Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

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 2032, growing at a CAGR of 13.92% during the forecast period 2026-2032.Global Real World Evidence Solutions Market DriversThe 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.

Xtract.io’s Pharmacy & Drug Store Location Data provides a complete geospatial view of pharmaceutical retail across the United States and Canada. This dataset includes handcrafted polygons and geocoded coordinates for each pharmacy location, making it a powerful resource for healthcare planners, market researchers, and retail strategists.

Organizations can leverage this dataset to:

• Conduct healthcare accessibility mapping and identify underserved areas.

• Evaluate market penetration and retail coverage across regions.

• Analyze the competitive landscape in pharmaceutical retail.

• Support site selection and expansion strategies.

How We Build Pharmacy Polygons

• Manually crafted polygons created using GIS tools like QGIS and ArcGIS, with aerial and street-level imagery.

• Integration of venue layouts and elevation plans from official sources for enhanced accuracy.

• Rigorous multi-stage quality checks ensure accuracy, completeness, and relevance.

What Else We Offer

• Custom polygon creation for any retail chain, healthcare facility, or point of interest.

• Enhanced metadata including entry/exit points, parking areas, and surrounding context.

• Flexible formats: WKT, GeoJSON, Shapefile, and GDB for smooth system integration.

• Regular updates tailored to client needs (30, 60, 90 days).

• Unlock the Power of Healthcare Geospatial Data

With detailed pharmacy polygon data and POI datasets, businesses can:

• Map healthcare service coverage and accessibility.

• Identify growth opportunities in underserved communities.

• Decode consumer behavior in the pharmaceutical retail space.

• Strengthen location-driven strategies with spatial intelligence.

Why Choose LocationsXYZ?

LocationsXYZ is trusted by enterprises worldwide to deliver 95% accurate, handcrafted POI and polygon data. With our pharma dataset, you gain actionable insights to support healthcare planning, retail expansion, and competitive benchmarking.

Discharges from pharmaceutical manufacturing facilities (PMFs) previously have been identified as important sources of pharmaceuticals to the environment. Yet few studies are available to establish the influence of PMFs to the pharmaceutical source contribution to wastewater treatment plants (WWTPs) and waterways at the national scale. Consequently, a national network of 13 WWTPs receiving PMF discharges and 7 WWTPs with no PMF input (controls) were selected from across the United States to assess the influence of PMF input for a diverse suite of pharmaceuticals being formulated for a range of WWTP sizes. WWTP effluent samples were collected and analyzed for 120 pharmaceuticals and pharmaceutical degradates. Of these, 33 pharmaceuticals had concentrations significantly higher in PMF-influenced effluent (maximum 555,000 nanograms per liter) compared to effluent from control sites (maximum 175 nanograms per liter). Concentrations in PMF discharges are episodic and indicate that production activities can vary substantially over relatively short (several months) periods and the potential to rapidly transition to other pharmaceutical products. Results show that PMFs are an important, national-scale source of pharmaceuticals to the environment. Additional research is needed to determine if the observed elevated pharmaceutical concentrations discharged from PMF-influenced WWTPs translate to an increase in adverse environmental effects in corresponding waterways.

Objectives: To develop and pilot a tool to measure and improve pharmaceutical companies’ clinical trial data sharing policies and practices. Design: Cross sectional descriptive analysis. Setting: Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015. Data sources: Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies. Main outcome measures: Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices. Results: Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval. Conclusions: It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study’s measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.

About the Dataset: Pharmaceutical Products Pricing and Availability Data in Pakistan

This dataset contains information about pharmaceutical product pricing and availability in Pakistan. The data was collected from various sources and compiled into a structured format for analysis. The dataset consists of 1630 entries with 7 columns, including:

Name: The name of the pharmaceutical product. Company: The company manufacturing or distributing the product. Price_before: The product's price before any discount is applied. Discount: The discount offered on the product, if applicable. Price_After: The price of the product after applying any discount. Pack_Size: The size or quantity of the product's packaging. Availability: The availability status of the product.

The dataset provides insights into the pricing trends and availability of pharmaceutical products in Pakistan, which can be valuable for various stakeholders including consumers, healthcare professionals, and policymakers. It can be used for analysis, research, and decision-making in the pharmaceutical industry.

Data Overview: Entries: 1630 Missing Values: Some columns have missing values, such as 'Name', 'Company', 'Price_before', 'Discount', 'Price_After', 'Pack_Size', and 'Availability'. Data Types: The dataset consists of object types for textual data and one float type for numerical data.

Potential Uses: This dataset can be used for a variety of purposes, including:

• Analyzing pricing trends and patterns of different drugs in Pakistan.
• Studying the affordability and accessibility of essential medicines in Pakistan.
• Tracking the performance of different pharmaceutical companies in the Pakistani market.
• Identifying potential areas for improving access to affordable medicines in Pakistan.

Limitations: It is important to note that this dataset only includes data on the maximum retail prices of pharmaceutical products. The actual price consumers pay may vary depending on the pharmacy and other factors. Additionally, the dataset does not include information on the quality of the pharmaceutical products.

I hope this description is helpful!

Pharma Source Direct Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

In 2024, research and development spending in the pharmaceutical industry stood at *** billion U.S. dollars globally. For comparison, R&D expenditures totaled *** billion dollars in 2016. Pharmaceutical R&D includes all steps from the initial research of disease processes, the compound testing over pre-clinical, and all clinical trial stages. At a certain point in the process – mostly during the pre-clinical phase – a governmental authority is involved to overview, regulate, and ultimately approve the drug. In the United States, the Food and Drug Administration is the principal agency associated with processes. The pressure to innovate In comparison to other industries, pharmaceutical companies are more driven by the imperative to manufacture innovative products, and thus to spend significant amounts on research and development. This is largely due to the time-limited patent protection of drugs and the following threat of sales erosion through generic and biosimilar competition. Two major effects of patent expirations for the pharma industry are a specific high R&D intensity and a growing focus on specialty drugs to diversify their product portfolio. The latest trends For the last several years, major developments in pharmaceutical research and development have begun to change the R&D landscape. A growing number of drug manufacturers are outsourcing large parts of R&D, mostly to clinical research organizations (also contract research organizations), with the main aim to reduce costs. Another important development is the use of big data in clinical research. Thus, a predictive modeling is possible which uses clinical and molecular data to develop safer and more efficient drugs. Particularly, real-time or real-world evidence (RWE) is becoming a greater interest. This makes cooperation with technology companies necessary and includes data gathered from various sources, even that of social media.

Knowledge Management in Pharmaceutical Market size was valued at USD 1.5 Billion in 2023 and is projected to reach USD 2.63 Billion by 2030, growing at a CAGR of 8.00% during the forecast period 2024-2031.

Global Knowledge Management in Pharmaceutical Market Drivers

The market drivers for the Knowledge Management in Pharmaceutical Market can be influenced by various factors. These may include:

• Regulatory Compliance: Businesses in the pharmaceutical industry work in a highly regulated setting. Clinical trial data, medication development records, and regulatory submissions are just a few examples of the vital material that effective knowledge management systems organize and manage to help guarantee regulatory compliance. • Rapid Technological Advancements: With the introduction of artificial intelligence (AI), big data analytics, and machine learning, the pharmaceutical sector is constantly changing. Knowledge management systems make it easier to integrate and use these technologies to boost innovation, expedite workflows, and enhance decision-making. • Growing Complexity of Drug Development: The processes involved in developing new drugs are getting more intricate and expensive. Pharmaceutical businesses can expedite drug discovery, development, and commercialization by managing and utilizing large amounts of scientific and clinical data through the use of knowledge management. • Globalization and Collaboration: When working on drug development initiatives, pharmaceutical corporations frequently collaborate with research groups, universities, and other business partners. Knowledge management systems make collaborative research more efficient by offering a central location for knowledge exchange and access, encouraging creativity, and boosting productivity. • Growing Significance of Personalized Medicine: Pharmaceutical companies must gather, examine, and handle vast amounts of patient data, genetic data, and clinical results as a result of the shift to personalized medicine. Knowledge management systems facilitate the amalgamation of heterogeneous data sources to bolster personalized medical endeavors, including the identification of biomarkers, patient classification, and the creation of tailored therapeutic approaches. The pharmaceutical sector is characterized by intense competition and cost pressures, as companies strive to get novel drugs to the market in a timely and economical manner. From discovery to commercialization, knowledge management supports pharmaceutical companies in maximizing resources, reducing risks, and enhancing operational effectiveness. • Risk management and patient safety: It is crucial to guarantee the security and effectiveness of pharmaceutical products. Pharmaceutical businesses use knowledge management systems to monitor post-market surveillance data to maintain patient safety and to identify, assess, and minimize risks related to medication research and manufacturing processes. • Demand for Evidence-Based Decision Making: In the areas of drug development, regulatory approval, and patient care, stakeholders in the healthcare industry, such as payers, regulators, healthcare providers, and patients, are calling for more and more evidence-based decision-making. Pharmaceutical businesses can produce, evaluate, and share scientific evidence to help educated decisions and enhance patient outcomes by using knowledge management systems.

Success.ai’s Pharma Data for the Global Pharmaceutical Industry provides a robust dataset tailored for businesses looking to connect with pharmaceutical companies, decision-makers, and key stakeholders worldwide. Covering pharmaceutical manufacturers, research organizations, biotech firms, and distributors, this dataset offers verified SIC codes, firmographic details, and contact information for executives and operational leads.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and business development strategies are driven by reliable, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is essential for navigating the competitive global pharmaceutical landscape.

Why Choose Success.ai’s Pharma Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of pharmaceutical executives, R&D leads, compliance officers, and procurement managers.
   • AI-driven validation ensures 99% accuracy, optimizing your campaigns and improving communication efficiency.

2. Comprehensive Coverage of the Global Pharmaceutical Sector

   • Includes profiles of pharmaceutical companies, biotech firms, contract manufacturing organizations (CMOs), and distributors across North America, Europe, Asia, and other major markets.
   • Gain insights into regional pharmaceutical trends, product pipelines, and market dynamics unique to global markets.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, mergers, product launches, and regulatory compliance shifts.
   • Stay aligned with the fast-paced pharmaceutical industry to capitalize on emerging opportunities and maintain relevance.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers, R&D specialists, and operational leaders in the pharmaceutical industry worldwide.
• 30M Company Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Verified SIC Codes: Understand industry classifications and product specializations to refine your targeting strategies.
• Leadership Contact Details: Connect with CEOs, COOs, medical directors, and regulatory managers influencing pharmaceutical operations.

Key Features of the Dataset:

1. Decision-Maker Profiles in Pharmaceuticals

   • Identify and engage with professionals overseeing R&D, clinical trials, supply chains, and regulatory compliance.
   • Target leaders responsible for drug development, vendor selection, and market entry strategies.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (biotech, generic pharmaceuticals, vaccines), geographic location, or revenue size.
   • Tailor campaigns to align with specific needs such as drug pipeline acceleration, production scaling, or market expansion.

3. SIC Codes and Firmographic Insights

   • Access verified SIC codes and detailed company profiles to understand market focus, operational scale, and specialization areas.
   • Use firmographic data to prioritize high-value targets and align product offerings with market demands.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with pharmaceutical stakeholders.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer technology solutions, research tools, or contract services to pharmaceutical manufacturers, R&D facilities, and distribution networks.
   • Build relationships with procurement teams and compliance officers responsible for vendor approvals and operational excellence.

2. Market Research and Product Development

   • Analyze global pharmaceutical trends, drug approval patterns, and regulatory frameworks to guide product innovation and market entry strategies.
   • Identify high-growth regions and emerging therapeutic areas to focus your resources effectively.

3. Partnership and Supply Chain Development

   • Connect with pharmaceutical companies seeking contract manufacturing, raw material sourcing, or distribution partnerships.
   • Foster alliances that streamline production, ensure quality, and accelerate time-to-market.

4. Regulatory Compliance and Risk Mitigation

   • Engage with regulatory officers and compliance managers overseeing adherence to local and international pharmaceutical standards.
   • Present solutions for documentation, reporting, and risk management to ensure compliance and operational efficiency.

Why Choose Success.ai?

1. Best Price Guarantee
   ...

Releaf Medical, a leading online platform for healthcare information, has built a reputation for providing trusted and reliable resources for medical professionals and patients alike. The company's website serves as a valuable hub for healthcare-related data, featuring a range of information on topics such as medical articles, clinical trials, and medical devices.

From medical news and research to patient education and treatment options, Releaf Medical's website offers a vast array of health-related information. With a focus on accuracy and comprehensiveness, the company's data is curated from reputable sources, making it an essential resource for healthcare professionals, researchers, and individuals seeking reliable health information.

Healthcare Data Analytics Market size was valued at USD 32.87 Billion in 2024 and is projected to reach USD 173.57 Billion by 2032, growing at a CAGR of 23.12% during the forecasted period 2026 to 2032.Growing Volume of Healthcare Data: The healthcare industry is generating an unprecedented volume of data from diverse sources, including electronic health records (EHRs), medical imaging, patient-generated data from wearables and mobile apps, genomic sequencing, and claims data. This explosion of big data necessitates advanced analytical tools to process, store, and derive meaningful insights. Without analytics, this vast data pool would remain a siloed and untapped resource.

The Pharmaceutical ERP market is experiencing robust growth, driven by the increasing need for efficient supply chain management, regulatory compliance, and data-driven decision-making within the pharmaceutical industry. The market's expansion is fueled by the rising adoption of cloud-based ERP solutions, offering scalability and cost-effectiveness. Furthermore, the growing complexity of pharmaceutical regulations and the pressure to reduce operational costs are compelling pharmaceutical companies to invest in advanced ERP systems capable of integrating diverse data sources and automating critical business processes. This includes streamlining manufacturing, managing inventory, tracking clinical trials, and ensuring compliance with stringent regulatory requirements like FDA 21 CFR Part 11. The competitive landscape is dynamic, with a mix of established players like Oracle Netsuite and Sage Group, and specialized niche providers such as AX for Pharma and BatchMaster Software Pvt. Ltd. These companies offer a range of solutions tailored to the specific needs of different pharmaceutical organizations, ranging from small to large multinational corporations. The forecast period (2025-2033) projects continued growth, driven by ongoing technological advancements, such as the integration of artificial intelligence and machine learning into ERP systems. These advancements will enhance predictive analytics, supply chain optimization, and overall business intelligence. Despite this positive outlook, challenges remain, including the high initial investment cost of ERP implementation and the need for extensive employee training and support. However, the long-term benefits in terms of improved efficiency, reduced errors, and better regulatory compliance outweigh these initial hurdles, securing the continued expansion of the Pharmaceutical ERP market. We estimate a conservative CAGR of 10% over the forecast period based on industry trends and historical growth patterns, reflecting both market expansion and the continuous adoption of newer ERP technologies within the sector.

Study design We conducted a cross-sectional study to quantify the number and describe the characteristics of prescription drugs on the Canadian market as of August 23, 2022 with a patient support program defined as services (including but not limited to financial assistance) offered to patients prescribed a specific drug and initiated and funded by the manufacturer. We then conducted a structured content analysis of web-based sources to understand the types and range of supports provided to patients through these programs. We chose to rely exclusively on publicly available data sources to both identify and describe manufacturer-sponsored patient support programs as these are sources currently available to patients when making program enrolment decisions and policymakers seeking to understand the extent and impact of this model of care. Sampling frame Because the European Medicines Association defines a patient support program as services for a specific drug offered by the company holding the marketing authorization, we first sought to identify all drug companies with currently marketed, prescription products in Canada. Between June 27, 2022 and August 23, 2022, two investigators independently extracted the names of all member companies listed on the websites of the three main trade associations for the Canadian pharmaceutical industry (Innovative Medicines Canada, representing the research-based pharmaceutical industry; BIOTECanada, representing the biotechnology industry; and the Canadian Generic Pharmaceutical Association, representing generic drug manufacturers). Because trade association membership is voluntary, we supplemented this list with non-member drug manufacturers identified in previous research. Using the Health Canada Drug Product Database,two investigators independently screened the list of companies and included those with marketed, prescription products and excluded companies that were not drug manufacturers (e.g., law firms) or without currently marketed prescription drugs (e.g., products under development). Discrepancies were resolved through discussion or adjudication by a third author. Sample and variables Using the Health Canada Drug Product Database, one investigator searched each identified drug manufacturer and extracted the product and active ingredient name(s) for all marketed, prescription drugs. We counted a single “drug” as all dosages, formulations, or routes of administration with the same active ingredients and manufacturer since industry patient support programs are brand-specific and do not typically differentiate among these factors. We selected variables that reflect known characteristics of drugs that may be associated with having a patient support program, and for which data were publicly available. One investigator also extracted Schedule D (biologic) status, route(s) of administration, and Level 1 Anatomical Therapeutic Chemical (ATC) code from the Drug Product Database and Product Monograph and identified whether the drug had Orphan Drug Status using the searchable United States database. On the basis of type of Health Canada regulatory review (i.e. innovator or subsequent entry), clinical expertise, and knowledge about the manufacturer, two investigators independently identified the brand status of each drug as brand (i.e. “innovator” products first to market); branded generic (i.e. “subsequent entry” products which are bioequivalent or biosimilar to an existing product on the market, but given a proprietary name); or generic (i.e. “subsequent entry” products which are bioequivalent to an existing product on the market). We classified biosimilars as branded generics. We resolved discrepancies through discussion, and/or adjudication by a third author. Identifying patient support programs and their characteristics Our primary outcome was whether a sampled drug had an associated manufacturer-sponsored patient support program. We defined a patient support program as any combination of services or resources related to medication access, administration, adherence, education, storage, or disposal for patients prescribed a specific product and initiated, sponsored and/or operated by the company holding the product’s marketing authorization. We distinguished patient support programs from “patient assistance programs,” excluding programs that exclusively provided financial assistance (e.g., coupons, co-pay coverage, etc.); expanded or compassionate access programs; risk management programs outlined in the Product Monograph (initiated by the regulator rather than the manufacturer); and programs delivered solely for a clinical study. Two investigators independently performed structured searches on Google (“[company name] AND patient support program AND Canada” and “[drug brand name] AND patient support program AND Canada”) to identify industry sponsored patient support programs in Canada and resolving discrepancies through discussion. Using Zotero, a reference management...

Numbers of new drugs UK 1971-2011Excel spreadsheet of annual numbers of new drugs launched in the UK from 1971 to 2011. Contains data in 6 columns: A: Year. B-D: Data from NIHR HSC, derived from new preparations added to the BNF from 1982-2011. Shows numbers of NCEs, new biologics and totals for each year. E: Data from CMR (1991), published by Tansey et al(1994). Shows numbers of NCEs for each year from 1971-1989. F: Combines data from columns D & E to show numbers of new drugs from 1971-2011. Years 1982-89 show the average of the two values, rounded to the nearest whole number.

Patient Matching for Clinical Trials Market Outlook

According to our latest research, the Global Patient Matching for Clinical Trials market size was valued at $1.2 billion in 2024 and is projected to reach $4.3 billion by 2033, expanding at a robust CAGR of 15.2% during the forecast period of 2025–2033. The exponential growth in the market is primarily driven by the increasing complexity and volume of clinical trials, which necessitate advanced patient matching solutions to enhance recruitment efficiency, reduce trial timelines, and improve data integrity. As the pharmaceutical and biopharmaceutical industries continue to innovate and develop targeted therapies, the need for precise patient identification and enrollment has become a critical success factor, propelling the adoption of sophisticated software and services in the patient matching for clinical trials market globally.

Regional Outlook

North America currently dominates the global patient matching for clinical trials market, accounting for the largest share in 2024. This region’s leadership is underpinned by a mature healthcare infrastructure, widespread adoption of electronic health records (EHRs), and a strong presence of leading pharmaceutical and biopharmaceutical companies. The United States, in particular, is a major contributor due to its high clinical trial activity and significant investments in health IT. Favorable regulatory policies, such as the 21st Century Cures Act, and a proactive approach towards data interoperability and patient privacy further support market growth. Additionally, the region benefits from robust collaborations between academic institutions, healthcare providers, and contract research organizations (CROs), which collectively foster innovation and rapid adoption of patient matching technologies.

The Asia Pacific region is emerging as the fastest-growing market, projected to register a remarkable CAGR of 18.7% from 2025 to 2033. Key growth drivers include increased investments in healthcare digitization, rising clinical research activity, and expanding pharmaceutical manufacturing capabilities, especially in China, India, and South Korea. Governments across the region are implementing supportive policies to attract global clinical trials, while local companies are investing in advanced data analytics and cloud-based patient matching solutions. The large and diverse patient population, combined with growing awareness of clinical trial opportunities, is accelerating enrollment rates and driving demand for accurate patient identification systems. Strategic partnerships between global pharma companies and regional CROs are also catalyzing market expansion in Asia Pacific.

Emerging economies in Latin America and the Middle East & Africa are witnessing steady adoption of patient matching solutions for clinical trials, albeit at a slower pace compared to developed regions. Challenges such as limited healthcare IT infrastructure, fragmented data sources, and regulatory inconsistencies impact the widespread implementation of advanced patient matching platforms. However, increasing participation in multinational clinical trials, coupled with the gradual modernization of healthcare systems, is creating localized demand for efficient patient recruitment and data management tools. Regional governments are beginning to recognize the importance of digital transformation in healthcare, and targeted policy reforms are expected to boost market growth over the forecast period.

Report Scope

The pharmaceutical cold chain monitoring market is experiencing robust growth, driven by the increasing need to ensure the integrity and efficacy of temperature-sensitive pharmaceuticals throughout their lifecycle. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a compound annual growth rate (CAGR) of 5% from 2025 to 2033. This growth is fueled by several key factors, including the rising prevalence of chronic diseases necessitating continuous medication, stringent regulatory requirements for maintaining product quality and safety, and the expansion of global pharmaceutical distribution networks. Advancements in sensor technology, the adoption of cloud-based data management systems, and the integration of IoT devices are further accelerating market expansion. The increasing demand for real-time monitoring and data analytics capabilities, combined with the growing adoption of proactive maintenance strategies, contributes significantly to the overall market expansion. Despite the considerable growth potential, the market faces challenges such as high initial investment costs associated with implementing monitoring systems and the potential for data security breaches. Furthermore, integrating disparate data sources and maintaining accurate and reliable data across the entire cold chain can be complex. However, the increasing emphasis on patient safety and the rising adoption of advanced technologies that address these challenges are expected to mitigate these limitations, driving continued growth in the pharmaceutical cold chain monitoring market in the forecast period. Major players in this space, including Sensitech, ORBCOMM, and Emerson, are actively contributing to this growth through continuous innovation and strategic partnerships.

IMS Data Analytics Function Market Outlook

According to our latest research, the IMS Data Analytics Function market size reached USD 3.42 billion in 2024, reflecting robust momentum across critical industries. The global market is poised to grow at a CAGR of 13.7% from 2025 to 2033, ultimately achieving a forecasted market value of USD 10.01 billion by 2033. This remarkable growth trajectory is primarily fueled by the rising adoption of advanced data analytics solutions in healthcare, pharmaceuticals, and insurance sectors, combined with the increasing digital transformation initiatives across both mature and emerging economies.

A significant growth factor for the IMS Data Analytics Function market is the escalating volume of data generated in healthcare and life sciences. With the proliferation of electronic health records (EHRs), wearable health devices, and connected medical equipment, organizations are increasingly reliant on sophisticated analytics platforms to extract actionable insights from massive datasets. The need to enhance patient outcomes, optimize operational efficiency, and comply with stringent regulatory requirements further accelerates the adoption of IMS Data Analytics Function solutions. Additionally, the integration of artificial intelligence and machine learning algorithms into analytics platforms is enabling real-time data processing and predictive analytics, which are becoming essential for clinical decision support and personalized medicine initiatives.

Another crucial driver is the growing demand for data-driven strategies in the pharmaceutical and insurance industries. Pharmaceutical companies are leveraging IMS Data Analytics Function tools to streamline drug discovery, monitor clinical trials, and ensure compliance with global regulations. Meanwhile, insurance providers are using advanced analytics to assess risk profiles, detect fraud, and personalize insurance products for their customers. The convergence of big data, cloud computing, and advanced analytics is empowering these sectors to make faster, evidence-based decisions, thereby enhancing competitiveness and profitability. As regulatory frameworks evolve to support data interoperability and privacy, organizations are investing heavily in scalable and secure analytics infrastructures.

The increasing focus on value-based care models and cost containment is also propelling market growth. Hospitals and research institutes are under pressure to deliver improved patient outcomes while managing operational costs. IMS Data Analytics Function solutions enable these entities to analyze clinical and financial data, identify inefficiencies, and implement process improvements. Furthermore, the shift towards cloud-based deployment models is making advanced analytics more accessible to small and medium enterprises (SMEs), fostering innovation and democratizing data-driven decision-making. The ability to integrate disparate data sources and provide real-time insights is transforming how healthcare providers, pharmaceutical companies, and insurers operate in a rapidly evolving digital landscape.

Regionally, North America continues to dominate the IMS Data Analytics Function market, accounting for the largest share in 2024, driven by a mature healthcare infrastructure, high adoption of digital technologies, and a strong regulatory environment. Europe follows closely, with significant investments in digital health and pharmaceutical research. The Asia Pacific region is witnessing the fastest growth, fueled by government initiatives to modernize healthcare systems, a burgeoning pharmaceutical sector, and increasing awareness about the benefits of data analytics. Latin America and the Middle East & Africa are also emerging as promising markets, supported by improving healthcare infrastructure and the gradual adoption of cloud-based analytics solutions.

Component Analysis

The IMS Data Analytics Function market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment is the largest contributor, driven by growing demand for advanced analytics platforms capable of processing and interpreting complex datasets. These platforms are increasingly incorporating artificial intelligence, machine learning, and natural language processing to deliver deeper insights and automate decision-making processes. The software segment is also witnessing rapid innovation, with vendors offering modu