Healthcare disparities and models for change.

With Healthy People 2010 making the goal of eliminating health disparities a national priority, policymakers, researchers, medical centers, managed care organizations (MCOs), and advocacy organizations have been called on to move beyond the historic documentation of health disparities and proceed with an agenda to translate policy recommendations into practice. Working models that have successfully reduced health disparities in managed care settings were presented at the National Managed Health Care Congress Inaugural Forum on Reducing Racial and Ethnic Disparities in Health Care on March 10-11, 2003, in Washington, DC. These models are being used by federal, state, and municipal governments, as well as private, commercial, and Medicaid MCOs. Successful models and programs at all levels reduce health disparities by forming partnerships based on common goals to provide care, to educate, and to rebuild healthcare systems. Municipal models work in collaboration with state and federal agencies to integrate patient care with technology. Several basic elements of MCOs help to reduce disparities through emphasis on preventive care, community and member health education, case management and disease management tracking, centralized data collection, and use of sophisticated technology to analyze data and coordinate services. At the community level, there are leveraged funds from the Health Resources and Services Administration's Bureau of Primary Health Care. Well-designed models provide seamless monitoring of patient care and outcomes by integrating human and information system resources

Healthcare queueing models.

Healthcare systems differ intrinsically from manufacturing systems. As such, they require a distinct modeling approach. In this article, we show how to construct a queueing model of a general class of healthcare systems. We develop new expressions to assess the impact of service outages and use the resulting model to approximate patient flow times and to evaluate a number of practical applications. We illustrate the devastating impact of service interruptions on patient flow times and show the potential gains obtained by pooling hospital resources. In addition, we present an optimization model to determine the optimal number of patients to be treated during a service session.Operations research; Health care evaluation mechanisms; Organizational efficiency; Management decision support systems; Time management; Queueing theory;

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Co-producing healthcare in a volume vs. value-based healthcare system: perspective of a parent of a patient and a health professions’ educator

The Institute for Healthcare Improvement’s Triple Aim framework represents an approach to optimizing a health system’s performance by focusing on improving the patient experience of care, improving the health of populations, and reducing healthcare costs. As the US healthcare system undergoes substantial reformation and a shift from fee-for-service payment to value-based models, an approach that emphasizes the co-production of healthcare, our healthcare system must work in concert with the Triple Aim to improve the health experience for patients across multiple environments. Co-production in healthcare means that patients contribute to the provision of health services as partners of professional providers. To highlight how the current healthcare model failed a patient by delaying diagnosis and subsequent care, thus causing undue suffering, the personal experience of one of the author’s children is reported as a narrative. The purpose of communicating this patient experience is to: 1) remind healthcare providers about the importance of not only listening, but hearing the patient and their parent’s concerns; 2) readily admit when a patient’s clinical presentation falls outside of their expertise; and 3) co-produce healthcare by working with the patient and their family. This patient experience serves to reinforce the commitment to co-produce health with patients and their families in a manner that emphasizes the value of care

Improving access to health services – Challenges in Lean application

Purpose: Healthcare organisations face significant productivity pressures and are undergoing major service transformation. This paper serves to disseminate findings from a Lean healthcare project using a NHS Single Point of Access environment as the case study. It demonstrates the relevance and extent that Lean can be applied to this type of healthcare service setting. Design/methodology/approach: Action research was applied and Lean tools used to establish current state processes, identify wastes and develop service improvement opportunities based upon defined customer values. Findings: The quality of referral information was found to be the root cause of a number of process wastes and causes of failure for the service. Understanding the relationship and the nature of interaction between the service‟s customer/supplier led to more effective and sustainable service improvement opportunities and the co-creation of value. It was also recognised that not all the Lean principles could be applied to this type of healthcare setting. Practical implications: The study is useful to organisations using Lean to undertake service improvement activities. The paper outlines how extending the value stream beyond the organisation to include suppliers can lead to improved co-production and generation of service value. Originality/value: The study contributes to service productivity research by demonstrating the relevance and limitations of Lean application in a new healthcare service setting. The case study demonstrates the practical challenges of implementing Lean in reciprocal service design models and adds validity to existing contextual models