The WHO declared the novel coronavirus disease a pandemic, with severe consequences for health and global economic activity and Italy is one of the hardest hit countries. This study aims to assess the socio-economic burden of COVID-19 pandemic in Italy through the estimation of Disability-Adjusted Life Years (DALYs) and productivity loss. The observational study was based on data from official governmental sources collected since the inception of epidemic until 28 April 2020. DALYs for a disease combines the years of life lost due to premature mortality in the population and the years lost due to disability of the disease. In addition to DALYs, temporary productivity loss due to absenteeism from work and permanent productivity loss due to premature mortality were estimated using the Human Capital Approach. The total DALYs amount to 2.01 per 1000 persons. The total permanent productivity loss was around EUR 300 million while the temporary productivity loss was around EUR 100 million. This evaluation does not consider other economic aspects related to lockdown, quarantine of contacts, healthcare direct costs etc. The burden of disease methodology is functional metric for steering choices of health policy and allowing the government to be accountable for the utilization of resources.

Pediatric rare diseases, typically resulting from genetic variations,¹ can emerge from before birth to childhood, posing notable clinical challenges. First, diagnosing rare diseases and other genetic conditions can be daunting due to vague or poorly defined clinical symptoms or lack of knowledge around many disorders as well as around the extension and functional impact of genetic variations.² Conventional diagnostic tests typically involve sequential single-gene analysis,³ while for many heterogeneous clinical conditions, multigene panels or genome-wide sequencing methods are necessary to identify the causative variations.⁴ Various factors greatly complicate diagnosing rare diseases in the pediatric population, such as the considerable number of disease-associated genes, the absence of pathognomonic features or signs, and the heterogeneous nature of the underlying pathogenic changes.⁵ The process is often lengthy, requires prompt recognition of suggestive features and timely referral to different specialists,⁶ and may require multiple attempts.

Objectives The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type. Methods A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials. Results Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety. Conclusion This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments.

The global spread of diabetes poses serious threats to public health requiring a patient-centered approach based both on interprofessional collaboration (IPC) given by the cooperation of several different health professionals, and patients’ perspective through the assessment of Patient-Reported Outcomes (PROs). The aim of the present study is to evaluate the impact of interprofessional collaboration interventions, for the management of type 2 diabetes in primary care settings, through PROs. A systematic review and meta-analysis was conducted querying the PubMed, Scopus and Embase databases. Out of the 1961 papers initially retrieved, 19 met the inclusion criteria. Interprofessional collaboration is significantly associated with an increase in both patient’s satisfaction (SMD 0.32 95% CI 0.05–0.59) and in the mental well-being component of the HRQoL (SMD 0.18; 95% CI 0.06–0.30), and there was also promising evidence supporting the association between an interprofessional approach and an increase in self-care and in generic and specific quality-of-life. No statistical differences were found, supporting the positive impact on IPC interventions on the physical component of the HRQoL, depression, emotional distress, and self-efficacy. In conclusion, the effect of IPC impacts positively on the few areas assessed by PROMs. Policymakers should promote the widespread adoption of a collaborative approach as well as to endorse an active engagement of patients across the whole process of care.

Background/Objectives: In hospital settings, the wide variability of acute and complex chronic conditions—among both adult and pediatric patients—requires advanced approaches to detect early signs of clinical deterioration and the risk of transfer to the intensive care unit (ICU). Nursing diagnoses (NDs), standardized representations of patient responses to actual or potential health problems, reflect nursing complexity. However, most studies have focused on the total number of NDs rather than the individual role each diagnosis may play in relation to outcomes such as ICU transfer. This study aimed to identify and rank the specific NDs most strongly associated with ICU transfers in hospitalized adult and pediatric patients. Methods: A retrospective, monocentric observational study was conducted using electronic health records from an Italian tertiary hospital. The dataset included 42,735 patients (40,649 adults and 2086 pediatric), and sociodemographic, clinical, and nursing data were collected. A random forest model was applied to assess the predictive relevance (i.e., variable importance) of individual NDs in relation to ICU transfers. Results: Among adult patients, the NDs most strongly associated with ICU transfer were Physical mobility impairment, Injury risk, Skin integrity impairment risk, Acute pain, and Fall risk. In the pediatric population, Acute pain, Injury risk, Sleep pattern disturbance, Skin integrity impairment risk, and Airway clearance impairment emerged as the NDs most frequently linked to ICU transfer. The models showed good performance and generalizability, with stable out-of-bag and validation errors across iterations. Conclusions: A prioritized ranking of NDs appears to be associated with ICU transfers, suggesting their potential utility as early warning indicators of clinical deterioration. Patients presenting with high-risk diagnostic profiles should be prioritized for enhanced clinical surveillance and proactive intervention, as they may represent vulnerable populations.