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Analítica de Big Data Analytics para o setor farmacêutico
e análises clínicas

nuvem Hortonworks é um líder. Leia o Forrester Wave.

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Encontrando a solução para dados inacessíveis

What happens when the data you need is hidden in silos, or when billions of dollars are riding on drug testing data you can’t access? How do you see a long-term view of 10 billion records to understand biological response to drugs? Researchers in the pharmaceutical industry turn to Hortonworks for advanced big data analytics on integrated translational data and to gain a holistic view of their pharmaceutical data.

Libere a força dos dados farmacêuticos

Big Data integration, pharmaceutical big data analytics, internal and external collaboration, portfolio decision support, more efficient clinical trials, faster time to market, improved yields, improved safety - these are just a few of the benefits pharmaceutical companies around the world achieve by tapping into the full power of their pharma big data.

casos de uso

Merck otimiza rendimentos de vacina: esforçar-se pelo "Lote de Ouro"

A Merck otimizou os rendimentos de suas vacinas por meio da análise de dados de produção para isolar as variáveis ​​preditivas mais importantes visando um "lote de ouro". Os líderes da Merck por muito tempo confiaram na produção Lean para aumentar os volumes e reduzir os custos, mas tornou-se cada vez mais difícil descobrir outras maneiras de melhorar os rendimentos. Eles olharam para o Open Enterprise Hadoop em busca de novos insights que pudessem reduzir ainda mais os custos e aumentar o rendimento. A Merck recorreu à Hortonworks para a descoberta de dados em registros de 255 lotes de uma vacina que de 10 anos atrás. Esses dados foram distribuídos em 16 sistemas de manutenção e gestão de edifícios e incluíram dados de sensores precisos sobre as configurações de calibração, pressão do ar, temperatura e umidade. Depois de agrupar todos os dados na Hortonworks Data Platform e processar 15 bilhões de cálculos, a Merck obteve novas respostas para as perguntas que fez por uma década inteira. Entre centenas de variáveis, a equipe da Merck conseguiu detectar aquelas que otimizaram o rendimento. A empresa começou a aplicar essas lições às suas outras vacinas, com um foco no fornecimento de medicamentos de qualidade com o menor preço possível. Assista à entrevista de Doug Henschen, da InformationWeek, com George Llado, da Merck.


Minimizando o desperdício ao longo do processo de produção de medicamentos

One Hortonworks pharmaceutical customer uses HDP for a single view of its supply chain and their self-declared “War on Waste”. The operations team added up the ingredients going into making their drugs, and compared that with the physical product they shipped. They found a big gap between the two and launched their War on Waste, using HDP big data analytics to identify where those valuable resources were going. Once it identifies those root causes of waste, real-time alerts in HDP notify the team when they are at risk of exceeding pre-determined thresholds.


Translational Research: transformando estudos científicos em medicamentos personalizados

The goal of Translational Research is to apply the results of laboratory research towards improving human health. Hadoop empowers researchers, clinicians, and analysts to unlock insights from translational data to drive evidence-based medicine programs. The data sources for translational research are complex and typically locked in data siloes, making it difficult for scientists to obtain an integrated, holistic view of their data. Other challenges revolve around data latency (the delay in getting data loaded into traditional data stores), handling unstructured and semi-structured types of data, and bridging lack of collaborative analysis between translation and clinical development groups. Researchers are turning to Open Enterprise Hadoop as a cost-effective, reliable platform for managing big data in clinical trials and performing advanced analytics on integrated translational data. HDP allows translational and clinical groups to combine key data from sources such as: Omics (genomics, proteomics, transcription profiling, etc) Preclinical data Electronic lab notebooks Clinical data warehouses Tissue imaging data Medical devices and sensors File sources (such as Excel and SAS) Medical literature Through Hadoop, analysts can build a holistic view that helps them understand biological response and molecular mechanisms for compounds or drugs. They’re also able to uncover biomarkers for use in R&D and clinical trials. Finally, they can be assured that all data will be stored forever, in its native format, for analysis with multiple future applications.


Sequenciamento de nova geração

IT systems cannot economically store and process next generation sequencing (NGS) data. For example, primary sequencing results are in large image format and are too costly to store over the long term. Point solutions have lacked the flexibility to keep up with changing analytical methodologies, and are often expensive to customize and maintain. Open Enterprise Hadoop overcomes those challenges by helping data scientists and researchers unlock insights from NGS data while preserving the raw results on a reliable, cost-effective platform. NGS scientists are discovering the benefits of large-scale processing and analysis delivered by HDP components such as Apache Spark. Pharmaceutical researchers are using Hadoop to easily ingest diverse data types from external sources of genetic data, such as TCGA , GENBank , and EMBL. Another clear advantage of HDP for NGS is that researchers have access to cutting-edge bioinformatics tools built specifically for Hadoop. These enable analysis of various NGS data formats, sorting of reads, and merging of results. This takes NGS to the next level through: Batch processing of large NGS data sets Integration of internal with publically available external sequence data Permanent data storage for large image files, in their native format Substantial cost savings on data processing and storage.

A HDP usa dados do mundo real para apresentar evidências do mundo real

Real-World Evidence (RWE) promises to quantify improvements to health outcomes and treatments, but this data must be available at scale. High data storage and processing costs, challenges with merging structured and unstructured data, and an over-reliance on informatics resources for analysis-ready data have all slowed the evolution of RWE. With Hadoop, RWE groups are combining key data sources, including claims, prescriptions, electronic medical records, HIE, and social media, to obtain a full view of RWE. With big data analytics in the pharmaceutical industry, analysts are unlocking real insights and delivering advanced insights via cost-effective and familiar tools such as SAS® ,R®, TIBCO™ Spotfire®, or Tableau®. RWE through Hadoop delivers value with optimal health resource utilization across different patient cohorts, a holistic view of cost/quality tradeoffs, analysis of treatment pathways, competitive pricing studies, concomitant medication analysis, clinical trial targeting based on geographic & demographic prevalence of disease, prioritization of pipelined drug candidates, metrics for performance-based pricing contracts, drug adherence studies, and permanent data storage for compliance audits.

Acesso perpétuo a dados brutos de pesquisas anteriores

A HDP utiliza dados do mundo real para entregar evidência do mundo real
A Evidência do Mundo Real (RWE) promete quantificar melhorias nos resultados de saúde e tratamentos, mas esses dados devem estar disponíveis em grande escala. Os altos custos do armazenamento e processamento de dados, os desafios referentes a dados não estruturados e estruturados, e um excesso de dependência dos recursos de informática para dados prontos para análise retardaram a evolução da RWE. Com o Hadoop, os grupos de RWE estão combinando fontes de dados-chave, incluindo reivindicações, prescrições, registros médicos eletrônicos, HIE, e redes sociais para obter uma visão completa da RWE. Os analistas estão liberando insights reais e fornecendo insights analíticos avançados por meio de ferramentas econômicas e conhecidas como SAS®, R®, TIBCO™ Spotfire® ou Tableau®. A RWE via Hadoop oferece valor com utilização ideal dos recursos de saúde em diferentes grupos de pacientes, uma visão holística sobre compensações de custo/qualidade, análise de vias de tratamento, estudos de preços competitivos, análise de medicação concomitante, segmentação de ensaios clínicos tendo como base a prevalência geográfica e demográfica da doença, priorização de medicamentos candidatos do pipeline, métricas para contratos de fixação de preços baseada no desempenho, estudos de adesão a medicamentos, e armazenamento de dados permanente para auditorias de conformidade.