Health Care Planning With Data Driven Resource Allocation
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Health Care Planning With Data Driven Resource Allocation
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Author : M. H. Birkin
language : en
Publisher:
Release Date : 2003
Health Care Planning With Data Driven Resource Allocation written by M. H. Birkin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Management categories.
Data Driven Healthcare
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Author : Laura B. Madsen
language : en
Publisher: John Wiley & Sons
Release Date : 2014-09-23
Data Driven Healthcare written by Laura B. Madsen 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 2014-09-23 with Business & Economics categories.
Healthcare is changing, and data is the catalyst Data is taking over in a powerful way, and it's revolutionizingthe healthcare industry. You have more data available than everbefore, and applying the right analytics can spur growth. Benefitsextend to patients, providers, and board members, and thetechnology can make centralized patient management a reality.Despite the potential for growth, many in the industry andgovernment are questioning the value of data in health care,wondering if it's worth the investment. Data-Driven Healthcare: How Analytics and BI are Transformingthe Industry tackles the issue and proves why BI is not onlyworth it, but necessary for industry advancement. Healthcare BIguru Laura Madsen challenges the notion that data have little valuein healthcare, and shows how BI can ease regulatory reportingpressures and streamline the entire system as it evolves. Madsenillustrates how a data-driven organization is created, and how itcan transform the industry. Learn why BI is a boon to providers Create powerful infographics to communicate data moreeffectively Find out how Big Data has transformed other industries, and howit applies to healthcare Data-Driven Healthcare: How Analytics and BI are Transformingthe Industry provides tables, checklists, and forms that allowyou to take immediate action in implementing BI in yourorganization. You can't afford to be behind the curve. The industryis moving on, with or without you. Data-Driven Healthcare: HowAnalytics and BI are Transforming the Industry is your guide toutilizing data to advance your operation in an industry wheredata-fueled growth will be the new norm.
Machine Learning Analytics For Data Driven Decision Support In Healthcare
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Author : Andrew Thomas Ward
language : en
Publisher:
Release Date : 2020
Machine Learning Analytics For Data Driven Decision Support In Healthcare written by Andrew Thomas Ward and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Machine learning has the potential to revolutionize the field of healthcare. With the increasing availability of electronic healthcare data, machine learning algorithms and techniques are able to offer novel data-driven insights in the form of descriptive, predictive, and prescriptive analytics. Research efforts in machine learning-driven clinical decision support systems have demonstrated performance comparable to, or surpassing, that of doctors across a wide range of disciplines. However, very few of these solutions are implemented and used. This may be due to the solution being too specialized, too difficult to operationalize, or both. My research in machine learning for clinical decision support has focused on delivering broadly applicable and clinically actionable predictions for heart disease and opioid use and misuse. As some of the leading causes of death in the US and worldwide, these are important public health concerns. A less-explored facet of decision support in healthcare lies on operational delivery of care: improving hospital efficiency, modeling patient admissions and discharges, and preventing medical errors. While these research topics are not as popular as their clinical counterparts, the potential for real-world improvement through the study of these issues is far greater in the near-term. In this dissertation, I present novel contributions spanning both the clinical and operational delivery of care. I focus on four lines of data-driven research which have the potential to deliver widespread impact: heart disease prediction, opioid use prediction in pediatric patients, medical error reduction, and hospital discharge planning and resource allocation.
Uncertainty In The Information Supply Chain
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Author : Monica Chiarini Tremblay
language : en
Publisher:
Release Date : 2007
Uncertainty In The Information Supply Chain written by Monica Chiarini Tremblay and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
ABSTRACT: Similar to a product supply chain, an information supply chain is a dynamic environment where networks of information-sharing agents gather data from many sources and utilize the same data for different tasks. Unfortunately, raw data arriving from a variety of sources are often plagued by errors (Ballou et al. 1998), which can lead to poor decision making. Supporting decision making in this challenging environment demands a proactive approach to data quality management, since the decision maker has no control over these data sources (Shankaranarayan et al. 2003). This is true in health care, and in particular in health planning, where health care resource allocation is often based on summarized data from a myriad of sources such as hospital admissions, vital statistic records, and specific disease registries. This work investigates issues of data quality in the information supply chain. It proposes three result-driven data quality metrics that inform and aid decision makers with incomplete and inconsistent data and help mitigate insensitivity to sample size, a well known decision bias. To design and evaluate the result-driven data quality metrics this thesis utilizes the design science paradigm (Simon 1996; Hevner, March et al. 2004). The metrics are implemented within a simple OLAP interface, utilizing data aggregated from several healthcare data sources, and presented to decision makers in four focus groups. This research is one of the first to propose and outline the use of focus groups as a technique to demonstrate utility and efficacy of design science artifacts. Results from the focus groups demonstrate that the proposed metrics are useful, and that the metrics are efficient in altering a decision maker's data analytic strategies. Additionally, results indicate that comparative techniques, such as benchmarking or scenario based approaches, are promising approaches in data quality. Finally, results from this research reveal that decision making literature needs to be considered in the design of BI tools. Participants of the focus groups confirmed that people are insensitive to sample size, but when attention was drawn to small sample sizes, this bias was mitigated.
Multiple Criteria Decision Engineering To Support Management In Military Healthcare And Logistics Operations
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Author : Nathaniel Bastian
language : en
Publisher:
Release Date : 2015
Multiple Criteria Decision Engineering To Support Management In Military Healthcare And Logistics Operations written by Nathaniel Bastian and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
The U.S. Department of Defense Military Health System (MHS) is a unique health system in that it recruits and trains its own medical staff, has a generally physically fit patient population, and is a closed, single-payer system. The unique mission of the MHS comes with its own set of healthcare and logistics challenges above and beyond those of a civilian US-based health system. At the surface, the MHS is charged with delivering quality healthcare to a diverse population. At the core, however, that charge includes maintaining peacetime healthcare delivery capacity while ensuring the deployment readiness of the active force, and deploying, establishing and running forward deployed healthcare facilities to provide the necessary health services support for combat, stability, peacekeeping, and humanitarian assistance operations. Further complicating the delivery of quality care is the transient nature of healthcare providers either due to deployments or routine personnel moves between hospitals, clinics, and field units. Due to the complexity of the MHS, effective management of healthcare resources and logistics is paramount. This is also important to senior military leaders since many real-world decision problems in the MHS exhibit the presence of multiple, conflicting objectives for judging alternatives, as well as the need for making compromises or trade-offs regarding the outcomes of alternate courses of action. Further, most of these same resource allocation decision problems are faced under inherent, underlying uncertainties. As a result of these challenges, this doctoral dissertation employs methods of multiple criteria decision engineering to assist strategic decision-making and to support the complex planning and management of military healthcare resources, personnel, logistics, and financial incentives. The primary motivation is the requirement to provide data-driven, managerial decision support for decision problems in: 1) resource allocation and performance within the military hospital network, 2) military medical workforce planning and future force structure, 3) supply chain network design for humanitarian assistance and disaster relief aerial delivery operations, and 4) effectiveness of healthcare financial incentives on hospital efficiency. Multi-criteria and stochastic optimization models that leverage mixed-integer programming, Monte Carlo simulation, discrete event simulation, text mining, clustering analysis, regression modeling and econometrics are developed to provide critical insights for military decision-makers. The multiple criteria decision engineering methods in this dissertation are applied to several real-world decision problems within military healthcare and logistics operations to illustrate the impact and relevance of the results. First, we proffer the Multi-Objective Auto-Optimization Model (MAOM) -- a resource allocation-based optimization model that adjusts resources (system inputs) automatically -- which provides decision-makers with a decision-support tool for re-allocating resources in large health systems that are centrally controlled and funded, such as the MHS. The necessity to efficiently balance and re-allocate system resources among hospitals in a hospital network is paramount, especially as health systems experience increasing demand and costs for health services. In these systems, inputs are fixed at certain levels and may only be adjusted within medical treatment facilities, while outputs must be maintained. Second, we proffer the Objective Force Model (OFM), a deterministic, mixed-integer linear weighted goal programming model to optimize workforce planning for the U.S. Army Medical Department (AMEDD) Personnel Proponency Directorate (APPD). We also develop two stochastic variants of the linear OFM, which incorporate probabilistic components associated with uncertain officer continuation rates. We employ a discrete event simulation model to verify and validate the results. These models allow for better transparency of personnel for both the senior AMEDD decision-makers and the health services human resource planners at APPD, while effectively projecting the manpower structure that provides the appropriate skill levels (by grade) to meet the demands of the current workforce structure. Third, we develop a multiple criteria decision analysis (MCDA) framework to optimize the military humanitarian assistance/disaster relief (HA/DR) aerial delivery supply chain network under uncertainty. The model uses stochastic, mixed-integer, weighted goal programming to optimize network design, logistics costs, staging locations, procurement amounts, and inventory levels. The MCDA framework enables decision-makers to explore the trade-offs between military HA/DR aerial delivery supply chain efficiency and responsiveness, while optimizing across a wide range of real-world, probabilistic scenarios to account for the inherent uncertainty in the location of global humanitarian disasters, as well as the amount of demand to be met. Fourth, we propose the Fuzzy Multi-Objective Auto-Optimization Model (FMAOM), an optimization model with fuzzy constraints that can be used for automatic resource re-allocation with respect to different levels of risk preferences. The efficient use of resources in health systems is crucial mostly due to the increasing demand and limited funding. The implications of the proposed fuzzy decision-making model for healthcare decision-makers and its relevance to healthcare policy and management are discussed. Fifth, we measure the effect of a monetary incentive model on hospital efficiency and outcomes. The Army component of the MHS implemented a pay-for-performance financial incentive program in 2007 in an effort to stimulate patient quality, access, and satisfaction improvements. Using a retrospective, quasi-experimental design, the empirical analysis incorporates data envelopment analysis (DEA) with time windows and difference-in-differences estimation. Hospitals are evaluated in the U.S. Army, Air Force, and Navy during the period of 2001--2012. The results indicate a statistically significant reduction in efficiency for the hospitals that received financial incentives. The health policy implications of this study are applicable in light of the national healthcare debate and may assist healthcare policy-makers in determining the efficacy and associated trade-offs of pay-for-performance financing models. Last, we introduce the Stochastic Multi-Objective Auto-Optimization Model (SMAOM) for resource allocation decision-making under uncertainty in the MHS. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The usefulness of the proposed model is illustrated by a computational experiment in which a traditional DEA model is compared to the proposed SMAOM for 128 hospitals in the three services (Air Force, Navy, Army) in the MHS using hospital-level data from 2009 - 2013. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18% over the DEA model while also accounting for uncertainty of health system inputs and outputs. In summary, the multiple criteria decision engineering models described in this dissertation focus on supporting the management of scarce military health system resources, military workforce planning and force structure, military humanitarian logistics network design, and military healthcare financial incentives. The challenge, however, remains in careful and continual coordination with senior MHS decision-makers and hospital managers so that models proffered here are used effectively as decision support tools.
Healthcare Analytics
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Author : Hui Yang
language : en
Publisher: John Wiley & Sons
Release Date : 2016-10-10
Healthcare Analytics written by Hui Yang 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 2016-10-10 with Business & Economics categories.
Features of statistical and operational research methods and tools being used to improve the healthcare industry With a focus on cutting-edge approaches to the quickly growing field of healthcare, Healthcare Analytics: From Data to Knowledge to Healthcare Improvement provides an integrated and comprehensive treatment on recent research advancements in data-driven healthcare analytics in an effort to provide more personalized and smarter healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance healthcare quality and operational efficiency. Organized into two main sections, Part I features biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient-monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part II focuses on healthcare delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician–patient interactions; insurance claims; and the role of social media in healthcare. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement also features: • Contributions from well-known international experts who shed light on new approaches in this growing area • Discussions on contemporary methods and techniques to address the handling of rich and large-scale healthcare data as well as the overall optimization of healthcare system operations • Numerous real-world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the healthcare industry • Plentiful applications that showcase analytical methods and tools tailored for successful healthcare systems modeling and improvement The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, healthcare systems, and economics. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement is also appropriate for graduate-level courses typically offered within operations research, industrial engineering, business, and public health departments.
Data Driven Science For Clinically Actionable Knowledge In Diseases
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Author : Daniel R. Catchpoole
language : en
Publisher: CRC Press
Release Date : 2023-12-06
Data Driven Science For Clinically Actionable Knowledge In Diseases written by Daniel R. Catchpoole and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-06 with Medical categories.
Data-driven science has become a major decision-making aid for the diagnosis and treatment of disease. Computational and visual analytics enables effective exploration and sense making of large and complex data through the deployment of appropriate data science methods, meaningful visualisation and human-information interaction. This edited volume covers state-of-the-art theory, method, models, design, evaluation and applications in computational and visual analytics in desktop, mobile and immersive environments for analysing biomedical and health data. The book is focused on data-driven integral analysis, including computational methods and visual analytics practices and solutions for discovering actionable knowledge in support of clinical actions in real environments. By studying how data and visual analytics have been implemented into the healthcare domain, the book demonstrates how analytics influences the domain through improving decision making, specifying diagnostics, selecting the best treatments and generating clinical certainty.
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
Data Driven Management In The Health Care Sector
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Author :
language : en
Publisher:
Release Date : 2018
Data Driven Management In The Health Care Sector written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Health Planning For Effective Management
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Author : William A. Reinke
language : en
Publisher: Oxford University Press
Release Date : 1988-04-14
Health Planning For Effective Management written by William A. Reinke and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988-04-14 with Medical categories.
Emphasizing practical considerations in designing and carrying out primary health care programs, this is a superb introductory text for public health students. It will be of particular interest to those working with rural populations in developing countries with limited resources. Part I covers policy issues and the conceptual framework for planning, management and evaluation. Part II reviews essential methods for effective implementation, considering the economic, political, epidemiologic, demographic and other components that contribute to the assessment of health needs and resource allocation. Part III discusses specific tools and techniques in program management related to decision analysis, network analysis, survey techniques, cost-effectiveness appraisal, and much more. Comprehensive and informative, this highly practical work is the result of many years of experience in teaching and working with health care planners from around the world.