How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data.

The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.

COVIDIAGNOSTIX: health technology assessment of serological tests for SARS-CoV-2 infection.

OBJECTIVE: In vitro diagnostic tests for SARS-COV-2, also known as serological tests, have rapidly spread. However, to date, mostly single-center technical and diagnostic performance's assessments have been carried out without an intralaboratory validation process and a health technology assessment (HTA) systematic approach. Therefore, the rapid HTA for evaluating antibody tests for SARS-COV-2 was applied. METHODS: The use of rapid HTA is an opportunity to test innovative technology. Unlike traditional HTA (which evaluates the benefits of new technologies after being tested in clinical trials or have been applied in practice for some time), the rapid HTA is performed during the early stages of developing new technology. A multidisciplinary team conducted the rapid HTA following the HTA Core Model® (version 3.0) developed by the European Network for Health Technology Assessment. RESULTS: The three methodological and analytical steps used in the HTA applied to the evaluation of antibody tests for SARS-COV-2 are reported: the selection of the tests to be evaluated; the research and collection of information to support the adoption and appropriateness of the technology; and the preparation of the final reports and their dissemination. Finally, the rapid HTA of serological tests for SARS-CoV-2 is summarized in a report that allows its dissemination and communication. CONCLUSIONS: The rapid-HTA evaluation method, in addition to highlighting the characteristics that differentiate the tests from each other, guarantees a timely and appropriate evaluation, becoming a tool to create a direct link between science and health management.

Change and Innovation in Healthcare: Findings from Literature.

BACKGROUND: Change is an ongoing process in any organizations. Over years, healthcare organizations have been exposed to multiple external stimuli to change (eg, ageing population, increasing incidence of chronic diseases, ongoing Sars-Cov-2 pandemic) that pointed out the need to convert the current healthcare organizational model. Nowadays, the topic is extremely relevant, rendering organizational change an urgency. The work is structured on a double level of analysis. In the beginning, the paper collects the overall literature on the topic of organisational change in order to identify, on the basis of the citation network, the main existing theoretical approaches. Secondly, the analysis attempts to isolate the scientific production related to the healthcare context, by analysing the body of literature outside the identified citation network, divided by clusters of related studies. METHODOLOGY: This review adopted a quantitative-based method that employs jointly systematic literature review and bibliographic network analysis. Specifically, the study applied a citation network analysis (CNA) and a co-occurrence keywords analysis. The CNA allowed detecting the most relevant papers published over time, identifying the research streams in literature. RESULTS: The study showed four main findings. Firstly, consistent with past studies, works reviewed pointed out a convergence on the micro-level perspective for change's analysis. Secondly, an organic viewpoint whereby individual, organization and change's outcome contribute to any organizational change's action has been found in its early stage. Thirdly, works reported change combined with innovation's concept, although the structure of the relationship has not been outlined. Fourth, interestingly, contributions have been limited within the healthcare context. CONCLUSION: Human dimension is the primary criticality to be managed to impede failure of the re-organizational path. Individuals are not passive recipients of change: individual change acceptance has been found a key input. Few papers discussed healthcare professionals' behaviour, and those available focused on technology-led changes perspective. In this view, individual acceptance of change within the healthcare context resulted being undeveloped and offers rooms for further analyses.