Improving Data Quality In Primary Care
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Improving Data Quality In Primary Care
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Author : Justin St-Maurice
language : en
Publisher:
Release Date : 2017
Improving Data Quality In Primary Care written by Justin St-Maurice and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Command and control systems categories.
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.
Improving Data Quality
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Author : Regional Office for the Western Pacific World Health Organization
language : en
Publisher: WHOWPRO
Release Date : 2003-01-01
Improving Data Quality written by Regional Office for the Western Pacific World Health Organization and has been published by WHOWPRO this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-01 with Language Arts & Disciplines categories.
This publication provides a set of guidelines for health care workers, health information managers and administrators to help them focus on improving the timeliness, accuracy and reliability of health care data. They describe key activities and tasks to be considered when addressing the question of data quality in health care, regardless of the setting or size of organisations.
Handbook Of Ehealth Evaluation
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Author : Francis Yin Yee Lau
language : en
Publisher:
Release Date : 2016-11
Handbook Of Ehealth Evaluation written by Francis Yin Yee Lau and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11 with Medical care categories.
To order please visit https://onlineacademiccommunity.uvic.ca/press/books/ordering/
Executing Data Quality Projects
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Author : Danette McGilvray
language : en
Publisher: Academic Press
Release Date : 2021-05-27
Executing Data Quality Projects written by Danette McGilvray and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-27 with Computers categories.
Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization’s standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach Contains real examples from around the world, gleaned from the author’s consulting practice and from those who implemented based on her training courses and the earlier edition of the book Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online
Race Ethnicity And Language Data
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Author : Institute of Medicine
language : en
Publisher: National Academies Press
Release Date : 2009-12-30
Race Ethnicity And Language Data written by Institute of Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-12-30 with Medical categories.
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.
Healthcare Analytics For Quality And Performance Improvement
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Author : Trevor L. Strome
language : en
Publisher: John Wiley & Sons
Release Date : 2013-10-02
Healthcare Analytics For Quality And Performance Improvement written by Trevor L. Strome and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-02 with Business & Economics categories.
Improve patient outcomes, lower costs, reduce fraud—all with healthcare analytics Healthcare Analytics for Quality and Performance Improvement walks your healthcare organization from relying on generic reports and dashboards to developing powerful analytic applications that drive effective decision-making throughout your organization. Renowned healthcare analytics leader Trevor Strome reveals in this groundbreaking volume the true potential of analytics to harness the vast amounts of data being generated in order to improve the decision-making ability of healthcare managers and improvement teams. Examines how technology has impacted healthcare delivery Discusses the challenge facing healthcare organizations: to leverage advances in both clinical and information technology to improve quality and performance while containing costs Explores the tools and techniques to analyze and extract value from healthcare data Demonstrates how the clinical, business, and technology components of healthcare organizations (HCOs) must work together to leverage analytics Other industries are already taking advantage of big data. Healthcare Analytics for Quality and Performance Improvement helps the healthcare industry make the most of the precious data already at its fingertips for long-overdue quality and performance improvement.
Quality Improvement In Primary Care
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Author : Stephen Gillam
language : en
Publisher: CRC Press
Release Date : 2022-02-14
Quality Improvement In Primary Care written by Stephen Gillam and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Business & Economics categories.
This book provides readers with an invaluable set of tools to convert the endless challenges for quality and myriad opportunities for improvement into meaningful and useful change. It considers how to manage primary care organisations in order to improve quality of care; how general practices are regulated and held accountable; various techniques used for assessing and measuring; and commonly used quality improvement frameworks.
Improving Data Quality In English Healthcare
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Author : Howard James Leicester
language : en
Publisher:
Release Date : 2004
Improving Data Quality In English Healthcare written by Howard James Leicester and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Data Driven Quality Improvement And Sustainability In Health Care
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Author : Patricia L. Thomas, PhD, RN, FAAN, FNAP, FACHE, NEA-BC, ACNS-BC, CNL
language : en
Publisher: Springer Publishing Company
Release Date : 2020-11-19
Data Driven Quality Improvement And Sustainability In Health Care written by Patricia L. Thomas, PhD, RN, FAAN, FNAP, FACHE, NEA-BC, ACNS-BC, CNL and has been published by Springer Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-19 with Medical categories.
Data-Driven Quality Improvement and Sustainability in Health Care: An Interprofessional Approach provides nurse leaders and healthcare administrators of all disciplines with a solid understanding of data and how to leverage data to improve outcomes, fuel innovation, and achieve sustained results. It sets the stage by examining the current state of the healthcare landscape; new imperatives to meet policy, regulatory, and consumer demands; and the role of data in administrative and clinical decision-making. It helps the professional identify the methods and tools that support thoughtful and thorough data analysis and offers practical application of data-driven processes that determine performance in healthcare operations, value- and performance-based contracts, and risk contracts. Misuse or inconsistent use of data leads to ineffective and errant decision-making. This text highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls. In addition, chapters feature key points, reflection questions, and real-life interprofessional case exemplars to help the professional draw distinctions and apply principles to their own practice. Key Features: Provides nurse leaders and other healthcare administrators with an understanding of the role of data in the current healthcare landscape and how to leverage data to drive innovative and sustainable change Offers frameworks, methodology, and tools to support quality improvement measures Demonstrates the application of data and how it shapes quality and safety initiatives through real-life case exemplars Highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls
Registries For Evaluating Patient Outcomes
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Author : Agency for Healthcare Research and Quality/AHRQ
language : en
Publisher: Government Printing Office
Release Date : 2014-04-01
Registries For Evaluating Patient Outcomes written by Agency for Healthcare Research and Quality/AHRQ and has been published by Government Printing Office this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-01 with Medical categories.
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.