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.

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.

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.

The rise of personalized medicine and its remarkable advancements have revealed new requirements for the availability of appropriate medical decision-making models. Computer science is an area that plays an essential role in the field of personalized medicine, where one of the goals is to provide algorithms and tools to extrapolate knowledge and improve the decision-support process. The minimum clinically important difference (MCID) is the smallest change in PROM scores that patients perceive as meaningful. Treatment that does not achieve the minimum level of improvement is considered inappropriate as well as a potential waste of resources. Using the MCID threshold to identify patients who fail to achieve the minimum change in PROM that results in a meaningful outcome may aid in pre-surgical shared decision-making. The decision tree algorithm is a method for extracting valuable information and providing further meaningful information to the domain expert that supports the decision-making. In the present study, different tools based on machine learning were developed. On the one hand, we compared three XGBoost models to predict the non-achievement of the MCID at six months post-operation in the SF-12 physical score. The prediction score threshold was set to 0.75 to provide three decision-making areas on the basis of the high confidence (HC) intervals; the minority class was re-balanced by weighting the positive class to penalize the loss function (XGBoost cost-sensitive), oversampling the minority class (XGBoost with SMOTE), and re-sampling the negative class (XGBoost with undersampling). On the other hand, we modeled the data through a decision tree (assessment tree), based on different complexity levels, to identify the hidden pattern and to provide a new way to understand possible relationships between the gathered features and the several outcomes. The results showed that all the proposed models were effective as binary classifiers, as they showed moderate predictive performance both regarding the minority or positive class (i.e., our targeted patients, those who will not benefit from surgery) and the negative class. The decision tree visualization can be exploited during the patient assessment status to better understand if those patients will benefit or not from the medical intervention. Both of these tools can come in handy for increasing knowledge about the patient’s psychophysical state and for creating an increasingly specialized assessment of the individual patient.

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One of the next frontiers in medical research, particularly in orthopaedic surgery, is personalized treatment outcome prediction. In personalized medicine, treatment choices are adjusted for the patient based on the individual’s and their disease’s distinct features. A high-value and patient-centered health care system requires evaluating results that integrate the patient’s viewpoint. Patient-reported outcome measures (PROMs) are widely used to shed light on patients’ perceptions of their health status after an intervention by using validated questionnaires. The aim of this study is to examine whether meteorological or light (night vs. day) conditions affect PROM scores and hence indirectly affect health-related outcomes. We collected scores for PROMs from questionnaires completed by patients (N = 2326) who had undergone hip and knee interventions between June 2017 and May 2020 at the IRCCS Orthopaedic Institute Galeazzi (IOG), Milan, Italy. Nearest neighbour propensity score (PS) matching was applied to ensure the similarity of the groups tested under the different weather-related conditions. The exposure PS was derived through logistic regression. The data were analysed using statistical tests (Student’s t-test and Mann−Whitney U test). According to Cohen’s effect size, weather conditions may affect the scores for PROMs and, indirectly, health-related outcomes via influencing the relative humidity and weather-related conditions. The findings suggest avoiding PROMs’ collection in certain conditions if the odds of outcome-based underperformance are to be minimized. This would ensure a balance between costs for PROMs’ collection and data availability.

Total hip (THA) and total knee (TKA) arthroplasty procedures have steadily increased over the past few decades, and their use is expected to grow further, mainly due to an increasing number of elderly patients. Cost-containment strategies, supporting a rapid recovery with a positive functional outcomes, high patient satisfaction, and enhanced patient reported outcomes, are needed. A Fast Track surgical procedure (FT) is a coordinated perioperative approach aimed at expediting early mobilization and recovery following surgery and, accordingly, shortening the length of hospital stay (LOS), convalescence and costs. In this view, rapid rehabilitation surgery optimizes traditional rehabilitation methods by integrating evidence-based practices into the procedure. The aim of the present study was to compare the effectiveness of Fast Track versus Care-as-Usual surgical procedures and pathways (including rehabilitation) on a mid-term patient-reported outcome (PROs), the SF12 (with regard both to Physical and Mental Scores), 3 months after hip or knee replacement surgery, with the use of Propensity score-matching (PSM) analysis to address the issue of the comparability of the groups in a non-randomized study. We were interested in the evaluation of the entire pathways, including the postoperative rehabilitation stage, therefore, we only used early home discharge as a surrogate to differentiate between the Fast Track and Care-as-Usual rehabilitation pathways. Our study shows that the entire Fast Track pathway, which includes the post-operative rehabilitation stage, has a significantly positive impact on physical health-related status (SF12 Physical Scores), as perceived by patients 3 months after hip or knee replacement surgery, as opposed to the standardized program, both in terms of the PROs score and the relative improvements observed, as compared with the minimum clinically important difference. This result encourages additional research into the effects of Fast Track rehabilitation on the entire process of care for patients undergoing hip or knee arthroplasty, focusing only on patient-reported outcomes.

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