EGEMS (Washington, DC)最新文献

The dissemination of evidence-based best practice through the entire health care system remains an elusive goal, despite public pressure and regulatory guidance. Many patients do not receive the same quality of care at different hospitals across the same health care system. We describe the role of a data driven learning collaborative, the High Value Healthcare Collaborative (HVHC), in the dissemination of best practice using adherence to the 3-hour-bundle for sepsis care. Compliance with and adoption of sepsis bundle care elements comparing sites with mature vs non-mature care delivery processes were measured during the improvement effort for a cohort of 20,758 patients. Non-mature sites increased their bundle compliance from 71.0 to 86.7 percent (p < 0.005). This compliance increase was primarily based on increased compliance with the fluid element of the bundle that improved for non-mature locations from 76.4 to 94.0 percent (p < 0.005).

Context: The High Value Healthcare Collaborative (HVHC) sepsis project was a two-year multi-site project where Member health care delivery systems worked on improving sepsis care using a dissemination & implementation framework designed by HVHC. As part of the project evaluation, participating Members provided 5 data submissions over the project period. Members created data files using a uniform specification, but the data sources and methods used to create the data sets differed. Extensive data cleaning was necessary to get a data set usable for the evaluation analysis.

Case description: HVHC was the coordinating center for the project and received and cleaned all data submissions. Submissions received 3 sequentially more detailed levels of checking by HVHC. The most detailed level evaluated validity by comparing values within-Member over time and between Member. For a subset of episodes Member-submitted data were compared to matched Medicare claims data.

Findings: Inconsistencies in data submissions, particularly for length-of-stay variables were common in early submissions and decreased with subsequent submissions. Multiple resubmissions were sometimes required to get clean data. Data checking also uncovered a systematic difference in the way Medicare and some members defined intensive care unit stay.

Conclusions: Data checking is a critical for ensuring valid analytic results for projects using electronic health record data. It is important to budget sufficient resources for data checking. Interim data submissions and checks help find anomalies early. Data resubmissions should be checked as fixes can introduce new errors. Communicating with those responsible for creating the data set provides critical information.

The purpose of this special issue is to disseminate learning from the High Value Healthcare Collaborative (HVHC). The HVHC is a voluntary, member-led organization based on trusted, working relationships among delivery system leaders. HVHC's mission is to be a provider-based learning health system committed to improving healthcare value through data, evidence, and collaboration. We begin by describing the organization and structure of HVHC in order to lay the context for a series of papers that feature work from this learning health system. HVHC was awarded a grant from the John and Laura Arnold Foundation to develop a generalizable model for dissemination and implementation. Implementation of the 3-hour sepsis bundle was used as a prototypic, complex intervention with an in-depth mixed methods evaluation across 16 member sites. The first four articles in this issue describe, in detail, various data and methodological challenges encountered together with strategies for overcoming these (see Knowlton et al., von Recklinghausen et al., Welch et al., and Taenzer et al.). Next, we illustrate how the Data Trust can support emerging questions relevant to member organizations. The paper by Albritton et al., explores the impact of observation stays on readmission rates. Knighton et al., explore the use of an area-based measure for health literacy to assess risk in disadvantaged populations. Two final papers illustrate the importance of fundamental data sources needed to support advanced data science.

Background: In June 2014, the Office of the National Coordinator for Health Information Technology published a 10-year roadmap for the United States to achieve interoperability of electronic health records (EHR) by 2024. A key component of this strategy is the promotion of nationwide health information exchange (HIE). The 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act provided significant investments to achieve HIE.

Objective: We conducted a systematic literature review to describe the use of HIE through 2015.

Methods: We searched MEDLINE, PsycINFO, CINAHL, and Cochrane databases (1990 - 2015); reference lists; and tables of contents of journals not indexed in the databases searched. We extracted data describing study design, setting, geographic location, characteristics of HIE implementation, analysis, follow-up, and results. Study quality was dual-rated using pre-specified criteria and discrepancies resolved through consensus.

Results: We identified 58 studies describing either level of use or primary uses of HIE. These were a mix of surveys, retrospective database analyses, descriptions of audit logs, and focus groups. Settings ranged from community-wide to multinational. Results suggest that HIE use has risen substantially over time, with 82% of non-federal hospitals exchanging information (2015), 38% of physician practices (2013), and 17-23% of long-term care facilities (2013). Statewide efforts, originally funded by HITECH, varied widely, with a small number of states providing the bulk of the data. Characteristics of greater use include the presence of an EHR, larger practice size, and larger market share of the health-system.

Conclusions: Use of HIE in the United States is growing but is still limited. Opportunities remain for expansion. Characteristics of successful implementations may provide a path forward.

The last in a series of four papers on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review describes how delivery system science provides a systematic means to answer questions that arise in translating complex interventions to other practice settings. When the focus is on translation and spread of innovations, the questions are different than in evaluative research. Causal inference is not the main issue, but rather one must ask: How and why does the intervention work? What works for whom and in what contexts? How can a model be amended to work in new settings? In these settings, organizational factors and design, infrastructure, policies, and payment mechanisms all influence an intervention's success, so a theory-driven formative evaluation approach that considers the full path of the intervention from activities to engage participants and change how they act to the expected changes in clinical processes and outcomes is needed. This requires a scientific approach to quality improvement that is characterized by a basis in theory; iterative testing; clear, measurable process and outcomes goals; appropriate analytic methods; and documented results. To better answer the questions that arise in delivery system science, this paper introduces a number of standard qualitative research approaches that can be applied in a learning health system: Pawson and Tilley's "realist evaluation," theory-based evaluation approaches, mixed-methods and case study research approaches, and the "positive deviance" approach.

Introduction: The New York City (NYC) Macroscope is an electronic health record (EHR) surveillance system based on a distributed network of primary care records from the Hub Population Health System. In a previous 3-part series published in eGEMS, we reported the validity of health indicators from the NYC Macroscope; however, questions remained regarding their generalizability to other EHR surveillance systems.

Methods: We abstracted primary care chart data from more than 20 EHR software systems for 142 participants of the 2013-14 NYC Health and Nutrition Examination Survey who did not contribute data to the NYC Macroscope. We then computed the sensitivity and specificity for indicators, comparing data abstracted from EHRs with survey data.

Results: Obesity and diabetes indicators had moderate to high sensitivity (0.81-0.96) and high specificity (0.94-0.98). Smoking status and hypertension indicators had moderate sensitivity (0.78-0.90) and moderate to high specificity (0.88-0.98); sensitivity improved when the sample was restricted to records from providers who attested to Stage 1 Meaningful Use. Hyperlipidemia indicators had moderate sensitivity (≥0.72) and low specificity (≤0.59), with minimal changes when restricting to Stage 1 Meaningful Use.

Discussion: Indicators for obesity and diabetes used in the NYC Macroscope can be adapted to other EHR surveillance systems with minimal validation. However, additional validation of smoking status and hypertension indicators is recommended and further development of hyperlipidemia indicators is needed.

Conclusion: Our findings suggest that many of the EHR-based surveillance indicators developed and validated for the NYC Macroscope are generalizable for use in other EHR surveillance systems.

Learning health systems use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning. Even without randomization, observational studies can play a central role as the nation's health care system embraces comparative effectiveness research and patient-centered outcomes research. However, neither the breadth, timeliness, volume of the available information, nor sophisticated analytics, allow analysts to confidently infer causal relationships from observational data. However, depending on the research question, careful study design and appropriate analytical methods can improve the utility of EHD. The introduction to a series of four papers, this review begins with a discussion of the kind of research questions that EHD can help address, noting how different evidence and assumptions are needed for each. We argue that when the question involves describing the current (and likely future) state of affairs, causal inference is not relevant, so randomized clinical trials (RCTs) are not necessary. When the question is whether an intervention improves outcomes of interest, causal inference is critical, but appropriately designed and analyzed observational studies can yield valid results that better balance internal and external validity than typical RCTs. When the question is one of translation and spread of innovations, a different set of questions comes into play: How and why does the intervention work? How can a model be amended or adapted to work in new settings? In these "delivery system science" settings, causal inference is not the main issue, so a range of quantitative, qualitative, and mixed research designs are needed. We then describe why RCTs are regarded as the gold standard for assessing cause and effect, how alternative approaches relying on observational data can be used to the same end, and how observational studies of EHD can be effective complements to RCTs. We also describe how RCTs can be a model for designing rigorous observational studies, building an evidence base through iterative studies that build upon each other (i.e., confirmation across multiple investigations).

Introduction: The use of information from clinical registries for improvement and value-based payment is increasing, yet information about registry use is not widely available. We conducted a landscape survey to understand registry uses, focus areas and challenges. The survey addressed the structure and organization of registry programs, as well as their purpose and scope.

Setting: The survey was conducted by the National Quality Registry Network (NQRN), a community of organizations interested in registries. NQRN is a program of the PCPI, a national convener of medical specialty and professional societies and associations, which constitute a majority of registry stewards in the United States.

Methods: We surveyed 152 societies and associations, asking about registry programs, governance, number of registries, purpose and data uses, data collection, expenses, funding and interoperability.

Results: The response rate was 52 percent. Many registries were self-funded, with 39 percent spending less than $1 million per year, and 32 percent spending $1-9.9 million. The typical registry had three full-time equivalent staff. Registries were frequently used for quality improvement, benchmarking and clinical decision support. 85 percent captured outpatient data. Most registries collected demographics, treatments, practitioner information and comorbidities; 53 percent captured patient-reported outcomes. 88 percent used manual data entry and 18 percent linked to external secondary data sources. Cost, interoperability and vendor management were barriers to continued registry development.

Conclusions: Registries captured data across a broad scope, audited data quality using multiple techniques, and used a mix of automated and manual data capture methods. Registry interoperability was still a challenge, even among registries using nationally accepted data standards.

The second paper in a series on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review summarizes study design approaches, including choosing appropriate data sources, and methods for design and analysis of natural and quasi-experiments. The primary strength of study design approaches described in this section is that they study the impact of a deliberate intervention in real-world settings, which is critical for external validity. These evaluation designs address estimating the counterfactual - what would have happened if the intervention had not been implemented. At the individual level, epidemiologic designs focus on identifying situations in which bias is minimized. Natural and quasi-experiments focus on situations where the change in assignment breaks the usual links that could lead to confounding, reverse causation, and so forth. And because these observational studies typically use data gathered for patient management or administrative purposes, the possibility of observation bias is minimized. The disadvantages are that one cannot necessarily attribute the effect to the intervention (as opposed to other things that might have changed), and the results do not indicate what about the intervention made a difference. Because they cannot rely on randomization to establish causality, program evaluation methods demand a more careful consideration of the "theory" of the intervention and how it is expected to play out. A logic model describing this theory can help to design appropriate comparisons, account for all influential variables in a model, and help to ensure that evaluation studies focus on the critical intermediate and long-term outcomes as well as possible confounders.

The third paper in a series on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review describes how analytical methods for individual-level electronic health data EHD, including regression approaches, interrupted time series (ITS) analyses, instrumental variables, and propensity score methods, can also be used to address the question of whether the intervention "works." The two major potential sources of bias in non-experimental studies of health care interventions are that the treatment groups compared do not have the same probability of treatment or exposure and the potential for confounding by unmeasured covariates. Although very different, the approaches presented in this chapter are all based on assumptions about data, causal relationships, and biases. For instance, regression approaches assume that the relationship between the treatment, outcome, and other variables is properly specified, all of the variables are available for analysis (i.e., no unobserved confounders) and measured without error, and that the error term is independent and identically distributed. The instrumental variables approach requires identifying an instrument that is related to the assignment of treatment but otherwise has no direct on the outcome. Propensity score methods approaches, on the other hand, assume that there are no unobserved confounders. The epidemiological designs discussed also make assumptions, for instance that individuals can serve as their own control. To properly address these assumptions, analysts should conduct sensitivity analyses within the assumptions of each method to assess the potential impact of what cannot be observed. Researchers also should analyze the same data with different analytical approaches that make alternative assumptions, and to apply the same methods to different data sets. Finally, different analytical methods, each subject to different biases, should be used in combination and together with different designs, to limit the potential for bias in the final results.