Hospital networks in Italy: state of the art and future pespectives. Insights from a qualitative research study.

Background Hospitals have undergone important that changes that have led, in recent decades at the international level, to the need for greater integration between hospitals and local healthcare services. The main institutional networks that have been developed in Italy are, as commended by the institutional levels, of 4 main types: the Emergency-Urgency Network, the Time-Dependent Networks, the Oncological Networks, and the Networks with primary care settings. It was important to assess the state of the art and analyze it in relation to possible future developments. Objective The aim of the study was to collect insights from both evidence-based knowledge and personal experience gained by experts in the field regarding the current condition and possible future developments of hospital networks. Material and methods A qualitative research methodology was chosen. Four mini-focus group meetings were organized among participants with proven expertise on the subject. Discussions were guided by four open-ended questions corresponding to the four areas of interest. Directed content analysis was chosen as the methodology for data analysis and final reporting of results. Results Four main categories were explored: "hospital networks and complexity", "hospital networks complexity and the need for integration", "levers for hospital networks governance" and "the COVID-19 challenge and future developments for hospital networks". In particular, the participants found that it is important to understand healthcare systems as complex systems and, therefore, to study the properties of complex systems. In this way it is possible to achieve value-based healthcare in complex contexts. It is also necessary to keep in mind that complexity represents a challenge for coordination/ integration in hospital networks. Mintzberg identified specific mechanisms to achieve it. Of them, mutual adaptation is the key to self-organization. Valentijn showed the organizational levels on which coordination/integration has to be obtained. Hospital network governance should include both hierarchy and self-determination logic to achieve integration in each of the four levels. The participants identified three key levers for governing complex organizations: "education", which consists of multi-professional and multi-level training in governance in complex systems; "information" consisting in considering the data registering as an integral part of the clinical care process to informative value; "leadership", which consists in convincing actors, directed towards personal gains, to achieve valuable goals. Finally, the challenge that COVID-19 served as an incentive for future developments of hospital networks. Discussion Various common points between the definitions of network and complex systems can be found. It is important to study the properties of complex systems in order to achieve value-based healthcare in the hospital networks context. The insights gained should be useful for all professionals from and across all levels of healthcare organizational responsibility, being able to orient roles and actions to achieve coordination/integration inside hospital networks. Conclusions Complexity literature can help understand how to achieve coordination/integration in healthcare settings and find levers for effective governance. It is important to study the current situation to anticipate and, possibly govern, future developments. In conclusion, governance of hospital networks should be interpreted as coordination/integration inside and across multiple organizational levels of co-responsibility.

Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study.

OBJECTIVE: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring. PATIENTS AND METHODS: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity. RESULTS: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated. CONCLUSIONS: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.

Donor-recipient incompatibility at CD31-codon 563 is a major risk factor for acute graft-versus-host disease after allogeneic bone marrow transplantation from a human leucocyte antigen-matched donor.

Disparities at minor histocompatibility antigens (mHA) are thought to be responsible for acute graft-versus-host disease (aGVHD) in patients receiving bone marrow transplantation (BMT) from a human leucocyte antigen (HLA)-matched donor. Although some mHA have been identified in humans, their role in aGVHD has not. Patients (n = 150) receiving a BMT from an HLA-matched donor were investigated for a correlation between aGVHD and donor/recipient incompatibility for seven polymorphisms previously proposed for mHA (HA-1, H-Y, CD31-codon 125, CD31-codon 563, HPA-1, HPA-3 and HPA-5). Only mismatch at CD31-codon 563 predicted grade II-IV aGVHD. The risk derived from CD31-codon 563 mismatch was the same as that derived from the use of bone marrow from an unrelated donor. We suggest that donor/recipient compatibility at CD31-codon 563 should be added to HLA-typing for donor selection and/or adjustment of aGVHD prophylaxis.