The Quest for System-Theoretical Medicine in the COVID-19 Era

Shifting from Theoretical Best Evidence to Practical Best Evidence: an Approach to Overcome Structural Conservatism of Evidence-Based Medicine and Health Policy

There is disparity in the healthcare sector between the extent of innovation in medical products (e. g., drugs) and healthcare structures. The reason is not a lack of ideas, concepts, or (quasi-) experimental studies on structural innovations. Instead, we argue that the slow implementation of structural innovations has created this disparity partly because evidence-based medicine (EBM) instruments are well suited to evaluate product innovations but less suited to evaluate structural innovations. This article argues that the unintentional interplay between EBM, which has changed significantly over time to become primarily theoretical, on the one hand, and caution and inertia in health policy, on the other, has resulted in structural conservatism. Structural conservatism is present when healthcare structures persistently and essentially resist innovation. We interpret this phenomenon as an unintended consequence of deliberate EBM action. Therefore, we propose a new assessment framework to respond to structural innovations in healthcare, centered on the differentiation between the theoretical best (possible) evidence, the practical best (possible) evidence, and the best available evidence.

Reducing uncertainty in evidence-based health policy by integrating empirical and theoretical evidence: An EbM+theory approach

BACKGROUND

To reduce their decisional uncertainty, health policy decision-makers rely more often on experts or their intuition than on evidence-based knowledge, especially in times of urgency. However, this practice is unacceptable from an evidence-based medicine (EbM) perspective. Therefore, in fast-changing and complex situations, we need an approach that delivers recommendations that serve decision-makers' needs for urgent, sound and uncertainty-reducing decisions based on the principles of EbM.

AIMS

The aim of this paper is to propose an approach that serves this need by enriching EbM with theory.

MATERIALS AND METHODS

We call this the EbM+theory approach, which integrates empirical and theoretical evidence in a context-sensitive way to reduce intervention and implementation uncertainty.

RESULTS

Within this framework, we propose two distinct roadmaps to decrease intervention and implementation uncertainty: one for simple and the other for complex interventions. As part of the roadmap, we present a three-step approach: applying theory (step 1), conducting mechanistic studies (EbM+; step 2) and conducting experiments (EbM; step 3).

DISCUSSION

This paper is a plea for integrating empirical and theoretical knowledge by combining EbM, EbM+ and theoretical knowledge in a common procedural framework that allows flexibility even in dynamic times. A further aim is to stimulate a discussion on using theories in health sciences, health policy, and implementation.

CONCLUSION

The main implications are that scientists and health politicians - the two main target groups of this paper-should receive more training in theoretical thinking; moreover, regulatory agencies like NICE may think about the usefulness of integrating elements of the EbM+theory approach into their considerations.

Perspectives of (/memorandum for) systems thinking on COVID-19 pandemic and pathology

Is data-driven analysis sufficient for understanding the COVID-19 pandemic and for justifying public health regulations? In this paper, we argue that such analysis is insufficient. Rather what is needed is the identification and implementation of over-arching hypothesis-related and/or theory-based rationales to conduct effective SARS-CoV2/COVID-19 (Corona) research. To that end, we analyse and compare several published recommendations for conceptual and methodological frameworks in medical research (e.g., public health, preventive medicine and health promotion) to current research approaches in medical Corona research. Although there were several efforts published in the literature to develop integrative conceptual frameworks before the COVID-19 pandemic, such as social ecology for public health issues and systems thinking in health care, only a few attempts to utilize these concepts can be found in medical Corona research. For this reason, we propose nested and integrative systemic modelling approaches to understand Corona pandemic and Corona pathology. We conclude that institutional efforts for knowledge integration and systemic thinking, but also for integrated science, are urgently needed to avoid or mitigate future pandemics and to resolve infection pathology.

'Medical Corona Science': Philosophical and systemic issues: Re-thinking medicine? On the epistemology of Corona medicine

RATIONALE, AIMS, AND OBJECTIVES

The disciplinary profile and the quality of production of knowledge on Corona pandemic is studied. This scientific field is called 'Medical Corona Science'.

METHODS

Criteria of analytical philosophy of science and science studies are systematically applied.

RESULTS

It is shown that mainly auxiliary medical disciplines such as virology and epidemiology but not clinical disciplines provide Corona knowledge. We see a laboratory-centered, technology- and data-driven science, largely ignoring clinical issues. Therefore we call these approaches "Medical Corona Science" (MCS). We see the need to adapt to features of a 'post-normal science', a 'mode 2 science' and of 'Integration and Implementation Science', especially as clinical knowledge must be integrated. There is also a severe lack of theoretical considerations that could help to frame the pandemic as a complex dynamic system.

CONCLUSIONS

We suggest a deeper meta-scientific discussion of the epistemic value of MCS and propose the application of tools from systems science.

Speech as a Biomarker for COVID-19 Detection Using Machine Learning

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.

Systems analysis shows that thermodynamic physiological and pharmacological fundamentals drive COVID-19 and response to treatment

Why a systems analysis view of this pandemic? The current pandemic has inflicted almost unimaginable grief, sorrow, loss, and terror at a global scale. One of the great ironies with the COVID‐19 pandemic, particularly early on, is counter intuitive. The speed at which specialized basic and clinical sciences described the details of the damage to humans in COVID‐19 disease has been impressive. Equally, the development of vaccines in an amazingly short time interval has been extraordinary. However, what has been less well understood has been the fundamental elements that underpin the progression of COVID‐19 in an individual and in populations. We have used systems analysis approaches with human physiology and pharmacology to explore the fundamental underpinnings of COVID‐19 disease. Pharmacology powerfully captures the thermodynamic characteristics of molecular binding with an exogenous entity such as a virus and its consequences on the living processes well described by human physiology. Thus, we have documented the passage of SARS‐CoV‐2 from infection of a single cell to species jump, to tropism, variant emergence and widespread population infection. During the course of this review, the recurrent observation was the efficiency and simplicity of one critical function of this virus. The lethality of SARS‐CoV‐2 is due primarily to its ability to possess and use a variable surface for binding to a specific human target with high affinity. This binding liberates Gibbs free energy (GFE) such that it satisfies the criteria for thermodynamic spontaneity. Its binding is the prelude to human host cellular entry and replication by the appropriation of host cell constituent molecules that have been produced with a prior energy investment by the host cell. It is also a binding that permits viral tropism to lead to high levels of distribution across populations with newly formed virions. This thermodynamic spontaneity is repeated endlessly as infection of a single host cell spreads to bystander cells, to tissues, to humans in close proximity and then to global populations. The principal antagonism of this process comes from SARS‐CoV‐2 itself, with its relentless changing of its viral surface configuration, associated with the inevitable emergence of variants better configured to resist immune sequestration and importantly with a greater affinity for the host target and higher infectivity. The great value of this physiological and pharmacological perspective is that it reveals the fundamental thermodynamic underpinnings of SARS‐CoV‐2 infection.

COVID-19 and mental health in children and adolescents: a diagnostic panel to map psycho-social consequences in the pandemic context

Since the beginning of the COVID-19 pandemic, much research has been done on the psycho-social consequences, especially for children, adolescents and families. In the long run, there is a large set of quantitative data available. However, these still seem to be not well understood. Theoretical classifications of the evidence also diagnostic tools still seem to be open. This paper elaborates a possible systematisation based on theoretical models of systemic self-organisation theories. This leads to a model for a comprehensive psycho-social child-in-environment diagnostic to map potential problem areas. Such a theoretical framing should enable both: a deeper understanding of the impact of pandemics on young people and hypotheses for intervention strategies in the context of pandemic management as well as in the context of diagnostic-systemic interventions in psycho-social working settings. In the coming months and years, it will be essential to be able to understand and describe psychosocial disabilities that have developed in the context of the pandemic in a differentiated way in order to establish targeted interventions.

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective-Results of a Qualitative Study

Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.