skills ) is one of 11 clinical data research networks ( cdrns ) funded by the patient centered outcomes research institute ( pcori ) in 2014 .
pcori , a non - governmental organization created under the patient affordable care act seeks to build an information technology ( it ) backbone to support comparative effectiveness research at a national scale across both cdrns and also patient powered research networks ( pprns ) .
scilhs engages patients , clinicians , health systems leadership , and key healthcare stakeholders as collaborators to build on an existing network of hospitals and health systems that have already adopted a common clinical and translational research it and regulatory framework .
scilhs , comprising 10 health systems ( box 1 ) , is a step toward answering the institute of medicine 's call for a learning healthcare system ( lhs)1 
 2 to  generate and apply the best evidence for the collaborative healthcare choices of each patient and provider ; to drive the process of discovery as a natural outgrowth of patient care ; and to ensure innovation , quality , safety , and value in health care. beth israel deaconess medical center boston children 's hospital boston health net ( boston medical center and community health centers ) cincinnati children 's hospital medical center columbia university medical center and new york presbyterian hospital morehouse school of medicine / grady memorial hospital ( research centers in minority institutions ) partners healthcare system ( includes massachusetts general and brigham & women 's hospital ) university mississippi medical center the university of texas health science center at houston wake forest baptist medical center fifteen years ago , scilhs informatics leaders began a quest to develop informatics infrastructure and regulatory innovation that would convert the emerging electronic health record ( ehr ) into a research tool for improving patient outcomes .
all of our work and open source toolkits have been supported by grants from the national institutes of health , centers for disease control and prevention , and office of the national coordinator of health information technology ( onc ) .
first , we built indivo,3 
 4 the first personally controlled health record , which gave patients their data , and apps to make those data useful
. then , i2b2 ( informatics for integrating biology and the bedside)57 created an open source analytic platform to the ehr , to fuse and analyze data produced by the delivery system , and identify research cohorts .
our next advance was shrine ( shared health research information network),810 a tool enabling investigators to query i2b2 nodes in real time across multiple sites for collaborative population research .
i2b2 has been successfully implemented at more than 100 sites across the usa , thereby enabling investigators to use delivery system data to identify patients with specific illnesses and clinical characteristics .
a recent pcori survey of all pcornet sites revealed that 37% of the existing cdrn nodes and 31% of the pprn nodes already used i2b2 .
finally , we built smart ( substitutable medical applications , reusable technologies)a platform to enable any developer to contribute to an  app store for health and research compatible with i2b2-shrine instances or compliant ehrs.1113 these informatics tools and associated research policy advances have already contributed to transformation in the clinical research enterprise  real - time , collaborative population health research is now enabled across shrine member sites distributed nationally  but they have yet to yield substantial improvements in the health of our patients .
now , in establishing pcornet , pcori has catalyzed a new national research dialog to answer patient - oriented questions and improve human health .
we directly address this challenge via a strategy intended to avoid prior mistakes of large - scale , top - down , costly software infrastructure efforts that failed to scale ( eg , cabig14 ) , instead building scilhs with open source , free , modular components5 
 15 with vibrant user and software developer communities that have already spread virally to scale across heterogeneous health systems . here , we detail the informatics approaches taken by scilhs to identify large cohorts of patients and engage them for research .
our technology strategy links lockstep to processes for regulatory innovation , development of robust governance constructs and policies , and local adoption by hospital leadership and institutional review boards .
sidecar warehouse to the ehr , effectively leveraging existing data collected by ehrs during routine care while avoiding costly , time - consuming ehr integrations ( figure 1 ) . developed intensively over the past 5  years at harvard medical school
, this approach employs vendor agnostic , free , open source , scalable , and interoperable technologies to produce the only research - based , shared repository of ehr data that can be queried in real - time .
of already proven value in the research ecosystem , these components support a cost - effective and sustainable research network of > 8 million patients .
each site will install the scalable collaborative infrastructure for a learning healthcare system ( scilhs ) sidecar , for identifying and reviewing cohorts and the myscilhs suite to : ( a ) manage linkage of contact data to the de - identified patient cohort list produced by the multisite shared health research information network ( shrine ) query ; ( b ) administer and store consent documents ; ( c ) outreach to patients through web - based survey and telephony ; and ( d ) promote ongoing patient engagement through outgoing messaging , including ( in the future ) return of research results to patients .
the web - based survey will be administered using redcap and indivo technologies , and will be accessible either by patients at home , or at the point of care , through tablet / kiosk - based interaction .
once completed , patient - reported data will have subject identifiers encoded ; its standardized survey metadata will then be loaded into the corresponding scilhs sidecar ( i2b2 node ) , enabling semantic data linkage with electronic health record data via shrine / i2b2 , while preserving subject confidentiality .
these software platforms will be provided to sites as self - contained , pre - configured virtual machines , enabling rapid dissemination of these technologies while minimizing administrative and software development overhead at each site .
we consider the heterogeneity of collaborating institutions to be a key measure of success ; via adoption of the sidecar approach , we enable any institution to join our scilhs network .
specifically , a primary goal is inclusion of diverse populations within our cdrn network , thereby enabling capture of the genetic , genomic , and socioeconomic variation that exists beyond insured populations in managed care settings alone .
further , by freely sharing the processes and software that have been developed and supported by harvard , we hope to catalyze the formation of many other new networks across heterogeneous health systems and institutions , and involve new partners in improving our core components , common data models , and ontologies .
the sidecar infrastructure is composed of the following : 
 i2b2 ( informatics for integrating biology and the bedside ) .
data analytic platform employed for ehr data analytics and clinical research at > 100 academic medical centers worldwide ( nih funded).shrine ( shared health research informatics network ) .
federated query and response system that enables investigators to discover ehr data housed in i2b2 nodes across multiple independent institutions ( nih ctsa funded).smart platforms .
first described in the new england journal of medicine,12 smart has programmatic interfaces and applications that transform both ehrs and their sidecars into platforms that run substitutable iphone - like apps.11 smart enables a national scale 
the original personally controlled health record3 
 4 
 16 
 17 links patients to clinical and research settings .
used by hundreds of thousands employees of dossia 's founding companies ( wal - mart , intel , and at&t ) , indivo was also the initial software codebase for microsoft 's healthvault platform ( nih , cdc , and onc funded).redcap ( research electronic data capture ) .
electronic data capture tool18 
 19 with 757 institutional partners , used to survey patients online ( nih ctsa funded ) .
data analytic platform employed for ehr data analytics and clinical research at > 100 academic medical centers worldwide ( nih funded ) . shrine ( shared health research informatics network ) . federated query and response system that enables investigators to discover ehr data housed in i2b2 nodes across multiple independent institutions ( nih ctsa funded ) .
smart platforms . first described in the new england journal of medicine,12 smart has programmatic interfaces and applications that transform both ehrs and their sidecars into platforms that run substitutable iphone - like apps.11 smart enables a national scale 
app store for pcor for rapid cycle innovation of pcor methods ( onc funded ) .
the original personally controlled health record3 
 4 
 16 
 17 links patients to clinical and research settings .
used by hundreds of thousands employees of dossia 's founding companies ( wal - mart , intel , and at&t ) , indivo was also the initial software codebase for microsoft 's healthvault platform ( nih , cdc , and onc funded ) .
electronic data capture tool18 
 19 with 757 institutional partners , used to survey patients online ( nih ctsa funded ) .
scilhs will combine ehr data with payer claims to facilitate longitudinal tracking of patients over time and across sites of care .
the sidecar approach provides the capability to implement new data models without transforming all of the stored source data  a key element in the scalability and interoperability of our platform ( table 1 ) . by enabling well - designed , cross - mapped ontologies that support a pcornet common data model
, this approach incorporates otherwise disparate clinical data sources into an easily - queried system that stores data in a flexible format .
data are stored in i2b2 using an entity  attribute  value model,20 
 21 employing a central  fact table based upon kimball 's star schema22 wherein each row stores a flexibly defined , atomic 
fact or observation for a patient.5 much of i2b2 's versatility arises from its focus on a semantic definition of patient observations that can represent various existing and newly defined data elements : claims , ehr , genetic and imaging data , as well as patient reported outcomes and demographics .
analogous to a capacious warehouse with adjustable shelves and bins , i2b2 accommodates various nomenclatures for data elements , and supports robust tags of associated modifiers and values .
this approach enables database indexing of facts and observations to support high performance execution of expressive queries and filters .
approaches to scalability and interoperability ehr , electronic health record ; omop , observational medical outcomes partnership ; pcori , patient centered outcomes research institute ; smart , substitutable medical applications , reusable technologies .
i2b2 employs an ontology - based approach that supports flexible , on - the - fly incorporation of new data elements and coding systems .
terminologies such as icd , ndc , and loinc may be pre - loaded as hierarchical concept trees ; new or ad - hoc terminologies including patient - reported outcome measures or locally defined data dictionaries readily coexist and may be cross - mapped in i2b2 .
concepts may be grouped using simple hierarchies and then optionally re - mapped into other reference coding systems and data models ( eg , observational medical outcomes partnership ( omop ) data model).23 in this way , i2b2 accommodates diverse real - world coding systems while maintaining a straightforward query interface for its users .
the shrine adaptor cell maps local i2b2 terminologies into a common , standards - based shrine ontology .
this enables a common shared ontology for federated queries while allowing individual i2b2 instances within institutions to retain local hierarchies and terminologies .
the adaptor transforms a federated shrine query into a query that runs on the local i2b2 database .
the adaptor then converts the result of that query back into the common shrine message format , using well - maintained standards including rxnorm , icd9 , and loinc .
in addition , shrine includes tools for ontology mapping and ontology - based data mining .
simple shrine customizations enable use of other query systems , for example the queryhealth distributed query system ( onc ) uses popmednet to query i2b2.24 
 25
shrine and i2b2-based research includes characterization of rare morbidities of common diseases,26 very rare diseases such as peripartum cardiomyopathy ( discovered in shrine and published in nature27 ) , detections of drug  drug interactions,28 and measures of quality and clinical efficacy across self - organized shrine networks in europe , the university of california healthcare systems , and a just - in - time network to study the prevalence of complication rates of type 1 and type 2 diabetes in hospitals across this country .
others have used shrine to characterize and track the rising incidence of colorectal cancer29 and further characterize it , and to identify and optimize practice variation in inflammatory bowel disease and intervene to change that practice.30 i2b2 and shrine have been implemented as the base infrastructure for a variety of enhanced chronic disease registry - based research efforts.31 the childhood arthritis and rheumatology research alliance uses the shrine / i2b2 registry framework to federate clinical care data and patient - reported data from 62 academic medical centers in the usa and canada32 
 33 and is currently piloting consensus treatment protocol trials.3437 the harvard inflammatory bowel disease ( ibd ) longitudinal data repository employs the same infrastructure.31 improvecarenow38 utilizes i2b2 as its centralized data warehouse for ibd - related quality improvement development at 50 centers .
the health systems that have joined scilhs reflect the american demographic  an essential requirement for reaching statistically valid , clinically meaningful , and patient - centric conclusions about therapies across the diverse spectrum of all healthcare consumers . in order to achieve the comprehensive , patient - centered outcomes infrastructure called for by pcori
, we introduce a new , patient - centric platform ( myscilhs ) based on the indivo system and incorporating the redcap electronic data capture tool .
myscilhs will support the blue button rest api for standards - based interactions with pprns and other patient - selected tools .
this api exposes up - to - date , structured clinical summary data for each participating patient . via a consumer - friendly workflow based on web standards including oauth2
, patients can authorize third - party apps and services , including pprns , to access their clinical data .
figure  2 shows the workflow from an initial query through the analytic phase in a comparative effectiveness study .
each node in the network maintains an instance of i2b2 containing claims and de - identified electronic medical record data .
scilhs is a true peer - to - peer network , meaning that any shrine - based node can initiate a query , using a common ontology , that aggregates results from all participating sites .
after the initial query , the investigator can automatically pass the query to each site where duly authorized local site investigators may review individual subject data for study eligibility using i2b2 smart apps ( figure 3 ) .
the myscilhs research contact management module links de - identified i2b2 records to patient demographics and contact information .
patients are engaged by web survey , telephony , or smart apps ; patient - reported data are returned to i2b2 and are then transferred into a secure comparative effectiveness ( ce ) study environment for analyses . in the ce environment ,
we anticipate that pcornet - level queries , which may launch against the full complement of 11 cdrns and 18 pprns , will be initiated at the pcornet adapter .
we anticipate that natural language processing ( nlp ) of provider notes will play an important role for adding complete longitudinal coded data to the hospital - based record.39 early findings demonstrate that nlp of hospital - based ehr notes provides quite complete longitudinal data even when compared with centers for medicare and medicaid services claims data ( personal communication , katherine liao , brigham and women 's hospital , 2014 ) .
using nlp on hospital and clinic notes will complement our strategy of concatenating ehr data with external sources such as claims and pharmacy data .
there will be important variations depending on the nature of the study , whether in - person consent is required , and whether patient identifiers are needed .
shared health research information network ( shrine ) architecture implemented as a modular framework . using a mapper toolkit
, each site exposes a common queryable data model , implemented in the ontology ( ont ) cell .
the ont cell manages the vocabulary of the data model and is one of several cells in the i2b2 architecture , including the broadcaster - aggregator cell ( agg , broadcasts the query across all i2b2 nodes in the shrine peer - to - peer network and aggregates the results ) , the identity management cell ( i m , used for authentication ) , the clinical research chart ( crc , manages the clinical data ) , the workplace cell ( work , manages the workflow ) , and the substitutable medical applications , reusable technologies ( smart ) cell ( manages the smart api ) .
a query from a patient centered outcomes research institute ( pcori ) approved study is translated to a shrine central node query either manually , or by a pcornet adaptor , the specifications for which are still to be determined .
the shrine central node broadcasts the query across the true peer - to - peer network ( arrow 1 ) .
i2b2 nodes containing coded data are queried at each site to identify appropriate patients returning obfuscated , aggregate patient counts ( arrow 2 ) .
patient identifiable data remains at each site where investigators can use smart apps to review records prior to aggregation ( arrow 3 ) . also , see figure 3 .
the patient list is passed to myscilhs for outreach to patients via apps , survey , or telephony ( arrow 4 ) .
patient generated data are imported into i2b2 via simple input formats ( csv , for example ) and placed into the i2b2 data model in a flexible schema that allows these to become first - class queryable data objects ( arrow 5 ) .
the adjudicated patient data ( reviewed by investigators using smart apps and confirmed as valid ) from each site , including patient - reported data can be added ( arrow 6 ) to a research data mart in one of several analytic data models ( including the pcori common data model ) with a level of identifiers appropriate to the level of consent obtained .
additional , outside data such as centers for medicare and medicaid claims can be added in this step .
a substitutable medical applications , reusable technologies ( smart ) platforms html5 app running on i2b2 , providing a richly featured electronic health record - like view of the data .
scilhs includes 10 legally and financially independent institutions whose ceo or equivalent senior institutional official has committed to active participation in governance , policy development , data sharing , and sustainability planning .
each member has pledged to invest additional personnel and resources to ensure the network meets local patient and clinical stakeholder needs . by harmonizing informatics infrastructure , data models , regulatory processes and policies , and patient participation within and across member institutions
, we anticipate that scilhs will achieve and remain a successful model for inter - institutional pcor . utilizing the innovative scilhs sidecar it approach to ehr access , we minimize local informatics burden , further enabling a sustainable and adaptable pcor infrastructure .
 we describe the architecture of the patient centered outcomes research institute ( pcori ) funded scalable collaborative infrastructure for a learning healthcare system ( scilhs , http://www.scilhs.org ) clinical data research network , which leverages the $ 48 billion dollar federal investment in health information technology ( it ) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients , plugging universally into the point of care , generating evidence and discovery , and thereby enabling clinician and patient participation in research during the patient encounter . 
 central to the success of scilhs is development of innovative  
 apps to improve pcor research methods and capacitate point of care functions such as consent , enrollment , randomization , and outreach for patient - reported outcomes . 
 scilhs adapts and extends an existing national research network formed on an advanced it infrastructure built with open source , free , modular components . 