AHRQ Funding Programs Focused on Building Infrastructure to Conduct CER with Electronic Prospective Clinical Data

Transcription

1 AHRQ Funding Programs Focused on Building Infrastructure to Conduct CER with Electronic Prospective Clinical Data I. Recovery Act Awards for Evidence Generation Awards EDM Forum for CER 1. Academy Health Electronic Data Methods (EDM) Forum for Comparative Effectiveness Research Erin C. Holve, Academy Health (U13 HS ) 1. Enhancing Clinical Effectiveness Research with Natural Language Processing of EMR Brian L. Hazlehurst, Kaiser Foundation Research Institute (R01 HS ) 2. Comparative Outcomes Management with Electronic Data Technology (COMET) Study Clete A. Kushida, Stanford University (R01 HS ) 3. Indiana PROSPECT J. M. Overhage, Indiana University-Purdue University at Indianapolis (R01 HS ) 4. Multi-Institutional Consortium for CER in Diabetes Treatment and Prevention Joseph V. Selby, Kaiser Foundation Research Institute (R01 HS ) 5. Washington Heights Initiative Community-based Comparative Effectiveness Research Adam B. Wilcox, Columbia University Health Sciences (R01 HS ) 6. The Population-Based Effectiveness in Asthma and Lung Disease Tracy A. Lieu, Harvard Pilgrim Health Care, Inc. (R01 HS ) II. Recovery Act Awards for Data Infrastructure Awards Building Patient Registries to Track Health Outcomes and Measure Quality and Performance Enhanced Registries for Quality Improvement and Comparative Effectiveness Research 1. Surgical Care and Outcomes Assessment Program Comparative Effectiveness Research David R.Flum, University of Washington (R01 HS ) 2. Building Modular Pediatric Chronic Disease Registries for QI and CE Research John J. Hutton, Children's Hospital Medical Center Cincinnati (R01 HS ) Distributed Data Research Networks, Including Linking Data Scalable Distributed Research Networks for Comparative Effectiveness Research 1. SCANNER: Scalable National Network for Effectiveness Research Lucila Ohno-Machado, University of California San Diego (R01 HS ) 2. Scalable Architecture for Federated Therapeutic Inquiries Network (SAFTINet) Lisa M. Schilling, University of Colorado Denver (R01 HS ) 3. Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings John F. Steiner, Kaiser Foundation Research Institute (R01 HS )

2 Recovery Act Awards for Evidence Generation Awards 1. Enhancing Clinical Effectiveness Research with Natural Language Processing of EMR Brian L. Hazlehurst, Kaiser Foundation Research Institute (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): To successfully use large linked clinical databases for comparative effectiveness research (CER) requires addressing some key informatics challenges associated with distributed, heterogeneous clinical data. Electronic networks of researchers are part of the solution because they can bridge the physical and organizational divides created by distinct health systems' individual electronic medical records (EMRs). In addition, informatics research has demonstrated the feasibility of automatically coding clinical text, enhancing the capacity to integrate both unstructured and nonstandardized clinical data from EMRs. With this study, we propose to develop CER infrastructure, make broadly available the proven MediClass technology for automated classification of EMRs containing both coded data and text clinical notes, and demonstrate the potential of this infrastructure for addressing CER questions within the asthma and tobacco-using patient populations of 6 diverse health systems. Asthma and smoking each impose huge and modifiable burdens on the healthcare system, and multiple morbidities related to asthma and smoking have been targeted by the IOM and AHRQ as priority areas in efforts to improve the healthcare system through comparative effectiveness research. We propose to develop, deploy, operate and evaluate the CER HUB, an Internet-based platform for conducting CER, and to demonstrate its utility in studying clinical interventions in asthma and smoking. Researchers who register to use the HUB, beginning with the research team from the 6 participating study sites, will be able to use a secure website to configure and download MediClass applications addressing CER questions within their respective healthcare organizations, to contribute these IRB-approved, processed datasets back to a centralized data coordinating center to be pooled with data similarly processed from other healthcare organizations, and to use the pooled database to answer diverse comparative effectiveness questions of large, real-world populations. A central function of the CER HUB will be facilitating (through online, interactive tools) development of a shared library of MediClass knowledge modules that afford uniform, standardized coding of EMR data. This shared library of knowledge modules could permit researchers to assess effectiveness in multiple areas of healthcare and gain access to data otherwise locked away in text clinical notes. A goal of the CER HUB is to accelerate creation of standardized knowledge used to normalize heterogeneous EMR data as representations of clinical events for CER. During the project period we will conduct 2 studies using this infrastructure to address the effectiveness of interventions for asthmatics and tobacco users across the 6 participating health systems. As an ongoing resource, the HUB will provide a collaborative development platform for enhancing comparative effectiveness research in potentially any health care domain. CER researchers can build software applications that will process their EMRs, creating standardized datasets permitting CER using a secure website to configure and download MediClass applications addressing CER questions within their respective healthcare organizations, to contribute these IRBapproved, processed datasets back to a centralized data coordinating center to be pooled with data similarly processed from other healthcare organizations, and to use the pooled database to answer diverse comparative effectiveness questions of large, real-world populations PUBLIC HEALTH RELEVANCE: Comparative effectiveness research (CER) requires that clinical data be in standard forms allowing multiple, large databases to be efficiently combined, and requires that all of the data be coded so that automated summarization of the data is possible. However, much of the clinical data necessary for CER is in the text clinical notes written by clinicians when caring for patients. We will build a centralized website where CER researchers can build software applications that will process their electronic medical records, including both the text and coded data, creating standardized datasets permitting comparative effectiveness research. We will demonstrate the utility of this infrastructure by conducting CER studies investigating the effectiveness of interventions in asthma and smoking, across the 6 participating health systems. 2

3 Recovery Act Awards for Evidence Generation Awards 2. Comparative Outcomes Management with Electronic Data Technology (COMET) Study Clete A. Kushida, Stanford University (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): The first phase of the proposed Comparative Outcomes Management with Electronic Data Technology (COMET) Study includes the development of a new electronic data network infrastructure that will prospectively enable access to and the sharing of clinical and research data between four Clinical Centers (CCs), located at Stanford University, University of Pennsylvania, University of Wisconsin-Madison, and Harvard University. The development of this new, broad, scalable, and sustainable electronic data network will enable the collection of longitudinal data content by each of the four CCs. However, the establishment of a relationship of this magnitude requires that multiple components be addressed during the specification phase. There are 18 essential components needed to be placed into concurrent agile evolutionary development in order to properly evolve specification and implementation goals of secure, authenticated, permissioned, and compliant data access to and sharing of digital and physical content among the clinical and research arms of multiple institutions. The components involved in development of the COMET network include creation of an informatics system that incorporates the following features: single-point access via the Internet; an evolving, customizable ontology mapped to existing variables collected by, and archived in, a wide variety of data models, database instances, and data elements comprising the informatics infrastructures of the clinical centers; an intuitive, federated query tool to produce result sets for a variety of research and clinical use cases; a request procedure to initiate the transfer of digital or physical specimen samples when they are needed for further analysis; procedures common to all clinical centers; request and transfer procedures enabling and logging the compliant, authorized, and authenticated exchange and sharing of digital and physical content; HIPAA compliance; and open-source software. The other phase is the collection of data from a comparative effectiveness trial evaluating positive airway pressure (PAP) vs. oral appliance (OA) therapy in improving hypertension and abnormalities in cardiovascular function in overweight/obese patients with obstructive sleep apnea (OSA), a highly prevalent sleep-related breathing disorder. The comparative effectiveness component of the proposed project is a novel multicenter single-blind (data collectors) two-arm randomized parallel group comparative trial. It is designed to determine the relative efficacy and differential degree of benefit of PAP versus OA therapy with respect to cardiovascular risk in predominantly female, overweight/obese OSA patients with hypertension. The electronic network infrastructure developed through COMET will link the prospective information collected from this comparative effectiveness trial via multiple CCs, diverse patient and research participant populations, different patient comorbidities and disease severities, various equipment and test types, and longitudinally across several patient visits. The data collected from this trial will be used to test the informatics infrastructure. PUBLIC HEALTH RELEVANCE: We plan to develop an electronic network that will enable the transfer of information from various hospitals and medical centers, patients and research subjects, different types and severity of medical problems, various equipment and test types, and across several patient visits. This network will allow physicians and scientists to access comprehensive information about their patients and research subjects, and the sharing of this information across several academic institutions may ultimately lead to improvement in medical outcomes. We also plan to conduct a study evaluating two common treatments for obstructive sleep apnea (OSA), a highly prevalent sleep-related breathing disorder, and we anticipate that this study will provide comparative data for determining the effectiveness of these treatments in reducing cardiovascular risk in a population at high risk for cardiovascular disease, and will enhance clinical decision making in determining the optimal treatment strategies for patients with OSA. 3. Indiana PROSPECT J. M. Overhage, Indiana University-Purdue University at Indianapolis (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): The Indiana PROSPECT builds on the nation's largest, most sophisticated health information exchange that already contains over 2.5 billion structured observations and 38 million text documents for over 10 million patients. This resource is already used for a variety of clinical outcomes and clinical effectiveness research. Based on our analysis of gaps in the infrastructure and experience gained by conducting a specific CER study as part of the PROSPECT, we will enhance this infrastructure to capture richer clinical data and particularly enhance our ability to incorporate patient 3

4 Recovery Act Awards for Evidence Generation Awards outcomes, improved capture of device utilization data, capabilities to incorporate genomic and other high throughput results, advanced technologies to identify patient for recruitment and enrollment technologies. In order to demonstrate the usability of this environment and to inform its further development, we propose to conduct a novel CER project evaluating treatment for Alzheimer's. The CER study will evaluate of medication treatment for behavioral symptoms of Alzheimer's disease. Alzheimer's disease has been identified as a first quartile CER priority. Because our approach is population based, we will incorporate a significant minority population. This application represents collaboration between the Medical Informatics Program at the Regenstrief Institute, Inc (a world leader in health information technology) and two Indiana University research programs: the Center for Aging Research and the Division of Clinical Pharmacology. These programs have an established track record in research relevant to under-served populations. Thus, this application combines considerable investigator, environment, and research strengths to continue to build a novel CER infrastructure in support of the nation's evidentiary CER priorities. We will build on our established governance approach to test and improve approaches for sharing access to these research databases and financial models for sustainability based on value created. PUBLIC HEALTH RELEVANCE: Project Narrative This proposal seeks to enhance the existing information technology infrastructure in Central Indiana to improve the nation's capacity to conduct comparative effectiveness research (CER) and apply these new capacities to a novel CER project evaluating treatment for Alzheimer's disease. 4. Multi-Institutional Consortium for CER in Diabetes Treatment and Prevention Joseph V. Selby, Kaiser Foundation Research Institute (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): Project Summary /Abstract: The HMO Research Network's (HMORN) Diabetes Multi-Center Research Consortium (DMCRC) Coordinating Center proposes to build a national research network and a multi-system distributed database for conducting comparative effectiveness research (CER) in the treatment and prevention of diabetes mellitus. The network is comprised of 31 investigators from 12 integrated healthcare delivery systems and academic institutions who study diabetes mellitus, gestational diabetes mellitus, childhood and adult obesity, weight management, medical informatics and biostatistics. The network has broad experience conducting system-based interventions to improve treatment of diabetes or to support lifestyle changes to prevent diabetes, as well as broad experience conducting CER. The 12-system database will capture and standardize detailed demographic and longitudinal electronic health record (EHR)-derived clinical data on more than 750,000 persons with diabetes and a much larger membership without diabetes. Once constructed, the database will be used to conduct and publish surveillance data on trends ( ) in the incidence, prevalence, treatment and outcomes of diabetes in this population. It will also be used to conduct two CER studies. The first is a cluster randomized, multi-system intervention that will rapidly identify diabetes patients with "early nonadherence" to newly prescribed anti-diabetic, antihypertensive, and lipid-lowering medications. Early nonadherence refers to patient failure to fill either the first prescription of a new medication or the first refill, and has been shown to be more frequent than "ongoing" nonadherence. This information will be provided to population management outreach teams in 3-5 health systems via the EHR or other electronic database, along with training in counseling patients on adherence problems. Study outcomes of subsequent risk factor change and adherence to the new medication will be compared within each system to those for patients on teams that do not receive the new information. The second CER study is an observational evaluation of various communication, counseling and referral strategies as provided to women who have had an abnormal glucose test (fasting glucose or oral glucose tolerance test) in the first 6 months after a pregnancy complicated by gestational diabetes mellitus (GDM) and who are therefore at very high risk for developing Type 2 diabetes. Study outcomes include weight change over one year and self-reported physical activity four months after the abnormal test. In each study, we will collect process of care data by applying natural language processing (NLP) to clinical text in EHRs and survey patients for information on demographic, clinical, and behavioral variables. These NLP-derived and patient-reported variables will be studied as possible mediators of treatment effectiveness or as potential modifiers of effectiveness (i.e. variables that identify patient subgroups that benefit less from usual interventions. Variables that prove to be important as mediators or modifiers may be good candidates for routine collection and incorporation into EHR's and/or future diabetes registries. PUBLIC HEALTH RELEVANCE: A network of 32 health services and intervention scientists affiliated with the HMO Research Network propose to build a national database for conducting comparative effectiveness research on treatment and on approaches to preventing diabetes mellitus. The database will include information on more than 750,000 persons with diabetes. It will be used to generate ongoing information on 4

5 Recovery Act Awards for Evidence Generation Awards the occurrence, treatments and outcomes of diabetes and to conduct 2 studies of approaches to treating and preventing this condition. 5. Washington Heights Initiative Community-based Comparative Effectiveness Research Adam B. Wilcox, Columbia University Health Sciences (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): The overall goal of this project is to advance comparative effectiveness research designed to improve hypertension care delivery and population outcomes by building on an existing institution-focused data infrastructure to create a robust community-focused data infrastructure that will support the kinds of innovative studies needed to effectively tackle seemingly intractable public health problems. This will be done through the Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research (WICER). WICER contains a research data warehouse that integrates patient-level data, including clinical data from multiple facilities, settings and sites of care, with person-level self-reported information and will map the linked data to variables that support prospective comparative effectiveness research studies. It also will provide tools to support researchers in accessing this infrastructure to perform retrospective analyses and recruit patients for research studies. We will then demonstrate how this infrastructure can support three comparative effectiveness studies on hypertension. These three studies will relate to the diagnosis, treatment and management of hypertension in patients. Our project will augment an existing data infrastructure, enhance access through state of the science tools, and achieve greater integration of both the data and the health care organizations that contribute to it. This in turn will enable us to conduct both the proposed and subsequent comparative effectiveness studies generating evidence to inform effective diagnosis and treatment of hypertension, and ultimately a wider range of related community health problems. PUBLIC HEALTH RELEVANCE: This research has the potential to improve the evidence base for treatment hypertension and other clinical conditions for a medically underserved, minority, low income, immigrant patient population. Currently, the evidence base for care delivery is not strong for this population, resulting in disparities in health care and outcomes. Insights gained through working with this specific population will then be generalizable to other such communities and populations. 6. The Population-Based Effectiveness in Asthma and Lung Disease Tracy A. Lieu, Harvard Pilgrim Health Care, Inc. (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): The burden of asthma falls disproportionately on vulnerable groups including racial/ethnic minorities and children. Enhancing the clinical infrastructure for comparative effectiveness research in asthma and lung diseases will have high impact because there is a continuing dearth of information to address current medication choices for these priority populations. To reduce asthma morbidity, we also need to better elucidate the factors that affect prescribing and adherence to asthma controller medications in diverse populations. Specific Aims - We will: (1) Build an innovative infrastructure for research on asthma and lung diseases by creating and linking standardized datasets from a state Medicaid population and four health plan populations; (2) Compare real-world adherence to and effectiveness of the major asthma controller regimens in diverse populations; and (3) Conduct an applied methodologic study that compares different design and analysis approaches for observational comparative effectiveness research. Research Design - For Aim 1, we will create a new distributed data network for research in asthma and lung diseases by building on extensive, currently available computerized datasets from our diverse populations - the TennCare Medicaid population and four HMO Research Network sites. We will build on the HMORN's Virtual Data Warehouse `and approaches used by other successful networks. For Aim 2, we will conduct cohort and nested case-control studies using data from claims, electronic medical records (EMRs), and surveys of patients and providers. Aim 2 analyses will evaluate variations in the rates of adverse asthma outcomes and costs among the major controller regimens, using propensity score weighting and alternative methods to adjust for confounding by indication. For Aim 3, we will conduct a methodologic study that compares the generalizability and validity of various study designs and statistical analysis approaches, and evaluates the benefits of collecting patient-reported data. Impact. This project is innovative in its plan to include all four important data sources - claims, EMRs, patients, and providers - and 5

6 Recovery Act Awards for Evidence Generation Awards a state Medicaid population in a new network for comparative effectiveness research on asthma and lung diseases. This large, diverse network will create a seminal model for comparative effectiveness research in chronic diseases and vulnerable populations. The current project will address key clinical and policy questions in asthma. The PEAL Network will lay a critical foundation for future population-based research in asthma and lung diseases, and research with priority populations. PUBLIC HEALTH RELEVANCE: Project Narrative This project will create a new infrastructure to accelerate comparative effectiveness research in asthma and other lung diseases in diverse populations. We will form the Population-Based Effectiveness in Asthma and Lung Diseases (PEAL) Network, creating highly detailed, standardized, linked computerized datasets from a state Medicaid plan and four health plans. We will conduct comparative effectiveness research on asthma controller medications, evaluating adherence and other real-world factors. This project is innovative in that we will link claims, electronic medical record, patient, and provider data, and will build new linkages with a Medicaid plan to include vulnerable groups who are disproportionately burdened by asthma. 6

7 Recovery Act Awards for Data Infrastructure Awards Enhanced Registries for Quality Improvement and CER 1. Surgical Care and Outcomes Assessment Program Comparative Effectiveness Research David R.Flum, University of Washington (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): The Surgical Care and Outcomes Assessment Program (SCOAP) is a voluntary, performance surveillance, sharing and feedback platform derived from clinical records. SCOAP improves quality by increasing adoption of evidence-based process of care measures and performing "real world" comparative effectiveness research (CER). In just four years, SCOAP was deployed across nearly all statewide hospitals. Versions of SCOAP exist for cardiac care, general surgical care, pediatric surgical care, and now vascular interventions (VI- SCOAP) for peripheral vascular disease. As a platform for both CER and improved adoption of CER-informed process of care measures for clinical quality improvement (QI), SCOAP is currently limited in scale by a lack of automated data gathering, linkage between data streams and connections to post-discharge and patient reported outcomes. Drawing information out of these different systems and linking them to other data sources for CER or QI requires an information technology solution that can interact with all available data systems and all types of data. We propose to deploy Amalga UIS" across the SCOAP Network to address the problem of inter-system connectivity by linking data systems in a common, framework. Amalga UIS" deployment within SCOAP will create automated data retrieval from each participating hospitals' varied electronic sources, doctor's offices and outpatient data streams. We will link automated clinical data with to administrative claims to track for all subsequent hospitalizations, vital status records for survival outcomes, healthcare payer claims datasets for outpatient encounters and medication utilization data, and point-of-care, longitudinal patient reported outcomes. The resulting SCOAP CER Translation Network (CERTN) will facilitate efficient CER as well as support SCOAP's QI initiatives around CER-driven process of care metrics. We will demonstrate the usability of the SCOAP CERTN Project for research and improved adoption of CER-informed process of care by using VI-SCOAP and comparing the treatment strategies of peripheral arterial disease and addressing procedural safety. Leveraging existing strengths of the SCOAP network and the VI-SCOAP registry with Microsoft's partnership, we propose several enhancements to improve our capability and scalability for longitudinal clinical and patient reported outcomes data collection. The SCOAP CERTN project will be a large-scale, real-world resource for CER based on clinical data using an automated data retrieval stream from diverse healthcare settings. This will greatly enhance existing SCOAP QI by delivering more comprehensive and timely data, and in relieving manual staff and resource burden to participating hospitals. SCOAP CERTN's initial CER focus will be in vascular surgery and interventions, but the project will be a gateway for investigators from all clinical disciplines to evaluate clinical outcomes, cost-impact, PROs and to translate evidence into QI. Importantly, surgical and interventional services span the continuum of healthcare conditions and SCOAP CERTN will have immediate capacity for investigators interested in almost any of AHRQ's priority conditions. PUBLIC HEALTH RELEVANCE: The SCOAP CERTN project will be a large-scale, real-world resource for CER based on clinical data using an automated data retrieval stream from diverse healthcare settings. SCOAP CERTN project's initial focus will be in vascular surgery and interventions, but this will be a gateway for investigators from across all clinical disciplines to evaluate clinical outcomes, cost-impact, patient reported outcomes and to translate evidence into clinical quality improvement. Surgical and interventional services span the continuum of healthcare conditions and SCOAP CERTN will have immediate capacity for investigators interested in almost any of AHRQ's priority conditions. 2. Building Modular Pediatric Chronic Disease Registries for QI and CE Research John J. Hutton, Children's Hospital Medical Center Cincinnati (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): A learning healthcare system comprises a community of front-line clinicians, patients, and scientists who view each clinical encounter as an opportunity to learn and to improve patient outcomes. In its most advanced state, it combines comparative effectiveness (CE) research with quality improvement (QI) science to ensure the delivery of new knowledge at the point of care. Electronic health records have the potential to become the tool for learning at the point-of-care, although 7

8 Recovery Act Awards for Data Infrastructure Awards Enhanced Registries for Quality Improvement and CER most are patient-focused and do not natively support the population management required by a learning healthcare system. Registries are designed to do this, but generally operate independently of institutional EHRs. We propose to change this by building upon existing open-source software to create a modular, versatile, and scalable registry that can be populated by EHRs. We will test its ability to support QI and CE research within the ImproveCareNow practice-based research and improvement initiative. Our specific aims are to: (Aim 1) enhance an existing registry to support a learning healthcare system for pediatric inflammatory bowel disease (IBD) by capturing needed data directly from electronic health records, improving the quality of collected data using new tools we have developed for recording clinical data during a patient encounter, and facilitating interventions to improve the quality of care for children; (Aim 2) use quality improvement methods to implement enhanced IBD-registry features to enable management of IBD care center populations and increase patient participation in care; (Aim 3) use data from the enhanced registry to compare the effectiveness of alternative treatment strategies for pediatric Crohn's Disease patients, with a special focus on timing of biologic agents; (Aim 4) develop governance structures for the network that engages patients and provides oversight of privacy, confidentiality, and data access, as well as scientific and technical concerns. The project will form a unique community of children, families, clinicians, informaticians, QI specialists, and CE researchers who work together to improve patient outcomes using learning healthcare system principles. Our long-term goal is to extend this work to other chronic disease communities devoted to advancing the health of children. PUBLIC HEALTH RELEVANCE: EHRs have the potential to anchor a learning healthcare system where each clinical encounter is an opportunity to learn and to improve clinical outcomes. We propose to link EHRs to disease-specific registries in a distributed network of pediatric healthcare providers and generate new evidence for the benefits and harms of different strategies and interventions to prevent, diagnose, and treat disease. 8

9 Recovery Act Awards for Data Infrastructure Awards Scalable Distributed Research Networks for CER 1. SCANNER: Scalable National Network for Effectiveness Research Lucila Ohno-Machado, University of California San Diego (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): We will develop a distributed network infrastructure for comparative effectiveness research that provides flexibility to participant sites in the means for data sharing. This flexibility will be implemented by allowing codification of data sharing policies - each institution will specify its own policies. The SCAlable National Network for Effectiveness Research (SCANNER) will connect diverse healthcare delivery settings with secure infrastructure that utilizes data collected at the point of care. Policies for data sharing will range from sharing of de-identified records to sharing aggregate results. Within this broad range, policies can be fine-tuned (for example, different levels of sharing for those with whom there is a DUA). When the policy specifies that data must remain at an institution, the network still could allow users outside that institution to execute queries that return summary results from simple patient counts or complex statistical procedures. The network will have a main node that manages policies, distributes queries, aggregate results, and maintains trust and security (authentication, authorization, auditing, etc). Each site will maintain a node that contains data from that site. The network will support (1) retrospective analyses, (b) prospective observational studies, (c) clinical trials, and (d) feedback to point-of- care users. Near real-time collection, analysis, dissemination of results, and feedback to the clinician will be enabled by an infrastructure that allows data to be exchanged according to policies specified by individuals and institutions. The network can scale because it is not dependent on a single trust management provider or data model, but is managed instead by a broker that assigns defined roles to selected entities. The specific aims of this project are to (1) develop and encode policy models based on multiple stakeholders, (2) implement a scalable and secure network and analytical tools that operates across multiple settings and different IT vendors, and (3) demonstrate the use of this network by collecting data on 4 pairs of cohorts. The particular Comparative Effectiveness Research studies that will be demonstrated relate to (1) the effectiveness of comanagement by pharmacists-clinicians for patients with (a) diabetes, and (b) hypertension, and (2) the effectiveness of new antiplatelet (prasugrel) and antithrombotic agents (dabigatran) as compared to their standard counterparts clopidogrel and warfarin, respectively. PUBLIC HEALTH RELEVANCE: Relevance SCANNER represents a novel design of an electronic infrastructure for research. The network infrastructure we propose uses data collected for clinical care and is scalable and secure. It implements policies derived from individuals and institutions, only allowing the type of data to be exchanged that are compliant with policies within a trust framework. It also implements tools for data analyses and feedback to the clinicians. Such a network will enable conduct of certain types of research studies to be conducted more efficiently and at a larger scale. 2. Scalable Architecture for Federated Therapeutic Inquiries Network (SAFTINet) Lisa M. Schilling, University of Colorado Denver (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): The overall goals of this project are to enhance the capacity and capability of a safety net focused distributed research network to conduct prospective comparative effectiveness research via a multi-setting, multi-state organization. The Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) will federate geographically dispersed safety net entities that collectively serve markedly diverse underserved populations. SAFTINet will both leverage and extend the established governance and technologic capabilities of the Distributed Ambulatory Research in Therapeutics Network (DARTNet) to allow more flexible options for participants and improved grid technology. This multistate project will allow researchers, health policy experts, payers, and clinicians to better understand the impact of a wide variety of health care interventions on health outcomes for minority, underserved and socioeconomically disadvantage populations by supporting observational comparative effectiveness research. We will assemble a learning community dedicated to the aforementioned populations and build secure technology to allow authorized and authenticated stakeholders to answer important questions regarding issues ranging from treatment options to care delivery options. The Specific Aims of this proposal 9

10 Recovery Act Awards for Data Infrastructure Awards Scalable Distributed Research Networks for CER are to: " Establish a broad, safety-net focused, research partnership and learning community to govern relationships, establish priorities, provide data quality oversight, and evaluate the purpose and value of the community's effort that leverages the established governance structure of DARTNet " Extend the DARTNet framework to build, deploy and assess a safety-net focused distributed research network which combines ambulatory and inpatient clinical data and Medicaid claims and eligibility data for clinical and research purposes " Develop and enhance four sentinel cohort pairs of patients with asthma (pediatric and adult), hypertension, and hypercholesterolemia distinguished by their care delivery characteristics that can support comparative effectiveness research PUBLIC HEALTH RELEVANCE: This multi-state project will allow researchers, health policy experts, payers, and clinicians to better understand the impact of a wide variety of health care interventions on health outcomes for minority, underserved and socioeconomically disadvantage populations. We will assemble a learning community dedicated to the aforementioned populations and build a secure technology to allow authorized and authenticated stakeholders to answer important questions regarding issues ranging from treatment options to care delivery options. 3. Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings John F. Steiner, Kaiser Foundation Research Institute (R01 HS ) Link to GOLD: DESCRIPTION (provided by applicant): The Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings, or SPAN, will develop a distributed research network that is interoperable across a range of health care systems and sites, incorporating large and diverse patient populations. The network infrastructure will have the capability to conduct large comparative effectiveness research (CER) studies using data collected on patient-reported outcomes collected at the point of care and real-time data collection. Furthermore, we address the important issue of governance to oversee all aspects of managing and conducting research with confidential health information. SPAN will leverage previous AHRQ support of the development of the DEcIDE Distributed Research Network to create the expanded and enhanced network suitable for conducting comparative effectiveness research (CER), compatible with AHRQ's mission to improve the quality and effectiveness of health care. One particularly unique feature of the SPAN network is the incorporation of integrated and less-integrated (community) health systems-an addition that poses potential data management challenges that are vastly outweighed by the benefits of adding diverse patient populations to distributed research networks. If this pilot effort on expanding across types of health systems is successful, we would propose future expansion of this feature. We selected initial cohorts for development that not only present opportunities for comparative effectiveness studies of treatments and processes of care, but that lend themselves to expansion for further cohort development-across AHRQ priority conditions and populations, different sites, and increased complexity of descriptive and treatment variables. Thus, the current proposal sets the stage for significant future CER by developing a network with the capacity to extract diagnostic, treatment, descriptive, and process of care data on more than 7.3 million individuals, the ability to grow and diversify, and the governance infrastructure to proactively address the complex issues that inevitably accompany investigations dependent on healthcare data. Results from research conducted with cohorts derived from this network will inform care delivery on individual levels as well as policy- level decisions on healthcare funding and reform. PUBLIC HEALTH RELEVANCE: The SPAN network will help researchers and public health officials study a broad spectrum of treatments, for multiple health conditions, in diverse patient populations, and across multiple types of health care systems. Findings will inform evidence-based health care and improve the national allocation of health care resources. 10