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Author: Kevin Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 65
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Book Description
It is widely known that the minority of patients make up the majority of healthcare costs. Research being done aims at identifying these patients through predictive modeling. In the hopes that providing targeted resources to these patients can prevent inurnment of the high-cost. Lowing the bottom line to the hospital and helping the patient. Yet what degree of utility do these models provide? Most models are applied in a less than realistic setting or fail to state which predicted patients can even be impacted. In this study, I went through patient's clinical notes to better understand how practical such predictive models are. First, I sought after literature to better understand what variables most predictive models use as a base. I compare these to what was available in the patient's profile. Then revise what necessary for me to predict high cost given the patient's clinical notes. With access to UWMC/Harborview and NW Hospital databases, I went through clinical notes to evaluate each patient's possible predictability. These determinations were later verified by a physician for accuracy. This was further reflected on Northwest(NW) Hospital data, which is a relatively smaller hospital with a focus on inpatient/outpatient patients. Each patient was categorized on the nature of their high expenditure. This work's importance is in how to consider predictive models moving forward. Assuming modeling will always have the solution to predict high-cost patients is misguided. Instead, understanding the underlying dynamic of the patient's cause is a better target. The conclusions made in this study can help better guide models to be more cognizant in how they approach predicting high-cost patients.
Author: Kevin Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 65
Get Book Here
Book Description
It is widely known that the minority of patients make up the majority of healthcare costs. Research being done aims at identifying these patients through predictive modeling. In the hopes that providing targeted resources to these patients can prevent inurnment of the high-cost. Lowing the bottom line to the hospital and helping the patient. Yet what degree of utility do these models provide? Most models are applied in a less than realistic setting or fail to state which predicted patients can even be impacted. In this study, I went through patient's clinical notes to better understand how practical such predictive models are. First, I sought after literature to better understand what variables most predictive models use as a base. I compare these to what was available in the patient's profile. Then revise what necessary for me to predict high cost given the patient's clinical notes. With access to UWMC/Harborview and NW Hospital databases, I went through clinical notes to evaluate each patient's possible predictability. These determinations were later verified by a physician for accuracy. This was further reflected on Northwest(NW) Hospital data, which is a relatively smaller hospital with a focus on inpatient/outpatient patients. Each patient was categorized on the nature of their high expenditure. This work's importance is in how to consider predictive models moving forward. Assuming modeling will always have the solution to predict high-cost patients is misguided. Instead, understanding the underlying dynamic of the patient's cause is a better target. The conclusions made in this study can help better guide models to be more cognizant in how they approach predicting high-cost patients.
Author: Agency for Healthcare Research and Quality/AHRQ
Publisher: Government Printing Office
ISBN: 1587634333
Category : Medical
Languages : en
Pages : 385
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Book Description
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.
Author: Jonathan Cylus
Publisher: Health Policy
ISBN: 9789289050418
Category : Medical
Languages : en
Pages : 264
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Book Description
In this book the authors explore the state of the art on efficiency measurement in health systems and international experts offer insights into the pitfalls and potential associated with various measurement techniques. The authors show that: - The core idea of efficiency is easy to understand in principle - maximizing valued outputs relative to inputs, but is often difficult to make operational in real-life situations - There have been numerous advances in data collection and availability, as well as innovative methodological approaches that give valuable insights into how efficiently health care is delivered - Our simple analytical framework can facilitate the development and interpretation of efficiency indicators.
Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309124999
Category : Computers
Languages : en
Pages : 334
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Book Description
In the realm of health care, privacy protections are needed to preserve patients' dignity and prevent possible harms. Ten years ago, to address these concerns as well as set guidelines for ethical health research, Congress called for a set of federal standards now known as the HIPAA Privacy Rule. In its 2009 report, Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research, the Institute of Medicine's Committee on Health Research and the Privacy of Health Information concludes that the HIPAA Privacy Rule does not protect privacy as well as it should, and that it impedes important health research.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309377722
Category : Medical
Languages : en
Pages : 473
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Book Description
Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.
Author: John W. Nelson
Publisher: John Wiley & Sons
ISBN: 1119747759
Category : Mathematics
Languages : en
Pages : 188
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Book Description
Using Predictive Analytics to Improve Healthcare Outcomes Winner of the American Journal of Nursing (AJN) Informatics Book of the Year Award 2021! Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement. Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a “how-to” manual, showing how to use existing theory and tools to achieve desired positive outcomes. You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes: A thorough introduction to data, caring theory, Relationship-Based Care®, the Caring Behaviors Assurance System©, and healthcare operations, including how to build a measurement model and improve organizational outcomes. An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes. Discussions of how to refine quality improvement initiatives, including the use of “comfort” as a construct to illustrate the importance of solid theory and good measurement in adequate pain management. An examination of international organizations using analytics to improve operations within cultural context. Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.
Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309316324
Category : Medical
Languages : en
Pages : 236
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Book Description
Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.
Author: Gary D. Miner
Publisher: Academic Press
ISBN: 012411640X
Category : Computers
Languages : en
Pages : 1111
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Book Description
With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner.Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. - Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research - Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations - Demonstrates methods to help sort through data to make better observations and allow you to make better predictions
Author: Pieter Kubben
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 219
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Book Description
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 030946921X
Category : Medical
Languages : en
Pages : 161
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Book Description
The Social Security Administration (SSA) administers two programs that provide benefits based on disability: the Social Security Disability Insurance (SSDI) program and the Supplemental Security Income (SSI) program. This report analyzes health care utilizations as they relate to impairment severity and SSA's definition of disability. Health Care Utilization as a Proxy in Disability Determination identifies types of utilizations that might be good proxies for "listing-level" severity; that is, what represents an impairment, or combination of impairments, that are severe enough to prevent a person from doing any gainful activity, regardless of age, education, or work experience.
