The Science of Individuality and Healthcare Quality

The Science of Individuality and Healthcare Quality

Anastasius S. Moumtzoglou (P. & A. Kyriakou Children's Hospital, Greece)
Copyright: © 2020 |Pages: 20
DOI: 10.4018/978-1-7998-2390-2.ch001
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The application of linear models to human systems and healthcare management and quality has improved our understanding of their system structure and function. However, such models often fall short of explaining experimental results or predicting future abnormalities in complex nonlinear systems which help in dissecting and analyzing individual system components. Nonlinear models may better explain how the individual components collectively act and interact to produce a dynamic system in constant flux. They also assist in filling in some of the results that are not adequately explained by linear models. In this context, we should consider the integration of linear and non-linear theories in healthcare quality and management, drawing the initial conditions of chaotic behavior from the standardization of the linear theory, and distinguishing between desirable and undesirable variation relegating statistical process control only to issues of high certainty regarding the outcome.
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Long-term thinking is necessary for the health and social care systems because significant changes in the population, society, technology and other areas are likely to shape social and health care in the future.

As an illustration, economic growth and its distribution will shape future standards of living. National governments’ policies are one set of factors that will impact the economy and living standards in the future. However, being unemployed, in insecure work, or working in impoverished conditions contributes to poor health, as work and health are inextricably linked. In this context, the future of work is widely debated, and the implications for health are complex and uncertain as there is significant uncertainty around macroeconomic forecasts for earnings and employment (Lovell & Bibby, 2018).

Furthermore, much has been written about the likely impact of automation and particularly the potential for job displacement, but predictions for the future vary widely (Office for National Statistics, 2019).

On the flip side, there is also optimism about opportunities technology could bring. As some roles are replaced, others will be adapted and integrated with technology, and new ones will be created. As automation replaces routine tasks, people could be freed to undertake more rewarding work (Willis et al., 2019).

Nonetheless, there are limits to the tasks that technology can perform, especially where human intelligence and perception are still essential (Frey & Osborne, 2017). As a result, social skills, intelligence and perception, are likely to be of enduring value, and the caring roles and skills that depend on human interaction could become sought after.

How well industries and governments prepare the current workforce with the knowledge, skills and flexibility needed to adapt to new types of work will also influence the impact of new technologies. As work changes, it is likely that social attitudes and expectations of working will change too. Accordingly, alternatives to traditional employment models have been proposed as policy responses to promote people’s economic security and wellbeing within a shifting labor market.

The economic, societal and technological shifts are likely to alter the way that people connect, as well as their communities and broader institutions (Pantel et al., 2013; Holt-Lunstad et al., 2015; Laugesen, et al., 2018). Still, social networks are essential for our health. People who are socially isolated have a higher risk of early mortality, comparable with other well-established risk factors.

Furthermore, the older population, which is particularly vulnerable to isolation, is growing while young people grow up in a time when technology has fundamentally changed the way that people interact. In digital terms, people are more connected than ever, although it is now recognized that technology use also has the potential to harm young people’s psychological health.

On the other hand, the world population is ageing and growing, as the proportion of older people increases due to improved life expectancy, which is projected to continue to rise over the coming years. As people live longer, they are spending more years in poor health and are increasingly likely to live with multiple conditions. Moreover, the burden of disease is shaped by the prevalence of the major health risk factors, but it is hard to predict how the prevalence of health risk factors will change over time.

Additionally, recent analysis has suggested that over the next years, health spending will need to rise to maintain current service levels, while the picture is starker for social care (Evans, 2018).

Moreover, even with additional funding, the health and social care system risk not having enough staff to deliver services in the future as there are no simple solutions to the workforce challenge. Increasing the pipeline of professionals and creating the working cultures and conditions that encourage retention takes time and investment.

Key Terms in this Chapter

Chaos Theory: The study of apparently random or unpredictable behaviour in systems governed by deterministic laws.

Patient Safety: The prevention of harm to patients. Emphasis is placed on the system of care delivery that prevents errors, learns from the errors, and is built on a culture of safety.

Statistical Process Control: A method of quality control which employs statistical methods to monitor and control a process.

Healthcare Quality: The degree to which health care services for individuals and populations increase the likelihood of desired health outcomes. In 1999, the Institute of Medicine released six domains to measure and describe healthcare quality, that is, safety, effectiveness, patient-centered care, timeliness, efficiency, equitability.

Complexity Theory: A set of concepts that attempts to explain complex phenomena which traditional theories do not explain.

Variation: A change or slight difference in condition, amount, or level, typically within specified limits.

Average: A number expressing the central or typical value in a set of data.

Science of Individuality: The science which acknowledges that each human needs to be seen and treated with utter respect for his or her individuality.

Nonlinear Models: A model which is not linear in at least one parameter.

Linear Models: Models which describe a continuous response variable as a function of one or more predictor variables.

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