This statistic displays selected top global pharmaceutical companies by the total number of Youtube views per post based on IMS Health Social Media Engagement Index data, as of January 2014 for the preceding 24 months. During this time, Pfizer received about ****** views per post.

Social media and top pharma companies

GlaxoSmithKline and Novartis have generated some of the highest viewerships from their Youtube channels. The companies received an average of ****** views and ****** views, respectively, as of 2012/2013. GlaxoSmithKline’s Youtube channel includes videos that show their TV advertisements, employee experiences, and pharmaceutical efforts. Johnson & Johnson has garnered some of the highest number of retweets, with *** retweets per tweet. The company has at least ***** twitter accounts with their news account followed by **** thousand followers as of March 2015.

Johnson & Johnson and GlaxoSmithKline have been ranked among the top pharmaceutical companies by the IMS Health Social Media Engagement Index in 2014, reaching scores of ** and **, respectively. This study found that only ** of the top 50 pharmaceutical companies used Twitter, Youtube, and Facebook to communicate with consumers. The use of social media in the pharmaceutical industry has increased at a slower rate than other industries, mostly due to regulatory risks. Since then, the FDA has provided a regulatory guideline for the pharma industry, while other companies have started to publish their own guidelines. As of 2013, the biotechnology and pharmaceutical industry had *** recruiters that were considered power users using LinkedIn, Twitter, and Facebook, compared to the ***** recruiters in the information technology industry.

Abstract: Building health management is an important part in running an efficient and cost-effective building. Many problems in a building’s system can go undetected for long periods of time, leading to expensive repairs or wasted resources. This project aims to help detect and diagnose the building‘s health with data driven methods throughout the day. Orca and IMS are two state of the art algorithms that observe an array of building health sensors and provide feedback on the overall system’s health as well as localize the problem to one, or possibly two, components. With this level of feedback the hope is to quickly identify problems and provide appropriate maintenance while reducing the number of complaints and service calls. Introduction: To prepare these technologies for the new installation, the proposed methods are being tested on a current system that behaves similarly to the future green building. Building 241 was determined to best resemble the proposed building 232 and therefore was chosen for this study. Building 241 is currently outfitted with 34 sensors that monitor the heating & cooling temperatures for the air and water systems as well as other various subsystem states. The daily sensor recordings were logged and sent to the IDU group for analysis. The period of analysis was focused from July 1st through August 10th 2009. Methodology: The two algorithms used for analysis were Orca and IMS. Both methods look for anomalies using a distanced based scoring approach. Orca has the ability to use a single data set and find outliers within that data set. This tactic was applied to each day. After scoring each time sample throughout a given day the Orca score profiles were compared by computing the correlation against all other days. Days with high overall correlations were considered normal however days with lower overall correlations were more anomalous. IMS, on the other hand, needs a normal set of data to build a model, which can be applied to a set of test data to asses how anomaly the particular data set is. The typical days identified by Orca were used as the reference/training set for IMS, while all the other days were passed through IMS resulting in an anomaly score profile for each day. The mean of the IMS score profile was then calculated for each day to produce a summary IMS score. These summary scores were ranked and the top outliers were identified (see Figure 1). Once the anomalies were identified the contributing parameters were then ranked by the algorithm. Analysis: The contributing parameters identified by IMS were localized to the return air temperature duct system. -7/03/09 (Figure 2 & 3) AHU-1 Return Air Temperature (RAT) Calculated Average Return Air Temperature -7/19/09 (Figure 3 & 4) AHU-2 Return Air Temperature (RAT) Calculated Average Return Air Temperature IMS identified significantly higher temperatures compared to other days during the month of July and August. Conclusion: The proposed algorithms Orca and IMS have shown that they were able to pick up significant anomalies in the building system as well as diagnose the anomaly by identifying the sensor values that were anomalous. In the future these methods can be used on live streaming data and produce a real time anomaly score to help building maintenance with detection and diagnosis of problems.

The Real World Evidence (RWE) solutions market for oncology is experiencing robust growth, projected to reach $1018 million by 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.8% from 2025 to 2033. This expansion is driven by several key factors. The increasing availability of large, diverse electronic health record (EHR) datasets provides rich sources of real-world data, enabling more efficient and cost-effective clinical trials and post-market surveillance. Furthermore, regulatory bodies are increasingly embracing RWE to supplement traditional clinical trial data, accelerating drug development and approval processes. The rising prevalence of cancer globally, coupled with a growing need for personalized medicine approaches, further fuels the demand for RWE solutions that can provide deeper insights into treatment efficacy and patient outcomes across diverse populations. Key players like Anthem, IMS Health, ICON Plc, and Thermo Fisher Scientific are actively investing in advanced analytics, data integration platforms, and specialized services to capitalize on this market opportunity. Competitive pressures are driving innovation, with companies developing sophisticated algorithms and AI-powered tools to extract meaningful insights from complex RWE datasets. The growth trajectory is expected to be influenced by advancements in data analytics techniques, particularly in areas like machine learning and artificial intelligence. These technologies enable the extraction of deeper insights from vast and complex datasets, leading to more precise predictions of treatment outcomes and improved decision-making. However, challenges such as data privacy concerns, interoperability issues across different healthcare systems, and the need for robust data standardization could potentially impede market growth to some degree. Nevertheless, the significant advantages of RWE in accelerating drug development, improving patient care, and reducing healthcare costs are expected to outweigh these challenges, ensuring continued market expansion throughout the forecast period. The development of more sophisticated risk stratification models, based on RWE, will further enhance the value proposition of this burgeoning market.

Abstract: Building health management is an important part in running an efficient and cost-effective building. Many problems in a building’s system can go undetected for long periods of time, leading to expensive repairs or wasted resources. This project aims to help detect and diagnose the building‘s health with data driven methods throughout the day. Orca and IMS are two state of the art algorithms that observe an array of building health sensors and provide feedback on the overall system’s health as well as localize the problem to one, or possibly two, components. With this level of feedback the hope is to quickly identify problems and provide appropriate maintenance while reducing the number of complaints and service calls. Introduction: To prepare these technologies for the new installation, the proposed methods are being tested on a current system that behaves similarly to the future green building. Building 241 was determined to best resemble the proposed building 232 and therefore was chosen for this study. Building 241 is currently outfitted with 34 sensors that monitor the heating & cooling temperatures for the air and water systems as well as other various subsystem states. The daily sensor recordings were logged and sent to the IDU group for analysis. The period of analysis was focused from July 1st through August 10th 2009. Methodology: The two algorithms used for analysis were Orca and IMS. Both methods look for anomalies using a distanced based scoring approach. Orca has the ability to use a single data set and find outliers within that data set. This tactic was applied to each day. After scoring each time sample throughout a given day the Orca score profiles were compared by computing the correlation against all other days. Days with high overall correlations were considered normal however days with lower overall correlations were more anomalous. IMS, on the other hand, needs a normal set of data to build a model, which can be applied to a set of test data to asses how anomaly the particular data set is. The typical days identified by Orca were used as the reference/training set for IMS, while all the other days were passed through IMS resulting in an anomaly score profile for each day. The mean of the IMS score profile was then calculated for each day to produce a summary IMS score. These summary scores were ranked and the top outliers were identified (see Figure 1). Once the anomalies were identified the contributing parameters were then ranked by the algorithm. Analysis: The contributing parameters identified by IMS were localized to the return air temperature duct system. -7/03/09 (Figure 2 & 3) AHU-1 Return Air Temperature (RAT) Calculated Average Return Air Temperature -7/19/09 (Figure 3 & 4) AHU-2 Return Air Temperature (RAT) Calculated Average Return Air Temperature IMS identified significantly higher temperatures compared to other days during the month of July and August. Conclusion: The proposed algorithms Orca and IMS have shown that they were able to pick up significant anomalies in the building system as well as diagnose the anomaly by identifying the sensor values that were anomalous. In the future these methods can be used on live streaming data and produce a real time anomaly score to help building maintenance with detection and diagnosis of problems.

Export: CSV, HTML and XLS Source: www.ecosante.fr Irdes based on IMS-Health data

HCTZ = hydrochlorothiazide; Amlodipine/Benazepril and other drug combinations with “/” represent fixed dose combination productsSource: IMS Health National Disease and Therapeutic Index, 1998–2012.Most frequently used drugs during hypertension treatment visits, 1998–2012.

The key objective of this research was to generate new evidence on outpatient antibiotic prescription rate and patterns in the private sector in India. We used 12-month period (May 2013 to April 2014) medical audit dataset from IQVIA (formerly IMS Health). We coded the diagnosis provided in the medical audit data to International Statistical Classification of Diseases and Related Health Problems (ICD-10) and the prescribed antibiotics for the diagnosis to Anatomic Therapeutic Chemical (ATC) classification of World Health Organization (ATC index-2016). We calculated and reported antibiotic prescription rate per 1,000 persons per year, by age groups, antibiotic class and disease conditions. Our main findings are—approximately 519 million antibiotic prescriptions were dispensed in the private sector, which translates into 412 prescriptions per 1,000 persons per year. Majority of the antibiotic prescriptions were dispensed for acute upper respiratory infections (J06) (20.4%); unspecified acute lower respiratory infection (J22) (12.8%); disorders of urinary system (N39) (6.0%); cough (R05) (4.7%); and acute nasopharyngitis (J00) (4.6%) and highest antibiotic prescription rates were observed in the age group 0–4 years. To conclude our study reports first ever country level estimates of antibiotic prescription by antibiotic classes, age groups, and ICD-10 mapped disease conditions.

Please note: on 7 February 2014 updated versions of the pdf report and the Excel spreadsheet were uploaded to correct under-reporting of the use of medicines in certain medicine groups. National figures have been revised as follows: - Alzheimer's Disease by five per cent, - ADHD by six per cent, - Alitretinoin by four per cent, - Riluzole by one per cent, - Exenatide and Liraglutide by less than one per cent. Within these medicine groups, some sub national data is affected more than others. All other elements of the report are unaffected. This was as a result of complications arising from the extraction of data relating to prescriptions issued in hospital and dispensed in the community. The National Institute for Health and Care Excellence (NICE) technology appraisal process assesses the clinical and cost effectiveness of new and existing drugs and treatments, and provides guidance on their use by the NHS. As part of the Pharmaceutical Price Regulation Scheme (PPRS) agreement which came into operation on 1 January 2009, the Department of Health (DH) and the Association of the British Pharmaceutical Industry agreed that the DH would review the variation in uptake of selected medicines in the NHS in England. The Health and Social Care Information Centre was commissioned to work with NICE, DH and the Pharmaceutical Industry to produce and publish a report looking at variation in the use of these medicines in relation to the number of eligible patients as estimated by NICE. Data on the number of patients being treated is not available and so estimated predicted use (using the estimated number of patients and the amount of medicine they would be expected to receive) was compared with observed use where possible. Data on observed use was taken from the primary care prescribing data provided by NHS Prescribing Services and secondary care data was provided by IMS Health. Several pharmaceutical companies also provided their own data. This report contains a new approach to measure variation in usage of medicines. The variation approach is used to compare changes over time and between organisations and has been adopted to enable a wider range of medicines to be included in the report. It enables those medicines where significant uncertainty remains in establishing an estimate of eligible patient population and/or estimate of usage to be included. Both methods report use of medicines under the new NHS structure. The report is factual and does not seek to find reasons for variation in prescribing. NHS medicine use for 54 medicines in 28 therapy groups, relating to 47 technology appraisals are included. For 10 groups the estimate approach is used and the variation approach is used for 18 groups. Have your say: This is an experimental publication and is the fourth of a planned annual series. We invite feedback to help inform how best to estimate uptake to allow meaningful interpretation of any variation across NHS organisations in future. Please give feedback using the form below.

This project was designed to evaluate the use of a drug detection instrument, the ion mobility spectrometer (IMS), and to integrate its use into an ongoing pretrial diversion program for nonviolent, first-time, drug-abusing offenders. The Pretrial Diversion Program in Orleans Parish, Louisiana, targeted offenders with limited arrest histories of nonviolent felony or misdemeanor violations. The majority of eligible participants were violators of simple drug possession statutes, primarily crack/cocaine or marijuana. Persons charged with drug distribution offenses were not eligible. In order to qualify for diversion, persons had to admit guilt regarding the acts for which they were arrested. The program was entirely voluntary. One of the unique aspects of this program was its aggressive use of drug testing, including urinalysis and hair analysis. This project evaluated the ability of the IMS to provide complete drug profile information to supervising agencies and assessed its usefulness to field staff engaged in drug monitoring duties. The project was based on the premise that enhanced information on offenders diverted into this program could create or improve several key aspects of program operation, such as client assessment, monitoring of compliance and progress, dispositional decision-making, client motivation, and staff morale. The study was designed to integrate the IMS into the normal operation of the New Orleans Pretrial Diversion Program with as little modification of existing treatment and supervision protocols as possible. Each client in the diversion program underwent an intensive intake assessment including an intake radioimmunoassay (RIA) hair assay and an additional RIA hair assay every 60 days. Each client was urine-tested at intake and assigned to a random test pool. The modified protocol for the project added an IMS-based scan or a hair specimen, skin wipe, and ten-second vacuum scan of clothing, hands, and axillae at intake. At each subsequent visit each client had a repeat IMS scan utilizing a skin swab and a scan of clothing or body area. Variables include self-reported cocaine use, self-reported marijuana use, IMS date, urinalysis date, hair assay results, urinalysis results, IMS detection, nicotine use, maximum amplitude, delta, cumulative amplitude, number of detections, whether the IMS showed a positive result, and the age, sex, and race of the client.

The National Institute for Health and Clinical Excellence (NICE) technology appraisal process assesses the clinical and cost effectiveness of new and existing drugs and treatments, and provides guidance on their use by the NHS. As part of the Pharmaceutical Price Regulation Scheme (PPRS) agreement which came into operation on 1 January 2009, the Department of Health (DH) and the Association of the British Pharmaceutical Industry agreed that the DH would review the variation in uptake of selected medicines in the NHS in England. The NHS Information Centre was asked to produce a bulletin looking at variation in the use of these medicines in relation to the number of eligible patients as estimated by NICE. Data on the number of patients being treated is not available and so predicted use (using the average dose and average length of treatment) was compared with observed use. Data on observed use in 2010 and 2011 was taken from the primary care prescribing data from NHS Prescribing Services and secondary care data provided by IMS Health. Several pharmaceutical companies also provided their own data. In all 52 medicines in 25 groups, relating to 35 technology appraisals, were considered. For 13 groups a comparison could be made (these are presented in Section 1 of the technology section results). For those appraisals where a valid comparison could not be made (these are presented in Section 2 of the technology results), the reasons why the comparison could not be made are presented along with a series of questions inviting the reader to suggest improvements to the method or data. A third section presents an approach to analysing the use of biologics medicines for the treatment of rheumatoid arthritis on which we welcome comments. The report was amended on 11 December 2012 to include NICE guidance on the use of denosumab for secondary prevention of fractures in osteoporosis (page 53). There are no other changes to the text or figures. Have your say This is an experimental publication and is the third of a planned annual series. We invite feedback to help inform how best to estimate uptake to allow meaningful interpretation of any variation across NHS organisations in future. Please give feedback using the form above.

Sources: Direct-to-consumer advertising data provided by Kantar Media; promotion to professionals data derived from the IMS Health Integrated Promotional Services™, 2010™ and excludes epromotion and expenditures for conferences and meetings depicted in Table 1; sales data derived from IMS Health Use of Medicines 2010.

The global ECG Information Management System (ECG IMS) market is experiencing robust growth, driven by the increasing prevalence of cardiovascular diseases, technological advancements in ECG systems, and the rising adoption of electronic health records (EHRs). The market's expansion is further fueled by the growing demand for efficient and accurate diagnosis and management of heart conditions, coupled with the increasing focus on improving healthcare infrastructure, particularly in developing economies. While precise figures for market size and CAGR were not provided, based on industry trends and the listed companies' activities, a reasonable estimate for the 2025 market size could be approximately $2.5 billion. Assuming a conservative CAGR of 7% over the forecast period (2025-2033), the market could reach approximately $4.8 billion by 2033. This growth is projected to be driven by the continued technological innovations like cloud-based systems improving data accessibility and remote patient monitoring capabilities, and expansion into emerging markets with increasing healthcare expenditure. The segmentation into cloud-based and web-based systems, along with applications across hospitals and clinics, reflects the market's diverse nature and caters to varying needs within the healthcare landscape. The presence of established players like Hillrom, Philips, and GE Healthcare, alongside emerging companies, highlights the competitive dynamics and innovation within the sector. The market's growth, however, faces some challenges. High initial investment costs associated with implementing and maintaining ECG IMS can be a barrier for smaller healthcare facilities. Data security and privacy concerns related to sensitive patient information also pose a significant restraint. Despite these obstacles, the long-term outlook for the ECG Information Management System market remains positive, with continued technological advancements, increasing healthcare spending, and a growing awareness of the importance of preventive cardiology driving the adoption of these systems globally. Strategic partnerships and collaborations between technology providers and healthcare institutions are expected to play a crucial role in overcoming the challenges and accelerating market penetration. The geographical distribution, encompassing North America, Europe, Asia-Pacific and other regions, signifies the global nature of this market and reflects the varied levels of healthcare infrastructure and technological adoption across different geographical locations.

The global healthcare informatics market size was valued at USD 674.52 million in 2025 and is projected to grow at a CAGR of 13.67% during the forecast period 2025-2033. The increasing adoption of electronic health records (EHRs), the growing need for healthcare data analytics, and the rising prevalence of chronic diseases are the major factors driving the growth of the market. The healthcare informatics market is segmented based on component, application, delivery model, and end user. The software segment is expected to hold the largest market share during the forecast period. The growing adoption of EHRs and other healthcare software solutions is the major factor driving the growth of this segment. The electronic health records (EHR) segment is expected to hold the largest market share during the forecast period. The increasing adoption of EHRs is the major factor driving the growth of this segment. The on-premise segment is expected to hold the largest market share during the forecast period. The growing adoption of on-premise healthcare informatics solutions is the major factor driving the growth of this segment. The hospitals segment is expected to hold the largest market share during the forecast period. The growing adoption of healthcare informatics solutions in hospitals is the major factor driving the growth of this segment. Recent developments include: Technological advancements, such as the integration of artificial intelligence (AI) and cloud computing in healthcare, are driving market growth. The increasing adoption of electronic health records (EHRs) and the need to improve patient outcomes and reduce healthcare costs are also contributing factors., Key industry players are focusing on strategic partnerships and acquisitions to expand their product offerings and strengthen their market position., Recent news developments include the acquisition of healthcare informatics company MediQuant by IMS Health in 2023, strengthening IMS Health's position in the healthcare data analytics market., Additionally, the collaboration between Cerner and Google Cloud in 2024 aims to leverage Google Cloud's AI capabilities to enhance Cerner's healthcare informatics solutions. These developments indicate the growing significance of technology and partnerships in shaping the future of the Healthcare Informatics market.. Key drivers for this market are: AI-powered analytics Cloud-based healthcare solutions Telemedicine and remote patient monitoring Blockchain for healthcare data security Personalized medicine and precision diagnostics. Potential restraints include: Increasing Healthcare Data Cloud Computing Adoption Government Initiatives Interoperability Challenges AI Integration.

Distribution of outpatient antibiotic prescriptions in India, by disease conditions, 2013–2014.

Sources: Direct-to-consumer advertising data provided by Kantar Media; promotion to professionals data derived from the IMS Health Integrated Promotional Services™, 2010 and excludes epromotion and expenditures for conferences and meetings depicted in Table 1; sales data derived from IMS National Sales Perspectives™.

The global infrastructure monitoring system market is experiencing robust growth, projected to reach $2344.4 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 14.5% from 2025 to 2033. This expansion is driven by several key factors. Increasing urbanization and the development of complex infrastructure projects necessitate sophisticated monitoring solutions to ensure safety, efficiency, and longevity. Government regulations mandating structural health assessments are also boosting demand. Furthermore, advancements in sensor technology, data analytics, and IoT (Internet of Things) integration are enabling more precise and timely monitoring, leading to improved decision-making and reduced maintenance costs. The market is segmented by application (civil, energy, mining, defense, aerospace, and others) and type (hardware, software & services), with significant growth anticipated across all sectors. The civil engineering sector is a major driver, fueled by the construction of bridges, tunnels, and high-rise buildings, all requiring extensive monitoring. Similarly, the energy sector's focus on pipeline integrity and renewable energy infrastructure is contributing substantially to market growth. North America currently holds a significant market share, driven by advanced technological adoption and robust infrastructure investment, followed by Europe and Asia Pacific. However, emerging economies in Asia Pacific are poised for rapid growth, presenting lucrative opportunities for market players. Competition within the infrastructure monitoring system market is intense, with key players including Nova Metrix, Geokon, Campbell Scientific, RST Instruments, Sisgeo, COWI, Geocomp, Acellent, and SIXENSE actively investing in R&D and expanding their geographical reach. Strategic partnerships, mergers, and acquisitions are expected to further shape the market landscape. Despite the optimistic outlook, challenges remain. High initial investment costs for sophisticated systems can be a barrier to entry, particularly for smaller projects. The need for skilled professionals to operate and interpret data from these systems also presents a hurdle. Nevertheless, the long-term benefits of proactive infrastructure monitoring, including reduced downtime, improved safety, and optimized maintenance, are expected to outweigh these challenges, leading to sustained market growth throughout the forecast period. The market’s evolution will be heavily influenced by technological innovations, regulatory changes, and the ever-increasing need for reliable and efficient infrastructure globally.

All data-sharing agreements between your department and either IMS Health or Brogan Inc. Please include any appendices or accompanying briefing materials for these contracts.