The National Hospital Care Survey (NHCS) collects data on patient care in hospital-based settings to describe patterns of health care delivery and utilization in the United States. Settings currently include inpatient and emergency departments (ED). From this collection, the NHCS contributes data that may inform emerging national health threats such as the current opioid public health emergency. The 2022 - 2024 NHCS are not yet fully operational so it is important to note that the data presented here are preliminary and not nationally representative. The data are from 24 hospitals submitting inpatient and 23 hospitals submitting ED Uniform Bill (UB)-04 administrative claims from October 1, 2022–September 30, 2024. Even though the data are not nationally representative, they can provide insight into the use of opioids and other overdose drugs. The NHCS data is submitted from various types of hospitals (e.g., general/acute, children’s, etc.) and can show results from a variety of indicators related to drug use, such as overall drug use, comorbidities, and drug and polydrug overdose. NHCS data can also be used to report on patient conditions within the hospital over time.

Patient Health and Medication Data

Column Descriptions

• Age: The age of the patient in years.
• Medication_Duration: The duration for which the patient has been on the current medication, measured in days.
• Allergy: Indicates the type of allergy the patient has, if any (e.g., Pollen, Peanuts, Shellfish, or None).
• Cholesterol: The patient's cholesterol level, categorized as HIGH or NORMAL.
• Sex: The sex of the patient, categorized as Male (M) or Female (F).
• BP (Blood Pressure): The patient's blood pressure reading, categorized as HIGH, NORMAL, or LOW.
• Na_to_K: The ratio of sodium to potassium in the patient's blood, a numerical value.
• Drug: The specific drug or medication prescribed to the patient, which could be the generic name or brand name of the drug.

Inspired from - https://www.kaggle.com/datasets/prathamtripathi/drug-classification

Washington’s PMP was created (RCW 70.225 (2007)) to improve patient care and to stop prescription drug misuse by collecting dispensing records for Schedule II, III, IV and V drugs, and by making the information available to medical providers and pharmacists as a patient care tool. Program rules, WAC 246-470, took effect August 27, 2011. The program started data collection from all dispensers October 7, 2011.

Under RCW 70.225.040(5)(a), the department is authorized to publish public data after removing information that could be used directly or indirectly to identify individual patients, requestors, dispensers, prescribers, and persons who received prescriptions from dispensers. The data available here are de-identified, and exclude patient, prescriber, and dispenser related information in alignment with program rules WAC 246-470-080. No requestor information is available here.

Prescriptions excluded from PMP include those dispensed outside of WA State, those prescribed for less than or equal to 24 hours, those administered or given to a patient in the hospital, and those dispensed from a Department of Corrections pharmacy (unless an offender is released with a prescription), an Opioid Treatment Program, and some federally operated pharmacies (Indian Health Services and Veterans Affairs report voluntarily since 2015).

Further information on collection and management of PMP data at DOH can be found at www.doh.wa.gov/pmp/data.

The data set includes the top 25 list for costliest prescribed drugs, most frequently prescribed drugs and the prescribed drugs with the highest monthly median out-of-pocket costs. Each of these top 25 lists are given for commercial plans and are broken out by brand or generic category (i.e., Brand or Generic, Brand, and Generic). The includes National Drug Code (NDC), Drug Name, number of prescriptions, number of individuals, total costs, cost per prescription and monthly median out-of-pocket costs for each NDC in each top 25 list.

The dataset, titled "2013年1月ｰ2022年12月 薬剤患者数," contains essential healthcare information over the ten-year period from January 2013 to December 2022 in Japan. It includes three key fields:

製品名 (Product Name): This field encompasses the names of pharmaceutical products used for medical treatment. These names serve as identifiers for the specific medications or drugs administered to patients.

実患者数 (Actual Number of Patients): This column provides data on the actual count of patients who received treatment with the mentioned pharmaceutical products during the specified timeframe. It serves as a crucial metric for evaluating the prevalence and usage of these medications.

金額 (Amount): The "金額" field represents the monetary value associated with the utilization of these pharmaceutical products. It signifies the total cost or expenditure linked to these medications within the stated period.

This dataset is invaluable for various stakeholders within the healthcare industry, including pharmaceutical companies, healthcare providers, researchers, and policymakers. It enables the analysis of trends in medication usage, patient demographics, and associated costs. Researchers can utilize this dataset to conduct pharmacoeconomic studies, assess the impact of specific medications, and make informed decisions regarding healthcare resource allocation. Additionally, pharmaceutical companies can gain insights into the performance of their products in the market. Overall, this dataset facilitates evidence-based decision-making and enhances the understanding of pharmaceutical utilization in Japan from 2013 to 2022.

This medical dataset contains the Application Submission part to build the Drugs@FDA database from the Food and Drug Administration (FDA) Approved Drug Products available on their official website. It provides information on drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status, and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.

Information about FDA-approved brand name and generic prescription and over-the-counter human drugs and biological therapeutic products. Drugs@FDA includes most of the drug products approved since 1939. The majority of patient information, labels, approval letters, reviews, and other information are available for drug products approved since 1998.

Track specialty drug utilization, analyze patient journeys, and predict earnings surprises based on domestic pharmacy claims capturing ~ 90 million patients. Pharmacy claims data is sourced from a large health services company with visibility into commonly blocked specialty pharmacy drugs and strong longitudinal integrity allowing for accurate patient journey analytics.

This dataset was used for the Winter 2018 Kaggle University Club Hackathon and is now publicly available. See Acknowledgments section for citation and licensing. Note: The types of data and recommendation based solutions provided by the contestants are purely for NLP learning purposes. They are not suitable for a real world drug recommendations solutions.

Welcome to the Kaggle University Club Hackathon!

If you are interested in joining Kaggle University Club, please e-mail Jessica Li at lijessica@google.com

This Hackathon is open to all undergraduate, master, and PhD students who are part of the Kaggle University Club program. The Hackathon provides students with a chance to build capacity via hands-on ML, learn from one another, and engage in a self-defined project that is meaningful to their careers.

Teams must register via Google Form to be eligible for the Hackathon. The Hackathon starts on Monday, November 12, 2018 and ends on Monday, December 10, 2018. Teams have one month to work on a team submission. Teams must do all work within the Kernel editor and set Kernel(s) to public at all times.

Prompt

The freestyle format of hackathons has time and again stimulated groundbreaking and innovative data insights and technologies. The Kaggle University Club Hackathon recreates this environment virtually on our platform. We challenge you to build a meaningful project around the UCI Machine Learning - Drug Review Dataset. Teams are free to let their creativity run and propose methods to analyze this dataset and form interesting machine learning models.

Machine learning has permeated nearly all fields and disciplines of study. One hot topic is using natural language processing and sentiment analysis to identify, extract, and make use of subjective information. The UCI ML Drug Review dataset provides patient reviews on specific drugs along with related conditions and a 10-star patient rating system reflecting overall patient satisfaction. The data was obtained by crawling online pharmaceutical review sites. This data was published in a study on sentiment analysis of drug experience over multiple facets, ex. sentiments learned on specific aspects such as effectiveness and side effects (see the acknowledgments section to learn more).

The sky's the limit here in terms of what your team can do! Teams are free to add supplementary datasets in conjunction with the drug review dataset in their Kernel. Discussion is highly encouraged within the forum and Slack so everyone can learn from their peers.

Here are just a couple ideas as to what you could do with the data:

• Classification: Can you predict the patient's condition based on the review?
• Regression: Can you predict the rating of the drug based on the review?
• Sentiment analysis: What elements of a review make it more helpful to others? Which patients tend to have more negative reviews? Can you determine if a review is positive, neutral, or negative?
• Data visualizations: What kind of drugs are there? What sorts of conditions do these patients have?

Top Submissions

There is no one correct answer to this Hackathon, and teams are free to define the direction of their own project. That being said, there are certain core elements generally found across all outstanding Kernels on the Kaggle platform. The best Kernels are:

1. Complex: How many domains of analysis and topics does this Kernel cover? Does it attempt machine learning methods? Does the Kernel offer a variety of unique analyses and interesting conclusions or solutions?
2. Original: What is the subject matter of this Kernel? Does it have a well-defined and interesting project scope, narrative or problem? Could the results make an impact? Is it thought provoking?
3. Approachable: How easy is it to understand this Kernel? Are all thought processes clear? Is the code clean, with useful comments? Are visualizations and processes articulated and self-explanatory?

Teams with top submissions have a chance to receive exclusive Kaggle University Club swag and be featured on our official blog and across social media.

IMPORTANT: Teams must set all Kernels to public at all times. This is so we can track each team's progression, but more importantly it encourages collaboration, productive discussion, and healthy inspiration to all teams. It is not so that teams can simply copycat good ideas. If a team's Kernel isn't their own organic work, it will not be considered a top submission. Teams must come up with a project on their own.

Submission Styling

The final Kernel submission for the Hackathon must contain the following information:

• All team members added as collaborators to the Kernel
• Somewhere at the top of your Kernel, find a space to put down all team member names, university name, club name, and team name (as specified whe...

Data on delay or nonreceipt of needed medical care, nonreceipt of needed prescription drugs, or nonreceipt of needed dental care during the past 12 months due to cost 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, Family Core, Sample Child, and 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.

Through the Medicines and Resources Information Project (GIP), the National Health Care Institute has an independent, reliable and representative information system that contains data on the use of medicines and resources in the Netherlands. The Zorginstituut uses this data to map the developments in the use of medicines and aids and the associated costs.

Since 2004, the data files of the GIP have been made accessible via www.gipdatabank.nl . The GIP database is a unique public data source with detailed figures on the use of medicines and aids in the Netherlands over the past five years. Here you will find detailed information about the volume (number of dispensations and number of standard daily doses), the associated costs and the number of users of medicines and aids.

The data files of the GIP are based on the claim data for pharmaceutical care (including diet and food) and medical aids, from 19 health insurers (risk-bearing labels). This concerns medicines and medical aids that have been prescribed extramurally by the general practitioner or the specialist, and subsequently dispensed by a pharmacist, dispensing general practitioner or supplier of medical aids. This concerns medicines and medical aids that are reimbursed by the health insurer on the basis of the Health Insurance Act (basic insurance).

Introduction: The goal of this dataset is to aid NLP research on recognizing safety critical information from drug usage guideline or patient handout data. This dataset contains annotated advice statements from 90 online DUG documents that corresponds to 90 drugs or medications that are used in the prescriptions of patients suffering from one or more chronic diseases. The advice statements are annotated in eight safety-critical categories: activity or lifestyle related, disease or symptom related, drug administration related, exercise related, food or beverage related, other drug related, pregnancy related, and temporal.

Data Collection: The data was collected from MedScape. It is one of the most widely used reference for health care providers. At first, 34 real anonymized prescriptions of patients suffering from one or more chronic diseases are collected. These prescriptions contains 165 drugs that are used to treat chronic diseases. Then, MedScape was crawled to collect the drug user guideline (DUG) / patient handout for these 165 drugs. But, MedScape does not have DUG document for all drugs. We found DUG document for 90 drugs in MedScape.

Data Annotation tool: The data annotation tool is developed to ease the annotation process. It allows the user to select a DUG document and select a position from the document in terms of line number. It stores the user log from the annotator and loads the most recent position from the log when the application is launched. It supports annotating multiple files for the same drug, as often there are multiple overlapping sources of drug usage guidelines for a single drug. Often DUG documents contain formatted text. This tool aids annotation of the formatted text as well. The annotation tool is also available upon request.

Annotated Data Description: The annotated data contains the annotation tag(s) of each advice extracted from the 90 online DUG documents. It also contains the phrases or topics in the advice statement that triggers the annotation tag, such as, activity, exercise, medication name, food or beverage name, disease name, pregnancy condition (gestational, postpartum). Sometimes disease names are not directly mentioned rather mentioned as a condition (e.g., stomach bleeding, alcohol abuse) or state of a parameter (e.g., low blood sugar, low blood pressure). The annotated data is formatted as following:
drug name, drug number, line number of the first sentence of the advice in the DUG document, advice Text, advice tag(s), medication, food, activity, exercise, and disease names mentioned in the advice.

Unannotated Data Description:
The unannotated data contains the raw DUG document for 90 drugs. It also contains the drug interaction information for the 165 drugs. The drug interaction information is categorized in 4 classes, contraindicated, serious, monitor closely, and minor. This information can be utilized to automatically detect potential interaction and effect of interaction among multiple drugs.

Citation: If you use this dataset in your work, please cite the following reference in any publication:

@inproceedings{preum2018DUG,
title={A Corpus of Drug Usage Guidelines Annotated with Type of Advice},
author={Sarah Masud Preum, Md. Rizwan Parvez, Kai-Wei Chang, and John A. Stankovic},
booktitle={ Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
publisher = {European Language Resources Association (ELRA)},
year={2018}
}

Dataset

This dataset was created by Ian Cornish

Contents

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/ ---

Real World Evidence Solutions Market size was valued at USD 1.30 Billion in 2024 and is projected to reach USD 3.71 Billion by 2031, growing at a CAGR of 13.92% during the forecast period 2024-2031.

Global Real World Evidence Solutions Market Drivers

The market drivers for the Real World Evidence Solutions Market can be influenced by various factors. These may include:

Growing Need for Evidence-Based Healthcare: Real-world evidence (RWE) is becoming more and more important in healthcare decision-making, according to stakeholders such as payers, providers, and regulators. In addition to traditional clinical trial data, RWE solutions offer important insights into the efficacy, safety, and value of healthcare interventions in real-world situations. Growing Use of RWE by Pharmaceutical Companies: RWE solutions are being used by pharmaceutical companies to assist with market entry, post-marketing surveillance, and drug development initiatives. Pharmaceutical businesses can find new indications for their current medications, improve clinical trial designs, and convince payers and providers of the worth of their products with the use of RWE. Increasing Priority for Value-Based Healthcare: The emphasis on proving the cost- and benefit-effectiveness of healthcare interventions in real-world settings is growing as value-based healthcare models gain traction. To assist value-based decision-making, RWE solutions are essential in evaluating the economic effect and real-world consequences of healthcare interventions. Technological and Data Analytics Advancements: RWE solutions are becoming more capable due to advances in machine learning, artificial intelligence, and big data analytics. With the use of these technologies, healthcare stakeholders can obtain actionable insights from the analysis of vast and varied datasets, including patient-generated data, claims data, and electronic health records. Regulatory Support for RWE Integration: RWE is being progressively integrated into regulatory decision-making processes by regulatory organisations including the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA). The FDA's Real-World Evidence Programme and the EMA's Adaptive Pathways and PRIority MEdicines (PRIME) programme are two examples of initiatives that are making it easier to incorporate RWE into regulatory submissions and drug development. Increasing Emphasis on Patient-Centric Healthcare: The value of patient-reported outcomes and real-world experiences in healthcare decision-making is becoming more widely acknowledged. RWE technologies facilitate the collection and examination of patient-centered data, offering valuable insights into treatment efficacy, patient inclinations, and quality of life consequences. Extension of RWE Use Cases: RWE solutions are being used in medication development, post-market surveillance, health economics and outcomes research (HEOR), comparative effectiveness research, and market access, among other healthcare fields. The necessity for a variety of RWE solutions catered to the needs of different stakeholders is being driven by the expansion of RWE use cases.

The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED) visits to hospitals since the early 1970s. First administered by the Drug Enforcement Administration (DEA) and the National Institute on Drug Abuse (NIDA), the responsibility for DAWN now rests with the Substance Abuse and Mental Health Services Administration's (SAMHSA) Center for Behavioral Health Statistics and Quality (CBHSQ). Over the years, the exact survey methodology has been adjusted to improve the quality, reliability, and generalizability of the information produced by DAWN. The current approach was first fully implemented in the 2004 data collection year. DAWN relies on a longitudinal probability sample of hospitals located throughout the United States. To be eligible for selection into the DAWN sample, a hospital must be a non-Federal, short-stay, general surgical and medical hospital located in the United States, with at least one 24-hour ED. DAWN cases are identified by the systematic review of ED medical records in participating hospitals. The unit of analysis is any ED visit involving recent drug use. DAWN captures both ED visits that are directly caused by drugs and those in which drugs are a contributing factor but not the direct cause of the ED visit. The reason a patient used a drug is not part of the criteria for considering a visit to be drug related. Therefore, all types of drug-related events are included: drug misuse or abuse, accidental drug ingestion, drug-related suicide attempts, malicious drug poisonings, and adverse reactions. DAWN does not report medications that are unrelated to the visit. The DAWN public-use dataset provides information for all types of drugs, including illegal drugs, prescription drugs, over-the-counter medications, dietary supplements, anesthetic gases, substances that have psychoactive effects when inhaled, alcohol when used in combination with other drugs (all ages), and alcohol alone (only for patients aged 20 or younger). Public-use dataset variables describe and categorize up to 16 drugs contributing to the ED visit, including toxicology confirmation and route of administration. Administrative variables specify the type of case, case disposition, categorized episode time of day, and quarter of year. Metropolitan area is included for represented metropolitan areas. Created variables include the number of unique drugs reported and case-level indicators for alcohol, non-alcohol illicit substances, any pharmaceutical, non-medical use of pharmaceuticals, and all misuse and abuse of drugs. Demographic items include age category, sex, and race/ethnicity. Complex sample design and weighting variables are included to calculate various estimates of drug-related ED visits for the Nation as a whole, as well as for specific metropolitan areas, from the ED visits classified as DAWN cases in the selected hospitals.This study has 1 Data Set.

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

Data Record 1: Drug ingredient combinations: 1-drugdb_drugs_1s.tsvData Record 1: Drug ingredient combinations: 2-drugsSee README.txt for Data Record 1: 1-drugdb_drugs_2s.tsvData Record 1: Drug ingredient combinations: 3-drugsSee README.txt for Data Record 1: 1-drugdb_drugs_3s.tsvData Record 1: Drug ingredient combinations: 4-drugsSee README.txt for Data Record 1: 1-drugdb_drugs_4s.tsvData Record 1: Drug ingredient combinations: 5-drugsSee README.txt for Data Record 1: 1-drugdb_drugs_5s.tsvData Record 2: Drug class combinations: 1-drugSee README.txt for Data Record 1: 1-drugdb_atc_classes_1s.tsvData Record 2: Drug class combinations: 2-drugsSee README.txt for Data Record 1: 1-drugdb_atc_classes_2s.tsvData Record 2: Drug class combinations: 3-drugsSee README.txt for Data Record 1: 1-drugdb_atc_classes_3s.tsvData Record 2: Drug class combinations: 4-drugsSee README.txt for Data Record 1: 1-drugdb_atc_classes_4s.tsvData Record 2: Drug class combinations: 5-drugsSee README.txt for Data Recor...

This dataset contains the Application Documents part to build the Drugs@FDA database. Application Documents refers to "document addresses or URLs to letters, labels, reviews, Consumer Information Sheets, FDA Talk Papers, and other types. Drugs at FDA provides information of drug name, active ingredient, strength, application number, label info, dosage form or route, marketing status as well as patient information, approval letters, review and other facts for drugs approved after 1997.

The global market for drug interaction checkers is experiencing robust growth, driven by increasing prescription drug usage, a rising elderly population with complex medication regimens, and a growing emphasis on patient safety and medication adherence. The market's expansion is further fueled by advancements in technology, enabling the development of more sophisticated and user-friendly interaction checkers that integrate seamlessly with electronic health records (EHRs) and mobile health (mHealth) applications. This integration facilitates quicker access to crucial information for both healthcare professionals and patients, reducing the risk of adverse drug events (ADEs) and improving overall healthcare outcomes. Key players in this market are leveraging AI and machine learning algorithms to enhance the accuracy and efficiency of their checkers, continually updating their databases with the latest drug information and interaction data to maintain reliability. However, despite considerable growth, the market faces challenges. Data privacy and security concerns related to patient medication information remain significant hurdles. Furthermore, the need for ongoing database maintenance and updates, to keep up with the continuously evolving pharmaceutical landscape, presents a cost and resource constraint for many providers. Despite these challenges, the substantial benefits of preventing ADEs—cost savings in healthcare, improved patient outcomes, and reduced liability for healthcare professionals—are powerful incentives for continued market expansion. The projected Compound Annual Growth Rate (CAGR) suggests a substantial increase in market value over the forecast period, highlighting the increasing importance of these tools in modern healthcare. Competition among established players like Medscape, WebMD, and DrugBank, alongside newer entrants, is expected to intensify, leading to further innovation and the development of more sophisticated drug interaction checkers.