There are many examples in healthcare, such as with bloodletting with leeches, where what was thought to be best practice, based on knowledge or evidence at the time, was later found to be harmful to patients. Our knowledge and its application must be in a continual state of assessment and re-assessment so that unreliable elements can be identified and action taken before, or at least minimal, harm is done . There is currently no agreement on standard metrics for safety and quality in healthcare and we are not going to attempt to establish standard definitions in this chapter . However, in order to discuss these issues, it is important to establish a common understanding of the terminologies and their meaning.
At a conceptual level, we conceive clinical safety as a strategic optimization problem in which the maximum level of permissible actuation must be considered and implemented in the simultaneous context of allowing the minimal degree of care-related harm. The objective is to design and implement a care system that minimizes safety risks to approach a goal of zero. The digitization of medicine affords a realistic chance of attaining this goal in an efficient and effective manner. The application of systems engineering principles also provides tools to design these kinds of systems.
The overall quality of healthcare is a summation of the experience of individuals, and for these individuals, there may be varying degrees of quality for different periods of their experience. Similar to safety, we also think of quality as a strategic optimization problem in which outcomes and benefits are maximized or optimized, while the costs and risks involved in the processes required to achieve them, are minimized. The provision of quality via optimized outcomes in clinical care is, to a large extent, a problem in engineering information reliability and flow, providing the best evidence at the right times to assist in making the best decisions . The concepts of the ‘best evidence’ and ‘best decisions’ themselves depend on input sources that range from randomized control trials to informed expert opinion to local best practices. To provide actual actuation, information flows must be supplemented by chemical (medications), mechanical (surgery, physical therapy, injections, human touch) and electromagnetic (imaging, ultrasound, radiation therapy, human speech) modalities, which can institute the processes indicated by those information flows.
Furthermore, quality may also be defined with respect to the degree of success in treatment of the disease state. Diseases addressed in modern medicine are, to a surprisingly large and increasingly recognized extent, those of control problems in bioengineering . These diseases may stem from control problems affecting inflammation, metabolism, physiological homeostasis, or the genome. However, these all represent failure in an element or elements of a normally well-controlled biological system. The quality of the clinical response to these failures is best improved by understanding them sufficiently and thoroughly enough so that targeted and tolerable treatments can be developed that control and/or eliminate the systems dysfunction represented by clinical disease. This should be accomplished in a way that minimizes undue costs in physical, mental, or even spiritual suffering. Ultimately, medical quality is based primarily on outcomes, but the nature of the processes leading to those outcomes must be considered. Optimal outcomes are desirable, but not at any cost, in the broad definition of the term. For example, prolonging life indefinitely is not an optimal outcome in some circumstances that are contextually defined by individual, family, and cultural preferences.
Having defined safety and quality in our context, the next step is to develop systems that capture, track and manage these concepts in retrospective, real-time, and predictive manners. It is only when we know precisely what static and dynamic elements of safety and quality we wish to ensure that we can design the systems to support these endeavors. These systems will involve the integration of hardware and software systems such as physiologic monitors with the EHR (including Computerized Provider Order Entry, Picture Archiving and Communication System, etc.), and will require a variety of specialized, domain-specific data analytics as well as technical innovations such as wireless body sensor networks to capture patient status in real time. The system will connect and communicate pertinent information among caregivers by populating standardized, essential access and alert nodes with timely and accurate information. It is also necessary that information flows bi-directionality (from the records of individuals to the population record, and from the population record to individuals) so that both can benefit from the data [2, 14]. Clearly, this will require an overall monitoring and information system that is interoperable, interactive both with its own components and its users, and actively but selectively informative. Future generations of clinicians will receive their education in an environment in which these systems are ubiquitous, selectively modifiable based on inputs such as crowdsourcing, and intrinsic to the tasks at hand, in contrast to the siloed and apparently arbitrarily imposed applications current clinicians may resist and resent [5, 8].
We noted the importance of control problems in disease, and control will also represent a fundamental component in the design of future safety and quality systems. The detection and prevention of adverse events is a significant challenge when depending on self-reporting methods or chart review and this issue is of high importance in the US [26, 27]. Predictive analytics can be developed as elements of the system to prospectively inform users of threats to safety and quality [19–21]. Carefully designed feed-forward components will inform participants in real time that an high risk activity is occurring so that it can be rectified without requiring retroactive analysis (Fig. 4.4—safety control loop below). Retrospective data analytics will track the factors affecting quality and safety so that practice, workflow, and technological systems can be accordingly modified. Such an ICS will be capable of monitoring medical errors, adverse events, regulatory and safety agency concerns and metrics, and compliance with best practice as well as meaningful use in parallel with costs and outcomes.