A comparison dataset of major healthcare software types, their functions, users, and 2025 trends.

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.

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.

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

Context: This synthetic healthcare dataset has been created to serve as a valuable resource for data science, machine learning, and data analysis enthusiasts. It is designed to mimic real-world healthcare data, enabling users to practice, develop, and showcase their data manipulation and analysis skills in the context of the healthcare industry.

Inspiration: The inspiration behind this dataset is rooted in the need for practical and diverse healthcare data for educational and research purposes. Healthcare data is often sensitive and subject to privacy regulations, making it challenging to access for learning and experimentation. To address this gap, I have leveraged Python's Faker library to generate a dataset that mirrors the structure and attributes commonly found in healthcare records. By providing this synthetic data, I hope to foster innovation, learning, and knowledge sharing in the healthcare analytics domain.

Dataset Information: Each column provides specific information about the patient, their admission, and the healthcare services provided, making this dataset suitable for various data analysis and modeling tasks in the healthcare domain. Here's a brief explanation of each column in the dataset -

Name: This column represents the name of the patient associated with the healthcare record. Age: The age of the patient at the time of admission, expressed in years. Gender: Indicates the gender of the patient, either "Male" or "Female." Blood Type: The patient's blood type, which can be one of the common blood types (e.g., "A+", "O-", etc.). Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as "Diabetes," "Hypertension," "Asthma," and more. Date of Admission: The date on which the patient was admitted to the healthcare facility. Doctor: The name of the doctor responsible for the patient's care during their admission. Hospital: Identifies the healthcare facility or hospital where the patient was admitted. Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," and "Medicare." Billing Amount: The amount of money billed for the patient's healthcare services during their admission. This is expressed as a floating-point number. Room Number: The room number where the patient was accommodated during their admission. Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent," reflecting the circumstances of the admission. Discharge Date: The date on which the patient was discharged from the healthcare facility, based on the admission date and a random number of days within a realistic range. Medication: Identifies a medication prescribed or administered to the patient during their admission. Examples include "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor." Test Results: Describes the results of a medical test conducted during the patient's admission. Possible values include "Normal," "Abnormal," or "Inconclusive," indicating the outcome of the test. Usage Scenarios: This dataset can be utilized for a wide range of purposes, including:

Developing and testing healthcare predictive models. Practicing data cleaning, transformation, and analysis techniques. Creating data visualizations to gain insights into healthcare trends. Learning and teaching data science and machine learning concepts in a healthcare context. You can treat it as a Multi-Class Classification Problem and solve it for Test Results which contains 3 categories(Normal, Abnormal, and Inconclusive). Acknowledgments: I acknowledge the importance of healthcare data privacy and security and emphasize that this dataset is entirely synthetic. It does not contain any real patient information or violate any privacy regulations. I hope that this dataset contributes to the advancement of data science and healthcare analytics and inspires new ideas. Feel free to explore, analyze, and share your findings with the Kaggle community.

Original Data Source: Healthcare Dataset

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.

According to a 2023/24 survey, of the 2,138 physician and advanced practice professional (APP) recruitment assignments conducted that year, 28 percent were for hospital settings. The share of hospital recruitments has been decreasing slowly in the past years. Instead, physicians and nurse practitioners (NPs) are being attracted to other outpatient facilities such as urgent care centers, retail clinics, and telemedicine platforms.

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

This dataset provides a classification of healthcare facilities in the State of Qatar by type. It includes public and private institutions such as hospitals, health centers, diagnostic centers, and specialized clinics. Each facility type is accompanied by the number of facilities in that category.The dataset supports healthcare infrastructure assessment, policy planning, and service coverage evaluation. It offers insight into the distribution and diversity of health service providers, useful for researchers, planners, and decision-makers.

Big Data Analytics In Healthcare Market size is estimated at USD 37.22 Billion in 2024 and is projected to reach USD 74.82 Billion by 2032, growing at a CAGR of 9.12% from 2026 to 2032.

Big Data Analytics In Healthcare Market: Definition/ Overview

Big Data Analytics in Healthcare, often referred to as health analytics, is the process of collecting, analyzing, and interpreting large volumes of complex health-related data to derive meaningful insights that can enhance healthcare delivery and decision-making. This field encompasses various data types, including electronic health records (EHRs), genomic data, and real-time patient information, allowing healthcare providers to identify patterns, predict outcomes, and improve patient care.

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.

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.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

In a historical and developmental sense, the former one-year reporting on employees employed in healthcare grew during 1990/91. in the continuous collection and monitoring of data through the state Register of Health Professionals. The department maintains data on all healthcare workers and healthcare associates, and on administrative and technical staff for now only numerically, according to the number of permanent employees at the end of the year. In the future, it is intended to register employees who are not health-oriented and work in healthcare, and healthcare professionals who work outside the healthcare system can also be registered.

Data on health workers and health care associates are required to be submitted not only by state and county-owned health institutions, but also by all private institutions, health workers who independently perform private practice, as well as trading companies for the performance of health activities, regardless of whether they have a contract with the Croatian Institute for health insurance.

All employees are assigned a registration number (code) upon entry into the Registry's database on the day of employment. The connection with the Croatian Health Insurance Institute exists through the use of the registration number when registering, recognizing within the CEZIH system, as well as when registering prescriptions, referrals and other documents of the HZZO. that is, in monitoring and building the health information system.

As an integral part of the same, relational databases also include data on health organizational units, representing the Register of Health Institutions. Namely, in addition to data on employees, the Registry, based on the decision of the Ministry of Health on work authorization, also records basic data on health institutions, surgeries and all other types of independent health units, regardless of the contract with the Croatian Health Insurance Institute or the type of ownership. As for employees, received data on the opening, closing, change of name, address, type and activity of the health organizational unit is also updated daily.

Thus, the organizational structure of healthcare is monitored through the database, according to levels of healthcare, types of healthcare institutions, healthcare activities performed by institutions, divisions with regard to the type of ownership as well as territorial distribution.

In addition to the importance of data on human potential and space, that is, the units where health care is provided, medical equipment is also an important factor in management and planning. One part of the department's work is related to the collection of data on this material resource. In the near future, it is planned to form a Register of Medically Expensive Equipment, which would be technologically and functionally connected with the existing two registers into a whole register of resources in healthcare.

Also, the statistical research aims to include those entities that are not part of the health system, and in which health workers work, i.e. health activities are performed, such as long-term care homes, which means expanding the existing data of the Register of Health Institutions.

In the last decade, a new IT application of the Registry of Health Care Professionals was created and an even better connection with the Croatian Institute for Health Insurance, for example through the use of the so-called population register or the register of insured persons. The register continues to be the source of data and the authorized institution for the delivery of data to international bodies such as the WHO and the joint WHO/Eurostat/OECD database. Within the scope of the Department's activities are also activities in international initiatives and programs, and with regard to the problems of statistical monitoring, shortages and planning of health workers. Since 2012, we have been involved in the implementation of the "Global Code of Practice on International Recruitment of Health Personnel", a recommendation that is also an instrument in the regulation, improvement and establishment of standards in the migration process.

In the same year, the Department was involved in the work in the part of the program platform on the topic of Joint Action on European Health Workforce Planning and Forecasting.

Also, during the past years, there has been cooperation on the topic of health workers within the framework of the South-eastern Europe Health Network (SEEHN).

This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Health Care Related Organization". 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.

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

According to a survey conducted in the U.S. in 2020, 74 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 12 percent of respondents mentioned the training was in development. On the other hand, only 30 percent of respondents were currently training their staff on how to effectively examine a patient remotely, while 32 percent mentioned the training is in development.

ContextHealth policy has long been preoccupied with the problem that health insurance stimulates spending (“moral hazard”). However, much health spending is costly healthcare that uninsured individuals could not otherwise access. Field studies comparing those with more or less insurance cannot disaggregate moral hazard versus access. Moreover, studies of patients consuming routine low-dollar healthcare are not informative for the high-dollar healthcare that drives most of aggregate healthcare spending in the United States.MethodsWe test indemnities as an alternative theory-driven counterfactual. Such conditional cash transfers would maintain an opportunity cost for patients, unlike standard insurance, but also guarantee access to the care. Since indemnities do not exist in U.S. healthcare, we fielded two blinded vignette-based survey experiments with 3,000 respondents, randomized to eight clinical vignettes and three insurance types. Our replication uses a population that is weighted to national demographics on three dimensions.FindingsMost or all of the spending due to insurance would occur even under an indemnity. The waste attributable to moral hazard is undetectable.ConclusionsFor high-cost care, policymakers should be more concerned about the foregone efficient spending for those lacking full insurance, rather than the wasteful spending that occurs with full insurance.