The Potential of Big Data Applications for the Healthcare Sector

1Big Data Minds 2014 BIG 318062

The Potential of BIG Data Applicationsfor the Healthcare Sector

Results of the User Needs & Requisites Study in BIG

Prof. Dr. Sonja ZillnerSiemens AG

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The EU Project BIGBig Data Public Private Forum

Europe needs a clear strategy for leveraging Big DataEconomy in Europe

Work at technical, business and policy levels, shaping thefuture through the positioning of Big Data in Horizon 2020.

Bringing the necessary stakeholders into a sustainableindustry-led initiative, which will greatly contribute toenhance the EU competitiveness taking full advantage ofBig Data technologies.

Objectives

Trigger

Type of project: Coordination & Support ActionProject start date: September 2012Duration: 26 monthsCall: FP7-ICT-2011-8Budget: 3,038 M€Consortium: 11 partners

Facts

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Data acquisition Dataanalysis

Datacuration

Datastorage

Datausage

Health Public Sector Telco, Media &Entertainment

Finance &insurance

Manufacturing,Retail, Energy,

Transport

Value Chain

• Structured data• Unstructured Data• Event processing• Sensors networks• Streams

• Data preprocessing• Semantic analysis• Sentiment analysis• Other features

analysis• Data correlation

• Trust• Provenance• Data augmentation• Data validation

• RDBMS limitations• NOSQL• Cloud storage

• Decision support• Decision making• Automatic steps• Domain-specific

usage

Technical areas

SupplyNeeds

Industry driven working groups

Project Structure(Sectorial forums and Technical working groups)

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Big Data in HealthcareWhat are we taking about?

Definition of Big Data in Healthcare IndustryBig Health Data technologies help to take existinghealthcare business intelligence, health data analytics andhealth data management applicationto the next level by providing means for the efficienthandling and analysis of complex and largehealthcare data by relying on

data integration,real-time analysis as well aspredictive analysis

Characteristics of Health data

Health data is not big in terms of large sizeExceptions are medical images and NGS, however the analysis of analytics approachesfor medical images and NGS is immature and in development

Health data is complexHeterogeneous data (images, structured, unstructured data, etc.)Various data domains (administrative, financial, patient, population, etc.)

often discussedunder the label„Advanced HealthData Analytics“

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Key FindingsImpact of Big Data Applications in Health Domain

Technology-wise = Evolution

Big Data Technology (e.g. scalable data analytics, semantictechnologies, machine learning, scalable data storage, etc.) is readyto be usedNow these techniques are combined and extended to address bigdata paradigmDomain-specific requirements needs to be addressed (e.g. healthdata anonymization, understand analytic needs)

Business-wise = Revolution

The lack of business cases is hindering blockIntegrating of heterogeneous data sources beyond organizationboundaries relies on effective cooperation of multiple stakeholderwith diverging interests

=> existing industrial business processes will change fundamentally,new players & business models will emerge

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User NeedsPotential Benefits and Advantages

Improved Efficiency of Care 1

Combine clinical, financial, and administrative data to monitor outcomes relative toresource utilizationMeasure physician performance against peers and other institutionsMine population level data for clinical researchHelps organizations manage regulatory compliance through detailed information reporting

Improved Quality of Care 1

Empowers users with key knowledge needed for effective decision makingIdentify high-risk patients and patient populationsDevelop predictive models leading to proactive patient careEnables uniform and multi-dimensional view of patient and population data

1= Frost & Sullivan “U.S. Hospital Health Data Analytics Market (2012) ”

Real Impact

of Big Data Analytics is expected on integrated data sets

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Multiple Data Pools in HealthcareMain impact by integrating various and heterogeneous data sources

Clinical Data

Owned by providers (such ashospitals, care centers, physicians,etc.)Encompass any information storedwithin the classical hospitalinformation systems or EHR, such asmedical records, medical images, labresults, genetic data, etc.

Claims, Cost &Administrative Data

Owned by providers and payorsEncompass any data sets relevant forreimbursement issues, such asutilization of care, cost estimates,claims, etc.

Pharmaceutical &R&D Data

Owned by the pharmaceuticalcompanies, researchlabs/academia, governmentEncompass clinical trials,clinical studies, population anddisease data, etc.

Patient Behaviour &Sentiment Data

Owned by consumersor monitoring deviceproducerEncompass anyinformation related tothe patient behavioursand preferences

Health data on theweb

Mainly open sourceExamples arewebsites such asPatientLikeMe,Linked Open Data,etc.

Highest Impacton integrated data sets

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Market Impact and CompetitionHype and Hope

Market ImpactThe market for big data technology in the healthcare domains is in anearly stageConcrete financial numbers are not availableAdoption health data analytics relies on the availability of EHR solutionsUS governance provides seed funding for the implementation EHRtechnology as well as health analytics technology

CompetitionThe market for big health data technology and solutions is emerging and highly competitive.Different types of vendors can be categorized:

IT vendors primary focusing on Big Data technology (e.g. IBM, Teradata, SAP, etc.),HIS vendors that offering a range of information solutions for health care providers (e.g.Cerner, Epic, Siemens, etc.)Vendors focusing primary on big data/data analytics solutions in markets with targetingonly one customer segment or functionality (e.g. MedeAnalytics, QlikView, Castlight, etc.)

McKinsey evaluation: since 2010 more than 200 businesses that offer innovative approaches forhealth data analytics and usage have emerged.Frost & Sullivan study: more than 100 competitors offering hospital health data analytics.

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Drivers and Constraints

DriversIncrease in volume of electronic health care dataNeed for improve operational efficiencyUS Healthcare Reforms HITECH & PPACATrend towards value-based healthcare deliveryTrend towards new system incentivesTrend towards increased patient engagement

ConstraintsOnly a limited portion of clinical data is yet digitizedlack of standardized health data (e.g. EHR, common models / ontologies)affects analytics usageData and Organizational silosData security and privacy issues hinder data exchangeHigh investments are neededExisting incentives hinder cooperationMissing business cases and unclear business models

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Value-Based Healthcare DeliveryA new paradigm for effective collaboration

The goal is to implement more effective healthcare delivery that allows to limit healthcareexpenditure and at the same time help to increase the quality of care settingsValue = Patient health outcomes per euro spentExample: US healthcare reform or provider starting to publishing high quality outcome data

Quality ImprovementsPrevention of illness, early detection, rightdiagnosis, right treatment to right patient, rapidcycle time of treatments, fewer complications, fewermistakes, slower disease progression, etc.

Goal: Better health and less treatments

Value-based healthcare is becoming focus of many healthcare reforms

Principles1 Example

1= Porter and Olmsted Teisberg. “Redefining German Health Care”, 2006

...will play an important role to establishmeans to track and analyze treatmentperformance of patients and patientpopulations

Big Data Technology....

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Big Data applications in the health domainSome examples

• Comparative Effectiveness Research: compare the clinical and financial effectiveness ofinterventions in order to increase efficiency and quality of clinical care services.

• Next generation of Clinical Decision Support Systems: use of comprehensiveheterogeneous health data sets as well as advanced analytics

• Clinical Operation Intelligence: identify waste in clinical processes in order to optimizethem accordingly, e.g. analyzing medical procedures to find performance opportunities, suchas improved clinical processes, fine-tuning and adaptation of clinical guidelines

• Secondary usage of health data is the aggregation, analysis and concise presentation ofclinical, financial, administrative as well as other related health data in order to discover newvaluable knowledge, for instance to identify trends, predict outcomes or influence patientcare, drug development, or therapy choices, e.g.

• Identification of patients with rare diseases• Patient recruiting and profiling• Forecast of clinical process performance• Healthcare Knowledge Broker

• Public Health Analysis aims to analyze comprehensive data sets of patient populations inorder to learn about the overall /population-wide effectiveness of treatments, the quality andcost structure of care settings, etc. By using nation-wide disease registries, i.e. databasescovering secondary data related to patients with a specific diagnosis, condition or procedure.

• Patient Engagement aims to establish communication portals that foster the activeengagement of patients in their healthcare process.

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RequirementsChallenges that need to be addressed

High InvestmentLong-term investments requireconjoint engagement of severalpartners

Data Digitalizationonly small percentage of data isdocumented (lack of time) withlow quality

Semantic Annotationtransform unstructured data intostructured format

Data SharingOvercome data silosand inflexibleinterfaces

Business CasesUndiscovered und unclaimedpotential business values

Value-based systemincentivesCurrent incentives enforce “highnumber” instead of “highquality” of care services

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Not-Technology-related

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Data SecurityLegal processes for data sharing& communication are needed

Regulation & Technology Technology-related

2 Data QualityReliable insights for health-relateddecisions require high data quality

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SummaryBig data revolution in healthcare is in a early stage

• Several developments in the healthcare domain, such as escalatinghealthcare cost, increased need for healthcare coverage and shifts inprovider reimbursement trends trigger the demand for big data technology.

• The availability and access of health data is continuously improving butmore efforts are needed

• The required big data technology, such as advanced data integration andanalytics technologies, are theoretically in place

• First-mover best-practice application demonstrate the potential of bigdata technology in healthcare

• Current roadblocks are the established system incentives of thehealthcare system which hinder collaboration and, thus, data sharing andexchange

• The trend towards value-based healthcare delivery will foster thecollaboration to enhance the treatment patient of the patient, and thus willsignificantly foster the need for big data applications

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Thank you foryour attention!Any Questions?

http://www.big-project.eu/

Contact: Prof. Dr. Sonja [email protected]

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Data & Analytics

The Potential of Big Data Applications for the Healthcare Sector