Content and Data Quality of Primary Care Routine Data Collection Projects: a Systematic Review of the Literature, Anglais PDF Download

Author: Camille Hagenbourger
Publisher:
ISBN:
Category :
Languages : en
Pages : 37

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CONTEXTE Les données issues des dossiers médicaux de soins primaires (DMSP) présentent une synthèse de l'histoire médicale du patient et une vue globale de la santé de la population. Leur extraction au sein de projets de recueil de données internationaux et leur analyse a permis de nombreuses avancées dans plusieurs domaines comme la pharmacovigilance, l'épidémiologie. OBJECTIFS Comparer le fond, la forme et la qualité des données recueillies au sein des projets de recueil et d'analyse de données de soins primaires internationaux. Proposer un système de recueil et d'analyse interopérable avec les projets de recueil de données existants. MÉTHODE Une revue systématique de la littérature à partir de PubMed et Google Scholar en suivant les critères PRISMA et AMSTAR. Les sites internet des projets ont également été inclus dans l'analyse et leur référent internet a été contacté par mail ou visioconférence. Un formulaire standardisé préétabli a été rempli à partir de toutes ces informations en suivant trois axes : contenu des bases de données, format des données recueillies, qualité des données. RÉSULTATS 39 articles ont été sélectionnés grâce aux équations de recherche, et 5 articles supplémentaires ont été repérés sur les sites internet des projets retenus. 6 projets de recueil de données de soins primaires ont été étudiés : CPRD, CPCSSN, Intego, SIDIAP, NIVEL, Fire Project. Un tableau comparatif des contenus des données extraites a permis de distinguer les catégories communes aux bases de données, notamment les données administratives, les données de consultation, la thérapeutique, les données importées. L'architecture des bases de données a également été répertoriée. Les données extraites sont principalement codées en utilisant des codes internationaux (CISP, ATC) ou structurées (IMC). La qualité des données est évaluée en interne ou en externe avec des critères variables selon les pays. L'implication des médecins sert souvent de levier pour améliorer la qualité des données. DISCUSSION Les projets de recueil et d'analyse de DMSP se basent sur une extraction partielle des informations des dossiers, essentiellement codée. A l'ère du big data, de nouvelles perspectives du traitement de l'information s'ouvrent notamment grâce au machine learning. Ces technologies peuvent-elles venir enrichir la recherche sur les dossiers médicaux informatisés en soins primaires ?

Author: Justin St-Maurice
Publisher:
ISBN:
Category : Command and control systems
Languages : en
Pages : 249

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Book Description
In an era where governments around the world invest heavily in data collection and data management, poor-quality data is expensive and has many direct and indirect costs. While there are different types of data quality challenges, some of the more complex data quality problems depend on the design and production processes involved in generating data. Therefore, it is important to design systems that support better data quality. This involves understanding what quality means in a specific context, understanding how it can be measured, and identifying ways to encourage better data quality behaviours. Healthcare is not immune to the challenges of data quality and can be classified as a complex socio-technical system by virtue of its characteristics. As such, the study of healthcare data quality and its improvement is well suited for the domain of systems design and human factors engineering. Cognitive Work Analysis (CWA) is especially well suited for this task, as it can be used to better understand the context and workflow of users in complex socio-technical domains. It is a conceptual framework that facilitates the analysis of factors that shape human-information interaction and has been used in healthcare for over 20 years. The approach is work-centred, rather than user-centred, and it analyses the constraints and goals that shape information behaviour in the work environment. I used CWA as a framework to help me analyse the problem of data quality in healthcare. My research uses an instrumental case study approach to understand data quality in primary care. My goal was to answer three questions: In primary care, how are individual users influenced by their environment to input high-quality data? What techniques could be used to design systems that persuade users to enter higher-quality data? Is it possible to improve data quality in primary care by persuading users with the user interface of information systems in these complex socio-technical systems? The scope of work included modelling data quality, defining and measuring data quality in a primary care system, establishing design concepts that could improve data quality through persuasion, and testing the viability of some design concepts. I began analysing this problem by creating an abstraction hierarchy of patient treatment with medical records. This model can be used to represent patient treatment from a primary care perspective. The model helped explain the patient treatment ecosystem and how data is generated through patient encounters. After creating my model to represent patient treatment, I incorporated it into two CWAs of data quality and data codification. The first model represented codification in the primary care ecosystem, whereas the second model represented codification in community hospitals. After developing abstraction hierarchies for both domains, I analysed similar tasks from each system with control task analysis, strategies analysis, and worker competencies analysis. The tasks that I analysed related specifically to data codification: in primary care, I modelled the record encounter task performed by clinicians at a Family Health Team (FHT), and in the community hospital, I modelled the abstract task performed by health information management professionals. I used the same record encounter task at the FHT as a continuing focus of my case study. I used both models of codification to perform a comparison. My goal was to identify the differences between the ecosystems and tasks that were present in primary care and the community hospital. Comparing CWA models is not a well-defined process in the literature, and I developed an approach to conduct this comparison based on seminal works. I used the approach to systematically compare each phase of my CWA models. I found that the analysis of both system domains in parallel enabled a richer understanding of each environment that may not have been achieved independently. In addition, I discovered that a rich environment exists around data codification processes, and this context influences and distinguishes the actions of users. While the tasks in both domains were seemingly similar, they took place with different priorities and required different competencies. After building and comparing models, I investigated the summarizing task in primary care more closely by analysing data within a FHT's reporting database. The goal of this study was to understand data quality tradeoffs between timeliness, validity, completeness, and use in primary care users. Data quality measures and metrics were developed through interviews with a focus group of managers. After analysing data quality measures for 196,967 patient encounters, I created baselines, modelled each measure with logit binomial regression to show correlations, characterized tradeoffs, and investigated data quality interactions. Based on the analysis, I found a positive relationship between validity and completeness, and a negative relationship between timeliness and use. Use of data and reductions in entry delay were positively associated with completeness and validity. These results suggested that if users are not provided with sufficient time to record data as part of their regular workflow, they will prioritize their time to spend more time with patients. As a measurement of the effectiveness of a system, the negative correlation between use and timeliness points to a self-reinforcing data repository that provides users with little external value. These findings were consistent with the modelling work and also provided useful insight to study data quality improvements within the system. I used my measures from the data analysis to select design priorities and behaviour changes that should, according to my ongoing case study, improve data quality. Then I developed several design concepts by combining CWA, a framework for behaviour change, and a design framework for persuasive systems. The design concepts adopted different persuasion principles to change specific behaviours. To test the validity of my design concepts, I worked with a FHT to implement some of my proposed interventions during a field study. This involved the introduction of a non-invasive summary screen into the user workflow. After the summary screen had been deployed for eight weeks, I received secondary data from the FHT to analyse. First, I performed a pre-post measurement of several data quality measures by doing a simple paired t-test. To further understand the results, I borrowed from healthcare quality improvement methodologies and used statistical process control charts to understand the overall context of the measures. The average delay per entry was reduced by 3.35 days, and the percentage of same-day entries increased by 10.3%. The number of records that were complete dropped by 4.8%. Changes to entry accuracy and report generation were not significant. Several additional insights could be extracted by looking at each the XmR chart for each variable and discussing the trends with the FHT. Feedback was also collected from users through an online survey. Through the use of a case study spanning several years, I was able to reach the following conclusions: data codification and data quality are manufactured within complex socio-technical systems and users are heavily influenced by a variety of factors within their ecosystem; persuasive design, informed with data from a CWA, is an effective technique for creating ecologically relevant persuasive designs; and data quality in primary care can be improved through the use of these designs in the system's user interface. There are interesting opportunities to apply the results of my work to other jurisdictions. A strength of this work lies in its usefulness for international readers to draw comparisons between different systems and health care environments throughout the world.

Author: OECD
Publisher: OECD Publishing
ISBN: 9264805907
Category :
Languages : en
Pages : 447

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Book Description
This volume, developed by the Observatory together with OECD, provides an overall conceptual framework for understanding and applying strategies aimed at improving quality of care. Crucially, it summarizes available evidence on different quality strategies and provides recommendations for their implementation. This book is intended to help policy-makers to understand concepts of quality and to support them to evaluate single strategies and combinations of strategies.

Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309140129
Category : Medical
Languages : en
Pages : 286

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Book Description
The goal of eliminating disparities in health care in the United States remains elusive. Even as quality improves on specific measures, disparities often persist. Addressing these disparities must begin with the fundamental step of bringing the nature of the disparities and the groups at risk for those disparities to light by collecting health care quality information stratified by race, ethnicity and language data. Then attention can be focused on where interventions might be best applied, and on planning and evaluating those efforts to inform the development of policy and the application of resources. A lack of standardization of categories for race, ethnicity, and language data has been suggested as one obstacle to achieving more widespread collection and utilization of these data. Race, Ethnicity, and Language Data identifies current models for collecting and coding race, ethnicity, and language data; reviews challenges involved in obtaining these data, and makes recommendations for a nationally standardized approach for use in health care quality improvement.

Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309120608
Category : Medical
Languages : en
Pages : 338

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Book Description
Successful development of clinical data as an engine for knowledge generation has the potential to transform health and health care in America. As part of its Learning Health System Series, the Roundtable on Value & Science-Driven Health Care hosted a workshop to discuss expanding the access to and use of clinical data as a foundation for care improvement.

Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309164257
Category : Medical
Languages : en
Pages : 267

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Book Description
Healthcare decision makers in search of reliable information that compares health interventions increasingly turn to systematic reviews for the best summary of the evidence. Systematic reviews identify, select, assess, and synthesize the findings of similar but separate studies, and can help clarify what is known and not known about the potential benefits and harms of drugs, devices, and other healthcare services. Systematic reviews can be helpful for clinicians who want to integrate research findings into their daily practices, for patients to make well-informed choices about their own care, for professional medical societies and other organizations that develop clinical practice guidelines. Too often systematic reviews are of uncertain or poor quality. There are no universally accepted standards for developing systematic reviews leading to variability in how conflicts of interest and biases are handled, how evidence is appraised, and the overall scientific rigor of the process. In Finding What Works in Health Care the Institute of Medicine (IOM) recommends 21 standards for developing high-quality systematic reviews of comparative effectiveness research. The standards address the entire systematic review process from the initial steps of formulating the topic and building the review team to producing a detailed final report that synthesizes what the evidence shows and where knowledge gaps remain. Finding What Works in Health Care also proposes a framework for improving the quality of the science underpinning systematic reviews. This book will serve as a vital resource for both sponsors and producers of systematic reviews of comparative effectiveness research.

Author: Heidi D. Nelson
Publisher: Lippincott Williams & Wilkins
ISBN: 1469885468
Category : Medical
Languages : en
Pages : 448

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Book Description
Systematic Evidence Reviews to Answer Health Care Questions is your most effective, A-to-Z guide to conducting thorough, comprehensive systematic reviews. By breaking down topics and essential steps, this volume teaches you how to form key questions, select evidence, and perform illuminating review not just in predictable circumstances, but when basic rules don’t apply—honing your ability to think critically and solve problems. You’ll learn how to define a review’s purpose and scope, develop research questions, build a team, and even manage your project to maximize efficacy. If you’re looking to refine your approach to systematic reviews, don’t just catalog and collect; use this powerful text to evaluate, synthesize, and deliver results that will help shape the health care industry. FEATURES Presented in standard format throughout to allow for more practical, easy to read approach Provides useful instruction on how to conduct a high-quality systematic review that meets the recent standards of the Institute of Medicine Accessible, concise information about the state-of-the-art methods of systematic review, from key question formulation to assessing the quality of included studies and reporting results Illustrated throughout with real-world examples from systematic reviews that have been used to inform practice guidelines and health policy

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 88

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The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.

Author: Moira Stewart
Publisher: SAGE Publications, Incorporated
ISBN:
Category : Medical
Languages : en
Pages : 312

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Book Description
The introductory chapters describe three decades of work by a family physician who recognized the importance of observing and questioning, and thoughtfully deliberated the challenges facing primary care researchers. Specific sections then go on to examine basic concepts such as identifying research questions and selecting an instrument, techniques such as choosing a sample and creating an original measure, as well as tools for measurement, data collection, and analysis.

Author: Jodi Nygaard
Publisher:
ISBN: 9781321807042
Category :
Languages : en
Pages :

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Book Description
The Agency for Healthcare Research and Quality (AHRQ) provided a three year grant to Academy Health in 2010 for an Electronic Data Methods (EDM) forum to gather the techniques used and lessons learned from leveraging clinical datasets for research in a number of ongoing projects. This included focusing on data quality management activities. As of July 2014, the results of the grant project have not been published, but this investment by AHRQ illustrates that there is a need to find the best methods to assess the quality of clinical datasets before leveraging them for clinical research studies. After conducting a literature review of best practices in Information Technology (IT) for assessing the quality of data, this thesis asserts that a method known as data profiling should be used to provide information about a data set to its consumers. The objective of this thesis project was to vet data profiling as a best practice for assessing the quality of a data set and to show that the output of the methods found fulfill the needs of principle investigators using retrospective clinical datasets for their research projects. A literature review was conducted to determine the best practices of data profiling and methods used to uncover the truth about the structure, content and quality of a data set. The methods and best practices were applied to a retrospective clinical data set and the principle investigator was interviewed to determine the value to his project. The profiling tool produced many statistics about the content of the database. Summary statistics and frequencies all uncovered the texture of the data. For example, the ages of patients in the database range from 28 to 94 and there are no Native Americans included in the dataset. 21.72% of the records in the database had some type of data quality issue. Out of 19,404 total values (attributes multiplied by records), only 0.5% were considered to be low quality. Five types of data quality rules uncovered these issues. The project took 35.5 hours for the business analyst to complete the entire assessment from beginning to end. Automated data profiling is an efficient and effective method of assessing the quality of retrospective clinical datasets. It should be implemented and formally documented to provide users of data with knowledge about the state of the data before it is leveraged to answer research questions. This can save time spent getting approvals via Institutional Review Boards (IRB) to use data that might not work for a specific project, or give a data consumer enough information to select the best source of data for their use. Data quality issues that are found can sometimes be remediated. In the case where they cannot, the limitation is at least understood by the researcher and the records can be thrown out if they compromise the study.