Open Data - Critical Capability for Open Healthcare
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This document summarizes a presentation on open data and information production processes in healthcare. It discusses that (1) open data is not always possible due to privacy and requires understanding how data is created, (2) information production involves data generation, manipulation, and use which can be opened at various levels, and (3) open data supports transparency, evidence-based practices, and open science by countering closed systems and politics.
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Open Data - Critical Capability for Open Healthcare
- 1. Translated and updated from the original presentation at the seminar of “Openness and the Future of Healthcare IS” by Service Factory, Aalto University School of Business at 18th of March 2014. OPENDATA-CriticalCapabilityinHealthcare InformationProductionProcesses Sami Laine, Doctoral Student Department of Computer Science and Engineering, Aalto University Email: sami.k.laine@aalto.fi
- 2. University of Turku, Finland •Information systems •Empirical field studies in hospital focusing on the use of IT. Turku University Hospital, Finland •Healthcare datawarehousing •Project management, system and service design. Aalto University, Finland •Usability Research •Healthcare data quality research across contexts. Personal background combines technical, social and healthcare perspectives Over 10 years involvement in healthcare sector
- 3. Idealistic View – Open Data is Completely Free Completely Free - Availability and Access Completely Free - Reuse and Redistribution Completely Free - Universal Participation http://okfn.org/ http://fi.okfn.org/ This is not Enough nor always Possible
- 4. Benchmarking claimed significant productivity differences in neurology specialty Pirkanmaa Hospital District Hospital District of Southwest Finland Laine, S., Niemi, E. (2013), “Transparency of Hospital Productivity Benchmarking in Two Finnish Hospital Districts”, In the Proceedings of the 29th annual Patient Classification Systems International (PCSI) Conference, Helsinki, Finland.
- 5. For example, fragmentation bias rewards splitting and heterogeneity Hospital District Hospital District DRG X Episode A DRG X DRG A DRG B DRG C Less production but more health for same money! More production but less health for same money! Laine, S., Niemi, E. (2013), “Transparency of Hospital Productivity Benchmarking in Two Finnish Hospital Districts”, In the Proceedings of the 29th annual Patient Classification Systems International (PCSI) Conference, Helsinki, Finland.
- 6. Enters data for primary purpose Builds data sets for secondary use Analyses and reports data Interprets data and makes decisions Medical Imaging System Electronic Patient Record Scripts collect data Scripts collect data Internal Service Reports Scripts produce internal reports Finnish Hospital Productivity Benchmarking Scripts produce data external sets DATA SUPPLY DATA MANUFACTURING DATA CONSUMPTION IPP has three problem themes: Human Errors, Software Features and Obscurity Data Warehouse Scripts produce external reports Data Warehouse Data Entry Errors Application Feature Bias Architecture Bias Scripting Error Interpretation Mismatch Laine, S., Niemi, E. (2013), “Transparency of Hospital Productivity Benchmarking in Two Finnish Hospital Districts”, In the Proceedings of the 29th annual Patient Classification Systems International (PCSI) Conference, Helsinki, Finland.
- 7. Enters data for primary purpose Builds data sets for secondary use Analyses and reports data Interprets data and makes decisions Medical Imaging System Electronic Patient Record Scripts collect data Scripts collect data Internal Service Reports Scripts produce internal reports Finnish Hospital Productivity Benchmarking Scripts produce data external sets DATA SUPPLY DATA MANUFACTURING DATA CONSUMPTION IPP has three problem themes: Human Errors, Software Features and Obscurity Data Warehouse Scripts produce external reports Data Warehouse Data Entry Errors Application Feature Bias Architecture Bias Scripting Error Scripting Error Obscurity Obscurity Obscurity Obscurity Laine, S., Niemi, E. (2013), “Transparency of Hospital Productivity Benchmarking in Two Finnish Hospital Districts”, In the Proceedings of the 29th annual Patient Classification Systems International (PCSI) Conference, Helsinki, Finland.
- 8. Inconsistent figures have been produced about the same issue at the same time… Ambulatory Procedures in Administrative Reports Ambulatory Procedures in Operation Room Reports Which one is correct? Laine, S. (2012), "APC-SIMULATOR: Demonstrating the Effects of Technical and Semantic Errors in the Accuracy of Hospital Reporting" In the Proceedings of the 17th International Conference on Information Quality (ICIQ), Paris, France.
- 9. Inaccuracies and semantic mismatches exist in healthcare data “Patient bills” “Manually duplicated codes” “Planned procedures” 1568710726 Both are more accurate than expected but only for a specific purpose. ”Actually Performed Ambulatory Procedures” - X % + Y % There exists semantic mismatches and error rates between contexts for good reasons. Laine, S. (2012), "APC-SIMULATOR: Demonstrating the Effects of Technical and Semantic Errors in the Accuracy of Hospital Reporting" In the Proceedings of the 17th International Conference on Information Quality (ICIQ), Paris, France.
- 10. You cannot trust Open Data unless you know exactly where data comes from and how it is actually created! All Ambulatory Procedures Planned or Billed Patient Bills or Municipality Invoices? Detailed level in actual socio-technical reality! Processes and Work Practices User Interfaces and Data Entry Protocols Data Models and Application Structures One must also describe the Information Production Process behind the interface to avoid black boxes
- 11. Enters data for primary purpose Builds data sets for secondary use Analyses and reports data Interprets data and makes decisions for secondary purposes Medical Imaging System Electronic Patient Record Scripts collect data Scripts collect data Datawarehouse Monthly Service Reports Scripts produce internal reports National Hospital Benchmarking Scripts produce data external sets DATA SUPPLY DATA MANUFACTURING DATA CONSUMPTION Information Production Process (IPP) consists of three phases based on Total Quality Management Wang, R. Y., Lee, Y. W., Pipino, L. L., Strong, D. M. (1998) “Manage Your Information as a Product”, Sloan Management Review, 39, 4, pp. 95-105. National Registry
- 12. Medical Imaging System Electronic Patient Record Scripts collect data Scripts collect data Management Reports Scripts produce internal reports Research Results DATA SUPPLY DATA MANUFACTURING DATA CONSUMPTION Open Data means also Open Methods and Open Processes Local Data Warehouse National Hospital Benchmarking Scripts produce external data sets National Data Registry Scripts produce public reports DATA METHODS OPEN DATA Scripts produce external data sets Scientific Data Set Scripts produce external analysis PROCESS Information Production Process of Researchers Information Production Process of Health Service Providers Information Production Process of National Organizations The problem is often the obscurity of actual data creation situations Another problem is earlier information production processes
- 13. Medical Imaging System Electronic Patient Record Scripts collect data Scripts collect data Management Reports Scripts produce internal reports Research Results DATA SUPPLY DATA MANUFACTURING DATA CONSUMPTION IPP capable for openness can be secured or opened at any phase Local Data Warehouse National Hospital Benchmarking Scripts produce external data sets National Data Registry Scripts produce public reports PRIVATE OPENNESS Scripts produce external data sets Scientific Data Set Scripts produce external analysis Information Production Process of Researchers Information Production Process of Health Service Providers Information Production Process of National Organizations What aggregation level can be opened to confidential or even public use? “Truly Ideologically Open Data” CONFIDENTIAL OPEN
- 14. Medical Imaging System Electronic Patient Record Scripts collect data Scripts collect data Management Reports Scripts produce internal reports Research Results DATA SUPPLY DATA MANUFACTURING DATA CONSUMPTION Interfaces, Processes and Methods can be always made open Local Data Warehouse National Hospital Benchmarking Scripts produce external data sets National Data Registry Scripts produce public reports DATA MODELS! APPLICATION INTERFACES! TOOLS! SCRIPTS! ALL OPEN! Scripts produce external data sets Scientific Data Set Scripts produce external analysis PROCESSES WORKFLOWS Information Production Process of Researchers Information Production Process of Health Service Providers Information Production Process of National Organizations These should be OPEN at least for INTERNAL USE!
- 15. Medical Imaging System Electronic Patient Record Scripts collect data Scripts collect data Management Reports Scripts produce internal reports Research Results DATA SUPPLY DATA MANUFACTURING DATA CONSUMPTION Open Data supports Evidence-Based Management, Open Government and Open Science Local Data Warehouse National Hospital Benchmarking Scripts produce external data sets National Data Registry Scripts produce public reports DATA METHODS OPEN X! Scripts produce external data sets Scientific Data Set Scripts produce external analysis PROCESSES OPEN SCIENCE EVIDENCE-BASED MANAGEMENT OPEN GOVERNMENT
- 16. Medical Imaging System Electronic Patient Record Scripts collect data Scripts collect data Management Reports Scripts produce internal reports Research Results DATA SUPPLY DATA MANUFACTURING DATA CONSUMPTION Closed Data leads to traditional habits and beliefs, insider power politics and flawed science. Local Data Warehouse National Hospital Benchmarking Scripts produce external data sets National Data Registry Scripts produce public reports DATA METHODS Closed & Obscure Scripts produce external data sets Scientific Data Set Scripts produce external analysis PROCESSES OBSCURITY OBSCURITY OBSCURITY Flawed Science Habits and Beliefs Insider Politics
- 17. Distributing Data Typically Open Data Product Data Organization Data Service Data Administrative Data Customer Instructions Potential but challenging Open Data Distributing Sensitive Data by using External Authorization Distributing Sensitive Data in Aggregated Form
- 18. Acquiring Data 3/20/2014Laitoksen nimi 18 Typically Open Data Geographical data Aggregated demographical data Aggregated statistics News and social media feeds Reference data Potential but challenging Open Data Full-scale master data (i.e. customers, services etc) External service transactions (i.e. Health services, transportation services etc) Sensitive individual level demographical data
- 19. All Information Production Processes should have Open Data capability Laitoksen nimi Open Data should not be ”ideological product” but ”practical capability” that is utilized everywhere Open Data as capability means Open Data, Open Methods and Open Processes For internal use For external users However, The level of Openness of Data Sets is controlled according their content requirements. Data Sets Data Models Application Interfaces Tools Workflows Documents Open Data Capability All processes Every workflow
- 20. Prevents Vendor-lock-in • Open standards and Application Interfaces • Open collaboration between stakeholders and use cases Improves Transparency • To own healthcare service production • To own information management and software systems Supports Quality Control • Own internal activity (patient services or medical device maintenance) • Service providers (e.g. software or logistics) • External Benchmarking (e.g. international productivity benchmarking) Open Information Production brings Transparency and Interoperability to Software Systems and Healthcare
- 21. Application for Strategic Research Opening at 11.6.2012 Project time schedule 1.9.2012-31.8.2014 QUALIDAT QUALITYOFDATAFOR VALID DECISIONS SupportingDiverseUsesandUsers Nieminen, Marko (prof.) Rossi, Matti (prof.) Borgman, Jukka Kaipio, Johanna Laine, Sami Mahlamäki, Katrine Niemi, Erkkahttp://qualidat.aalto.fi/
- 22. Enters data for primary purpose Builds data sets for secondary use Analyses and reports data Interprets data and makes decisions for secondary purposes Big Data (e.g. medical device) Social Data (e.g. Facebook) Operative Data (e.g. hospital ERP) Scripts collect manually entered data Application inspects machine generated event data Analytical Datawarehouse Statistical Reports Innovative algorithms produce analyses QUALIDAT RESEARCH = Tracking down the entire information flow Open Data Scripts construct data sets for external use DATA GENERATION DATA MANIPULATION DATA UTILIZATION http://qualidat.aalto.fi/
- 23. The Researched Domains in the Information Production Process USER INTERFACE ”Screens” ”Interactions” DATA FLOW ”Script logic” DATA UTILIZATIONDATA MANIPULATIONDATA GENERATION WORK PRACTICES ”Processes”,”Guidelines” APPLICATION LOGIC ”Data model” ”Interfaces” DATA MODEL ”Models” ”Descriptions” ANALYTICS ”Business rules” ”Aggregation logic” MEASUREMENT ”Method” BUSINESS FUNCTION ”Domain terminology” BUSINESS CASE ”Situation” ”Conclusions” Enters data for primary purpose Builds data sets for secondary use Analyses and reports data Interprets data and makes decisions for secondary purposes http://qualidat.aalto.fi/
- 24. Scientific publications about open data and open science Nosek, B. A., & Bar-Anan, Y. (2012), Scientific Utopia: I. Opening scientific communication. Psychological Inquiry, 23, pp. 217–243. Nosek, B. A., Spies, J. R. and Motyl, M. (2012), Scientific Utopia: II. Restructuring Incentives and Practices to Promote Truth Over Publishability. Perspectives on Psychological Science, 7(6), pp. 615- 631.
- 25. Some webpages about Openness Open Science National Research Data Initiative (TTA) Open activism Open Knowledge Finland ry Openness of ICT The Roadmap for Open ICT Ecosystems, Berkman Center for Internet & Society at Harvard Law School
- 26. Questions? sami.k.laine@aalto.fi https://www.researchgate.net/profile/Sami_Laine/ http://www.linkedin.com/pub/sami-laine/2/a61/970 Versio 19.1.2014 Sami Laine, Doctoral Student Department of Computer Science and Engineering Email: sami.k.laine@aalto.fi