Infrastructure For Comparative Effectiveness Research Research Articles

Building electronic data infrastructure for comparative effectiveness research: accomplishments, lessons learned and future steps.

There are large gaps in our knowledge on the potential impact of diagnostics and therapeutics on outcomes of patients treated in the real world. Comparative effectiveness research aims to fill these gaps to maximize effectiveness of these interventions. Health information technology has the potential to dramatically improve the practice of medicine and of research. This is an overview of about US$100 million of American Recovery and Reinvestment Act investment in 12 projects managed by the Agency for Healthcare Research and Quality to build an electronic clinical data infrastructure that connects research with healthcare delivery. The achievements and lessons learned from these projects provided a foundation for the National Patient-Centered Clinical Research Network (PCORnet)and will help to guide future infrastructure development needed to build an efficient, scalable and sustainable learning health system.

American Recovery and Reinvestment Act-comparative effectiveness research infrastructure investments: emerging data resources, tools and publications.

The Recovery Act provided a substantial, one-time investment in data infrastructure for comparative effectiveness research (CER). A review of the publications, data, and tools developed as a result of this support has informed understanding of the level of effort undertaken by these projects. Structured search queries, as well as outreach efforts, were conducted to identify and review resources from American Recovery and Reinvestment Act of 2009 CER projects building electronic clinical data infrastructure. The findings from this study provide a spectrum of productivity across a range of topics and settings. A total of 451 manuscripts published in 192 journals, and 141 data resources and tools were identified and address gaps in evidence on priority populations, conditions, and the infrastructure needed to support CER.

Workforce development for comparative effectiveness research: training programs funded by the American Recovery and Reinvestment Act

We conducted a midstream assessment of the comparative effectiveness research (CER) training programs funded by the American Recovery and Reinvestment Act (ARRA) by examining program characteristics, planned curriculum development activities and core competencies. We examined all 43 training projects funded by the US$46 million ARRA CER investment, collecting data from key informant discussions and a technical expert panel. The majority of projects leveraged institutional resources to provide an individualized combination of didactic and experiential learning supported by strong mentorship. Core competencies included skills in statistical modeling, evidence synthesis (systematic reviews and meta-analysis) and general research design skills. ARRA-supported CER training programs enhanced workforce capacity by developing curricula and preparing CER researchers to apply emerging methods and utilize new CER infrastructure.

Creating an infrastructure for comparative effectiveness research in emergency medical services.

Emergency medical services (EMS) providers deliver the initial care for millions of people in the United States each year. The Institute of Medicine noted a deficit in research necessary to improve prehospital care, created by the existence of data silos, absence of long-term outcomes, and limited stakeholder engagement in research. This article describes a regional effort to create a high-performing infrastructure in southwestern Pennsylvania addressing these fundamental barriers. Regional EMS records from 33 agencies in January 2011 were linked to hospital-based electronic health records (EHRs) in a single nine-hospital system, with manual review of matches for accuracy. The use of community stakeholder engagement was included to guide scientific inquiry, as well as 2-year follow up for patient-centered outcomes. Local EMS medicine stakeholders emphasized the limits of single-agency EMS research and suggested that studies focus on improving cross-cutting, long-term outcomes. Guided by this input, more than 95% of EMS records (2,675 of 2,800) were linked to hospital-based EHRs. More than 80% of records were linked to 2-year mortality, with more deaths among EMS patients with prehospital hypotension (30.5%) or respiratory distress (19.5%) than chest pain (5.4%) or nonspecific complaints (9.4%). A prehospital comparative effectiveness research infrastructure composed of patient-level EMS data, EHRs at multiple hospitals, long-term outcomes, and community stakeholder perspectives is feasible and may be scalable to larger regions and networks. The lessons learned and barriers identified offer a roadmap to answering community and policy-relevant research questions in prehospital care.

Comparisons Among Health Behavior Surveys: Implications for the Design of Informatics Infrastructures that Support Comparative Effectiveness Research

Introduction:To address the electronic health data fragmentation that is a methodological limitation of comparative effectiveness research (CER), the Washington Heights Inwood Informatics Infrastructure for Comparative Effectiveness Research (WICER) project is creating a patient-centered research data warehouse (RDW) by linking electronic clinical data (ECD) from New York Presbyterian Hospital’s clinical data warehouse with ECD from ambulatory care, long-term care, and home health settings and the WICER community health survey (CHS). The purposes of the research were to identify areas of overlap between the WICER CHS and two other surveys that include health behavior data (the Behavioral Risk Factor Surveillance System (BRFSS) Survey and the New York City Community Health Survey (NYC CHS)) and to identify gaps in the current WICER RDW that have the potential to affect patient-centered CER.Methods:We compared items across the three surveys at the item and conceptual levels. We also compared WICER RDW (ECD and WICER CHS), BRFSS, and NYC CHS to the County Health Ranking framework.Results:We found that 22 percent of WICER items were exact matches with BRFSS and that there were no exact matches between WICER CHS and NYC CHS items not also contained in BRFSS.Conclusions:The results suggest that BRFSS and, to a lesser extent, NYC CHS have the potential to serve as population comparisons for WICER CHS for some health behavior-related data and thus may be particularly useful for considering the generalizability of CER study findings. Except for one measure related to health behavior (motor vehicle crash deaths), the WICER RDW’s comprehensive coverage supports the mortality, morbidity, and clinical care measures specified in the County Health Ranking framework but is deficient in terms of some socioeconomic factors and descriptions of the physical environment as captured in BRFSS. Linkage of these data in the WICER RDW through geocoding can potentially facilitate patient-centered CER that integrates important socioeconomic and physical environment influences on health outcomes. The research methods and findings may be relevant to others interested in either integrating health behavior data into RDWs to support patient-centered CER or conducting population-level comparisons.

Security approaches in using tablet computers for primary data collection in clinical research.

Next-generation tablets (iPads and Android tablets) may potentially improve the collection and management of clinical research data. The widespread adoption of tablets, coupled with decreased software and hardware costs, has led to increased consideration of tablets for primary research data collection. When using tablets for the Washington Heights/Inwood Infrastructure for Comparative Effectiveness Research (WICER) project, we found that the devices give rise to inherent security issues associated with the potential use of cloud-based data storage approaches. This paper identifies and describes major security considerations for primary data collection with tablets; proposes a set of architectural strategies for implementing data collection forms with tablet computers; and discusses the security, cost, and workflow of each strategy. The paper briefly reviews the strategies with respect to their implementation for three primary data collection activities for the WICER project.

A tall order on a tight timeframe: stakeholder perspectives on comparative effectiveness research using electronic clinical data

BACKGROUND & SIGNIFICANCE: The AcademyHealth Electronic Data Methods Forum aims to advance the national dialogue on the use of electronic clinical data (ECD) for comparative effectiveness research (CER), patient-centered outcomes research, and quality improvement by facilitating exchange and collaboration among eleven research projects and external stakeholders. AcademyHealth conducted a mixed-method needs assessment with the Electronic Data Methods Forum's key stakeholders to assess: stakeholder views on developing new infrastructure for CER using ECD; current gaps in knowledge with respect to CER; and expectations for a learning health system. AcademyHealth conducted 50 stakeholder interviews between August 2011 and November 2011 with participants from the following seven stakeholder groups: government, business/payer, industry, healthcare delivery, patient/consumer, nonprofit/policy and research. With input from key collaborators, AcademyHealth designed a semi-structured interview guide and a short survey. Reviewers used the qualitative data analysis software NVivo to code the transcripts and to identify and manage complex concepts. Quantitative data from the questionnaire has been integrated with the final analysis as relevant. The analysis of recurring concepts in the interviews focus on five central themes: stakeholders have substantial expectations for CER using ECD, both with respect to addressing the limitations of traditional research studies, and generating meaningful evidence for decision-making and improving patient outcomes; stakeholders are aware of many challenges related to implementing CER with ECD, including the need to develop appropriate governance, assess and manage data quality, and develop methods to address confounding in observational data; stakeholders continue to struggle to define 'patient-centeredness' in CER using ECD, adding complexity to attaining this goal; stakeholders express that improving translation and dissemination of CER, and how research can be 'useful' at the point of care, can help mitigate negative perceptions of the CER 'brand'; and stakeholders perceive a need for a substantial 'culture shift' to facilitate collaborative science and new ways of conducting biomedical and outcomes research. Many stakeholders proposed approaches or solutions they felt might address the challenges identified.

Three Perspectives on the Impact of Comparative Effectiveness Research on Decision Making

BaCkgRound: Comparative effectiveness research (CER) is rapidly add- ing to the amount of data available to health care coverage and payment decision makers. Medicare’s decisions have a large effect on coverage and reimbursement policies throughout the health insurance industry and will likely influence the entire u.S. health care system; thus, examining its role in integrating CER into policy is crucial. oBjECtIvES: to describe the potential benefits of CER to support payment and coverage decisions in the Medicare program, limitations on its use, the role of the Centers for Medicare & Medicaid Services (CMS) in improv- ing the infrastructure for CER, and to discuss challenges that must be addressed to integrate CER into CMS’s decision-making process. SuMMaRy: a defining feature of CER is that it provides the type of evidence that will help decision makers, such as patients, clinicians, and payers, make more informed treatment and policy decisions. Because CMS is responsible for more than 47 million elderly and disabled beneficiaries, the way that Medicare uses CER has the potential to have a large impact on public and individual health. Currently many critical payment and coverage decisions within the Medicare program are made on the basis of poor- quality evidence, and CER has the potential to greatly improve the quality of decision making. despite common misconceptions, CMS is not prohibited by law from using CER apart from some reasonable limitations. CMS is, however, required to support the development of the CER infrastructure by making their data more readily available to researchers. While CER has substantial potential to improve the quality of the agency’s policy decisions, challenges remain to integrate CER into Medicare’s processes. these chal- lenges include statutory ambiguities, lack of sufficient staff and internal resources to take advantage of CER, and the lack of an active voice in set- ting priorities for CER and study design. ConCLuSIon: although challenges exist, CER has the potential to greatly enhance CMS’s ability to make decisions regarding coverage and payment that will benefit both the agency and their patient population. J Manag Care Pharm. 2012;18(4-a):S5-S8 Copyright © 2012, academy of Managed Care Pharmacy. all rights reserved. • Without comparative effectiveness research (CER), payers and Centers for Medicare & Medicaid Services (CMS) must make decisions based on the best evidence available, which often lacks headto-head data comparison, uses surrogate endpoints, and excludes the specific elderly or disabled population that Medicare covers. • There is a common misperception that the CMS is not allowed to use CER data when making decisions. What is already known about this subject • Contrary to a common misperception, CMS is allowed to use CER data when making coverage decisions, albeit with some limitations. • CER could be used when creating patient decision aids, establishing value-based insurance design, and in coding (determining whether there is enough of a significant therapeutic distinction for a particular product to assign it a new code). • CMS could use CER data in pricing decisions—specifically in the area of least-costly alternative (or reference pricing). What this article adds • Greater use of Coverage with Evidence Development is a mechanism to stimulate the generation of relevant CER for the Medicare

Quasi-random reflections on randomized controlled trials and comparative effectiveness research

Comparative effectiveness research (CER) is still an evolving framework for which much needs to be done to improve the ability of randomized controlled trials (RCTs) to supply the necessary evidence. Perhaps, most important is to start with a clearly specified decision and decision maker in mind when the RCTs are designed. Second is to initiate RCTs with clinically relevant outcomes and comparators earlier in the evaluation process. Third is to specify and measure factors that might modify the intervention's effect, subject to logistical constraints of complexity and cost, so the trial is maximally informative, about how and to whom the intervention should be administered. It will be necessary to borrow observational methodologies and approaches to extract meaningful causal and subgroup inferences from such trials. Process variables should be seen as potentially part of that framework of effect-modifying factors, perhaps amenable to embedded experimental assessment with a trial. Perhaps most importantly, we need to improve the nationwide CER infrastructure to allow for rapid initiation and accrual for CER trials to reduce the trade-off that often exists between the speed of evidence development and its quality.

Electronic Health Record Use to Classify Patients with Newly Diagnosed versus Preexisting Type 2 Diabetes: Infrastructure for Comparative Effectiveness Research and Population Health Management

Use of electronic health record (EHR) content for comparative effectiveness research (CER) and population health management requires significant data configuration. A retrospective cohort study was conducted using patients with diabetes followed longitudinally (N=36,353) in the EHR deployed at outpatient practice networks of 2 health care systems. A data extraction and classification algorithm targeting identification of patients with a new diagnosis of type 2 diabetes mellitus (T2DM) was applied, with the main criterion being a minimum 30-day window between the first visit documented in the EHR and the entry of T2DM on the EHR problem list. Chart reviews (N=144) validated the performance of refining this EHR classification algorithm with external administrative data. Extraction using EHR data alone designated 3205 patients as newly diagnosed with T2DM with classification accuracy of 70.1%. Use of external administrative data on that preselected population improved classification accuracy of cases identified as new T2DM diagnosis (positive predictive value was 91.9% with that step). Laboratory and medication data did not help case classification. The final cohort using this 2-stage classification process comprised 1972 patients with a new diagnosis of T2DM. Data use from current EHR systems for CER and disease management mandates substantial tailoring. Quality between EHR clinical data generated in daily care and that required for population health research varies. As evidenced by this process for classification of newly diagnosed T2DM cases, validation of EHR data with external sources can be a valuable step.

Comparative effectiveness research: Policy context, methods development and research infrastructure

Comparative effectiveness research (CER) has received substantial attention as a potential approach for improving health outcomes while lowering costs of care, and for improving the relevance and quality of clinical and health services research. The Institute of Medicine defines CER as 'the conduct and synthesis of systematic research comparing different interventions and strategies to prevent, diagnose, treat, and monitor health conditions. The purpose of this research is to inform patients, providers, and decision-makers, responding to their expressed needs, about which interventions are most effective for which patients under specific circumstances.' Improving the methods and infrastructure for CER will require sustained attention to the following issues: (1) Meaningful involvement of patients, consumers, clinicians, payers, and policymakers in key phases of CER study design and implementation; (2) Development of methodological 'best practices' for the design of CER studies that reflect decision-maker needs and balance internal validity with relevance, feasibility and timeliness; and (3) Improvements in research infrastructure to enhance the validity and efficiency with which CER studies are implemented. The approach to addressing each of these issues should be informed by the understanding that the primary purpose of CER is to help health care decision makers make informed clinical and health policy decisions.

One System For Electronic Health Records

Letters Health AffairsVol. 29, No. 6: Moving Forward On Health Reform One System For Electronic Health RecordsBarry Saver Affiliations University of Massachusetts Medical School Worcester, Massachusetts PUBLISHED:June 2010No Accesshttps://doi.org/10.1377/hlthaff.2010.0514AboutSectionsView articleView Full TextView PDFPermissions ShareShare onFacebookTwitterLinked InRedditEmail ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions View article"One System For Electronic Health Records." Health Affairs, 29(6), p. 1273TOPICSElectronic medical recordsPrivate sectorCosts and spending Loading Comments... Please enable JavaScript to view the comments powered by Disqus. DetailsExhibitsReferencesRelated Article MetricsCitations: Crossref 1 History Published online 1 June 2010 Information Project HOPE—The People-to-People Health Foundation, Inc. PDF downloadCited byBuilding the Informatics Infrastructure for Comparative Effectiveness Research (CER)Medical Care, Vol. 50

Comparative effectiveness research priorities: Identifying critical gaps in evidence for clinical and health policy decision making

In the debate on improving the quality and efficiency of the United States healthcare system, comparative effectiveness research is increasingly seen as a tool for reducing costs without compromising outcomes. Furthermore, the recent American Recovery and Reinvestment Act explicitly describes a prioritization function for establishing a comparative effectiveness research agenda. However, how such a function, in terms of methods and process, would go about identifying the most important priorities warranting further research has received little attention. This study describes an Agency for Healthcare Research and Quality-funded pilot project to translate one current comparative effectiveness review into a prioritized list of evidence gaps and research questions reflecting the views of the healthcare decision makers involved in the pilot. To create a prioritized research agenda, we developed an interactive nominal group process that relied on a multistakeholder workgroup scoring a list of research questions on the management of coronary artery disease. According to the group, the areas of greatest uncertainty regarding the management of coronary artery disease are the comparative effectiveness of medical therapy versus percutaneous coronary interventions versus coronary artery bypass grafting for different patient subgroups; the impact of diagnostic testing; and the most effective method of developing performance measures for providers. By applying our nominal group process, we were able to create a list of research priorities for healthcare decision makers. Future research should focus on refining this process because determining research priorities is essential to the success of developing an infrastructure for comparative effectiveness research.