Engineering a better health care system: a report from the President's Council of Advisors on Science and Technology

Reversal of Azithromycin Resistance in Staphylococcus saprophyticus by Baicalin

In recent years, the efficacy of antibiotics has been threatened by the evolution of bacterial resistance. We previously demonstrated that baicalin (Bac) showed synergies with azithromycin (Azm) against Azm-resistant Staphylococcus saprophyticus (ARSS). The aim of this study was to explore the roles of Bac in reversing the resistance of ARSS to Azm. The ARSS was sequentially passaged for 20 days with the sub-MIC (minimum inhibitory concentration) of Bac. The strain that recovered sensitivity to Azm was named Azm-sensitive S. saprophyticus (ASSS). The sub-MIC of Bac reversed the resistance of ARSS to Azm. The MIC of Azm against the ASSS strain was 0.488 mg/l, and it was stable within 20 passages. The highest rate of resistance reversal reached 3.09% after ARSS was exposed to 31.25 mg/l Bac for 20 days. Furthermore, semiquantitative biofilm and RT-PCR assays reflected that the ability of biofilm formation and the transcript levels of msrA, mphC, and virulence-associated genes in the ASSS strain were significantly lower than those of the ARSS strain (p < 0.05). Simultaneously, Azm delayed the start time of death, alleviated the injury of the kidney, and decreased the bacterial burden in organs and cytokine levels in mice infected with ASSS. In contrast, Azm did not have a good therapeutic effect on mice infected with ARSS. Therefore, Bac has the potential to be an agent that reversed the resistance of ARSS to Azm.

Health Care 4.0: A Vision for Smart and Connected Health Care

Industry 4.0 has transformed manufacturing industry into a new paradigm. In a manner similar to manufacturing, health care delivery is at the dawn of a foundational change into the new era of smart and connected health care, referred to as Health Care 4.0. In this paper, we discuss the historical evolution of Health Care 1.0 to 4.0, describe the characteristics of smart and connected care in Health Care 4.0, identify multiple research challenges and opportunities of Health Care 4.0 in terms of data, model, dynamics, and integration, and outline the implications of people, process, system and health outcomes. Finally, conclusions and recommendations are presented in the areas of (1) involvement of multiple disciplines and perspectives, (2) development of technologies and methodologies with combination of quantitative and qualitative approaches, (3) closed-loop integration of sociotechnical system, and (4) design of person-centered system with specific attention to human needs and health equity.

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Failure to Rescue Event Mitigation System Assessment: A Mixed-methods Approach to Analysis of Complex Adaptive Systems

Although it is widely acknowledged that health care delivery systems are complex adaptive systems, there are gaps in understanding the application of systems engineering approaches to systems analysis and redesign in the health care domain. Commonly employed methods, such as statistical analysis of risk factors and outcomes, are simply not adequate to robustly characterize all system requirements and facilitate reliable design of complex care delivery systems. This is especially apparent in institutional-level systems, such as patient safety programs that must mitigate the risk of infections and other complications that can occur in virtually any setting providing direct and indirect patient care. The case example presented here illustrates the application of various system engineering methods to identify requirements and intervention candidates for a critical patient safety problem known as failure to rescue. Detailed descriptions of the analysis methods and their application are presented along with specific analysis artifacts related to the failure to rescue case study. Given the prevalence of complex systems in health care, this practical and effective approach provides an important example of how systems engineering methods can effectively address the shortcomings in current health care analysis and design, where complex systems are increasingly prevalent.

A Systems Approach to Design and Implementation of Patient Assessment Tools in the Inpatient Setting

Failure to rescue events, or events involving preventable deaths from complications, are a significant contributor to inpatient mortality. While many interventions have been designed and implemented over several decades, this patient safety issue remains at the forefront of concern for most hospitals. In the first part of this study, the development and implementation of one type of highly studied and widely adopted rescue intervention, algorithm-based patient assessment tools, is examined. The analysis summarizes how a lack of systems-oriented approaches in the design and implementation of these tools has resulted in suboptimal understanding of patient risk of mortality and complications and the early recognition of patient deterioration. The gaps identified impact several critical aspects of excellent patient care, including information-sharing across care settings, support for the development of shared mental models within care teams, and access to timely and accurate patient information. This chapter describes the use of several system-oriented design and implementation activities to establish design objectives, model clinical processes and workflows, and create an extensible information system model to maximize the benefits of patient state and risk assessment tools in the inpatient setting. A prototype based on the product of the design activities is discussed along with system-level considerations for implementation. This study also demonstrates the effectiveness and impact of applying systems design principles and practices to real-world clinical applications.

Challenges And Opportunities For Improving Patient Safety Through Human Factors And Systems Engineering

Despite progress on patient safety since the publication of the Institute of Medicine's 1999 report, To Err Is Human, significant problems remain. Human factors and systems engineering (HF/SE) has been increasingly recognized and advocated for its value in understanding, improving, and redesigning processes for safer care, especially for complex interacting sociotechnical systems. However, broad awareness of HF/SE and its adoption into safety improvement work have been frustratingly slow. We provide an overview of HF/SE, its demonstrated value to a wide range of patient safety problems (in particular, medication safety), and challenges to its broader implementation across health care. We make a variety of recommendations to maximize the spread of HF/SE, including formal and informal education programs, greater adoption of HF/SE by health care organizations, expanded funding to foster more clinician-engineer partnerships, and coordinated national efforts to design and operationalize a system for spreading HF/SE into health care nationally.

A systems approach to healthcare: from thinking to -practice

Medicine is increasingly complex, involving a highly connected system of people, resources, processes, and institutions. Attempts to improve care involve disruptions to this system, with the potential for wide-ranging consequences, both positive and negative. Despite this, many improvement methodologies are poorly equipped to manage either complexity or risk - instead focusing on discrete interventions whose effects are narrowly monitored. Engineers have long understood that complex problems require a systems view, and that attempts to make things better can themselves introduce new risk into a system. Given this, an engineering systems approach may be of significant value to those trying to improve healthcare. Two fundamental questions emerge from such an approach: what can we do better, and what could possibly go wrong? This paper describes the evolution of a systems approach to healthcare, and explores a recently co-developed framework outlining a systems approach based upon a synergy between healthcare and engineering.

Adverse Events and Over-the-Counter (OTC) Drugs: Is Inappropriate Labeling the Problem? - The Case of Acetaminophen

Incorrect use of acetaminophen that results in severe liver injury is among the most common adverse drug events (ADEs) in the United States of America (USA), and other countries around the world. Compared to any other drug, it is also on the top for emergency calls, emergency room visits, and acute liver failure in the USA. Consequently, regulating acetaminophen has become a heated and unresolved public debate. During the last decades, multiple efforts have taken place to address the issue, mostly focused on the premise that inappropriate labeling is a leading cause of such ADEs, for failure in communicating the associated risks through warnings. Unlike previous studies, this paper illustrates the importance of considering human factors principles. Three different warning labels were designed and evaluated. The results showed no statistically significant effect, despite the application of best practices for label warning design. Non-design factors, the warning process and why a warning could fail are also illustrated. Implications could help researchers, policy makers, and manufacturers to consider focusing less on traditional labeling interventions, and to invest efforts more systematically considering also the current trends and the current needs of consumers.

The evolution of healthcare quality measurement in the United States

Quality measurement is fundamental to systematic improvement of the healthcare system. Whilst the United States has made significant investments in healthcare quality measurement and improvement, progress has been somewhat limited. Public and private payers in the United States increasingly mandate measurement and reporting as part of pay-for-performance programmes. Numerous issues have limited improvement, including lack of alignment in the use of measures and improvement strategies, the fragmentation of the U.S. healthcare system, and the lack of national electronic systems for measurement, reporting, benchmarking and improvement. Here, we provide an overview of the evolution of U.S. quality measurement efforts, including the role of the National Quality Forum. Important contextual changes such as the growing shift towards electronic data sources and clinical registries are discussed together with international comparisons. In future, the U.S. healthcare system needs to focus greater attention on the development and use of measures that matter. The three-part aim of effective care, affordable care and healthy communities in the U.S. National Quality Strategy focuses attention on population health and reduction in healthcare disparities. To make significant improvements in U.S. health care, a closer connection between measurement and both evolving national data systems and evidence-based improvement strategies is needed.

Essentials of navigating and teaching surgical quality improvement

Quality improvement (QI) has become a focus of contemporary surgical practice. The purpose of this review is to provide a framework of working knowledge regarding QI for the practicing surgeon. QI design, implementation methods, measurement tactics, statistical analysis, and presentation tools based on the Institute of Healthcare Improvement model are reviewed. Additional principles to optimize QI success such as fostering communication, building a culture of QI, leadership involvement, and financial planning are also explored. Lastly, resources for QI education and teaching are provided for those interested in learning more about this expanding field.