This database is part of the National Medical Information System (NMIS). The National Health Care Practitioner Database (NHCPD) supports Veterans Health Administration Privacy Act requirements by segregating personal information about health care practitioners such as name and social security number from patient information recorded in the National Patient Care Database for Ambulatory Care Reporting and Primary Care Management Module.

The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

The State Emergency Department Databases (SEDD) are part of the family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The SEDD are a set of databases that capture discharge information on all emergency department visits that do not result in an admission. The SEDD combined with SID discharges that originate in the emergency department are well suited for research and policy questions that require complete enumeration of hospital-based emergency departments within market areas or states. Data may not be available for all states across all years.

Discover the booming medical database software market, projected to reach $45 billion by 2033, with a CAGR of 12%. This analysis explores key drivers, trends, restraints, and regional insights for EHR and HIM systems, featuring leading companies like NextGen and Epic. Learn more about this rapidly evolving sector.

The State Inpatient Databases (SID) are part of the family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The SID are a set of hospital databases containing the universe of the inpatient discharge abstracts from participating States, translated into a uniform format to facilitate multi-State comparisons and analyses. The SID can be used to investigate questions and identify trends unique to one state, to compare data from two or more states, and to conduct market area research or small area variation analyses. Data may not be available for all states across all years.

Specific injuries in patients seen in the ED with football injuries by hospital admission (N = 819,000).

Contains data to inform healthcare decision-making from Cochrane and other systematic reviews, clinical trials, and more. Cochrane reviews bring you the combined results of the worlds best medical research studies, and are recognized as the gold standard in evidence-based health care. Consists of a regularly updated collection of evidence-based medicine databases, including The Cochrane Database of Systematic Reviews. This database includes systematic reviews of healthcare interventions that are produced and disseminated by The Cochrane Collaboration. It is published on a monthly basis and made available both on CD-ROM and the Internet. The review abstracts are available to browse and search free of charge on this website. The Cochrane Library Users'' Group (CLUG) provides a forum for discussion of usability, readability, searchability, and formatting issues related to the use of The Cochrane Library. The Cochrane Collaboration is an international not-for-profit and independent organization, dedicated to making up-to-date, accurate information about the effects of healthcare readily available worldwide. Funded by John Wiley and Sons Limited. The individual entities of The Cochrane Collaboration are funded by a large variety of governmental, institutional and private funding sources, and are bound by organisation-wide policy limiting uses of funds from corporate sponsors.

BackgroundAdministrative healthcare databases are useful and inexpensive tools that can provide a comprehensive assessment of the burden of diseases in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources. However, a crucial issue is the reliability of information gathered. The aim of this study was to validate ICD-9 codes for several major cardiovascular conditions, i.e., acute myocardial infarction (AMI), atrial fibrillation/flutter (AF), and heart failure (HF), in order to use them for epidemiological, outcome, and health services research.MethodsData from the centralised administrative database of the Umbria Region (890,000 residents, located in Central Italy) were considered. Patients with a first hospital discharge for AMI, AF/flutter, and HF, between 2012 and 2014, were identified using ICD-9-CM codes in primary position. A sample of cases and non-cases was randomly selected, and the corresponding medical charts reviewed by specifically trained investigators. For each disease, case ascertainment was based on all clinical, laboratory, and instrumental examinations available in medical charts. Sensitivity, specificity, and predictive values with 95% confidence intervals (CIs), were calculated.ResultsWe reviewed 458 medical charts, 128 for AMI, 127 for AF/flutter, 127 for HF, and 76 of non-cases for each condition. Diagnostic accuracy measures of the original discharge diagnosis were as follows. AMI: sensitivity 98% (95% CI, 94–100%), specificity 91% (95% CI, 83–97%), positive predictive value (PPV) 95% (95% CI, 89–98%), negative predictive value (NPV) 97% (95% CI, 91–100%). AF/flutter: sensitivity 95% (95% CI, 90–98%), specificity 95% (95% CI, 87–99%), PPV 97% (95% CI, 92–99%), NPV 92% (95% CI, 84–97%). HF: sensitivity 96% (95% CI, 91–99%), specificity 90% (95% CI, 81–96%), PPV 94% (95% CI, 88–97%), NPV 93% (95% CI, 85–98%).ConclusionThe case ascertainment for AMI, AF and flutter, and HF, showed a high level of accuracy (≥ 90%). The healthcare administrative database of the Umbria Region can be confidently used for epidemiological, outcome, and health services research.

ObjectiveWe evaluated the validity of physician billing claims to identify deceased organ donors in large provincial healthcare databases.MethodsWe conducted a population-based retrospective validation study of all deceased donors in Ontario, Canada from 2006 to 2011 (n = 988). We included all registered deaths during the same period (n = 458,074). Our main outcome measures included sensitivity, specificity, positive predictive value, and negative predictive value of various algorithms consisting of physician billing claims to identify deceased organ donors and organ-specific donors compared to a reference standard of medical chart abstraction.ResultsThe best performing algorithm consisted of any one of 10 different physician billing claims. This algorithm had a sensitivity of 75.4% (95% CI: 72.6% to 78.0%) and a positive predictive value of 77.4% (95% CI: 74.7% to 80.0%) for the identification of deceased organ donors. As expected, specificity and negative predictive value were near 100%. The number of organ donors identified by the algorithm each year was similar to the expected value, and this included the pre-validation period (1991 to 2005). Algorithms to identify organ–specific donors performed poorly (e.g. sensitivity ranged from 0% for small intestine to 67% for heart; positive predictive values ranged from 0% for small intestine to 37% for heart).InterpretationPrimary data abstraction to identify deceased organ donors should be used whenever possible, particularly for the detection of organ-specific donations. The limitations of physician billing claims should be considered whenever they are used.

Demographics of patients seen at the ED with football injuries by admission to hospital 2010 to 2013 ((N = 819,000).

Integrated database from three primary sources: West Health-Gallup partnership surveys since 2019, nationally representative 2025 survey, and Gallup Poll Social Series health modules since 2001

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.

Our highly-targeted consumer healthcare database includes:

🗸 Name 🗸 Postal Address, Email Address, Telephone Number 🗸 Age, Gender 🗸 Most likely to ask a Doctor About an Advertised Prescription Medicine 🗸 Most likely looked for Medical Information on the Web 🗸 Most Likely to Prefer Brand Name Medicines 🗸 Most Likely to Buy Prescriptions through the Mail

The dataset is available for purchase by US region: 🗸 New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont) 🗸 Middle Atlantic (New Jersey, New York, and Pennsylvania) 🗸 East North Central (Illinois, Indiana, Michigan, Ohio, and Wisconsin) 🗸 West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota) 🗸 South Atlantic (Delaware; Florida; Georgia; Maryland; North Carolina; South Carolina; Virginia; Washington, D.C. and West Virginia) 🗸 East South Central (Alabama, Kentucky, Mississippi, and Tennessee) 🗸 West South Central (Arkansas, Louisiana, Oklahoma, and Texas) 🗸 Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming) 🗸 Pacific (Alaska, California, Hawaii, Oregon, and Washington)

Abstract

THE DATA ON THIS PAGE SHOULD NOW ONLY BE USED TO FINISH UP EXISTING PROJECTS. NO NEW PROJECTS SHOULD BE STARTED WITH THIS COPY OF THE DATA.

The MarketScan Commercial Database (previously called the 'MarketScan Database') contains real-world data for healthcare research and analytics to examine health economics and treatment outcomes.

This page also contains the MarketScan Commercial Lab Database starting in 2018.

Starting in 2026, there will be a data access fee for using the full dataset. Please refer to the 'Usage Notes' section of this page for more information.

Methodology

MarketScan Research Databases are a family of data sets that fully integrate many types of data for healthcare research, including:

• De-identified records of more than 188 million patients (medical, drug and dental)

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• Laboratory results

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• Hospital discharges

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The MarketScan Databases track millions of patients throughout the healthcare system. The data are contributed by large employers, managed care organizations, hospitals, EMR providers, and Medicare.

Usage

This page contains the MarketScan Commercial Database.

We also have the following on other pages:

• The MarketScan Medicare Supplemental Database
• The MarketScan Dental Database
• The MarketScan Benefit Plan Design data
• MarketScan Redbook (The MarketScan Code Reference Book)

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**Starting in 2026, there will be a data access fee for using the full dataset **(though the 1% sample will remain free to use). The pricing structure and other **relevant information can be found in this **FAQ Sheet.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to support@stanfordphs.freshdesk.com for approval prior to journal submission.

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For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Data Documentation

Data access is required to view this section.

Section 2

Metadata access is required to view this section.

Section 3

Metadata access is required to view this section.

Usage FAQs (Answers provided in User Guide starting on page 56)

Metadata access is required to view this section.

The PHARMO Database Network is a population-based network of electronic healthcare databases, combining anonymous data from several primary and secondary healthcare providers in the Netherlands, such as general practitioners and outpatient pharmacies. The databases are also linked to external registries such as the Cancer and Pathology registries. As of the 1st of January 2018, the network included around 7 million active patients. All patients registered with the contributing healthcare providers are included in the network unless patients request to opt out. The frequency of data collection for each healthcare provider is at least annually, whilst the linkage is updated every year.

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.

Image Credit:

Image by BC Y from Pixabay

The data package contains NPI related datasets. The NPI number of all the covered health care professionals, the deactivated NPI's and dfferent codes used within the NPI dataset

ABSTRACT Objective: to analyze demographic Brazilian medical data from the national public healthcare system (SUS), which provides free universal health coverage for the entire population, and discuss the problems revealed, with particular focus on surgical care. Methods: data was obtained from public healthcare databases including the Medical Demography, the Brazilian Federal Council of Medicine, the Brazilian Institute of Geography and Statistics, and the National Database of Healthcare Establishments. Density and distribution of the medical workforce and healthcare facilities were calculated, and the geographic regions were analyzed using the public private inequality index. Results: Brazil has an average of two physicians for every 1,000 inhabitants, who are unequally distributed throughout the country. There are 22,276 board certified general surgeons in Brazil (11.49 for every 100,000 people). The country currently has 257 medical schools, with 25,159 vacancies for medical students each year, with only around 13,500 vacancies for residency. The public private inequality index is 3.90 for the country, and ranges from 1.63 in the Rio de Janeiro up to 12.06 in Bahia. Conclusions: A significant part of the local population still faces many difficulties in accessing surgical care, particularly in the north and northeast of the country, where there are fewer hospitals and surgeons. Physicians and surgeons are particularly scarce in the public health system nationwide, and better incentives are needed to ensure an equal public and private workforce.

This spreadsheet provides the list of indicators related to the assessment of the quality of child healthcare collected from two type of sources: open-access international databases and national experts. It has been adopted to the Paper 'Quality of child healthcare in European countries: common measures across international databases and national agencies'.

IntroductionInteractions between pharmaceutical companies and healthcare providers are increasingly scrutinized by academics, professionals, media, and politicians. Most empirical studies and professional guidelines focus on unilateral donor-recipient types of interaction and overlook, or fail to distinguish between, more reciprocal types of interaction. However, the degree of goal alignment and potential for value creation differs in these two types of interactions. Failing to differentiate between these two forms of interaction between pharmaceutical companies and healthcare providers could thus lead to biased conclusions regarding their desirability. This study reviews the empirical literature regarding the effects of bilateral forms of interactions between pharmaceutical companies and healthcare providers in order to explore their effects.Material and methodsWe searched two medical databases (i.e. PubMed and Cochrane Library) and one business database (i.e. EBSCO) for empirical, peer-reviewed articles concerning any type of bilateral interaction between pharmaceutical companies and healthcare providers. We included quantitative articles which were written in English and published between January 1st, 2000 and October 31st, 2016, and where the title or abstract included a combination of synonyms of the following keywords: pharmaceutical companies, healthcare providers, interaction, and effects.ResultsOur search results yielded 10 studies which were included in our analysis. These studies focused on either research-oriented interaction or on education-oriented interaction. The included studies reported various outcomes of interaction such as prescribing behavior, ethical dilemmas, and research output. Regardless of the type of interaction, the studies either reported no significant effects or ambivalent outcomes such as affected clinical practice or ethical issues.Discussion and conclusionThe effects of bilateral interactions reported in the literature are similar to those reported in studies concerning unilateral interactions. The theoretical notion that bilateral interactions between pharmaceutical companies and healthcare providers have different effects given their increased level of goal alignment thus does not seem to hold. However, most of the empirical studies focus on intermediary, provider-level, outcomes such as altered prescribing behavior. Outcomes at the health system level such as overall costs and quality of care are overlooked. Further research is necessary in order to disentangle various forms of value created by different types of interactions between pharmaceutical companies and healthcare providers.