the other hand, only ** percent of respondents were currently training their staff on how to effectively examine a patient remotely, while ** percent mentioned the training is in development.

This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Health Care Activity". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.

Technical notes and documentation on the common data model of the project CONCEPT-DM2.

This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes.

Aims of the CONCEPT-DM2 project:

General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems.

Main specific aims:

• To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations
• To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines
• To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.
• To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.

Study Design: It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register.

Cohort definition: All patients with code of Type 2 Diabetes in the clinical health records

• Inclusion criteria: patients that, at 01/01/2017 or during the follow-up from 01/01/2017 to 31/12/2022 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)
• Exclusion criteria:
  • patients with no contact with the health system from 01/01/2017 to 31/12/2022
  • patients that had a T1D (DM1) code opened after the T2D code during the follow-up.
• Study period. From 01/01/2017 to 31/12/2022

Files included in this publication:

• Datamodel_CONCEPT_DM2_diagram.png
• Common data model specification (Datamodel_CONCEPT_DM2_v.0.1.0.xlsx)
• Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data)
  • sample_data1_dm_patient.csv
  • sample_data2_dm_param.csv
  • sample_data3_dm_patient.csv
  • sample_data4_dm_param.csv
  • sample_data5_dm_patient.csv
  • sample_data6_dm_param.csv
  • sample_data7_dm_param.csv
  • sample_data8_dm_param.csv
• Datamodel_CONCEPT_DM2_explanation.pptx

According to a survey carried out in 2023, most people in Mexico were public health insured. Close to half of the Mexican population were covered by public health programs and were not affiliated to the country's social security institutions or private insurances, while around 43 percent were insured with the Mexican Social Security Institute (IMSS). In that year, three to four in ten respondents who had no health insurance and sought out medical services attended a private health care facility for medical attention.

Health Care Service Type Codes are used to identify the classification of service or benefits. This external code list is for use in ASC X12 Transaction Sets 270, 271 and 278, versions 006010 and higher. Version 005010 codes are available within the ASC X12 TR3 Implementation Guide. This dataset also contains information on the different service type codes and their descriptions, the start and modified dates, and the status for each code.

The HCUP Summary Trend Tables include monthly information on hospital utilization derived from the HCUP State Inpatient Databases (SID) and HCUP State Emergency Department Databases (SEDD). Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD. The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics: Overview of monthly trends in inpatient and emergency department utilization All inpatient encounter types Inpatient stays by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Inpatient encounter type -Normal newborns -Deliveries -Non-elective inpatient stays, admitted through the ED -Non-elective inpatient stays, not admitted through the ED -Elective inpatient stays Inpatient service line -Maternal and neonatal conditions -Mental health and substance use disorders -Injuries -Surgeries -Other medical conditions Emergency department treat-and-release visits Emergency department treat-and-release visits by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Description of the data source, methodology, and clinical criteria

In 2023, follow-up appointments were the primary application of telemedicine use in the United States, with almost **** of respondents having this type of healthcare virtually. More than ** percent of patients surveyed used telemedicine for regular check-ups, medication management and refills, and mental health appointments. Other health care services used by patients to a lesser extent were reviewing test or lab results, non-emergency appointments, and remote monitoring device check-ups.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a California Department of Public Health data system created to manage state licensing-related data. This file lists the bed types and bed type capacities that are associated with California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification. This file can be linked by FACID to the Healthcare Facility Locations (Detailed) Open Data file for facility-related attributes, including geo-coding. The L&C Open Data facility beds file is updated monthly. To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. A list of healthcare facilities with addresses can be found at: https://data.chhs.ca.gov/dataset/healthcare-facility-locations.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses and certifies more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a CDPH data system created to manage state licensing-related data and enforcement actions. This file includes California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification.

To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. Facility geographic variables are updated monthly, if latitude/longitude information is missing at any point in time, it should be available when the next time the Open Data facility file is refreshed.

Please note that the file contains the data from ELMS as of the 11th business day of the month. See DATA_DATE variable for the specific date of when the data was extracted.

Map of all Health Care Facilities in California: https://go.cdii.ca.gov/cdph-facilities

This dataset provides the number of health facilities in Qatar for the year 2019, categorized by type of health facility such as government hospitals, private hospitals, healthcare centers, and specialized clinics. It supports health infrastructure monitoring and sectoral analysis for planning and development.

This map shows the predominant type of health insurance that the young population have in the U.S. The pop-up shows a breakdown of the count of people under 19 by the type of health insurance they have. The pattern is shown by states, counties, and tracts. There are bookmarks in the map to help you jump to different cities. You can also search for any city in the Untied States to learn more about that area. The data is from the most current 5-year estimates put together by the American Community Survey (ACS) branch of the U.S. Census Bureau. The data is updated each year when the ACS releases their new data values. To learn more about the layer and data used in this map, click here.

As of June 2022, 14 percent of Americans surveyed reported delaying or avoiding dental care services in the last 12 months in order to afford other household expenses. Furthermore, 13 percent of respondents said they had avoided going to the doctor to pay household expenses instead. This graph shows the share of U.S. adults who cut back on selected healthcare services in the past year to pay for other household expenses in 2022.

Abstract: The study aimed to estimate the effect of health insurance on overall survival and disease-free survival in breast cancer patients undergoing surgery at the Las Américas Oncology Institute in Medellín, Colombia, with data from the institutional registry. The variables were compared between subsidized coverage and contributive coverage with chi-squared test (χ2) or Student t test, Kaplan-Meier, and log-rank test. The target variable was adjusted with Cox regression. There were 2,732 patients with a median follow-up of 36 months. Ten percent of the women with contributive coverage died, compared to 23% of the subsidized coverage group. There were differences in time-to-treatment (contributive group with 52 days versus subsidized group with 112 days, p < 0.05). Disease-free survival and overall survival were better in women with contributive coverage compared to those with subsidized coverage (p < 0.05), and overall survival varied according to tumor and treatment variables. Overall survival and disease-free survival and early time-to-diagnosis and treatment were better in patients with contributive coverage compared to those with subsidized coverage.

In 2020, the Washington State Legislature enacted Engrossed Substitute Senate Bill (ESSB) 6404 (Chapter 316, Laws of 2020, codified at RCW 48.43.0161), which requires that health carriers with at least one percent of the market share in Washington State annually report certain aggregated and de-identified data related to prior authorization to the Office of the Insurance Commissioner (OIC). Prior authorization is a utilization review tool used by carriers to review the medical necessity of requested health care services for specific health plan enrollees. Carriers choose the services that are subject to prior authorization review. The reported data includes prior authorization information for the following categories of health services: • Inpatient medical/surgical • Outpatient medical/surgical • Inpatient mental health and substance use disorder • Outpatient mental health and substance use disorder • Diabetes supplies and equipment • Durable medical equipment The carriers must report the following information for the prior plan year (PY) for their individual and group health plans for each category of services: • The 10 codes with the highest number of prior authorization requests and the percent of approved requests. • The 10 codes with the highest percentage of approved prior authorization requests and the total number of requests. • The 10 codes with the highest percentage of prior authorization requests that were initially denied and then approved on appeal and the total number of such requests. Carriers also must include the average response time in hours for prior authorization requests and the number of requests for each covered service in the lists above for: • Expedited decisions. • Standard decisions. • Extenuating-circumstances decisions. Engrossed Second Substitute House Bill 1357 added additional prescription drug prior authorization reporting requirements for health carriers beginning in reporting year 2024. Carriers were provided the opportunity to submit voluntary prescription drug prior authorization data for the 2023 reporting period. Prescription drug reporting was required for the 2024 reporting period.

The dataset covers the 2601 counties' Health plan selection either the presence or absence of Advanced premium tax credit for 2015. It also includes the total number of unique individuals with non-canceled plan selection for March 2015 of the 37 states that use the HealthCare.gov platform, Federally-facilitated, State-Partnership and supported State-based Marketplaces. Plan selections are from November 15, 2014, to February 15, 2015, plus the special enrollment period from February 22, 2015.

This classification includes different types of health care establishments.

The Global Patient Data Hub Solutions Market size is expected to reach $2.64 billion by 2032, rising at a market growth of 7.3% CAGR during the forecast period. These systems enable healthcare providers, payers, and researchers to access real-time patient information, consolidate electronic health r

This dataset contains data for the Healthcare Payments Data (HPD) Healthcare Measures report. The data cover three measurement categories: Health conditions, Utilization, and Demographics. The health condition measurements quantify the prevalence of long-term illnesses and major medical events prominent in California’s communities like diabetes and heart failure. Utilization measures convey rates of healthcare system use through visits to the emergency department and different categories of inpatient stays, such as maternity or surgical stays. The demographic measures describe the health coverage and other characteristics (e.g., age) of the Californians included in the data and represented in the other measures. The data include both a count or sum of each measure and a count of the base population so that data users can calculate the percentages, rates, and averages in the visualization. Measures are grouped by year, age band, sex (assigned sex at birth), payer type, Covered California Region, and county.