Hewlett Packard Enterprise Files IPO Prospectus Highlighting Haven Big Data Platform for Healthcare

In today’s society, data is doubling every two years—a fascinating phenomenon in itself. More notably, entrepreneurs are actively collecting data and extracting valuable insights to monitor market trends, enhance corporate competitiveness, and transform operational practices and even business models. In the healthcare sector, big data is increasingly regarded by many companies as the pathway to the future.

In the 2013 ranking of the world’s most influential big data companies, HP ranked second with a total big data revenue of $664 million in 2012. Below, we will examine the core components of HP’s data platform and explore case studies of HP’s big data applications.

In November 2015, Hewlett Packard Enterprise, led by current HP CEO Meg Whitman, will begin trading on the New York Stock Exchange. Hewlett Packard Enterprise will focus on enterprise-level IT solutions, infrastructure, as well as software and cloud services. Among these, the creation of the Haven big data platform is the company's top priority.

HP Haven is the industry’s first comprehensive, scalable, and open big data analytics platform for security. By leveraging it, entrepreneurs can instantly access actionable insights on demand to drive business outcomes and gain a competitive edge, while clinicians can obtain the most comprehensive and objective descriptions of medical conditions, thereby enhancing diagnostic and treatment standards and reducing healthcare costs.

HP Haven Big Data Platform’s healthcare analytics address the challenges and complexities of big data in the healthcare industry through a modular, flexible solution. It connects data silos and intelligently leverages both structured data (such as scheduling data and billing codes) and unstructured data (such as clinical narratives). Capable of ingesting all types of information regardless of location, format, or language, it enables clinicians to gain comprehensive insights and derive actionable intelligence that enhances care quality and operational efficiency while reducing healthcare costs.

The Haven Big Data Platform comprises Enterprise Big Data, Big Data Cloud, Haven Hadoop, and Haven Predictive Analytics. Among these, Enterprise Big Data is an on-premises big data solution primarily designed for structured and unstructured data; Big Data Cloud is a suite of cloud services that rapidly delivers data-driven insights and leverages APIs to create next-generation applications and services; Haven Hadoop enables access to and analysis of massive datasets within the industry’s most widely used Hadoop distributions; and Haven Predictive Analytics accelerates the implementation of large-scale machine learning and advanced analytics.



Let us examine four common solution use cases with high potential and high returns:

• Search

Vast amounts of clinical records involve a wide variety of medical devices and specialized expertise. Due to the diversity of information, physicians must spend considerable time searching for specific clinical data, whether within an individual patient’s medical record or across thousands of patient cases. Since many records consist of clinical narratives that are unstructured, with each patient and clinician employing unique narrative and documentation styles, these narratives constitute free text characterized by individual stylistic preferences and terminology. This heterogeneity of information impedes search efficiency, making the process both time-consuming and labor-intensive.

HP Haven Big Data Platform leverages taxonomies to query information, making searches more convenient and efficient. In extracting data related to medical information, clinical practices, business intelligence, and research activities, the platform employs mature probabilistic models to automatically identify clinical concepts and highlight relevant search results based on query content. Consequently, HP Haven Big Data Platform helps reduce manual, error-prone steps, thereby lowering administrative overhead and shortening the time required for information collection.

• Report

Healthcare providers regularly collect key performance indicators (KPIs) as a reference to evaluate recent operational effectiveness. However, these metrics are largely limited to structured transactional data that complies with commercial processing standards. This means that non-standard reports typically require batch processing queries, executed with the assistance of information services.

HP Haven’s big data platform for healthcare analytics integrates coded and free-text data, enabling comprehensive intelligent services that range from enterprise-wide automation to reporting. Its interactive user interface delivers intuitive reports and visualizations of clinical data, while the self-service analytics model of the HP big data platform allows users to easily leverage these capabilities. Relevant analytical results can also be rapidly traced back to the underlying supporting data, ensuring greater accuracy in reporting.

• Identifying Differences

Financial reimbursement and clinical reporting are almost entirely based on structured data, yet such data can never be fully accurate. Both overcoding and undercoding introduce bias. Therefore, identifying discrepancies between diagnosis/procedure codes and clinical documentation is critical, but doing so is fraught with challenges.

HP addressed this issue by leveraging healthcare analytics on the Haven big data platform, enabling users to identify discrepancies between fault analysis codes and clinical documentation. This was achieved because the Haven big data platform streamlined process steps and enhanced search effectiveness through methods such as automating search processes and eliminating boundaries between structured and unstructured data.

• Process Optimization

In today’s society, people seek to achieve the best therapeutic outcomes at the lowest cost. Healthcare providers are exploring various strategies to enhance operational efficiency and the quality of patient care, such as reducing operational waste and optimizing IT system performance through proactive management.

HP Haven big data platform can be integrated with existing healthcare systems, such as those from Cerner Corporation, to accelerate the analysis of large volumes of system performance data. Cerner’s RTMS (Response Time Measurement System) timers can detect how long specific application functions take, such as adding or expediting the transmission of patient information, entering medication orders, or documenting medical procedures. Furthermore, Cerner’s analytics system features data alerting and proactive monitoring capabilities to minimize the risk of degraded system performance.

The healthcare analytics capabilities of the HP Haven big data platform are modular, enabling its data analysis functions to be extended to other use cases, such as investigating comorbidity patterns, relationship discovery, and advanced cohort analysis for population health management.



Finally, the practical application value of HP's big data platform is demonstrated through application cases.

Case 1:Kainos Leads the National Health Service into the Paperless Era with Support from HP Autonomy Technology

The UK government aims to achieve a paperless National Health Service (NHS) by 2018. Paperlessness refers to the replacement of cumbersome paper-based medical records with electronic health records (EHRs). In the UK, Kainos is a renowned company specializing in electronic health records.
Powered by HP Autonomy’s automated platform, Kainos’ flagship product—Evolve—is spearheading a digital revolution in the healthcare industry.

Perhaps we can understand the important role of HP Autonomy in the formation of electronic medical records from the creation process of Evolve.

Evolve aims to digitize medical records, making them more focused and centralized to address issues related to patients, wards, and hospitalization processes. This enables health and social care practitioners to access critical information with a single keystroke. To achieve this goal, Kainos is striving to identify a technology capable of reading and indexing complex information derived from paper-based medical records, extracting file types, and recognizing concepts in specific domains such as clinical specialties. The technology must also be able to access and retrieve information from other systems.

HP Autonomy’s enterprise information management solution is fully capable of meeting Kainos’s needs. With HP Autonomy, users can gain real-time visibility into all their data for information analytics, archiving, discovery, content management, data protection, and marketing optimization.

Autonomy’s software system features powerful management and analytics tools capable of processing human-generated information and unstructured data, such as emails, social media content, videos, audio files, text documents, and web pages. Based on this capability, Kainos has established a partnership with HP Autonomy.

Kainos has two long-term objectives: first, to establish a unified interface accessible both locally and remotely, through which users can obtain various clinically relevant information; and second, to develop a tool capable of conducting leading-edge research and analysis on such information.

HP IDOL, the analytics engine under HP Autonomy, automatically categorizes medical records and documents based on content and concepts, facilitating the search and analysis of specific information.

The adoption of HP Autonomy has shortened technology development cycles, reduced R&D costs, and enabled Evolve to reach the market earlier. This robust technical support has also bolstered confidence in Evolve. Leveraging HP Autonomy’s technology, Evolve has implemented electronic medical records (EMR), allowing physicians to access patients’ medical histories more conveniently while reducing costs associated with paper-based records.

Case 2:HP Establishes Presence in Guizhou to Boost Big Data Industry



The informatization of medical services not only facilitates the management of healthcare institutions but also brings numerous benefits to doctors and patients, becoming one of the key trends in societal development. In this process, cloud computing plays a crucial role, and the establishment of smart healthcare cloud platforms has become an important component of medical informatization.

Information systems across various industries in today’s society face challenges such as high investment costs, significant risks, severe information silos, and the absence of a comprehensive and scientific standard framework. These issues seriously hinder information sharing, decision-making inference, and business operations.

Cloud computing features virtualization, massive scale, high scalability, high reliability, cloud storage, and cloud analytics. It can adapt to a wide variety of applications, perform big data storage and analysis, enable real-time monitoring of resource status, and support effective decision-making.

Smart healthcare can connect community health service centers, cloud infrastructure, computing centers, cloud services, and service recipients into an integrated system, thereby eliminating information silos, enabling data sharing, and facilitating informed decision-making for enterprises.

HP Haven’s search and analytics capabilities enable it to excel in the field of smart healthcare. Among its offerings, HP Vertica OnDemand is a cloud-based, enterprise-grade big data analytics platform that delivers high-performance, enterprise-level data analysis, empowering users to make informed decisions with maximum speed—precisely meeting the demands of smart healthcare.

HP’s big data platform has been applied in the field of smart healthcare. It is reported that on March 2, 2015, HP announced plans to establish a smart healthcare cloud platform in Huizhou. The company also stated that it would build a powerful data center in Guiyang, covering an area of 10,000 square meters and housing 50,000 servers, to enhance the overall technical capabilities and service levels of Guiyang’s cloud computing infrastructure. Leveraging big data analytics platform technologies, HP aims to create the “Guizhou International Financial and Trade Cloud” big data platform. Currently, HP has partnered with Guiyang to establish a unified smart healthcare cloud platform. In the initial phase, data from nine municipal hospitals will be integrated to create a citywide unified electronic database. In the future, residents will be able to perform medical insurance-related operations through the healthcare cloud platform, while hospitals and relevant medical insurance agencies will be able to centrally manage and settle data via the backend system.

Chen Gang, a member of the Standing Committee of the Guizhou Provincial Party Committee and Secretary of the Guiyang Municipal Party Committee, spoke highly of HP: “HP is one of the pioneers in boosting Guiyang’s development of the big data and cloud computing industries. HP’s establishment in Guiyang has made the city a ‘hotspot’ for big data industry development, significantly enhancing Guiyang’s appeal in attracting big data industry investments.”

The establishment of the Smart Healthcare Cloud Platform will make significant contributions to local healthcare, with HP’s big data platform demonstrating its value in this context. Secretary Chen further stated, “The launch of the HP project will not only positively promote the development of big data and cloud computing in Guiyang but also create more job opportunities and provide more convenient public medical services, benefiting a broader segment of the general population.” Let us look forward to the role that HP’s Smart Cloud Healthcare will play in advancing Guizhou’s development.

By Chen Kun
Editor: Zhang Nan