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Data-driven Healthcare for the Pharmaceutical Industry

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The payer and provider challenge imposes a new set of constraints on the pharmaceutical industry. Payers impose new cost constraints on providers and scrutinize the value medicines offer much more carefully. Drugs face pricing pressures from consumer watchdog groups, industry trade organizations, and regulators, and pharma will need to optimize operations and identify new segments of customers. Payers want better therapies and medication, along with data that backs the superiority claim. Pharma has no choice but to engage with its customers/patients to demonstrate the value of drugs, justify the price, provide relevant information to regulators, and get through FDA approval quickly. Pharma has traditionally dealt with slower yields on new drugs because of ineffective manufacturing and approval processes and lack of effective tools to analyze population health trends increase costs. Read more at https://www.solix.com/solutions/data-driven-solutions/healthcare/

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Data-driven Healthcare for the Pharmaceutical Industry

  1. 1. Includes Forrester Research report, ‘Seven Ways Big Data Improves Healthcare Outcomes’ Empowering the Data-driven Enterprise
  2. 2. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical2 EXECUTIVE SUMMARY Healthcare data and its impact upon the patient care decision process via accurate, real-time, reliable data from disparate sources is creating a digital health revolution. Physician groups, nursing facilities, hospitals, pharmaceutical companies, clinical researchers, and medical equipment manufacturers are all churning out vast amounts of data during their daily operations. This data has tremendous value and can revolutionize patient care, diagnosis, real-time decisions and help deliver new, unimagined innovations with quality of patient care. Data-driven healthcare approach by the pharmaceutical industry can address lower volume of medicines consumed globally and address issues of pricing, market-access pressures, along with generic drug push into the marketplace. The healthcare and policy reform will affect the pharmaceutical industry and the future of US healthcare policy evolution is imminent. How will the industry react to not only serve the needs of the population, but also proactively harness data to optimize the business models and lead to sustainability and growth? A key challenge that the industry is grappling with is how the pharmaceutical industry will rebound from a recent low in new drug approvals. How can that be accelerated via simple automation that takes into account all the evolving regulatory policies? How will the industry address the challenges with reimbursement and demanding patients that have access to more information at their fingertips, to better engage with the consumer and demonstrate clear value. The tremendous opportunity of a data-driven strategy is apparent to pharmaceutical industry, as all these informational assets exhibiting volume, variety, and velocity need to be ingested and analyzed for enhanced insight leading to better business decisions to address proactively the needs of patient care, while getting to market cheaper, faster, with better products. Data-driven technology solution such as the Solix Common Data Platform (CDP) provides a next generation data management platform that not only meets the analytic demands of the data-driven organization but also addresses the cost, compliance, and governance challenges that come along. The Solix CDP combines human and computer analysis based on huge volumes of data to produce optimal decisions at every level of the healthcare business. Providers can take complete advantage of the data-driven healthcare revolution by adopting such a technology foundation significantly enhancing patient care, and achieve tremendous efficiencies themselves. Healthcare Revolution and Challenges En Route We are witnessing a data-driven healthcare revolution with widespread digitization of electronic health record systems. But with compelling opportunities, we also see massive data volumes, evolving patient expectations, and expanding regulations. Data in varying formats from an increasing array of sources must be integrated to ensure optimal outcomes, whether obtaining a diagnosis, ensuring accurate claims processing, developing new pharmaceutical treatments, or addressing regulatory challenges. To accelerate this healthcare revolution, the industry has to manage key challenges such as government regulations, information security, privacy protocols, changing technology landscape (such as electronic health records, data analytics), while also containing the cost of rolling out new drugs, plans, and products into the healthcare market. Government healthcare programs are growing rapidly and cannot be ignored.
  3. 3. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical3 The healthcare organization’s need to comply with government requirements such as accountability, the performance improvement mandate, and evidence-based outcomes, will require considering technology options to create efficiencies. The pharmaceutical industry have unique challenges that are largely intertwined with payers, providers and require a concerted industry plan crafted in unison. The payer and provider challenge imposes a new set of constraints on the pharmaceutical industry. Payers impose new cost constraints on providers and scrutinize the value medicines offer much more carefully. Drugs face pricing pressures from consumer watchdog groups, industry trade organizations, and regulators, and pharma will need to optimize operations and identify new segments of customers. Payers want better therapies and medication, along with data that backs the superiority claim. Pharma has no choice but to engage with its customers/patients to demonstrate the value of drugs, justify the price, provide relevant information to regulators, and get through FDA approval quickly. Pharma has traditionally dealt with slower yields on new drugs because of ineffective manufacturing and approval processes and lack of effective tools to analyze population health trends increase costs. Data-driven Healthcare Can Help Overcome Industry Challenges The healthcare industry has recognized the emerging challenges well, is reconciled to the new versus traditional business model, and is embracing the technology innovation that will position players for long-term success. Every healthcare ecosystem partner will need to optimize its business models, grow its customer base, address regulatory pressures with emerging technologies such as artificial intelligence, machine learning, block chain, and virtual reality, along with data mining, Big Data, and analytics-based approaches. Data powered tools can accelerate this healthcare revolution with innovations shaping and improving the healthcare system to respond better to patient needs via accurate, collated, aggregated, and meaningful data that provides information and actionable insights for every segment. Addressing the demand for accurate, reliable data is key to success. Healthcare data is created at the source by providers such as physician groups, pharmacies and medical equipment manufacturers. Ultimately, some of this information ends up with the pharma companies for analysis and fine tuning their products availability. Optimize operation and identify new segments of customers Optimize the FDA and regulatory process to get to market faster Predictive analytics to address supply chain demands Reimbursement and demanding patients Clear value of medicines backed by data Address pricing and generic drug pressure O s O t P d R C A Pharma CHALLENGES
  4. 4. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical4 The current landscape of healthcare systems is complicated and fragmented across the industry with many data sources. The cost and complexity of integrating, managing, and storing exabytes of data is a constant issue for everyone within the healthcare ecosystem and more so with pharma considering all the data sources that need to be consumed. The multiple disparate systems, exhibiting a variety of data formats make integrating, exchanging, and harnessing data a challenge. Analyzing all this critical data from all these sources and staging them for advanced integration and analytics is hard but very fruitful. Big Data Can Revolutionize all the Healthcare segments The healthcare world has created a volume, variety and velocity of healthcare data, a unique trifecta that, once addressed, can make huge strides in healthcare decision-making and patient care. The volume of data in healthcare, a lack of standardization of healthcare data from various sources such as providers, payers, disease- management groups, social media, medical laboratories, personalized genetic testing companies, patients’ personal information, along with the need for urgency and real-time analytics that could potentially save lives, makes Big Data ideally suited to work its magic in healthcare. Big Data can be applied to prevent deaths, identify medical conflicts, even predict epidemics and cure diseases. It can proactively identify a child’s potential upcoming health issues and recommend protective measures, and chart out a plan to alleviate the spend in healthcare disbursements over the child’s lifespan. Big Data and advanced analytics can improve healthcare decisions on patient care at all levels, from supporting Real-Time Health Systems (RTHS) to all forms of digital medicine. Big Data can reduce the cost of healthcare and of insurance significantly, helping to make a huge expansion of healthcare coverage a reality. Decision algorithms can provide an additional layer of support and interaction with the patient, in addition to the doctor. Big Data analysis can incorporate patient lab results, the longitudinal patient record, medical imaging, etc., to make treatment recommendations, providing better treatment while relieving the busy, overtly stretched medical professional from hours of work, allowing her to focus on higher value activities. Growing Pharma Data Sources Billing EDI Distribution/ Supply Chain Prescription information Clinical Trials PubMed Sunshine Act FDA 1572 EMR/EHR
  5. 5. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical5 Providers can optimize their existing offerings by leveraging intelligent data-driven strategies to reduce soaring healthcare costs. Big Data analysis can optimize provider resources, distributing it among patients based on their condition and specific need. For the payer, application areas range from fraud detection to real-time continuous patient monitoring outside the clinical setting using personal/ IoT sensors. Other verticals have successfully targeted customers with campaigns that have increased business. The healthcare industry can do the same, but in this case to provide better patient care, to optimize existing resources, and ultimately increase revenue, providing immense benefit to the patients. Hospitals are starting to apply Big Data to sift through complex variables such as lab tests, family history, and diagnosis, taking into account a variety of disparate data elements, in some cases to provide proactive intervention with a patient to head off a long-term costly health challenge. Payers are leveraging Big Data analysis to identify and prevent medical fraud early, saving billions annually. Pharmaceutical companies are leveraging Big Data to streamline and reduce the cost of screening compounds in drugs discovery research. Predictive analysis models working on massive virtual databases of molecular and clinical data can accelerate the process and reduce cost, identify risk factors and can optimize yield from the drug manufacturing process. Big Data’s impact upon order management for medical equipment manufacturers can improve demand planning, identify customer behaviors, and provide insights to deliver goods in a timely fashion. These are only some of the many use cases that benefit from applying Big Data. BIG DATA BENEFITS Mine Patient data to improve care Predict adverse outcome Determine populations at risk for illness Pinpoint where education and prevention is need Detect medical fraud Reduce Readmission Identify procedures likely to succeed Customer experience Supply chain optimization; timely deliveries Reduce the cost to care for by predictive maintenance insights Provide additional insights to end-user; Upsell Ensure even utilization and wear and tear of machines Accelerated drug discovery Targeted Marketing Reduce drug fatalities via predictive modeling Patient compliance via IOT devices Payer Medical Equipment Pharma Provider
  6. 6. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical6 Big Data Needs a Big Technology Shift Traditionally, organizations depended on the Enterprise Data Warehouses (EDW) for all their analytic and business intelligence requirements. However, with the rapidly evolving analytics landscape and the adoption of Big Data, traditional EDWs are falling short of the capabilities needed. Not only are EDWs prohibitively expensive, they lack the ability to store and process unstructured data, and the healthcare industry has more unstructured than structured data. Additionally, due to its schema-on-write requirement, EDWs cannot support the ad-hoc rapid exploration of data which is now become a key requirement of every data driven organization. A Big Data technology platform such as Apache Hadoop provides in-built advantages to help realize the data-driven healthcare vision by ingesting a wide variety of healthcare data, whether structured, semi- structured, or unstructured, in a single repository in low cost bulk storage, eliminating costly and slow ETL processes. The data is stored “as-is” and applies a schema on read. This allows ad-hoc analytic query and in-memory processing in real-time as and when needed. Apache Hadoop also provides massively scalable distributed processing, which is required for complex machine learning and analytic use cases. Finally, Hadoop enables advanced text and voice search, structured queries and advanced analysis tools working seamlessly against multiple data types and formats. Hadoop provides the ability to ask ad-hoc questions to get quick responses, along with the ability to drill down to precise information based upon a natural language search. However, Apache Hadoop does not provide enterprise grade capabilities such as codeless data ingestion, metadata management, Information Lifecycle Management (ILM), data governance and security. Big Data in Action Power clinical recommendation engines using electronic medical record data. The University of Michigan Medical School harnesses intensive care signals and integrates them with their ICU patient charts. It mines data and creates tools that combine bedside real-time facts with clinical rules to signal potential dangers within the ICU. This solution flags risk and recommends diagnostic and treatment options for the critically ill patients. Like most of these types of development initiatives, the school uses its own institution as its spearhead client. It is developing the business programs necessary to bring these insights to market once it feels confident of the efficacy of the solution. Create an institutional benchmark for cancer treatment. Memorial Sloan Kettering Cancer Center built a longitudinal repository of individuals with cancer with great fidelity. It combined publicly available Centers for Medicare and Medicaid Services (CMS) data’s administrative facts such as diagnosis, procedure codes, and provider IDs with clinical facts, such as what cancer stage, from the National Program of Cancer Registries. This greatly enhances the meaning of the administrative data allowing the center to compare one institution’s results for similar cancers to another. The melding of two public data sources to gain insight about the efficacy of cancer treatment across the US is a significant achievement. Ref: Forrester Research report, ‘Seven Ways Big Data Improves Healthcare Outcomes’ (included)
  7. 7. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical7 Additionally, the constantly evolving Hadoop ecosystem makes it a daunting task for enterprises to identify which newer Hadoop technologies are worth incorporating as part of their Hadoop cluster. What exacerbates the problem is that Apache open source technologies are not designed to work together and have no industry standard interfaces, making building a full technology stack a daunting task requiring scarce skills. Organizations need an enterprise grade Big Data management system built on Apache Hadoop such as the Solix Common Data Platform (CDP) for Healthcare. Introducing Solix Common Data Platform (CDP) for Healthcare The Solix Common Data Platform (CDP) is a highly scalable and robust next-generation Big Data management platform that features uniform data collection, metadata management, data governance, ILM, data security, data discovery, and a full set of interfaces to support plug-and-play stack creation and modernization. It leverages the high-performance and low-cost characteristics of the open source Apache Hadoop framework to allow economical storage and real-time processing of petabytes of structured and unstructured healthcare data. Solix CDP stores data “as-is” to eliminate costly ETL operations during data ingestion and provides an ability to transform data post-ingestion to feed the unique needs of downstream NoSQL and analytic applications. It includes modern Big Data processing engines like Apache Spark, Impala and Hive, to meet the machine learning and advanced analytic needs of today’s real-time Data-driven organizations. With a built-in enterprise data lake, enterprise archiving, application retirement, and eDiscovery solutions, Solix CDP provides organizations with an unparalleled enterprise data management and analytic tools and framework. This makes it possible for organizations to leverage data for effective medical diagnosis, clinical trials, drug discovery, and fraud prevention, while saving on storage costs and complying with complex healthcare regulations (including HIPPA, HITECH, CFR etc.). Solix CDP is certified to operate with both the Cloudera and Hortonworks Hadoop distributions. Additionally, it can be deployed on-prem or on the cloud (supports AWS, Azure, Oracle and Google cloud).
  8. 8. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical8 Solutions Overview: Enterprise Data Lake for Machine Learning and Advanced Analytics The Solix CDP-enabled healthcare data lake is a self-contained enterprise data hub that provides robust data collection, data governance and data preparation tools with self-service visualization and business intelligence. It provides authorized data consumers with a singular repository of structured and unstructured healthcare data from a wide range of data sources including EHR, PACS, health trackers, diagnostic equipment, published research, and more. This data is captured into the repository by Solix CDP in an “as is” form along with its associated metadata. This eliminates the need for costly ETL during the ingestion process, while making it easy to discover, understand, and consume data. It would be nearly impossible and extremely expensive for any traditional EDW to incorporate such variety and large volume of information at such velocity. The metadata captured during data ingestion coupled with the strong data governance and data security features of the Solix CDP ensure the data in the healthcare data lake is made securely available to the right people with little or no support from IT. Additionally, the in-depth data preparation features and the inclusion of advanced open source data processing engines, like Apache Spark and Impala, make the healthcare data lake an ideal platform for machine learning and advanced healthcare analytics. Owing to its advanced data storage and data processing capabilities, the healthcare data lake can enable a wide range of predictive and prescriptive analytics necessary to support delivery of quality healthcare services leading to better patient outcomes, cost reduction, identification of abuse and fraud, better clinical research, and more. DATA MART SONOGRAPHY PATHOLOGY PHARMACOLOGY SCANS MEDICAL RECORDS UNSTRUCTURED DATA SEMI STRUCTURED DATA FITNESS TRACKERS IOT SENSORS EHR/EMR DATA IMAGER/PACS DATA RESEARCH DATA STRUCTURED DATA HISTORIC PATIENT DATA BIOMETRIC CRM DATATRANSACTION/DATABASE DISCOVERY SEARCH STAGE TRANSFORM ARCHIVE DATA LAKE HIVEHIVE ANALYTICS REPORTINGDATA MINING
  9. 9. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical9 Enterprise Archiving and Application Retirement In a typical enterprise, up to 80 percent of data in core production applications is inactive and up to 40 percent of enterprise applications are rarely used. This holds true even in the healthcare industry with large volumes of unused data in EHR, PACS, ERP systems, and the many legacy applications occupying the IT environment. At a time when organizations are looking to reduce costs, reallocate resources to high ROI driven IT activities, enterprise archiving and application retirement are a boon. As part of enterprise archiving and application retirement, application data running online is first moved into Tier 2 or Hadoop infrastructure, and then purged from its source location, according to data retention policies defined as part of the ILM strategy. Archived data is further classified for security and compliance requirements such as legal hold, and universal access is provided for business users through role-based structured reports and full text search. Enterprise archiving and application retirement frees up valuable resources in production environment and eliminates unnecessary license and maintenance costs. This could translate into millions in potential savings for a healthcare organization. SOLIX ENTERPRISE ARCHIVING Information Lifecycle Management (ILM) Data Archiving Application Retirement • Manage data growth • Improve application performance • Improve availability • Reduce infrastructure costs • Structured, unstructured data • Print stream archiving • Eliminate maintenance cost • Meet compliance & governance objectivities • Data center consolidation • Print stream retirement Semi/Unstructured Data Universal Access Native Access BI Reporting Analytics Solix Big Data Suite Archiving Solix EDMS Database Archiving Archive Database DB ActiveData Structured Data MOVE & COPY MOVE, COPY, PRINT Enterprise Business Record Print Stream Capture Search & Query Access Retention Management and Legal Hold SOLIXCOMMONDATAPLATFORM Semi-ActiveData (RDBMS) InActiveData (Hadoop ) Reporting/BITools Solix BigData Suite Solix APM (Repository, Query, Search) HISTORIC PATIENT DATA BIOMETRIC CRM DATA TRANSACTION/ DATABASE FITNESS TRACKERS IOT SENSORS EHR/EMR DATA IMAGER/PACS DATA RESEARCH DATA
  10. 10. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical10 Enterprise Business Records (EBRs) By modeling, ingesting, and managing all types of data into a single Hadoop repository, the Solix CDP enables the creation of an Enterprise Business Record (EBR). An EBR is a denormalized, point-in-time snapshot of a business transaction, which may include structured, semi-structured, or unstructured data elements. EBRs support both the regulatory and analytic use cases by providing a quick and well-structured access to complete transactional data along with a history of changes. EBRs are accessible via text or voice search and Restful APIs. Data Governance, Security and Compliance Proper data governance requires that compliance and security measures be in place, and nowhere is data governance more vital than in the highly regulated healthcare industry. One key question in any patient privacy audit is who has the access to sensitive information. Each time a hospital employee needs to access a patient record, proper authentication must occur to ensure that only those with permission to access records can do so. Furthermore, all parties must handle data in compliance with the Health Insurance Portability and Accountability Act (HIPAA) and Security Rule for electronic Protected Health Information (ePHI). Certain healthcare organizations must adopt HL7 standards and create Healthcare Information Exchanges (HIEs) to allow for secure submission and retrieval of patient data.
  11. 11. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical11 The Solix CDP provides a robust, multi-layered security model: • Perimeter: Kerberos and AD/LDAP protect the Hadoop cluster with authentication and network isolation. • Access Control: Apache Sentry manages what the data users and applications can access by roles based permissions and authorizations. • Encryption/Masking: End-to-end encryption for data when in motion and at rest, tokenization and data masking to restrict unauthorized usage • Audit: Audit trail and reporting on the complete data lifecycle including security classification, lineage, access, retention, legal hold, etc. Additionally, the Information Lifecycle Management (ILM) capability discovers and classifies enterprise data and then establishes rules and retention policies to manage the data throughout its lifecycle. Comprehensive retention policies with exception handling such as legal hold and GDPR help further in meeting complex regulatory and compliance requirements.
  12. 12. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical12 Data-driven Finance - Emagia Receivables Management Suite The ready-to-deploy Emagia Receivables Management Suite (ERMS) is about finding the most cost efficient resources to accelerate cash flow. EMRS ensures the most effective receivables, credit policy management, and automation of credit-to-cash (CTC) and order-to-cash (OTC) processes. EMRS is a leading data-driven solution helping customers improve their return on cash. With the introduction of new reimbursement plans (MACRA rules, QPP, MIPS, ACO) a huge amount of data needs to be analyzed to arrive at an appropriate reimbursement formula to maximize incentives. Emagia Cash provides enterprise OTC and CTC solutions to transform, automate, and optimize receivables, credit, and collections. Furthermore, hospital networks have decentralized silos of financial information, each with separate cash management systems. By consolidating disparate cash systems with the Solix CDP, EMRS delivers dramatic credit risk reduction, DSO improvement and cash flow maximization. Conclusion Pharmaceutical companies now have access to vast amounts of structured, semi-structured, and unstructured data from which they can potentially identify patterns that could lead to cures for diseases, patient care improvements, and reducing the price of healthcare. To be able to draw meaningful correlations from these patterns, pharmaceutical companies need to embrace the best of Big Data technologies. Unfortunately, these technologies can be quite complex and daunting. The good news is Solix CDP is an enterprise grade Big Data management platform that leverages the best of open source technologies combined with enterprise class data collection, governance, and discovery features. In a world where data analysis is the key to success and data is measured in exabytes, the Solix CDP is vital. Access ShareMeasure PredictAnalyze Data-driven Finance TM
  13. 13. Empowering the Data-driven Enterprise Data-driven Healthcare for Pharmaceutical13 Solix Technologies, Inc. 4701 Patrick Henry Dr., Bldg 20 Santa Clara, CA 95054 Toll Free: +1.888.GO.SOLIX (+1.888.467.6549) Telephone: +1.408.654.6400 Fax: +1.408.562.0048 URL: http://www.solix.com Copyright ©2017, Solix Technologies and/or its affiliates. All rights reserved. This document is provided for information purposes only and the contents hereof are subject to change without notice. This document is not warranted to be error-free, nor subject to any other warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchant- ability or fitness for a particular purpose. We specially disclaim any liability with respect to this document and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without our prior written permission. Solix is a registered trademark of Solix Technologies and/or its affiliates. Other names may be trademarks of their respectively. Empowering the Data-driven Enterprise
  14. 14. Forrester Research, Inc., 60 Acorn Park Drive, Cambridge, MA 02140 USA Tel: +1 617.613.6000 | Fax: +1 617.613.5000 | www.forrester.com Seven Ways Big Data Improves Healthcare Outcomes by Skip Snow, March 25, 2015 For: CIOs KEY TAKEAWAYS Mining Genetic Data Reveals New Treatment Approaches Research scientists crunch big data to discover how gene expression interacts with the omics environment, which includes our genes and all of the interactions between molecules, bacterium, and genes that constitute microphysiology. Insights gained from this data allow researchers to propose new therapies that ameliorate diseases by altering the genetic environment. Drug Companies Harvest Social Media Streams To Find Victims Of Rare Diseases A great problem in fighting rare disease is diagnosing it. When a vendor can mine social media to understand whom to rule out as potentially having a rare disease, big data becomes a powerful clinical tool, shepherding victims of rare disease through a door of social triage and into a consultation with the correct specialist. Big Data Fuels A Possible Paradigm Switch For Epidemiology Google has all but single handedly changed how we do disease surveillance. In the past six years, it has determined where flu is based on search queries that users enter into their phones and computers. It is now tackling new diseases, and health ministries around the world are starting to depend on these results. New Business Models Emerge As Big Data Fuels Solutions Offered By Major Healthcare Providers By harvesting internal and third-party data, tier 1 hospital systems embed insight from data into software solutions. They seek to monetize it by licensing solutions to other hospitals. Monetizing data drives partnerships with tech vendors, creating compelling solutions and accelerating the globalization of care delivery.
  15. 15. © 2015, Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester® , Technographics® , Forrester Wave, RoleView, TechRadar, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. To purchase reprints of this document, please email clientsupport@forrester.com. For additional information, go to www.forrester.com. FOR CIOS WHY READ THIS REPORT In the healthcare industry, knowledge driven by big data is changing the shape of research, clinical, and administrative operations; standards of care; and even fundamental business models. It is providing new revenue opportunities previously unattainable in healthcare. Healthcare CIOs are at the center of these groundbreaking initiatives as they struggle to build the business cases for their own programs. However, they often ask Forrester for examples of big data in practice and the resulting ROI behind successful big data initiatives. This report catalogs some major applications of big data Forrester has observed in its research. Table Of Contents Big Data Insight Feeds A New Data Economy Individual And Population Data Combine, Improving Clinical Outcomes Big Data Powers Breakthroughs In Research And Epidemiology Healthcare Payers Add Value-Based Products Based On Their Unique Data Access WHAT IT MEANS Big Data To Provide Revenue For Large Healthcare Companies Supplemental Material Notes & Resources Forrester interviewed 42 vendor and user companies for this report. Related Research Documents Healthcare Meets Cognitive Computing February 13, 2015 Healthcare Transformation Is Driving Disruption For Payers’ Business Capabilities December 3, 2014 Predictions 2015: The BT Agenda Underpins Healthcare Transformation November 17, 2014 Seven Ways Big Data Improves Healthcare Outcomes Compelling Business Cases For Big Data by Skip Snow with Patti Freeman Evans, Brian Hopkins, Abigail Komlenic, and Shaun McGovern 2 7 8 MARCH 25, 2015
  16. 16. FOR CIOS Seven Ways Big Data Improves Healthcare Outcomes 2 © 2015, Forrester Research, Inc. Reproduction Prohibited March 25, 2015 BIG DATA INSIGHT FEEDS A NEW DATA ECONOMY The healthcare industry has realized data is one of its most valuable assets. Tier 1 institutions across the healthcare ecosystem innovate by combining unlikely data streams to generate new insights. Often this process can be turned into new products (e.g., software solutions) that other healthcare organizations will buy. CIOs are struggling to understand the compelling business cases that underlie a great deal of these activities. They are often heads-down responding to requests for data analytics from their workforce without the capacity to respond to the paradigm switch as a new economy segment within healthcare emerges. Enterprises can win a competitive advantage by focusing their teams, developing big data initiatives to harness their organization’s unique data assets. Below we enumerate seven important use cases to stimulate conversations about these switches that are taking place. The insights gained facilitate value-based care, inform payers of their reimbursement policies efficiently, forge new fraud and waste capabilities, help in the discovery of gene interactions, and change the shape of epidemiology (see Figure 1). Figure 1 The Three Main Environments Of The Healthcare Ecosystem Seek Common Data Entities Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.117433 • Finding patients with rare disease • Creating clinical test beds • Population management • Health optimization • Fraud and waste detection • Employee behavior benchmark Problems Clinical Administrative Care domain • Finding new drug therapies • Finding clinical care paths Research • Claims data • Clinical data • Social data • Epidemiological data • Consumer behavior data • Location data • Criminal history data • Credit data • Consumer behavior data • Omics data • Molecule pathway data • Corpus of knowledge
  17. 17. FOR CIOS Seven Ways Big Data Improves Healthcare Outcomes 3 © 2015, Forrester Research, Inc. Reproduction Prohibited March 25, 2015 Individual And Population Data Combine, Improving Clinical Outcomes The perfect storm is brewing. Technology has learned how to find insight from within both structured and unstructured data, and, because clinical records are now mostly digital, combining clinical, administrative, and publicly available data often yields unanticipated insight (see Figure 2). Complex pattern-matching algorithms, the need to create a value-based environment, and fast, inexpensive clusters of commodity computers running open source software have changed what is possible. Forrester has found examples where big data combined with various other data sources to: ■ Power clinical recommendation engines using electronic medical record data. The University of Michigan Medical School harnesses intensive care signals and integrates them with their ICU patient charts. With its partners with IBM and AirStrip Technologies, it mines data and creates tools that combine bedside real-time facts with clinical rules to signal potential dangers within the ICU. This solution flags risk and recommends diagnostic and treatment options for the critically ill patients. Like most of these types of development initiatives, the school uses its own institution as its spearhead client. It is developing the business programs necessary to bring these insights to market once it feels confident of the efficacy of the solution. ■ Create an institutional benchmark for cancer treatment.1 Memorial Sloan Kettering Cancer Center built a longitudinal repository of individuals with cancer with great fidelity. It combined publicly available Centers for Medicare and Medicaid Services (CMS) data’s administrative facts such as diagnosis, procedure codes, and provider IDs with clinical facts, such as what cancer stage, from the National Program of Cancer Registries. This greatly enhances the meaning of the administrative data allowing the center to compare one institution’s results for similar cancers to another. The melding of two public data sources to gain insight about the efficacy of cancer treatment across the US is a significant achievement. ■ Diagnose rare disease by marrying big data and social media communities. Often, clinicians cannot diagnose people with rare diseases correctly because they have never seen a case in their practices. Corcept Therapeutics, a niche pharmaceutical company, partners with Liquid Grid to mine social media for synonyms and semantic equivalents to the clinical descriptions of Cushing syndrome to promote its therapy Mifepristone.2 According to Liquid Grid’s CEO Malcolm Bohm: “We start with our own ontology of medical terms sentiment. We mine Facebook, Twitter, Tumblr, WordPress, and the metadata of YouTube. This takes no more than a matter of weeks, and we are ready to use the insight we have on how a lay community describes a condition.”3
  18. 18. FOR CIOS Seven Ways Big Data Improves Healthcare Outcomes 4 © 2015, Forrester Research, Inc. Reproduction Prohibited March 25, 2015 Figure 2 Facebook Page Used By Concept Therapeutics To Steer Potential Patients To Doctors Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.117433 Source: Corcept Therapeutics’ Cushing’s Connection Facebook page
  19. 19. FOR CIOS Seven Ways Big Data Improves Healthcare Outcomes 5 © 2015, Forrester Research, Inc. Reproduction Prohibited March 25, 2015 Big Data Powers Breakthroughs In Research And Epidemiology Big data can speed time-to-market for therapies, and novel ways of doing disease surveillance foretell a new paradigm in epidemiology: ■ Labs use big data to disrupt traditional research models and methods. Mount Sinai Hospital has invested heavily in data scientists and equipment, creating a “dry lab” infrastructure.4 They use computer and data science to uncover networks of interactions, revealing new targets for genetic interventions. Clinical trials are already underway as the output of several dry lab discoveries. Unlike traditional genetic research using computers to sequence genes and human intellect to interpret the meaning of these sequences, computers with knowledge of what drugs do to target gene expression suggest possible therapies to the clinicians based on gene network pathology. Mount Sinai plans to monetize its best-of-breed ability to find patterns in the genetic data.5 ■ Google’s disease surveillance invigorates epidemiology. Google parses its search stream to detect disease instances, e.g., number of dengue fever and flu cases in many nations of the world. Google works with major academic research institutions and public health officials to curate and validate its epidemiology algorithms. The company also uses national epidemiology databases to benchmark and validate its results. Over the six-year span of the project, Google’s results have become quite accurate.6 The health departments of nations that do not have surveillance infrastructures seem to depend on Google’s weekly updates on dengue fever.7 The potential to change the game in epidemiology is real, and we have seen at least one startup that is trying to capitalize on these business ideas (see Figure 3).
  20. 20. FOR CIOS Seven Ways Big Data Improves Healthcare Outcomes 6 © 2015, Forrester Research, Inc. Reproduction Prohibited March 25, 2015 Figure 3 Google Has Hit The Mark For US Flu Prediction As Compared With CDC Data Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.117433 Note: Google and the Google logo are registered trademarks of Google, Inc., used with permission. Google flu Data source: Google Flu Trends (http://www.google.org/flutrends)
  21. 21. FOR CIOS Seven Ways Big Data Improves Healthcare Outcomes 7 © 2015, Forrester Research, Inc. Reproduction Prohibited March 25, 2015 Healthcare Payers Add Value-Based Products Based On Their Unique Data Access As CIOs become more embedded in their organizations’ customer-facing initiatives, they will find many opportunities to drive customer value, and thus revenue, via big data. Whether it is in population management or fraud detection, big data initiatives provide new value and ways to reduce costs in providing care: ■ Health insurance companies target waste and fraud with big data solutions. The insurance industry is a leader in fraud detection. It rivals the credit card industry in detecting patterns in data that indicates fraud. Harvard Pilgrim Health Care uses LexisNexis’ Intelligent Investigator to ferret out fraud. It links unexpected relationships between a provider billing address and risky individuals associated with that address to uncover more “bad guys.” The program has helped the organization initiate many new criminal and civil investigations. ■ Pacific Blue Cross preserves privacy and provides aggregated reports to large employers. Privacy concerns make it impossible for employers to gain access to their staff’s health records. Pacific Blue Cross creates reports for large companies showing patterns of opportunities to optimize staff health without exposing employees’ individual health data.8 Such advances in data-driven, value-based products will differentiate insurance companies for the next decade as less competent competitors strive to catch up. W H AT I T M E A N S BIG DATA TO PROVIDE REVENUE FOR LARGE HEALTHCARE COMPANIES Big data will be a major driver altering the operational models of healthcare. Developing these solutions is expensive. New forms to monetize these solutions evolve to fund the necessary investments. New organizational models are emerging; for example, we see health companies that traditionally only sell hospital beds to consumers hiring business-to-business (B2B) marketing staffs. Technology vendors with global reach are looking to the major centers of excellence in care management to create cobranded products that embed insight locked up in data. Over the next 15 years, the increased globalization of care delivery tools fueled by big data will accelerate. As trends like retail medicine continue to mature, the data itself will allow clinical standards to evolve. These standards will be available for economically and geographically challenged communities consuming these solutions. The successful CIO in large healthcare companies increasingly will run business units selling solutions. The hiring practices for BT executives is evolving; former software and consulting executives increasingly are being recruited by traditional technology buyers now turning into solution vendors.
  22. 22. FOR CIOS Seven Ways Big Data Improves Healthcare Outcomes 8 © 2015, Forrester Research, Inc. Reproduction Prohibited March 25, 2015 SUPPLEMENTAL MATERIAL Companies Interviewed For This Report Accenture Aetna Anthem Insurance Ayasdi Blue Shield of California CitiusTech Cognizant Corcept Therapeutics Dell Elsevier Epic Systems Explorys Google Harvard Pilgrim Health Care HCL Technologies Health Integrated IBM Informatica InterSystems Kaiser Permanente Koninklijke Philips KPMG McKesson Memorial Sloan Kettering Cancer Center Mercy Hospital Mount Sinai Hospital MVP Health Care Nuance Communications Optum Orion Health Pacific Blue Cross Health Benefits Society Practice Fusion PwC SAS United Healthcare United States Department of Health and Human Services University of Michigan Medical School Verisk Health Virtual Radiologic Webtrends West ZirMed
  23. 23. FOR CIOS Seven Ways Big Data Improves Healthcare Outcomes 9 © 2015, Forrester Research, Inc. Reproduction Prohibited March 25, 2015 ENDNOTES 1 Memorial Sloan Kettering Cancer Center combined data from the national cancer registry and administrative claims data for Medicare patients from the CMS. Source: Forrester interview with John Gunn, COO of Memorial Sloan Kettering Cancer Center, September 2014. 2 Check out Medscape for epidemiology on almost any disease. Source: David S. Liebeskind, MD, “Hemorrhagic Stroke,” Medscape, January 8, 2015 (http://emedicine.medscape.com/article/1916662- overview#a0156) and Gail K. Adler, MD, “Cushing Syndrome,” Medscape, April 4, 2014 (http://emedicine. medscape.com/article/117365-overview#aw2aab6b2b4aa). 3 Source: Forrester interview with Liquid Grid, Q3 2014. 4 A dry lab is an increasingly important term of art in the biomedical research world. It refers to a lab that consists of computers and computer models that pour over clinical and other data to find patterns of insight. 5 For more information on cognitive computing, see the February 13, 2015, “Healthcare Meets Cognitive Computing” report. 6 For more information on Google’s work with the flu, see the June 20, 2014, “Google Flu Trends — A Big Data Fail? Not Exactly” report. 7 Google is working with new data sources in the lab to refine its results. It is collaborating in the academic and public health spheres to refine its results. Yet it has no current plans to monetize these innovations. In an interview with Steve Crossan, Google’s product manager for disease surveillance, told Forrester: “We are lucky enough not to need a business model to measure the work we are doing. We are not trying to build a business around public health surveillance.” He went on to say that when its cycle runs slowly, it does get feedback from the countries that need the data because it does not have public surveillance for diseases. 8 This information was gathered in an interview with Cindy Bratkowski, senior vice president, information technology and client services, and Akiko Campbell, director, innovation center and security officer at Pacific Blue Cross in September 2014.
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