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.

Mental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps. This study aimed to conduct a review of current apps which offer screening and/or aid diagnosis of mental health conditions on the Apple app store (iOS), Google Play app store (Android), and using the m-health Index and Navigation Database (MIND). In addition, the study aimed to evaluate user experiences of the apps, identify common app features and determine which features are associated with app use discontinuation. The Apple app store, Google Play app store, and MIND were searched. User reviews and associated metadata were then extracted to perform a sentiment and thematic analysis. The final sample included 92 apps. 45.65% (n = 42) of these apps only screened for or diagnosed a single mental health condition and the most commonly assessed mental health condition was depression (38.04%, n = 35). 73.91% (n = 68) of the apps offered additional in-app features to the mental health assessment (e.g., mood tracking). The average user rating for the included apps was 3.70 (SD = 1.63) and just under two-thirds had a rating of four stars or above (65.09%, n = 442). Sentiment analysis revealed that 65.24%, n = 441 of the reviews had a positive sentiment. Ten themes were identified in the thematic analysis, with the most frequently occurring being performance (41.32%, n = 231) and functionality (39.18%, n = 219). In reviews which commented on app use discontinuation, functionality and accessibility in combination were the most frequent barriers to sustained app use (25.33%, n = 19). Despite the majority of user reviews demonstrating a positive sentiment, there are several areas of improvement to be addressed. User reviews can reveal ways to increase performance and functionality. App user reviews are a valuable resource for the development and future improvements of apps designed for mental health diagnosis and screening.

Code sheet: Metrics and parameters.

The Real World Data (RWD) solution market is experiencing robust growth, driven by the increasing adoption of digital health technologies, the rising prevalence of chronic diseases, and the urgent need for more efficient and cost-effective clinical trials. The market's expansion is fueled by several key factors, including the growing availability of large datasets from electronic health records (EHRs), wearable sensors, and mobile health applications. Pharmaceutical and biotechnology companies are increasingly leveraging RWD to accelerate drug development, gain deeper insights into patient populations, and improve post-market surveillance. Furthermore, regulatory agencies are showing greater acceptance of RWD in clinical decision-making, further stimulating market growth. We estimate the market size to be approximately $15 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 18% projected through 2033. This growth is anticipated to be driven primarily by the increased integration of AI and machine learning into RWD platforms, enhancing data analysis and generating actionable insights. The market is segmented by application (e.g., clinical trials, drug safety surveillance, regulatory submissions, payer decision support) and by type of data (e.g., EHRs, claims data, patient-generated health data, registry data). North America currently holds a significant share of the global RWD market, due to advanced healthcare infrastructure and early adoption of digital health technologies. However, Asia-Pacific is projected to witness the highest growth rate in the forecast period, driven by rapid technological advancements and increasing healthcare expenditure in emerging economies like India and China. Significant restraints include data privacy concerns, data interoperability challenges, and the need for robust data governance frameworks to ensure data quality and security. However, ongoing technological advancements and supportive regulatory policies are expected to mitigate these challenges in the coming years. Key players in the market are actively investing in RWD solutions, strengthening their competitive landscape through strategic partnerships, mergers, and acquisitions.

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.

This dataset provides information about Allegheny County vendors accepting WIC who participate in the Pennsylvania Department of Agriculture's Farmers Market Nutrition Program (FMNP). These markets provide the public, including WIC recipients, with fresh, nutritious, locally grown fruits, vegetables, and herbs from approved farmers in Pennsylvania.

Each row in the data includes details about location, days/hours of operation, and the length of the season. Additional directions and affiliations have also been provided when available.

Users may also be interested in the PA Department of Agriculture's new PA FMNP Market Locator app, a free mobile tool to help residents find markets closest to them across the entire state. The FMNP Market Locator app is available both in the Apple Store (https://apple.co/2KNJ4dk) and Google Play (http://bit.ly/2Z86Ytg).

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

ANN sensitivity (excluding description text data).

The size of the Diabetes Care Devices Market market was valued at USD 210.9 Million in 2023 and is projected to reach USD 349.9 Million by 2032, with an expected CAGR of 5.10% during the forecast period. Diabetes Care Devices Market can be referred to as a broad category of products aimed at the management as well as tracking of blood glucose levels in diabetic patients. Some of the devices include blood glucose meters, CGMs, insulin pumps, syringes, and lancets among others. Both overt and latent autoimmune diabetes in adults and T2DM need insulin as a means of regulating blood sugar so as to minimize the incidence of secondary conditions including cardio-vascular complications, stroke, and nephropathy. New developments in the market are introduction and growth of minimal invasive devices, rise in sales of CGMs and insulin pumps, linkage of these devices to mobile health apps for data analysis and increased consciousness of diabetes care in the global market. A growing number of people diagnosed with diabetes, and especially in developing countries, is stimulating sales. Recent developments include: March 2023: Abbott announced that the U.S. Food and Drug Administration cleared its FreeStyle Libre 2 and FreeStyle Libre 3 integrated continuous glucose monitoring system sensors for integration with automated insulin delivery (AID) systems. Abbott modified the sensors to enable integration with AID systems., January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes - one of the largest combined blood glucose meter and mobile diabetes app datasets ever published.. Key drivers for this market are: Rising Prevalence of Cancer Worldwide, Technological Advancements in Diagnostic Testing; Increasing Demand for Point-of-care Treatment. Potential restraints include: High Cost of Molecular Diagnostic Tests, Lack of Skilled Workforce and Stringent Regulatory Framework. Notable trends are: The continuous glucose monitoring segment is expected to witness a healthy growth rate over the forecast period.

The size of the Russia Self-monitoring Blood Glucose Devices Market was valued at USD 226.17 Million in 2023 and is projected to reach USD 326.39 Million by 2032, with an expected CAGR of 5.38% during the forecast period. The SMBG devices market is the most important sector of the health care industry in Russia. SMBG stands for self-monitoring of blood glucose, and it is the process through which diabetic patients take their own blood sugar checks at home that enable them to manage their situation effectively. These are critical monitoring equipment for diabetes patients because they keep updating them with real-time information on their level of blood glucose, thus enabling them to take informed decisions on how much insulin to administer, what to eat, and how much exercise to take. The use of SMBG in Russia has been rising year by year due to increased prevalence of diabetes, a growing awareness of the necessity for control of blood sugar, and improved SMBG technology. These are instruments used by patients with type 1 and type 2 diabetes to measure the blood glucose at several points of the day. Noting trends of blood glucose could help an individual detect hypoglycemic or hyperglycemic conditions and optimize his or her management strategy. Recent developments include: May 2023: LifeScan announced positive data from a study of real-world evidence supporting its Bluetooth-connected blood glucose meter. Evidence from more than 55,000 people with diabetes demonstrated sustained improvements in readings in range. The analysis focuses on changes over 180 days. LifeScan published results in the peer-reviewed journal Diabetes Therapy. The company’s OneTouch Bluetooth-connected blood glucose meter and mobile diabetes app provide simplicity, accuracy, and trust., January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence from Over 144,000 People with Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes – one of the largest combined blood glucose meter and mobile diabetes app datasets ever published.. Key drivers for this market are: Rising Prevalence of Cancer Worldwide, Technological Advancements in Diagnostic Testing; Increasing Demand for Point-of-care Treatment. Potential restraints include: High Cost of Molecular Diagnostic Tests, Lack of Skilled Workforce and Stringent Regulatory Framework. Notable trends are: Rising diabetes prevalence.

This paper focuses on the evaluation and recommendation of healthcare applications in the mHealth field. The increase in the use of health applications, supported by an expanding mHealth market, highlights the importance of this research. In this study, a data set including app descriptions, ratings, reviews, and other relevant attributes from various health app platforms was selected. The main goal was to design a recommendation system that leverages app attributes, especially descriptions, to provide users with relevant contextual suggestions. A comprehensive pre-processing regime was carried out, including one-hot encoding, standardisation, and feature engineering. The feature, “Rating_Reviews”, was introduced to capture the cumulative influence of ratings and reviews. The variable ‘Category’ was chosen as a target to discern different health contexts such as ‘Weight loss’ and ‘Medical’. Various machine learning and deep learning models were evaluated, from the baseline Random Forest Classifier to the sophisticated BERT model. The results highlighted the efficiency of transfer learning, especially BERT, which achieved an accuracy of approximately 90% after hyperparameter tuning. A final recommendation system was designed, which uses cosine similarity to rank apps based on their relevance to user queries.

AI Age-Spot Formation Predictive App Market Outlook

According to our latest research, the global AI Age-Spot Formation Predictive App market size reached USD 368 million in 2024, reflecting the increasing adoption of artificial intelligence in dermatological and skincare applications. The market is expected to expand at a robust CAGR of 18.7% from 2025 to 2033, with the forecasted market size projected to reach USD 1,795 million by 2033. This impressive growth trajectory is primarily driven by the rising prevalence of skin-related concerns, technological advancements in AI-driven diagnostics, and an increasing consumer focus on preventative skincare solutions.

One of the primary growth factors propelling the AI Age-Spot Formation Predictive App market is the escalating demand for personalized skincare solutions. Consumers are increasingly seeking tailored recommendations and early warning systems for age-related skin changes, such as age spots, to maintain a youthful appearance and prevent long-term skin damage. The integration of AI algorithms in mobile and web applications enables precise analysis of skin images, predicting the likelihood of age spot formation based on individual skin type, environmental exposure, and lifestyle factors. This capability not only empowers consumers to take proactive measures but also supports dermatologists in providing more accurate and data-driven consultations, thus fueling the adoption of AI-powered predictive apps across the globe.

Another significant driver accelerating market growth is the surge in investments and partnerships between technology companies and cosmetic or healthcare providers. As AI continues to demonstrate its efficacy in medical imaging and diagnostics, major players in the skincare and healthcare sectors are leveraging these advancements to enhance their service offerings. The ability of AI age-spot prediction apps to process vast datasets and deliver real-time, actionable insights has attracted considerable interest from both established enterprises and startups. Furthermore, the increasing penetration of smartphones and wearable devices, coupled with rising awareness about early intervention in skin health, is creating a fertile environment for the proliferation of these applications.

The market is also being shaped by a growing emphasis on preventive healthcare and wellness, particularly in developed economies. With the aging global population and heightened awareness regarding UV exposure and its long-term effects on skin health, both healthcare professionals and consumers are prioritizing tools that facilitate early detection and prevention of age spots. Regulatory support for digital health solutions and the integration of AI in telemedicine platforms are further supporting the widespread adoption of predictive apps. However, challenges such as data privacy concerns, the need for high-quality training datasets, and the risk of algorithmic bias remain areas that stakeholders must address to ensure sustained market growth and consumer trust.

From a regional perspective, North America currently dominates the AI Age-Spot Formation Predictive App market, owing to advanced healthcare infrastructure, high consumer awareness, and the presence of leading technology innovators. Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, increasing disposable incomes, and a burgeoning middle-class population that is becoming more conscious of skincare and aesthetics. Meanwhile, Europe continues to witness steady growth, supported by robust regulatory frameworks and growing investments in digital health. Collectively, these regional dynamics are contributing to the global expansion and diversification of the AI age-spot predictive app ecosystem.

Component Analysis

The Component segment of the AI Age-Spot Formation Predictive App market is bifurcated into Software and Services</b&

m-health applications categories and re-categorization as per WHO.

FC m-health applications adoption and environmental determinants.

PC m-health applications adoption and organizational determinants.

FC m-health applications on adoption and combined TOE effect variables.

PC m-health applications adoption and environmental determinants.