A national data infrastructure for patient-centered outcomes research.

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A national data infrastructure for patientcentered outcomes research

Concerted efforts are underway to improve healthcare decision-making through patient-centered outcomes research. These efforts are supported by the PatientCentered Outcomes Research Trust Fund, which was established within the Patient Protection and Affordable Care Act. This article focuses on describing national data infrastructure efforts that support patient-centered outcomes research. A national data infrastructure has the potential to decrease research costs and improve research throughput. We describe early and current efforts that demonstrated this potential, how the national effort is utilizing the lessons learned from these predecessor efforts and remaining challenges. Keywords: comparative effectiveness research • data infrastructure • national data infrastructure • patient-centered outcomes research • research data

Imagine that you walk into your provider’s office for a regular check-up. Your doctor tells you that your hypertension is still high even though you are already taking three blood pressure medications. You ask her what your options are and wonder if there might be research that helps inform your options, especially as you are already taking a total of eight medications and are worried about drug–drug interactions and side effects. She checks your electronic health record (EHR) and tells you about a clinical trial testing the effectiveness, in patients like you who have multiple medical conditions, of a new hypertension medication compared with an older medication. She explains the study and the pros and cons of participation, including potential benefits, harms and risks. She additionally indicates that they are particularly interested in the impact the medication has on your life, including your quality of life and any side effects. Your doctor explains that you will come back to the office for regular clinic visits so that the staff can check your blood pressure, monitor any side effects and collect your patient-reported outcomes. All your data will be collected

10.2217/CER.14.54 © 2015 Future Medicine Ltd

in your EHR, pooled with data from other participants and shared in a way that protects your confidentiality and identity. After you consider everything, you agree to participate and sign an electronic consent form. When the study is completed, the evidence generated from the study, but not your individual results, will be easily accessible to patients, caregivers and clinicians to help others like you make informed decisions about whether or not the new hypertension medication is right for them. The above scenario is an example of the power and potential for health information technology (HIT) – the use of an EHR to alert a provider to potential clinical trials of interest, to perform informed consent, to collect data including patient-reported outcomes and share data across individual patients and providers – to enable patient-centered outcomes research (PCOR) and, specifically, comparative clinical effectiveness research (CER) that engages patients in determining the impact on health outcomes of two or more preventive, diagnostic, treatment or healthcare delivery approaches. While this scenario might not even be imaginable in most

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Christine Dymek*,1, Janelle Gingold1, Avinash Shanbhag2, Doug Fridsma2,3 & Pierre L Yong1 1 Office of the Assistant Secretary for Planning & Evaluation, US Department of Health & Human Services, 200 Independence Ave SW, Washington, DC 20201, USA 2 Office of the National Coordinator for Health Information Technology, US Department of Health & Human Services, 200 Independence Ave SW, Washington, DC 20201, USA 3 AMIA, 4720 Montgomery Lane, Suite 500, Bethesda, MD 20814, USA, previously Office of the National Coordinator for Health Information Technology *Author for correspondence: Tel.: +1 202 690 7807 Fax: +1 202 260 2524 [email protected]

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Review Dymek, Gingold, Shanbhag, Fridsma & Yong providers’ offices currently, there is a national focus on making this vision a reality. The Patient Protection and Affordable Care Act (ACA) of 2010 established the Patient-Centered Outcomes Research Trust Fund (PCORTF). The PCORTF provides a multi-billion dollar investment in PCOR that will ultimately: • Provide the information patients, caregivers, providers and policymakers need to make more informed health decisions; • Contribute to building the national capacity and infrastructure for conducting PCOR; • Train researchers to more effectively conduct PCOR; • Integrate findings into clinical practice. In this paper we describe efforts, now commencing, to create a national research data infrastructure made possible by the PCORTF. We begin by discussing the need for and the needs of a national research data infrastructure and describing early and current data infrastructure efforts that contributed to requirements for the national PCOR data infrastructure. We conclude by discussing remaining challenges for fully realizing the promise of the national data infrastructure for PCOR. Why do we need a national data infrastructure for PCOR? Evidence suggests that as much as 30% of healthcare spending reflects care that is of questionable value [1] . PCOR is seen as a mechanism to combat ineffective, low-value care and answer questions that are meaningful to patients. However, the time and expense of conducting high-quality research that produces definitive results present a key barrier to conducting needed research. As noted by the Director of the NIH in a recent post, “it’s often a long, costly process to identify trial sites, recruit volunteers, run the study, compile data, and, finally, analyze the results. The time is right to revamp the way we do this” [2] . To generate PCOR that is useful to patients and providers requires more than funding the right research studies. It also requires an underlying data infrastructure that leverages HIT to facilitate identification and enrollment of patients in clinical trials, collection of data as part of the regular care process (and use of data collected as part of the regular care process), linking and analysis of patient data while protecting confidentiality and dissemination of results so that patients, clinicians, payers and policy makers can make informed health decisions. If built properly, a national data infrastructure can support the efficient collection, linkage and analysis

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of data for PCOR across a multitude of settings, including individual clinical and patient research networks, academic medical centers and provider practices. Rather than having individual investigators ‘start fresh’ with each new study, a national research data network will allow for greater data sharing and reuse of infrastructure, while maintaining the privacy and security of patient data. The breadth of available data across a national data infrastructure would also result in the greater feasibility of studies that heretofore were difficult to conduct because they involve rare conditions and populations. What does a national data infrastructure for PCOR need to be successful? Our national data infrastructure will be successful if it enables more PCOR than previously, while reducing the time and cost of conducting it. Achieving success requires implementing an infrastructure that supports the vision depicted in Figure 1. This vision displays various participants in the healthcare system, including patients, providers and researchers, all sharing information seamlessly from different distributed sources in a single ‘virtual’ network. The boxes (labeled A–F) depict a set of potential interoperable data resources that will be available within the PCOR infrastructure. The arrows depict the information flows between those data sources that enable a learning process (box D), the results of which are then disseminated back into the health system and to patients. Examples of information flows include the extraction of clinical data from EHRs and the contribution of patient-generated data to the health system and learning process. We can reduce research time and cost by enabling the virtual connections depicted in this scenario, which requires interoperable HIT, data in electronic form and governance that facilitates the security, privacy, reliability and legality of the virtual connections. For a national data infrastructure, the ability to consistently perform the interoperable data flows depicted by the arrows will require: • Electronic data linked from a variety of PCOR sources, including clinical data generated by the healthcare delivery system; • Standards and services to facilitate interoperable data flows, data linkage and analysis; • Policies and governance structures to manage the network; • The means for network sustainability. Electronic data requirements for a particular study may include data from multiple EHRs that need to be linked with billing information to determine the

future science group

A national data infrastructure for patient-centered outcomes research

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Direct patient–study data exchange Patient controlled health record

Dissemination of learning

C

Clinical Provider data EHR

Payer admin data A

Query clinical data

D

Data extraction Clinical data repository

Pre-analysis flow of PCOR data Payer claims databases

Learning process (e.g., clinical trials)

B

Key:

Post-analysis dissemination of learning

Clinical report Form/Retrieve form function

E

Approved repository access

Approved database access

F

Figure 1. Vision for the patient-centered outcomes research data infrastructure. EHR: Electronic health record; PCOR: Patient-centered outcomes research.

effectiveness of a particular care intervention. Patientreported outcomes data collected from a mobile device can provide patients’ perspectives to supplement clinical data extracted from EHRs. Standards are nationally accepted specifications that have been widely approved and adopted via market forces, community consensus or regulatory requirements. These include specifications for capturing, storing, representing, linking and exchanging data in a secure manner so that accurate information is conveyed to the recipient of the data. For example, the Quality Reporting Document Architecture standard published by Health Level Seven International, an international standards developing organization, is a standard document format for the exchange of electronic clinical quality measure data from electronic health records. The Meaningful Use Incentive Program, described below, has adopted the Quality Reporting Document Architecture as a standard for data submission. Services refer to resources that researchers can employ to capture, store, link, analyze or exchange PCOR data or evidence through a distributed model provided remotely (such as through the internet), rather than provided locally or on-site. These services would be made available over a network or through a cloud-based model. Such services will enable researchers to perform critical tasks that


they may not have the capacity, expertise or resources to perform on their own. Developed services could include data query services, such as application programming interfaces for enabling access to certain data sources. popHealth [3] is an example of an open-source reference implementation software service that supports the above-mentioned Health Level Seven International Quality Reporting Document Architecture standard to automate the reporting of electronic clinical quality measures from electronic health records. Policies are federal rules or guidelines that need to be established in order to: • Lead to a scalable trust framework that ensures all stakeholders within the national PCOR data infrastructure that needed identity checking and security and privacy rules are assessed appropriately; • Protect patients and their data; and • Ensure the use of established standards and services. For example, national policies could be established for obtaining consent from patients prior to any patient-generated, mobile-device data being released for PCOR. Governance structures are needed to develop and apply the standards and policies required for an

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Review Dymek, Gingold, Shanbhag, Fridsma & Yong interoperable national data research network. While individual research networks will determine the form of governance structure that works best for them, there will be broader governance needs for the national research data infrastructure. For example, the above-mentioned patient and patient data protection policies may necessitate a need for a third-party governing body, which contains patient advocates, to develop requirements for standards and policies regarding patient-generated data collected via a mobile device. Sustainability requirements for the national research network will include the identification of business models that ensure adequate revenues to fund the activities of the network. In addition to business models, organizational designs and organizational development pathways to support the business model will need to be identified. In the two sections that follow, we describe early and current infrastructure efforts and their contributions to these requirements. We then, in the section below on PCORTF efforts , explain how PCORTF investments are not only leveraging early and current data infrastructure efforts, but are further investing in the development of these requirements. Early efforts While the ACA represents the largest federal investment in PCOR infrastructure to date, earlier federal efforts began laying the foundation for PCOR infrastructure and, specifically, for a network architecture that is distributed or federated rather than centralized. Federated or distributed architecture allows data holders to maintain control over their own data and only share what is needed to answer specific research questions. Both the Medicare Modernization Act of 2003, which established the Effective Health Care Program at the Agency for Healthcare Research and Quality (AHRQ), and the 2009 American Recovery and Reinvestment Act (ARRA) enabled substantial investments in the development of interoperable research networks and infrastructure to exchange electronic data for CER [4] . Together, these investments increased the quantity of electronic health data, established infrastructure for future HIT building blocks to enable national PCOR and demonstrated proofs-of-concept for the interoperable exchange of health data among multiple institutions for research. Below, we describe three investments within the aforementioned programs. The first is AHRQ’s Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) investment, which was part of the Effective Health Care Program. The second and third are the ARRA CER and the ARRA Health Information Technology for Economic and Clinical Health (HITECH) investments.

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AHRQ: DEcIDE

In 2007, AHRQ’s DEcIDE investment funded two efforts whose purpose was to create prototype distributed research networks (DRNs) within the Distributed Ambulatory Research in Therapeutics Network (DARTnet) and in the Health Maintenance Organization Research Network (HMORN), and to explore the issues associated with using this new infrastructure for CER [5] . DARTnet was successful in piloting a DRN within a primary care practice-based research network to conduct CER related to depression and diabetes [4,6]. The HMORN was successful in first developing a report that described the technical and organizational design recommendations for DRNs and then piloting the design to conduct CER on therapies for hypertension [5,7,8] . Table 1 summarizes the data, standards, services, policies and governance structure contributions of these projects. These projects demonstrated the potential for exchanging electronic clinical data between independent organizations while protecting patient information and privacy and enabling data holders to maintain local control over their data. ARRA CER investments

In 2009, the ARRA expanded federal resources devoted to CER by providing US$1.1 billion to the Department of Health and Human Services (HHS) for CER investment. ARRA also established the Federal Coordinating Council on Comparative Effectiveness Research (FCCCER), which set priorities for federal CER investment and advised that a portion of these funds should be used to establish data infrastructure for CER. ARRA CER data infrastructure investments were diverse. They included development of new registries and databases, establishment of new data linkages, enhancements to existing data sources and further development of DRNs [9] . In particular, three coordinated grant programs through AHRQ were intended to demonstrate proof-of-concept for conducting large-scale CER using electronic clinical data, building on the lessons learned in earlier pilots [10] . Together, these three programs funded 11 projects, each for 3 years. The three grant programs were the Prospective Outcome Systems Using Patient-Specific Electronic Data to Compare Tests and Therapies (PROSPECT) program (six grantees), the Scalable Distributed Research Network for CER program (three grantees) and the Enhanced Registry for Quality Improvement and CER program (two grantees)1. AHRQ also funded the Electronic Data Methods Forum to act as a convener and to support learning and dialogue among the diverse stakeholders involved in the 11 projects. The requirements for the programs reflected the challenges experienced in the earlier AHRQ-funded pilot projects. Each awardee was required to: link



Different approaches to harmonizing data: common data models and mapping of data and terminologies Identified need for methods to address data validity and data quality in multisite data exchange

Standardized data elements and recommended use of a common data model within a DRN

Platform, in the S&I Framework, for establishing HIT standards to improve interoperability

Establishment of the HIT Standards Committee to recommend standards to develop a national HIT infrastructure and promote interoperability

Standards, such as MU standards and Query Health, for collection and exchange of electronic data

Ref.

[14]

[8,11,13,14,15]

[5,9]

[8,9,13]

AHRQ: Agency for Healthcare Research and Quality; ARRA: American Recovery and Reinvestment Act; CER: Comparative Effectiveness Research; DEcIDE: Developing Evidence to Inform Decisions about Effectiveness; DRN: Distributed data network; EHR: Electronic health record; HITECH: Health Information Technology for Economic and Clinical Health; IRB: Institutional review board; MU: Meaningful use; S&I: Standards and Interoperability.

Standards

Multiple research networks demonstrating ability to integrate electronic data, including EHR data, from multiple sites. Several demonstrated ability to collect and use patient-reported data. Most focused on particular population (i.e., pediatric) or disease (obesity, asthma, etc)

Increased quantity and availability of electronic clinical data

New data linkages, registries, enhancements and aggregation to enable CER

DRN design and pilots that called for each network member to link EHR, lab, billing, registry, pharmacy and other electronic data in a physical or virtual database that could be queried to conduct CER across sites

Electronic data from a variety of sources

HITECH Act

ARRA CER infrastructure investments

AHRQ Effective Health Care DEcIDE pilot projects

National data infrastructure requirement

Table 1. Contribution of early efforts to national data infrastructure requirements.



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Some funding by parent institutions (e.g., academic medical centers) and continued grants, but long-term sustainability is an issue

Some funding by parent institutions (e.g., academic medical centers) and continued grants, but long-term sustainability is an issue

ARRA-funded with continuing MU support by ONC and CMS

Ref.

[5,8,13]

[5,8,13]

[16]

[13]

[3,13,18]

AHRQ: Agency for Healthcare Research and Quality; ARRA: American Recovery and Reinvestment Act; CER: Comparative Effectiveness Research; DEcIDE: Developing Evidence to Inform Decisions about Effectiveness; DRN: Distributed data network; EHR: Electronic health record; HITECH: Health Information Technology for Economic and Clinical Health; IRB: Institutional review board; MU: Meaningful use; S&I: Standards and Interoperability.

Sustainability

Lesson learned that governance requires considerable effort and project management, especially as diversity of network increases

Design and pilot of centralized network management functions, including query management, IRB engagement and oversight

Establishment of the HIT Policy Committee to recommend policy to develop a national HIT infrastructure and promote interoperability

Established governance and recognized importance of governance structures that sustain partnerships and data use agreements and that also facilitate IRB approval across multiple institutions

The result of these designs and pilots eventually became PopMedNet

Reference implementations of services such as popHealth

HITECH Act

Policy and organizational structure of local control of patient-level data to protect privacy and confidentiality

Design and pilot of services within the portal, such as a query manager and access control manager

Governance (policies and structures)

Development and demonstrated use of various informatics tools and services, such as those to query data, process queries, analyze data, view aggregated data, use clinical decision support tools to identify patients or collect data for studies, and conduct natural language processing of data. Some projects reused previously developed tools (e.g., at least one project used PopMedNet), and some developed project-specific tools (such as one project’s Research Data Explorer [RedX] tool to view de-identified data and support data queries)

Design and pilot of a centralized web portal for network management (including operations such as query scheduling and security functions such as authentication of users)

Services

ARRA CER infrastructure investments

AHRQ Effective Health Care DEcIDE pilot projects


Table 1. Contribution of early efforts to national data infrastructure requirements (cont).

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multiple healthcare delivery sites; connect multiple databases across multiple data architectures; focus on AHRQ priority populations and the Institute Of Medicine’s priority CER topics; collect prospective, patient-centered outcomes; conduct CER, generate valid and generalizable conclusions; establish governance; and, develop a plan for sustainability. These efforts were successful in establishing data infrastructure for CER and in conducting CER. Although each project focused on a particular population or disease, the infrastructures developed provided potentially scalable proofs of concept for infrastructure to collect, link and analyze electronic data. Although ARRA funding has ended, many of these projects continue to enhance their infrastructure for conducting PCOR and many are engaging in current efforts, such as Mini-Sentinel and PCORnet, described below. Their contributions to national data infrastructure requirements are included in Table 1. ARRA HITECH investments

ARRA also made foundational investments in HIT and interoperability through the HITECH Act, which established the underpinning for enabling PCOR using health IT. The HITECH Act provided HHS with the authority to establish programs to improve healthcare quality, safety and efficiency through the promotion and use of HIT. The Meaningful Use (MU) Incentive Program, the largest investment authorized by the HITECH Act, laid the foundation for utilizing EHR data not only for quality improvement but research as well. The MU Program is a joint effort between the Centers for Medicare and Medicaid Services (CMS) and the Office of the National Coordinator for HIT (ONC) to incentivize providers to use EHRs in a meaningful and standardized way. ONC uses a collaborative process engaging diverse stakeholders to establish criteria to certify HIT, and CMS incentivizes providers to use HIT that meets those criteria. For example, ONC requires that EHRs be able to collect data, such as vital signs, in a standardized way in order to achieve certification. Those standardized data are then incorporated into the care summaries that CMS incentivizes providers participating in the MU Program to provide to patients and other providers contributing to a patient’s care. Broad use of certified EHRs creates a foundation of commonly collected data, engages patients with health data, empowers patients to share their data and standardizes the exchange of data. Additionally, the HITECH Act has accelerated the adoption of EHRs and contributed to the increased availability of electronic clinical data that may be used to drive quality improvement and research.


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Moreover, the HITECH Act called for the creation of HIT Policy and Standards Committees to recommend policy and standards for a national HIT infrastructure. It also established other core programs to further enable HIT adoption and interoperability [11] . One of these programs is the Standards and Interoperability Framework, which ONC uses to engage diverse stakeholders in establishing consensus-based standards for the collection, exchange and use of electronic data [12] . PCOR data infrastructure efforts will continue to leverage the HIT building blocks established through the contributions of the HITECH Act. These contributions to PCOR national data infrastructure requirements are also summarized in Table 1. While these early efforts contributed significantly to our understanding of the promise and the requirements for a national data infrastructure for PCOR, they also demonstrated remaining challenges. In addition to sustainability issues, key challenges include the time and effort required to harmonize and ensure the quality of data from heterogeneous data sources and to create governance structures to conduct PCOR via distributed data networks [5,14] . Current efforts Several current federal and non-federal efforts are utilizing the contributions of the early efforts described above while continuing to address remaining challenges. Here we describe three of the largest current data infrastructure projects, which together demonstrate the increasing maturity of research networks in addressing earlier challenges, while also scaling up to incorporate cross-network data sharing. Their contributions to national data infrastructure components are summarized in Table 2. Mini-Sentinel

The US FDA launched the Sentinel Initiative in 2008 to track the safety and adverse event reports of regulated drugs, biologics and medical devices [19] . Mini-Sentinel is a pilot project in the Sentinel Initiative. Mini-Sentinel has developed a distributed data infrastructure for conducting safety surveillance of medical products. Mini-Sentinel allows for distributed data querying of various data partners, including health systems and health plans, each of which control their own data but adapt it to a common data model for purposes of sharing it via the network [20] . Mini-Sentinel currently has 18 data partners. Although Mini-Sentinel is currently used primarily for surveillance, it has potential for other purposes. HMO Research Network

The HMO Research Network (HMORN) began in 1994 as an informal collaboration of 10 health


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Annual member contribution to a common operations fund Identified challenge that project-specific funding does not support resourceintense cross-project and cross-site collaboration

Funded by the US FDA

OMOP: Observational Medical Outcomes Partnership; VDW: Virtual data warehouse

Sustainability

Policy and organizational structure Policy and organizational structure of of local control of patient-level data local control of patient-level data to to protect privacy and confidentiality protect privacy and confidentiality

VDW includes a board for setting overall Use of a coordinating center to policy, an operations committee and data provide central coordination and and implementation workgroups administration of network and queries

Use of a coordinating center to provide central coordination and administration of network and queries

Funded by the NIH

Policy and organizational structure of local control of patient-level data to protect privacy and confidentiality

Robust governance structure involving active engagement from participating organizations through leadership and topic- or process-specific committees

Robust governance structure involving active engagement from participating organizations through leadership and topic- or process-specific committees

Robust governance structure involving active engagement from participating organizations through leadership and topic- or processspecific committees

Governance (policies and structure)

Use of PopMedNet to test the feasibility Use of PopMedNet as a platform and viability of obtaining certain data via to administer the distributed queries among data partners research network

Adherence to and use of national Query Health standards

Use of PopMedNet as a platform to administer the distributed research network

Pilot and use of national Query Health standards

Any data model is allowed; data partners can decide whether they want to, or are able to, respond to queries

Distributed infrastructure enabling data collection across eight health plan and health system data partners for seven specific demonstration pragmatic trials

NIH Collaboratory

Services

Common data model specific to the VDW

Adaptation of the OMOP common data model

Standards

VDW incorporating clinical and administrative data from 18 large health systems for research in multiple areas. Data domains currently include: lab results, health plan enrollment, demographics, cancer, pharmacy, health system utilization, vital signs and census

HMO Research Network

Distributed network of primarily claims data from 18 data partners, but increasingly adding clinical data, specific to surveillance of drugs, biologics and medical devices

Mini-Sentinel



Table 2. Contribution of current efforts to national data infrastructure requirements.

[21]

[23,25]

[20,22,25]

[20,22,25]

[16,17,24]

[15]

[20,21,24]

[20,21,23,24]

Ref.



systems. With support from federal investments, including the NIH, FDA, AHRQ (including ARRA CER data infrastructure funds described above) and CDC, the HMORN has grown to a formal collaboration between 18 research centers based in large healthcare systems. Its purpose is to improve individual and population health via a learning healthcare system [21] . The HMORN’s data infrastructure is called the Virtual Data Warehouse, which is a distributed data infrastructure leveraging a common data model that allows collaborators to control their own data and only share what is needed to respond to a specific scientific inquiry. A total of 13 HMORN sites also participate in Mini-Sentinel [21] . NIH Collaboratory

Finally, the NIH Health Care System Research Collaboratory is another multi-institutional collaboration that uses distributed network infrastructure to conduct research. It was established in 2012. The NIH Collaboratory’s goal is to enable providers and patients to make decisions based on the best evidence by creating new infrastructure to enable collaborative research and execute high-impact pragmatic clinical trial demonstration projects, engaging healthcare delivery systems in research. The NIH Collaboratory is currently facilitating seven demonstration projects, including a pragmatic trial of population-based programs to prevent suicide attempts, and studying strategies to reduce the prevalence of colon cancer in specific populations [25] . Many of the research institutions and organizations involved in both MiniSentinel and the HMORN are engaged in the NIH Collaboratory [24] . Collectively, these efforts demonstrate progress toward developing infrastructure that enables organizations to participate in multiple networks as evidenced by cross-network participation, which foreshadows the promise of a national network for PCOR. Additionally, unlike the early efforts that pilot-tested different models, tools and standards, current efforts have begun to exhibit similar characteristics, including: • Use of common data models to improve interoperability and sharing of data; • Robust governance structures with active engagement from participating organizations; • Use of reusable tools or services based on national standards. However, challenges remain in scaling up data models and governance structures to establish a national data infrastructure for conducting PCOR.


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The primary scaling challenges reflect the challenges raised by the early efforts. Namely, every time networks add a new data sharing partner, time and effort are involved in harmonizing data (i.e., having the new data partner map its data to the network’s common data model) and ensuring that the data partner adheres to the governance requirements of the network. Additional work is needed to identify and develop the national standards, services, policies and governance structures that will enable less timeconsuming and resource-intense sharing of electronic data for PCOR across all research networks and data sources. PCORTF efforts The ACA created and charged the Patient-Centered Outcomes Research Institute (PCORI) with advancing the evidence on health outcomes through research. PCORI’s efforts are supported by the PCORTF. The PCORTF also dispenses funds to the HHS Secretary to enable a comprehensive, interoperable and sustainable data network infrastructure to collect, link and analyze data from multiple sources to facilitate PCOR; these funds are managed by the Office of the Secretary (OS). In addition, AHRQ receives PCORTF funding to train researchers to effectively conduct PCOR and to integrate PCOR results into clinical practice. In this section, we discuss the PCORI and OS data infrastructure efforts. PCORnet

In December 2013, PCORI made a significant investment in the development of PCORnet, a national network of research networks that aims to improve the speed, efficiency and use of patient-centered CER [26] . PCORnet includes patient-powered research networks (PPRNs) and clinical data research networks (CDRNs), as well as a coordinating center. With this investment, PCORI seeks to move beyond the current model of individual research networks and to enable interoperability and exchange of data across research networks, thereby increasing the amount and types of data available for PCOR. Each CDRN represents at least one million lives and will have the ability to capture complete clinical information on those lives over time. Each PPRN has the ability to collect information on 50,000 patients (less for patients with rare disorders) [27] . Given these large patient populations, PCORnet will facilitate handling varied types of data queries and studies at a scale not possible before. PCORnet, building upon earlier efforts, will make use of the Mini-Sentinel common data model [20] and PopMedNet [16] platform to facilitate cross-network queries.


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Review Dymek, Gingold, Shanbhag, Fridsma & Yong Governance within PCORnet will also make use of lessons learned from earlier DRN governance models by providing organizational structures that actively engage network members in governance. Task forces will develop PCORnet policies, procedures and infrastructure with the goal of facilitating the conduct of PCOR across CDRNs and PPRNs. These task forces consist of CDRN and PPRN representatives, one of whom serves as co-chair alongside a member of the coordinating center. A steering committee, which also contains CDRN and PPRN representatives, will vet the proposed task force policies, procedures and infrastructure and make recommendations to PCORI leadership for approval [27] . Additionally, a PCORnet Patient Council advises the PCORnet Steering Committee and PCORI leadership to ensure that patient input is provided appropriately across all policies and procedures [28] . Federal efforts

HHS is supporting the evolution of PCORnet and furthering data capacity for PCOR by making targeted OS PCORTF investments in the necessary ‘building blocks’ or components that will enable researchers to conduct the core functions of collecting, linking and analyzing data in an interoperable, secure, scalable and sustainable manner that will allow the nation to realize the vision of using HIT in a manner described in the opening scenario to seamlessly integrate clinical care and research. HHS will focus on investments in the following types of components, most of which were described above: • Standards;

able to obtain the patient’s recent blood pressure readings via a service that would automatically populate the eCRF from previously entered EHR data. The data captured in the eCRF would be automatically sent to an electronic database for research use. Data Access Framework is another PCORTFfunded ONC initiative [30] that is creating the standards necessary to enable health providers and researchers with a specific question to easily access and extract information in EHRs, quickly and at a lower cost. In order to facilitate this enhanced EHR data access for research within an organization, across organizations, and within multi-institutional scenarios, the ONC is employing a step-wise progression: • Local access: Create a standard language and template in which a provider can ask a question and have all EHR systems have the same understanding of the question. This unlocks the data from the EHR and is the most important part of the proposed work, since, without this building block, the targeted and distributed query approaches mentioned below will either be delayed, or will result in non-standard or difficult-to-extend solutions that will limit research utility; • Targeted query: Create a secure way to remotely access information so that a properly authenticated inquirer can ask for a specific report from a specific entity. This allows researchers to access specific kinds of information and leverages existing query standards through secure, remote access;

• Services; • Policies; • Governance structures; • Federal data. For example, the OS PCORTF is supporting two specific consensus-based standards initiatives that could be leveraged in the future by PCORnet or others engaged in a national PCOR network, as well as by developers of tools and services. The first initiative, ONC’s Structured Data Capture initiative [29] , facilitates PCOR by standardizing information collected for clinical purposes through an EHR with information collected for research purposes. ONC is developing standards for the structure of common data elements that can be used for the interaction of the EHR system with electronic case report forms (eCRFs) used in clinical research. With services utilizing the new standards, existing data from an EHR can ‘pre-populate’ the eCRF. For example, a researcher trying to determine the effect of a new treatment on blood pressure would be

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• Distributed query: Create an information model that describes a shared understanding among multiple data holders of what the data mean and how the data are organized. This shared understanding enables automated data aggregation and analysis with optimal accuracy. Establishment and adoption of these national standards will facilitate data collection and data access for PCOR in a cost-effective and scalable manner. Federal data sources are also PCOR infrastructure components. Federal data that are useful for PCOR include: clinical data in EHR systems maintained by the Indian Health Service and the Veteran’s Administration; current and planned federal quality reporting datasets (e.g., Physician Quality Reporting System, Hospital Inpatient Quality Reporting, Hospice Quality Reporting); disease registries (e.g., the US Renal Data System), vital statistics (e.g., birth and death data); surveillance data collected by the CDC and the FDA; claims data maintained by CMS; and



biospecimens and biorepository data maintained by the NIH. HHS aims to identify investments that will leverage federal data sources that can be most useful for PCOR by making them more usable and accessible within the national research network. Early examples of a new way of accessing federal data resources include the virtual data access infrastructures implemented by both the CDC and CMS. CDC’s Research Data Center and CMS’s Virtual Research Data Center allow researchers to access and analyze data stored on CDC and CMS’s servers, rather than physically receiving a copy of the data on portable media. These environments are enabling researchers to access federal data more quickly and cost-effectively than before, while providing for greater security and protections [31] . Table 3 summarizes existing PCORTF data infrastructure contributions to the four national data infrastructure requirements outlined earlier in this paper. Additional contributions will be made through the life of the PCORTF. Challenges exist, however. For example, by making use of a common data model to harmonize data, PCORnet will experience the same scalability issues experienced by earlier data networks. As PCORnet and investment in needed national infrastructure components evolve, the time and effort required to harmonize data among network partners should decrease. Similarly, as national-level policies and governance structures evolve, the time needed to address governance issues should decrease. By working closely with PCORnet and others conducting PCOR to understand evolving infrastructure needs, HHS will continue to identify and develop the nationallevel components needed to efficiently enable the core PCOR functions of collecting, linking and analyzing data in a distributed national network. A look to the future: remaining challenges Current and past infrastructure efforts highlight the challenges of governance and data harmonization

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for a national PCOR data infrastructure. PCORTF efforts are working to address these challenges via the creation of PCORnet and federal investments in key building blocks, the adoption of which will ultimately lessen harmonization and governance burdens. These investments will not necessarily address the issue of sustainability, however, which was also identified as a key challenge in current and past efforts. The sustainability rows of Tables 1, 2 and 3 mostly point to federal funding as networks’ primary source of revenue; some networks are partially member-funded. Sustainability of not only individual networks but also the national data infrastructure for PCOR remains a challenge. Understanding the value proposition of a national PCOR data infrastructure for various stakeholders and translating that value proposition into business models that will sustain the national data infrastructure is still a work in progress. Disclaimer The ideas expressed in this do not, nor intend to, represent the stated policies and programs of the federal government or its agencies. Any appearances of such are coincidental. The authors of this report are solely responsible for its content.

Financial & competing interests disclosure The authors, during the writing of this paper, were employed by the Department of Health and Human Services and have contributed to the development of the Federal PCORTF efforts mentioned in this paper. C Dymek’s salary is funded by the PCORTF and she has no other conflicts of interest. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

Table 3. Patient-Centered Outcomes Research Trust Fund contributions to national data infrastructure requirements. National data infrastructure requirement

PCORTF


Increased and varied patient data brought by PCORnet CDRNs and PPRNs Increased accessibility of Federal data

Standards and services

Mini-Sentinel common data model adapted by PCORnet. Structured data capture and data access framework national standards initiatives

Governance

PCORnet governance model created

Sustainability

PCORTF and other federal support, but long-term sustainability is still an issue

CDRN: Clinical data research network; PCORTF: Patient Centered Outcomes Research Trust Fund; PPRN: Patient-powered research network.



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Executive summary Background • We define patient-centered outcomes research (PCOR), as comparative clinical effectiveness research (CER) that engages patients in determining the impact on health outcomes of two or more preventive, diagnostic, treatment or healthcare delivery approaches. • The Patient Protection and Affordable Care Act (ACA) of 2010 established the Patient-Centered Outcomes Research Trust Fund (PCORTF). The PCORTF provides a multi-billion dollar investment in PCOR that will provide the information patients, caregivers, providers and policy makers need to make more informed health decisions.

Why do we need a national data infrastructure for PCOR? • It is resource-intensive to conduct high-quality research. • A national data infrastructure is needed to improve the efficiency of research by allowing researchers to re-use existing data and infrastructure. • The breadth of available data across a national data infrastructure would also result in the greater feasibility of heretofore difficult studies on rare conditions and populations.

What does a national data infrastructure for PCOR need to be successful? • A national data infrastructure needs to enable interoperable information flows among a variety of data sources. • To do this consistently over time, a national infrastructure requires: linked electronic data; standards; services; policies; governance structures; and, the means for network sustainability.

Early efforts • The Agency for Healthcare Research and Quality made early investments in data infrastructure that lay a foundation of network architecture that is federated or distributed rather than centralized and made other key contributions to data infrastructure requirements: electronic data; standards; services; policies; and, governance structures. • These efforts demonstrated proofs of concept but experienced challenges with scaling. • The Health Information Technology for Economic and Clinical Health Act, under the 2009 American Recovery and Reinvestment Act, made investments that enable the use of health IT for PCOR by increasing the amount of electronic data available, increasing standardization and interoperability of data and establishing frameworks for further developing national policies and standards.

Current efforts • Current efforts addressing data infrastructure for research include the US FDA’s Mini-Sentinel program, the HMO Research Network and the NIH Collaboratory. • These efforts share key characteristics that include: use of a common data model, robust governance structures and use of reusable tools and services based on national standards. They have made additional contributions to the national building blocks. • These efforts demonstrate a greater ability to scale, but still experience challenges.

PCORTF efforts • The PCORTF funds the Patient-Centered Outcomes Research Institute to conduct PCOR. The Patient-Centered Outcomes Research Institute has also invested in data infrastructure for PCOR through a project called PCORnet. • The PCORTF provides funds to the Department of Health and Human Services, and the Department of Health and Human Services supports PCORnet by investing in national ‘building blocks’ or components that PCORnet and other PCOR research networks can use.

A look to the future: remaining challenges • Current and future PCORTF investments funded by the Department of Health and Human Services will address gaps in governance and interoperability. • Sustainability of a national data infrastructure for PCOR is still a work in progress.

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