Margunn Aanestad Some comments on «socio-digital generativity» InfraGlobe workshop 29th of April 2014
Is generativity desirable? Shouldn t we rather study degrowth, how to simplify and scale down IT portfolios? Methods for Digital Decluttering «Decluttering» 3
Transposing the notion of generativity from consumer technologies to public health care A different regulatory and economic environment Different «actor complexes» Untempered growth of ICT not desirable, change needs to be coordinated 4
Information-related generativity In IS: generativity is a quality of technology (applications, platforms, infrastructures) The value of personal health information Empirical examples: NHS England s care.data initative Biobanks + genomics global information ecology (WHO: Universal Health Coverage) (Norway s health platform ) 5
Example 1: NHS England s care.data scheme Aim: Extract data from GP s records: «Lacking pieces of the puzzle» Clinical and biomedical information: Family history, referrals, diagnoses, prescriptions etc. Blood pressure, body mass index, cholesterol level etc. NHS no., postcode, birth date, etc. (not name) To be collected by HSCIC (est. April 2013) The Health and Social Care Act 2012 Not for care (SCR) - intended for research, planning, audit etc. 6
Increasingly debated (late 2013- early 2014): The risks associated with data extraction/transfer The opt-out option The use by third parties The information campaign (Jan 2014) Feb 17th 2014: postponed 6 months: to permit "more time to build understanding of the benefits of using the information, what safeguards are in place, and how people can opt out if they choose to". But. 7
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The Institute and Faculty of Actuaries: research paper aimed at improving the accuracy of pricing and in no way caused insurance price rises. HSCIC s Information Governance Assessment (August 2013) access to individual patients records can enable insurance companies to accurately calculate actuarial risk so as to offer fair premiums to its customers. Such outcomes are an important aim of Open Data, an important government policy initiative." This is generativity! 9
Example 2: Biobanks + genomics Longterm storage of biological material and personal data Clinical biobanks and population biobanks An «infrastructure for research» 40 % of clinical research in Norway is based on use of biobanks Expectations/desires: Make more broadly available, enable re-use and further use of samples and information 10
Regulatory challenges Further use of information vs. Informed consent: From: «One PI, one project and one jurisdiction» To: Who will wish to use which data for what purpose? With global open data sharing: under what jurisdiction? Countries seek dynamic mechanisms for informed consent (Participant-centered initatives) 11
«Living Consent» mechanisms using «MyRecord»: 12
Interpretation of genetic variants «normal» variants? List of variants from sample of individuals DNA Databases of gene-disease relations LOVD HGMD LSDB BIC Other LSD Myriad Genetics dbsnp 1000Genomes NHLBI GO ESP
Enforcing openness The Bermuda Principles (1996): all DNA sequence data be released in publicly accessible databases within twenty-four hours after generation NIH s attempts to offer access to genomic data dbgap and Genomic Data Sharing Policy Major medical journals enforce disclosure before publications 14
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Example 3: WHO Universal Health Coverage Equity of access, high quality of services, and sustainable financial models. Indicators of performance and efficiency of health system Requires highly granular data (person/encounter-level) 17
Example 4: «Helsenorge.no» Citizens point of access A platform for third-party innovation Emerging concerns as visions materializes 18
If we take generativity as an aim: It needs to be qualified: When, where and how is it a desirable quality? Is data/information-related generativity different? What are characteristics of the dynamics, what implications are seen, what responses are attempted? etc 19
Ref Zittrain (2006) Characteristics that enable informationrelated generativity: Increased amounts of data (datafication) Accessibility (from analog to digital) Richness of data sets (breadth of variables/attributes) Granularity of data (level/frequency) Volume (amount) Combinability (based on standardization/harmonization), 20