Montserrat Cases|Laura I. Furlong|Joan Albanell|Russ B. Altman|Riccardo Bellazzi|Scott Boyer|Angela Brand|Anthony J. Brookes|Søren Brunak|Tim W. Clark|Joaquim Gea|Peter Ghazal|Norbert Graf|Roderic Guigó|Teri E. Klein|Núria López-Bigas|Víctor Maojo|Barend Mons|Mark A. Musen|José Luís Oliveira|Anthony Rowe|Patrick Ruch|Amnon Shabo|Edward H. Shortliffe|Alfonso Valencia|Johan van der Lei|Miguel Angel Mayer|Ferrán Sanz
Improving data and knowledge management to better integrate health care and research
Pompeu Fabra University|Pompeu Fabra University|Pompeu Fabra University|Stanford University|University of Pavia|AstraZeneca (Sweden)|Maastricht University|University of Leicester|Technical University of Denmark|University of Copenhagen|MaineGeneral Medical Center|Massachusetts General Hospital|Pompeu Fabra University|Centro de Investigación Biomédica en Red de Enfermedades Respiratorias|University of Edinburgh|Saarland University|Stanford University|Pompeu Fabra University|Institució Catalana de Recerca i Estudis Avançats|Universidad Politécnica de Madrid|Leiden University Medical Center|Netherlands Bioinformatics Centre|Stanford University|University of Aveiro|Janssen (United Kingdom)|IBM Research - Haifa|Arizona State University|Columbia University|Spanish National Cancer Research Centre|Erasmus MC|Pompeu Fabra University|Pompeu Fabra University
upon time several engineers biologists clinicians realized lot information biomedicine partitioned silos intercommunicate silos side effect existence different disciplines required example develop new drugs engineers decided dispose silos put information axiomatic form facilitate automatic reasoning multiple data sources also decided open way effort duplicated seemed reasonable step welcomed much axiomatization engineers found still issues lack agreement many seemingly uncomplicated facts employ curators resolve issues said curators losing plot also found discipline silos also intradiscipline silos intradiscipline silos partitions different evidences assertions developed evidences earlier assertions based even earlier assertions webs disagreement also chains error found connecting facts various silos uncomplicated even axiomatization results scientific experiments axioms even may treated way perform isolated bits tasks w clark fig 1 illustrates challenge scientists clinical practitioners face world contains vast array complex diverse data locating connecting information difficult 13 deriving definitive knowledge data guide research andor clinical practice even harder many barriers make difficult progress field recently discussed scientific meeting held barcelona 3 july 2012 4 july 2012 4 general title beyond omics revolutions integrative knowledge management empowered healthcare research meeting focused six topics dealing biomedical knowledge explosion better healthcare identifying actionable knowledge items point care exploiting patient information enrich basic biomedical research standards clinicalomics integration semantic challenge new information technology supporting massive biomedical data management systems medicine making systems biology translational integrative knowledge management improving drug rd main ideas conclusions arising event presented new biomedical discoveries emerge everincreasing rate translation health care typically occurs slowly lack sufficient systems astutely identify clarify hand advances relevant practitioners usable formats example thousands biomarkers exist comprising truly useful ones amongst many others less useful nonactionable valuable new biomarkers diagnostic prognostic therapeutic therefore advanced effectively health care decision ones progress simply onerous given cost modern clinical trials deficiency incentives expertise amongst researchers would best placed advance markers development hence translation occur usually major pull clinical world rather push researchers clearly need methods systems reliably routinely identify connect informative reliable useful information least biomarkers generated research community efforts better structure scientific knowledge instance means nanopublications 5 investigation study assay isa commons 6 could provide key components solution challenge magnified fact relevant information spread across research resources eg literature patents laboratory reports market data medical reports biobanks also realms less professional rigour social networks patient communities eg wikis blogs social media platforms progress therefore necessitate addressing crosslanguage crossjargon barriers well traditional targets interoperability standards data syntax semantics beyond connecting integrating research findings lies challenge understanding information education important indeed proposed lack appropriate training explains slow uptake companion diagnostics clinical practice 7 tackling require robust guidelines use pharmacogenomic information also provision accompanying pharmacokinetic metabolic drug interaction knowledge derived latest biomedical research thus arguable researchers responsibility make clinically relevant findings understandable healthcare sector perhaps form userfriendly web portals software 8 9 electronic health record ehr developers computerized physician order entry designers clinical decision support system cdss creators vendors likewise need involved delivering additional content portals connecting platforms intended end users considering issues well key challenges data interpretation experts concluded overall challenge one knowledge engineering ke rather simply need better informatics research medical practice hence may difficult make real progress biomedical researchers clinical practitioners alone need new group multidisciplinary engineers 10 goes back tradition ke health field stemmed artificial intelligence research 1990s 11 however contrast previous ke approaches aimed organize data reveal absolute knowledge flawed concept illustrated need far pragmatic approach ke 20 identify make directly useful limited set data knowledge items reliably proven clinically actionable aim would explicitly address two core information problems faced clinicians much existing new data ii time resources discern reliably uncertain erroneous information shown supplementary table s1 many international projects aim integrate various types data related specific diseases pharmacological treatments general however using ke 20 approach developing new methodologies tools data integration exploitation novel strategies massive data storage handling types projects make progress consolidating unifying relevant data ke 20 approaches begin explored however succeed data must suitable quality breadth petabytes potentially useful biomedical data captured structured format andor made available use others many situations include molecular omics profiles genomes transcriptomes proteomes epigenomes etc exposure environmental chemicals exposomes phenotype data eg recorded clinical settings dynamic data eg measurements different points time space could contribute improved research health care instance research world primary data highthroughput studies large number subjects eg genomewide association studies 12 typically never leave laboratory generated healthcare world molecular profiles individual patient sometimes recorded per time period starting recorded poorly exploited 13 clear simply handling diversity scale data challenge motivate focusing effort problem rather providing reason allowing data lost many considerations relate quality completeness reliability reproducibility primary data knowledge derived relevant judgements may well context dependent example whether biopsy heterogeneous tumour might considered usefully representative whole tumour contextual metadata data data therefore important information often properly collected recorded directly related current discussions reproducibility research findings comparability different analytical procedures approaches allow consistent repeated analysis data sets becoming necessary eg galaxy genepatterns questions reproducibility concern data produced knowledge gleaned data derived important studies statistical experimental design problems contemporary scientific publications recently reported 14 15 one notable problem applying ke biomedical research data nature knowledge engineered specifically active opposed consolidated scientific knowledge consists assertions supported evidence consider knowledge snapshot consensus scientific community particular subject given time active knowledge subjected continuous reevaluation new findings change perspective facts may refuted years essentially knowledge truly absolute particular complication human bias error underlying citation distortion least review articles example provided recent review role inclusion body myositis aetiopathology alzheimers disease suggested following chain assertions underlying evidence found cases grounding evidence cases meaning distorted results misapplied misconstrued 16 issues contribute existence intradiscipline silos disconnect facts assertions underlying evidence cases discrepancies data collected different sources clearly argues need information accessibility structure less reliance subjective human opinion must balanced risk proposing many hypotheses extensive highthroughput data could easily lead spurious associations context ongoing multiparty curation efforts different initiatives useful way identify organize relevant information represent costly timeconsuming tasks efforts harmonization standardization well development software supporting curation tasks therefore needed improve assist curators work important point emphasize different levels evidence needed cdsss compared required research grade knowledge discovery medical reasoning may represented epistemological models amenable partial automation 17 18 cases data generated chosen fit purpose researchers example must design experiments simulations record much detailed information possible facilitate comprehensive exploration biomedical question contrast clinicians must carefully define healthcare questionnaires register salient medical variables pertaining patients aid clinical decisionmaking ideally however avoid silos data groups always also consider possible likely reuse data part data provenance carefully recorded make possible retrieval original sources ensure reliability reproducibility undoubtedly effect generation useful predictions 19 time constraints barcelona meeting precluded extending discussion areas ethical legal frameworks information found elsewhere 20 21 also noted european parliament currently discussing data protection directive underpin new legal framework 22 increasingly genomic information likely relevant health care ideally stored within medical records example current use would personalized drug dosing pharmacogenomic tests used routine clinical practice however vastly underused key biological data individuals encapsulated native format clinical data structures bubbledup items associated phenotypic data using clinical data standards generates question standards required allow efficient translation key research findings clinical practice paradigms needed support biomedical data management controlled vocabularies ontologies integration diverse heterogeneous biomedical information provide part answer fortunately several current initiatives support development ontologies describe different aspects biology biomedicine eg national center biomedical ontology 23 open biological biomedical ontologies 24 ricordo project 25 yet needs done instance difficult reconcile medical records disease descriptions associated public molecular data due inherent complexity diseases way traditionally classified described also disease descriptions heterogeneous often dynamic case mental illness 26 beyond standards perhaps actually equal need understandards words efforts aim deliver standardization capabilities required ke 20 standards semantic integration irrespective common understanding need make sure next generation cerebro silico reasoning strategies understood meant node edge network associations resolve issue expressive standard less interoperable constraining standards crucial also enables capturing similarities whilst preserving disparities specifically health data semantics context cannot faithfully represented using flat structures eg list entries rather compositional language required meaningfully connects various data entries furthermore health data standards need accommodate unstructured data text eg clinicians narrative whilst links structured data entries lifetime comprehensive recording personal health information including omics data certainly desirable arguably calls new model data stewardship independent health record banks ihrb vision 27 would support implementation lifelong crossinstitutional interoperable ehrs would constitute escape fixation legacy systems long healthcare providers also record keepers continue poor archives proprietary based isolated silos data semantics represented explicitly making hard impossible cdsss really effective instead proposed limited number independent regulated third parties specialized sustaining individual lifetime ehr continuously curating record running various analyses prepare right infostructure cdsss tasks require unique specialization new kind archive provide complete coherent information framework support health individual challenge improving biomedical knowledge management goes hand hand need suitable education training relevant stakeholders patients clinicians researchers regulators policymakers particular clinicians need support improve ability interpret use research findings researchers must learn take actionable findings closer clinicians concomitantly researchers need better comprehend problems raised clinical practice solved laboratory intensive use reinforces need forums interaction active participation biomedical researchers bioinformaticians physicians experience clinical research hence move onesizefitsall education stratified medicine towards truly individualized clinical exercise following paradigm shift towards concept predictive preventive personalized participatory medicine p4 28 finally active participation citizens via blogs social networks provides way improve general level health literacy thereby empower individuals regarding role healthcare system experts took part aforementioned debates barcelona also offer following consensus statements summary medicine increasingly dataintensive discipline growing need link individual patient health records rapidly changing research knowledge better differential diagnosis prognosis prediction treatment response equally biomedical research gain enormously integrative analysis clinical multiomics information fig 2 capitalization opportunities must guided precise understanding many complex issues related integration large amounts diverse information partly involves overcoming barriers different disciplines biology medicine computer sciences implying key role knowledge engineers review based debates held barcelona 3 july 2012 4 july 2012 active participation authors debates organized bdebate initiative biocat obra social la caixa universitat pompeu fabra barcelona event held within framework european inbiomedvision project funded eu seventh framework programme research technological development fp7 grant agreement 270107 addition received support eu fp7 project 200754 gen2phen innovative medicines initiative joint undertaking grant agreement 115002 etox 115191 open phacts resources composed financial contribution eu fp7 kind contributions companies european federation pharmaceutical industries associations lif received support instituto de salud carlos iii fondo europeo de desarollo regional cp1000524 anthony rowe works janssen research development originally developed transmart platform prior making open source russ b altman founder personaliscom conflict interest declared please note publisher responsible content functionality supporting information supplied authors queries missing content directed corresponding author article