Ebook: Engineering the System of Healthcare Delivery

Kim, aged 3 years, lies asleep, waiting for a miracle. Outside her room, the nurses on the night shift pad softly through the half-lighted corridors, stopping to count breaths, take pulses, or check the intravenous pumps. In the morning, Kim will have her heart fixed. She will be medicated and wheeled into the operating suite. Machines will take on the functions of her body: breathing and circulating blood. The surgeons will place a small patch over a hole within her heart, closing off a shunt between her ventricles that would, if left open, slowly kill her.

Kim will be fine if the decision to operate on her was correct; if the surgeon is competent; if that competent surgeon happens to be trained to deal with the particular anatomic wrinkle that is hidden inside Kim's heart; if the blood bank cross-matched her blood accurately and delivered it to the right place; if the blood gas analysis machine works properly and on time; if the suture does not snap; if the plastic tubing of the heart-lung machine does not suddenly spring loose; if the recovery room nurses know that she is allergic to penicillin; if the “oxygen” and “nitrogen” lines in the anesthesia machine have not been reversed by mistake; if the sterilizer temperature gauge is calibrated so that the instruments are in fact sterile; if the pharmacy does not mix up two labels; and if when the surgeon says urgently, “Clamp, right now,” there is a clamp on the tray.

If all goes well, if ten thousand “ifs” go well, then Kim may sing her grandchildren to sleep some day. If not, she will be dead by noon tomorrow.

If Kim were an astronaut, strapped into her seat at the top of some throbbing rocket, the crowd assembled would hold their breath in the morning Florida sun. “How can it possibly work?” they would whisper. “How many parts are there in that machine? A million? What if one fails? My toaster fails. Please let it all work right.” The machine would bellow smoke, the gantry fall away, and slowly the monster would rise, Kim on top.

If it worked, they would cheer. “A miracle,” they would shout, in awe that the millions of tiny lines of effort, the millions of tiny lines of cause and effect, from job shops in Ohio and laboratories in Pasadena, criss-crossing through time and space, could converge so magnificently in a massive, gleaming rocket launched exactly right. Perfect.

If it failed, they would cry. So would the rocket's makers, who had done their very best. No one wanted it to end this way. Poor Kim. What was the trouble? What went wrong? Why?

The lines of cause will converge around Kim in the morning as she wheels toward the operating room. Thousands upon thousands of elements weaving a basket to hold her safely, all hope. No crowd holds its breath tonight; but wouldn't they if they knew?

From: Berwick DM. Controlling variation in health care: a consultation from Walter Shewhart. Medical Care 1991; 29: 1212–1225.

Patient safety is a global challenge that requires knowledge and skills in multiple areas, including human factors and systems engineering. In this chapter, numerous conceptual approaches and methods for analyzing, preventing and mitigating medical errors are described. Given the complexity of healthcare work systems and processes, we emphasize the need for increasing partnerships between the health sciences and human factors and systems engineering to improve patient safety. Those partnerships will be able to develop and implement the system redesigns that are necessary to improve healthcare work systems and processes for patient safety.

The role of systems in addressing the needs of elderly and chronically ill populations remains a far from universal way of thinking, much less practice, in health care. Re-engineering the current fragmented system to align providers, patients and payment models to facilitate proactive management of conditions associated with advanced age and/or one or more chronic diseases – rather than responding to costly consequences of a health care system optimized for acute care conditions – will be a major challenge for all stakeholders. There are, however, promising success stories that are taking place in the United States today that may provide a model for improvement. The authors define the issues faced by the health care providers and payers that arise when providing care for the elderly and those with chronic conditions – issues that threaten to overwhelm the financial and human health care resources that exist to serve these populations. They define innovative ways of thinking about systems of care, and provide examples of unique systems that have applied theory into practice. These successful leaders may offer lessons in proactively managing complex health conditions, overcoming communication barriers and using technology to complement the necessary human touch that is essential to health care delivery.

Health care provided in the final year of life is typically costly and often delivers unintended outcomes. High value can be defined for end of life care. High value clinical practices exist for end of life care and a common set of high value processes can be identified. The current system structure of healthcare delivery does not consistently support those high value processes. An improved organizational schema could foster sustained delivery of high value delivery operations. The healthcare ecosystem needs to evolve to provide appropriate incentives and support for an appropriately designed care system.

This chapter provides an overview of health care costs in the United States, including trends, sources and uses of funds, employers' role, and factors driving costs. It also reviews what analysts believe are cost drivers especially compared to other countries that have significantly lower health care costs and, often, better health outcomes. Within the US, there are also important differences by geography, further demonstrating that higher US costs do not reflect higher quality and greater patient and physician satisfaction. In fact, the opposite is often the case.

Information technology in health care (HIT) is getting a major boost in the United States through the passage of the American Recovery and Reinvestment Act (ARRA) of 2009. The portion of the Act that relates to health information technology (HITECH) seeks to achieve widespread implementation of electronic health records (EHRs) across the land and assure that these EHRs achieve sufficient levels of ‘meaningful use’ to improve care, reduce costs, and result in better outcomes. This chapter sets the stage for the other chapters that follow in this section. The chapter will review current thinking about how HIT will facilitate collection, dissemination, and evaluation of information throughout the system. Further, it will discuss the role and potential for HIT to support a learning organization [7,8]. Finally, it will outline the current widely identified barriers to progress, e.g., standards development, lack of interoperability and connectivity, and limited decision support that uses evidence-based guidelines created and maintained explicitly to be actionable through computer-based records and systems. Further, with the passage of HITECH, there is a continued attention given to privacy policy at the expense of access to person-specific health information for legitimate social purposes including research and community health. More will be said about this near the end of the chapter. Finally, the chapter will end with a discussion of the difference between information and communication and it will advocate for greater attention to the use of technology as a tool for improve communications and not simply storage and transmission of information.

A radical change in technical approach is needed to achieve electronic health records suitable to support an engineered system of healthcare. This chapter suggests a redefinition of interoperable health information. It provides examples of how to break the electronic health record challenge into component parts to match computational technique to the scale of the problem handled by a component.

Whether for the generation or application of evidence to guide healthcare decisions, the success of evidence-based medicine is grounded in principles common to engineering. In the Learning Healthcare System envisioned by the Institute of Medicine's (IOM) Roundtable on Evidence-Based Medicine, evidence emerges as a natural by-product of care delivery, which is thoroughly documented, pooled for continuous monitoring and analysis, integrated with insights from related studies, and fed back seamlessly to improve the consistency and appropriateness of care decisions by clinicians and their patients. Drawing from lessons shared at the IOM/NAE symposium, Engineering a Learning Healthcare System, this paper provides an overview of the state-of-play in health care today, some of its key challenges, the vision and features of a learning healthcare system, applicable commonalties and principles from engineering, and potential collaborative opportunities moving forward to the benefit of both fields.

The United States faces tremendous challenges with its healthcare system. By any standard, it is expensive and performs poorly in most measures of health and thus, is in great need of reform. But how do we reform things without making the situation worse? Some of the more fundamental problems arise from the combination of a fee-for-service payment system for physicians with insurance-based financing care. This combination results in conflicts among the interests of patients, physicians and payers. This paper examines this issue from a decision analytic perspective, starting with a definition of the patient-centered view, and an assessment of the practicality of controlling costs by making healthcare more patient-centric. It then illustrates how fee-for-service models corrupt decision-making and other solutions designed to reign in the abuses of the fee-for-service model and also negatively impacts the quality of decision making for individual patients. Whatever the strategies for health reform, the degree of patient-centeredness of care is a benchmark that allows policy makers to understand how far they have had to deviate from optimal to achieve the desired ends of cost control.

Health care spending and more importantly, health care spending growth rates, are unsustainable. Past strategies of price controls, reliance on administered pricing for Medicare and the dominance of a la carte fee for service reimbursement have been part of the problem and do not represent promising strategies for the future. Too much time has been spent debating whether Medicare has done better or worse than the private sector since neither represents an acceptable path going forward. Understanding the effects of innovative payment strategies – including those that affect the patient – will be an important part in learning how to “bend the curve”. Making sure that there are strategies to implement the results of successful pilots and demonstrations will also be important.

Texas Bix Bender is not a known health economist. In fact, he's not an economist at all. He is the author of “Don't Squat with Yer Spurs On! The Cowboy's Guide to Life”, and in that book he provides some insight into the issues that affect improving healthcare effectiveness and efficiency. One of his guides to life is as follows: “If you find yourself in a hole, the first thing to do is stop digging” [3].

America needs a far more efficient health care financing and delivery system than the one we have. Our present system is a serious threat to public finances and is pricing itself out of reach. At the root of the problem are incentives and organization. The present fragmented fee-for-service small practice model is filled with cost-increasing incentives. There are some relatively efficient organized delivery systems, mostly based on large multi-specialty group practices. Unfortunately, most consumers are not offered the opportunity to save money and get better care by choosing such a system. This situation presents great opportunities for improvement in performance by re-engineering the system. However, for this to happen, incentives must be fundamentally changed so that everyone is cost conscious and care is organized in accountable care systems seeking improvement.

This chapter offers a systems view of healthcare delivery and outlines a wide range of concepts, principles, models, methods and tools from systems engineering and management that can enable the transformation of the dysfunctional “as is” healthcare system to an agreed-upon “to be” system that will provide quality, affordable care for everyone. Topics discussed include systems definition, design, analysis, and control, as well as the data and information needed to support these functions. Barriers to implementation are also considered.

In Evita, Andrew Lloyd Webber and Tim Rice wrote: Politics, the Art of the Possible. To those of us in the operations research community, we postulate: Operations Research, the Science of Better – (i.e. better processes, better systems and better decisions). Using our own and other scientific, engineering, mathematical, and social sciences methodologies, operations researchers help decision makers make better decisions; decisions leading to improvements: greater quality, lower costs, greater revenues, better access, better scheduling, lower risks, more satisfaction – with the goal of always striving for the best or optimal decisions.

Engineering has and will continue to have a critical impact on healthcare; the application of technology-based techniques to biological problems can be defined to be technobiology applications. This paper is primarily focused on applying the technobiology approach of systems engineering to the development of a healthcare service system that is both integrated and adaptive. In general, healthcare services are carried out with knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Indeed, the engineering design of a healthcare system must recognize the fact that it is actually a complex integration of human-centered activities that is increasingly dependent on information technology and knowledge. Like any service system, healthcare can be considered to be a combination or recombination of three essential components – people (characterized by behaviors, values, knowledge, etc.), processes (characterized by collaboration, customization, etc.) and products (characterized by software, hardware, infrastructures, etc.). Thus, a healthcare system is an integrated and adaptive set of people, processes and products. It is, in essence, a system of systems which objectives are to enhance its efficiency (leading to greater interdependency) and effectiveness (leading to improved health). Integration occurs over the physical, temporal, organizational and functional dimensions, while adaptation occurs over the monitoring, feedback, cybernetic and learning dimensions. In sum, such service systems as healthcare are indeed complex, especially due to the uncertainties associated with the human-centered aspects of these systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation.

With its primary focus on community health, the public health system focuses on intervention and prevention of disease and injury to protect entire populations. As a federation of city, county and state entities operating independently under a complicated array of local, state and federal laws, public health can best be understood as a complex adaptive system. The dynamic nature of this system and the need for public health agencies to relate and respond to numerous stimuli in terms of new regulations, changing health status, emerging threats and shifting policy, can mask the commonality of underlying business processes performed within the public health sector. Heightened demand for interoperable, adaptive information systems across the broader US health system necessitates the recognition of this commonality and highlights the need for comprehensive analysis and understanding of these core business processes. In turn, this analysis paves the way for public health to apply proven systems engineering techniques to streamline, automate and facilitate those processes. Here, we look at the nature of the public health system and the evolution of a purpose-built methodology for process engineering within public health. We also present a case study based on the application of the methodology to develop requirements for public health laboratory information management systems.

Focusing on pandemic influenza, this chapter approaches the planning for and response to such a major worldwide health event as a complex engineering systems problem. Action-oriented analysis of pandemics requires a broad inclusion of academic disciplines since no one domain can cover a significant fraction of the problem. Numerous research papers and action plans have treated pandemics as purely medical happenings, focusing on hospitals, health care professionals, creation and distribution of vaccines and anti-virals, etc. But human behavior with regard to hygiene and social distancing constitutes a first-order partial brake or control of the spread and intensity of infection. Such behavioral options are “non-pharmaceutical interventions.” (NPIs) The chapter employs simple mathematical models to study alternative controls of infection, addressing a well-known parameter in epidemiology, R₀, the “reproductive number,” defined as the mean number of new infections generated by an index case. Values of R₀ greater than 1.0 usually indicate that the infection begins with exponential growth, the generation-to-generation growth rate being R₀. R₀ is broken down into constituent parts related to the frequency and intensity of human contacts, both partially under our control. It is suggested that any numerical value for R₀ has little meaning outside the social context to which it pertains. Difference equation models are then employed to study the effects of heterogeneity of population social contact rates, the analysis showing that the disease tends to be driven by high frequency individuals. Related analyses show the futility of trying geographically to isolate the disease. Finally, the models are operated under a variety of assumptions related to social distancing and changes in hygienic behavior. The results are promising in terms of potentially reducing the total impact of the pandemic.

Dental decay is the most prevalent chronic disease among both children and adults in the U.S. The Surgeon General's Report on Oral Health found that there had been marked improvement in oral health in many Americans over the last 50 years and that good oral health could be achieved by all Americans largely due to the presence of safe and effective interventions to prevent and control oral disease However, recent national data suggest that several disparities in dental care exist. In this chapter, we present a model of the dental health system as well as key differences with the general medical health system. We further discuss the major issues that the dental care delivery system will have to address in order to ensure that all Americans have access to effective interventions to prevent and control disease in an environment of decreasing supply of dentists per capita and potentially increasing demand. We then discuss strategies and policies to address these emerging issues in the context of this model. Finally, we conclude with suggestions on how engineering techniques could be used to improve the system.