MIPLM research projekt data driven business models in healthcare
- 1. MIPLM RESEARCH PROJECT NORA REHÁKOVÁ: Data-driven Business Models and its Regulations Limits
- 2. PRESENTANTATION TABLE OF CONTENTS • Big Data Overview in Healthcare Industry & Big Data Challenges • Business Models • Data-driven Business Models (DDBM`s) • Data-driven Business Models related Regulation/Legal limits
- 3. BIG DATA DEFINITION – 3/4V`S RESPECTIVELY 10V`S CHARACTERISTICS • Dough Laney`s 3V`s definition: Volume, Velocity and Variety (4V`s + Veracity) • Big Data 10V`s characteristics: Volume, Variety, Velocity, Veracity, Validity, Value, Variability, Venue, Vocabulary and Vagueness • Information vs. Data: • - Information to be deemed as knowledge of concerning objects, such as facts, events, things, processes or ideas. • - Data is reinterpretable representation of information in a formalized manner suitable for interpretation (processed by humans or machines).
- 4. BIG DATA LEVERAGING • Companies regardless of industry started to be IT/data-driven in order to stay competitive and avoid risk getting left behind. • How companies leverage their data to their advantage: • 1. Improving Decision Making Process, • II. Improving Processing-Operations, • III. Data Monetization, • Big Data enables companies gain better market and customer intelligence (customers needs, preferences, behavior, customers opinion).
- 5. BIG DATA OVERVIEW IN HEALTHCARE INDUSTRY • Healthcare industry was identified as one significant sector, where Big Data analytics will have one of the greatest impact on peoples daily life`s. • The main tipping points for innovation are as follows: • I. Demand driven pressure for better data is increasing as the cost pressure intensifies, structural reforms are arising and continue whilst first movers and early adopters showcase the respective advantage, • II. The supply site represented by national collectors of treatment and clinical data outcomes starting to become available (Electronic Health Record in EU – Directive 2011/24 on patient`s rights in cross border healthcare), • III. Investment is catalysing the pace of technological development of data anonymization and aggregation in hospitals and private treatment centers, where such data are processed through BI SW, • IV. Governments are committed to accelerate the market change via setting interoperability standards to encourage private sector to participate.
- 6. BIG DATA IN HEATHCARE CHALLENGES • Main Big Data Challenges: • I. Cross-cultural aspects of privacy/GDPR – key issue to be addressed in order to leverage data analysis by multitude healthcare providers • II. EHR/Medical data are available in unstructured form • III. Data interoperability and quality of data and data integration • VI. Current systems which we use are not scalable to manage and maintain structures of Big Data (advanced method to handle missing data together with systematic, large scale and privacy measurements)
- 7. BUSINESS MODEL VS. BUSINESS STRATEGY • Business Model: • - articulates the logic, data and other supportive measures for a value proposition for customer as well as viable revenue and costs structure of the enterprise that delivers that value. • - simplifies what benefit will be delivered to customer, how enterprise will be organized and how it captures a portion of the value which delivers. • Business Strategy: • - BM to be coupled with a competitive strategy analysis that requires market segmentation, value proposition per each segment, setting up the mechanism to deliver particular value and designing ,,isolating mechanism” that can be used to prevent the competitive strategy and business model from competitors and disintermediated by customers.
- 9. DATA-DRIVEN BUSINESS MODELS (DDBM`S) • I. Informed Decision Making • II. Data Brokerage • III. Data Analytics as a Service • IV. Consultancy • V. Tool Providers
- 10. DDBM`S REGULATION/LEGAL LIMITS • I. Data Regulation - GDPR (EU Regulation 2016/679), Competition Law. • II. Contracting for Data – Data ownership and access (rights in personam), data ownership does not exist in EU, discussions on non- exclusive data ownership rights to be established vs. e.g. USA patient data ownership to serve the purpose of data transmission within the HCP. • III. IP Rights in Relation to Data – Copyright Law, Database Directive, Trade Secret Directive (rights in rem).
- 11. CONCLUSION • Big Data/Big Data analysis to significantly improve patients journeys and outcomes. • Due to technical and legal challenges Big Data in Healthcare are not optimally leveraged, new DDBS`s to be discovered accordingly, cross-domain approach to be applied while addressing those challenges. • Data to become a key business asset and to be treated accordingly, Data Analytics to be used for smarter/qualified decisions, improve daily operations and performance as well as to become sustainable competitive advantage. • Aforementioned rights and duties arise via IPR, EU Regulation and Contracts and we can perceive its importance either: • - Positively: IPR and contracts to be monetized, • - Negatively: IPR infringement and breach of contract to rise damages or other remedies. • Identified Legal Limits: • I. When running a global data driven business different law protections are applicable to your project (e.g. Database right in EU does not apply to database created in the US, copyright protection based on registration principle vs. EU automatic protection to copyright), • II. Directives vs. Regulations, in case of Directives only objectives are binding while approach selection is left in competencies in each member state, which can cause insecurity and discrepancies in national approaches. • III. In case of Data Contracting – Governing Law to be chosen out of 28 national systems.