Medical knowledge integration and "systems medicine": Needs, ambitions, limitations and options

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.

Eleven quick tips for data cleaning and feature engineering

Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the computational analysis. In this framework, data cleaning and feature engineering are key pillars of any scientific study involving data analysis and that should be adequately designed and performed since the first phases of the project. We call "feature" a variable describing a particular trait of a person or an observation, recorded usually as a column in a dataset. Even if pivotal, these data cleaning and feature engineering steps sometimes are done poorly or inefficiently, especially by beginners and unexperienced researchers. For this reason, we propose here our quick tips for data cleaning and feature engineering on how to carry out these important preprocessing steps correctly avoiding common mistakes and pitfalls. Although we designed these guidelines with bioinformatics and health informatics scenarios in mind, we believe they can more in general be applied to any scientific area. We therefore target these guidelines to any researcher or practitioners wanting to perform data cleaning or feature engineering. We believe our simple recommendations can help researchers and scholars perform better computational analyses that can lead, in turn, to more solid outcomes and more reliable discoveries.

Toward a big picture of COVID-19

The paper aims to illustrate and explain the problems and opportunities for improvement in Covid management that become evident when taking a systems perspective. Critical time delays occurred in the regulation of the pandemic that the management cycle of political cybernetics makes explicit. In general, the executive management of the pandemic in global, regional, and national organizations was unprepared in detecting and responding to the onset of the waves and making appropriate decisions towards differential instead of general lockdowns based on available data. This was further complicated by the mutants of SARS-CoV2 that perpetuated the high dynamics of the pandemic. In addition, the diversity of medical specialisms, without appropriate big picture thinking, led to an imbalanced response that failed to appreciate the role of virology and epidemiology compared to clinical and public health-related issues. In consequence, laboratory experts suggested everyday regulations for the citizens without taking into account wider considerations for empirical research. There was an insufficient effort made for proposed treatment studies using existing agents based on the established understanding of essential physiology and the role of local and systemic chronic inflammation. In contrast, driven by media popularization, drugs that later proved beneficial were put in doubt and other drugs that lacked benefit and potentially caused harm were driven to clinical trials and utilization. Person-centered systems view backed by scientific knowledge and established data would have set better priorities. Finally, we need to take a step back and consider the Corona crisis pandemic in the context of the unidimensional utility-driven handling of natural ecosystems by the culture of industrialized countries. This ever-accelerating destruction of life spaces for species drives adaptations are the basis of zoonoses. There is strong evidence that future pandemics should be faced with a more systemic socio-ecological conceptual framework that also reflects the fatal impact of human civilization on natural ecosystems, no matter if SARS CoV2 is a zoonosis or a laboratory accident. It is critical for the future of our species that we collectively learn from this experience, address limitations in our perspectives, enhance our system-based science and bolster global, regional, and national crisis management. The impact of climate change and biodiversity loss has crossed the horizon and is now clearly in full sight.

How does the 'environment' come to the person? The 'ecology of the person' and addiction

Currently, psychiatry lacks a field that can be called "theoretical psychiatry", which uses theoretical concepts and explanatory models: The main stream of research is to collect data of all kinds in the hope that the computational Big Data approach will shed a bright light on the black box of mental disorders. Accordingly, the biology-based Research Domain Criteria of the National Institute of Mental Health have been established. However, as philosophical analyses of concepts and methods have shown, several epistemological gaps stand in the way of a consistent multilevel understanding of mental disorders. Also, the implicit ontological problems in the biological reduction of the psychosocial level and in the integration of so-called hard and soft disciplines are mostly left out. As a consequence, a non-reductive psychological theory of mental disorders is sought that also integrates correlating biological and sociological issues. In this context, one example of promising nonreductive psychiatric research is the option of systems/network psychopathology. The possibilities for integrating different psychological perspectives are highlighted for the field of addiction research and treatment, where pragmatic behaviorist approaches dominate over the theory-based practice of psychoanalysis. In comparing the theoretical constructs of these two approaches, the relevance of the concept of "(social) environment" as the wealth of influential sociocultural factors is discussed at levels superior to the interpersonal micro-level, namely the organizational meso- and societal macro level, which is not sufficiently considered in current biopsychiatry. On this basis of argumentation, the usefulness of grounding and framing psychiatry through the field of ecological sciences, especially human ecology, is demonstrated. Finally, to this end, an outline of an ecological model of mental health and illness is presented.

Future of processing and facilitating change and learning

The field of the sciences of the mind is evolving fast. With the diversification of knowledge and accumulation of data, often lacking integration and reproducibility, questions arise. The role of critical thinking and research is evident. As the science of the unconscious, psychoanalysis provides a method and theory to understand human minds and mentalities, helping the patient know his mind and transform action into reflection. Mental activities, including social skills, develop in the social context, depending on the social environment’s demands and resources put onto the individual. Encoding emotional signals, markers of meaning for the individual, is ontogenetically necessary and has influences on memory encoding. Beyond theoretical understanding, implicit relational knowledge is actualized in the therapeutic setting. With a strong focus on experiencing emotional reconsolidation of memories, previous relationships’ repercussions are enriched with broadening viewpoints in the analytic environment. The long-term effects of psychotherapeutic treatments have been examined. A sufficient explanation of the specific factors contributing to success or an answer when an impact is lacking is still under investigation. When investigating subliminal and implicit mechanisms leading to memory reconsolidation and the formation of functional object relations and interaction patterns, the focus is set on affective interplay and processing prior/during and after social interactions. The present paper discusses which parameters might contribute to the reshaping of memories and the linkage of memory with the emotional load of experience. Providing insights into such dynamic mental phenomena could enhance process research by investigating moment by moment interactions in psychoanalysis, treatment, and learning processes. Due to the research subject’s complexity, different research methods and integration of associated research fields are required.

Implementing a Clinical Research Department to Support Pediatric Studies: A SWOT Analysis

The safety, tolerability, pharmacokinetics and efficacy of most drugs used in pediatrics have not been studied in different age groups and are administered "off-label use". Clinical pediatric drug trials require specific and stringent compliance with laws, regulations, guidelines, and patient/parent/public involvement, which in turn increases resource use and makes support useful from a medical, qualitative, economic, and system perspective. We examined the strengths, weaknesses, opportunities and threats of implementing a Research Department for the Support of Pediatric Studies (RDPS) in Vienna. We used the SWOT ("strengths", "weaknesses", "opportunities", and "threats") analysis to collect comprehensive data and facts on the internal strengths, weaknesses (company analysis), and external opportunities and threats (environmental analysis). The company analysis revealed a productivity gain, due to a highly specialized team and standardized processes. The environmental analysis outlined a considerable 360-degree potential for a qualitative and quantitative medical- and social-scientific expansion of the service portfolio. The establishment of a RDPS leads to the centralization of pediatric studies by bundling tasks and concentration of specialist knowledge, which enables the exploitation of synergies, the standardization of processes, the promotion of professionalism, flexibility, innovations and the reduction of inefficiencies in the form of duplication of tasks. RDPS offers tailored advice and support for different types of pediatric studies.