1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Buffalo Blue Cloud Health Information Center: the vision Werner Ceusters, MD Barry Smith, PhD Ontology Research Group NYS CoE in Bioinformatics & Life Sciences

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ambition: BBC-HIC A global center to analyze, mine, and model personal health data into information that assists payers, providers, and the public in making value-based health decisions. Key question: how to differentiate BBC-HIC from the hundreds of other centers that have the same goal ?

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U To answer that question Assess the state of the art Find shortcomings and areas for improvement Provide a unique and novel solution

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The state of the art and its shortcomings

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Today’s data generation and use observation & measurement data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example 1: clinician Δ = outcome observation & measurement data organization diagnosis use add Generic beliefs verify treatment

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example 2: device manufacturer / supplier observation & measurement data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Slightly different: payer / health plan data organization model development use add Generic beliefs verify influence Δ = outcome $ data organization window dressing

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Key components data information knowledge hypotheses Players HIT Outcomes generates influences

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Key components data information knowledge hypotheses Players HIT Outcomes generates influences representation reality about

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Current deficiencies At the level of reality: –Desired outcomes different for distinct players Competing interests –Multitude of HIT applications and paradigms At the level of representations: –Variety of formats –Silo formation –Doubtful semantics In their interplay: –Very poor provenance or history keeping –No formal link with that what the data are about –Low quality

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Prevailing paradigms ignore many dynamic aspects of reality

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Prevailing paradigms ignore many dynamic aspects of reality They focus on observations, but should focus on what the observations are about !

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Wake up calls ‘the current system of publication in biomedical research provides a distorted view of the reality of scientific data that are generated in the laboratory and clinic’. ‘scientists may uncritically follow paths of investigation that are popularized in prestigious publications, neglecting novel ideas and truly independent investigative paths’. (1)Ioannidis JPA Why most published research findings are false. PLoS Med 2(8): e124, 2005. (2)Young NS, Ioannidis JPA, Al-Ubaydli O. PLoS Med 5(10): e201. doi: 10.1371/journal.pmed.0050201, 2008.

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Wake up calls ‘the current system of publication in biomedical research provides a distorted view of the reality of scientific data that are generated in the laboratory and clinic’. ‘scientists may uncritically follow paths of investigation that are popularized in prestigious publications, neglecting novel ideas and truly independent investigative paths’. (1)Ioannidis JPA Why most published research findings are false. PLoS Med 2(8): e124, 2005. (2)Young NS, Ioannidis JPA, Al-Ubaydli O. PLoS Med 5(10): e201. doi: 10.1371/journal.pmed.0050201, 2008.

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Where should we go?

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some suggestions Acknowledge mainstream thinking, but stand above it Pursue a truly unique objective which –Uses what is usable from mainstream approaches –Remediates what is wrong –Unifies paradigms which are internally consistent but not compatible Covers all value chains for all players Make it “big”

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The core of biomedical ontology in Buffalo extending the methodology of high quality ontologies to other domains of biology and medicine, and to EHRs and coding systems combining ontology with referent tracking

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) The Open Biomedical Ontologies Foundry Nature Biotechnology 2007; 25 (11): 1251-1255

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Current funded biomedical ontology projects Protein Ontology (PRO) (NIH/NIGMS) Infectious Disease Ontology (IDO) (NIH/NIAID) Realism-Based Versioning for Biomedical Ontologies (SNOMED) (NIH/NLM) Ontology for Risks Against Patient Safety (RAPS) (EU) DSM Ontology (to support work on revision of Diagnostic and Statistical Manual of Mental Disorders Cleveland Clinic Semantic Database in Cardiothoracic Surgery

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ultimate goal A digital copy of the world

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Requirements for this digital copy R1:A faithful representation of reality R2… of everything that is digitally registered, what is generic  scientific theories what is specific  what individual entities exist and how they relate R3:… throughout reality’s entire history, R4… which is computable in order to … … allow queries over the world’s past and present, … make predictions, … fill in gaps, … identify mistakes,...

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U In fact … the ultimate crystal ball

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U General principle

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Distinction between Ontologies and Information Models Ontologies should represent only what is always true about the entities of a domain (whether or not it is known to the person that reports), Information models (or data structures) should only represent the artifacts in which information is recorded. –Such information may be incomplete and error-laden which needs to be accounted for in the information model rather than in the ontology itself.

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Perfect ‘semantic’ tools are useless … … if data captured at the source is not of high quality Prevailing EHR information models don’t allow data to be stored at acceptable quality level: –No formal distinction between disorders and diagnosis –Messy nature of the notions of ‘problem’ and ‘concern’ –No unique identification of the entities about which data is stored Unique IDs for data-elements cannot serve as unique IDs for the entities denoted by these data-elements

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Principles for Success’ Evolutionary change Radical change: Principle 6: Architect Information and Workflow Systems to Accommodate Disruptive Change »Organizations should architect health care IT for flexibility to support disruptive change rather than to optimize today’s ideas about health care. Principle 7: Archive Data for Subsequent Re-interpretation »Vendors of health care IT should provide the capability of recording any data collected in their measured, uninterpreted, original form, archiving them as long as possible to enable subsequent retrospective views and analyses of those data. Ceusters & Smith, “Strategies for Referent Tracking in Electronic Health Records” Journal of Biomedical Informatics 39(3):362-378, June 2006. Willam W. Stead and Herbert S. Lin, editors; Committee on Engaging the Computer Science Research Community in Health Care Informatics; National Research Council. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions (2009)

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Understanding data and what data is about

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Combining Referent Tracking with Ontology #105 caused by RT-based Data model instance-of at t Realism-based Ontology

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A huge, but noble, multi-disciplinary enterprise Find, combine and systematize existing data Build master patient index, physician index, disorder index, body part index, … anything index Connect indices to existing data elements in systems –Initially partial connections –Migration path for existing applications SOA and middleware development Data access policies and implementation Case-based reasoning facilities In summary: track everything