How Registries Can Help Performance Measurement Improve Care

Suggests ways to better utilize databases of clinical information to evaluate care processes and outcomes and improve measurements of healthcare quality and costs, comparative clinical effectiveness research, and medical product safety surveillance

The need for a definition of big data for nursing science: A case study of disaster preparedness

© 2016 by the author; licensee MDPI, Basel, Switzerland. The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term âBig Dataâ, which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing.Link_to_subscribed_fulltex

Electronic health records (EHRs) in clinical research and platform trials: Application of the innovative EHR-based methods developed by EU-PEARL

Electronic health records; Platform trialsRegistros médicos electrónicos; Pruebas de plataformaRegistres mèdics electrònics; Proves de plataformaObjective Electronic Health Record (EHR) systems are digital platforms in clinical practice used to collect patients’ clinical information related to their health status and represents a useful storage of real-world data. EHRs have a potential role in research studies, in particular, in platform trials. Platform trials are innovative trial designs including multiple trial arms (conducted simultaneously and/or sequentially) on different treatments under a single master protocol. However, the use of EHRs in research comes with important challenges such as incompleteness of records and the need to translate trial eligibility criteria into interoperable queries. In this paper, we aim to review and to describe our proposed innovative methods to tackle some of the most important challenges identified. This work is part of the Innovative Medicines Initiative (IMI) EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project’s work package 3 (WP3), whose objective is to deliver tools and guidance for EHR-based protocol feasibility assessment, clinical site selection, and patient pre-screening in platform trials, investing in the building of a data-driven clinical network framework that can execute these complex innovative designs for which feasibility assessments are critically important. Methods ISO standards and relevant references informed a readiness survey, producing 354 criteria with corresponding questions selected and harmonised through a 7-round scoring process (0–1) in stakeholder meetings, with 85% of consensus being the threshold of acceptance for a criterium/question. ATLAS cohort definition and Cohort Diagnostics were mainly used to create the trial feasibility eligibility (I/E) criteria as executable interoperable queries. Results The WP3/EU-PEARL group developed a readiness survey (eSurvey) for an efficient selection of clinical sites with suitable EHRs, consisting of yes-or-no questions, and a set-up of interoperable proxy queries using physicians’ defined trial criteria. Both actions facilitate recruiting trial participants and alignment between study costs/timelines and data-driven recruitment potential. Conclusion The eSurvey will help create an archive of clinical sites with mature EHR systems suitable to participate in clinical trials/platform trials, and the interoperable proxy queries of trial eligibility criteria will help identify the number of potential participants. Ultimately, these tools will contribute to the production of EHR-based protocol design.“EU-PEARL has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 853966-2. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and CHILDREN'S TUMOR FOUNDATION, GLOBAL ALLIANCE FOR TB DRUG DEVELOPMENT NON PROFIT ORGANISATION, SPRINGWORKS THERAPEUTICS INC.

Ready or Not? Protecting the Public's Health From Diseases, Disasters, and Bioterrorism, 2009

Based on ten indicators, assesses progress in the readiness of states, federal government, and hospitals to respond to public health emergencies, with a focus on the H1N1 flu. Outlines improvements and concerns in funding, accountability, and other areas

J Registry Manag

BackgroundIn 2005, a pilot project was started at the Centers for Disease Control and Prevention (CDC) to expand an existing birth defects surveillance program, the Metropolitan Atlanta Congenital Defects Program (MACDP), to conduct active surveillance of stillbirth. This pilot project was evaluated using CDC\u2019s current guidelines for evaluating surveillance systems.MethodsWe conducted stakeholder interviews with the staff of MACDP\u2019s stillbirth surveillance system. We reviewed the published literature on stillbirth ascertainment including 4 previous publications about the MACDP stillbirth surveillance system. Using fetal death certificates (FDC) as a second, independent data source, we estimated the total number and prevalence of stillbirths in metropolitan Atlanta using capture-recapture methods, and calculated the sensitivity of the MACDP stillbirth surveillance system.ResultsThe MACDP stillbirth surveillance system is useful, flexible, acceptable, and stable. The system\u2019s data quality is improved because it uses multiple sources for case ascertainment. Based on 2006 data, estimated sensitivities of FDCs, MACDP, and both sources combined for identifying a stillbirth were 78.5%, 76.8%, and 95.0%, respectively. The prevalence of stillbirths per 1,000 live births and stillbirths was 8.2 (95% confidence interval [CI]: 7.5-9.0) based on FDC data alone and 9.9 (95% CI: 9.1-10.8) when combined with MACDP data.ConclusionUse of MACDP as an additional data source for stillbirth surveillance resulted in higher levels of case ascertainment, better data quality, and a higher estimate of stillbirth prevalence than using FDC data alone. MACDP could be considered as a model to enhance stillbirth surveillance by other active birth defects surveillance programs.CC999999/Intramural CDC HHS/United States2015-08-11T00:00:00Z23270086PMC4532308vault:787

A Learning Health Sciences Approach to Understanding Clinical Documentation in Pediatric Rehabilitation Settings

The work presented in this dissertation provides an analysis of clinical documentation that challenges the concepts and thinking surrounding missingness of data from clinical settings and the factors that influence why data are missing. It also foregrounds the critical role of clinical documentation as infrastructure for creating learning health systems (LHS) for pediatric rehabilitation settings. Although completeness of discrete data is limited, the results presented do not reflect the quality of care or the extent of unstructured data that providers document in other locations of the electronic health record (EHR) interface. While some may view imputation and natural language processing as means to address missingness of clinical data, these practices carry biases in their interpretations and issues of validity in results. The factors that influence missingness of discrete clinical data are rooted not just in technical structures, but larger professional, system level and unobservable phenomena that shape provider practices of clinical documentation. This work has implications for how we view clinical documentation as critical infrastructure for LHS, future studies of data quality and health outcomes research, and EHR design and implementation. The overall research questions for this dissertation are: 1) To what extent can data networks be leveraged to build classifiers of patient functional performance and physical disability? 2) How can discrete clinical data on gross motor function be used to draw conclusions about clinical documentation practices in the EHR for cerebral palsy? 3) Why does missingness of discrete data in the EHR occur? To address these questions, a three-pronged approach is used to examine data completeness and the factors that influence missingness of discrete clinical data in an exemplar pediatric data learning network will be used. As a use-case, evaluation of EHR data completeness of gross motor function related data, populated by providers from 2015-2019 for children with cerebral palsy (CP), will be completed. Mixed methods research strategies will be used to achieve the dissertation objectives, including developing an expert-informed and standards-based phenotype model of gross motor function data as a task-based mechanism, conducting quantitative descriptive analyses of completeness of discrete data in the EHR, and performing qualitative thematic analyses to elicit and interpret the latent concepts that contribute to missingness of discrete data in the EHR. The clinical data for this dissertation are sourced from the Shriners Hospitals for Children (SHC) Health Outcomes Network (SHOnet), while qualitative data were collected through interviews and field observations of clinical providers across three care sites in the SHC system.PHDHlth Infrastr & Lrng Systs PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162994/1/njkoscie_1.pd

The Use of the Agency Healthcare Research and Quality Patient Safety Indicator 11 Toolkit to Decrease Postoperative Respiratory Failure

Postoperative respiratory failure incidents can lead to adverse outcomes, including prolonged hospitalizations, increased admissions to intensive care units, and the risk of complications such as ventilator-associated pneumonia, sepsis, and mortality. This project aimed to assess the effectiveness of implementing the Agency for Healthcare Research and Quality Patient Safety Indicator 11 toolkit intervention for noninvasive positive-pressure ventilation in reducing postoperative respiratory failure rates compared to traditional practices. Adopting evidence- based toolkits, such as those provided by the Agency for Healthcare Research and Quality, aids healthcare organizations in enhancing the quality of patient care. The quality improvement project employed a quasi-experimental design, comparing two groups: one receiving the toolkit intervention and another adhering to traditional practices. Postoperative respiratory failure incidences in the year prior within the same timeframe were compared to the outcomes of the quality improvement project. These positive outcomes underscore the importance of implementing the Agency for Healthcare Research and Quality Patient Safety Indicator 11 toolkit intervention as a quality improvement initiative in healthcare organizations. This intervention has the potential to substantially reduce postoperative respiratory failure rates and associated complications

A study assessing the characteristics of big data environments that predict high research impact: application of qualitative and quantitative methods

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible

Advisory Committee on Immunization Practices (ACIP) summary report : September 22, 2020, Atlanta, Georgia

Publication date from document properties.min-2020-09.pdf2020https://www.cdc.gov/vaccines/acip/meetings/downloads/min-archive/min-2020-09.pdf878