A Multicomponent Primary-Care Intervention for Preventing Falls in Older Adults Living in the Community: The PREMIO Study.

BACKGROUND: Falls are a common cause of morbidity and functional impairment in the elderly and represent a significant health problem. General practitioners (GPs) are the first point of contact for health issues and may provide preventive services. The randomized clinical trial PREMIO was conducted by GPs to evaluate the effects of a multicomponent intervention for the prevention of falls in older adults aged ≥ 65 years at high risk of falling. METHODS: 117 GPs enrolled 1757 patients (1116 F, 641 M) and randomized them into 2 groups (intervention and control). The intervention group received medical and behavioral counseling, home risk-factor assessment, a physical-activity program and nutritional counseling. The control group received only the nutritional counseling. Both groups were followed for one year. The primary outcome was the rate of falls at home over 12 months. RESULTS: 1225 patients completed the study. Subjects receiving the intervention had, on average, fewer falls at home (percentage change -31.2%, p < 0.02) and fewer total falls (-26.0%, p < 0.02), although the reduction in the number of fallers was small (-3.9%, p = 0.05). Among the secondary endpoints, rates of general hospital or emergency-department admission and GP visits showed slight improvements (not statistically significant), while the risk of fractures was unexpectedly increased in the intervention group compared to the controls (odds ratio 2.39, p = 0.023). CONCLUSIONS: Future studies and public-health interventions to prevent domestic falls among community-dwelling older people at high risk of falling could benefit from a multicomponent approach including medication review, physical exercise and home risk assessment and should include assessment of risk factors for fractures.

A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing.

Objective Decision support systems (DSS) have been developed and promoted for their potential to improve quality of health care. However, there is a lack of common clinical strategy and a poor management of clinical resources and erroneous implementation of preventive medicine. Methods To overcome this problem, this work proposed an integrated system that relies on the creation and sharing of a database extracted from GPs' Electronic Health Records (EHRs) within the Netmedica Italian (NMI) cloud infrastructure. Although the proposed system is a pilot application specifically tailored for improving the chronic Type 2 Diabetes (T2D) care it could be easily targeted to effectively manage different chronic-diseases. The proposed DSS is based on EHR structure used by GPs in their daily activities following the most updated guidelines in data protection and sharing. The DSS is equipped with a Machine Learning (ML) method for analyzing the shared EHRs and thus tackling the high variability of EHRs. A novel set of T2D care-quality indicators are used specifically to determine the economic incentives and the T2D features are presented as predictors of the proposed ML approach. Results The EHRs from 41237 T2D patients were analyzed. No additional data collection, with respect to the standard clinical practice, was required. The DSS exhibited competitive performance (up to an overall accuracy of 98%±2% and macro-recall of 96%±1%) for classifying chronic care quality across the different follow-up phases. The chronic care quality model brought to a significant increase (up to 12%) of the T2D patients without complications. For GPs who agreed to use the proposed system, there was an economic incentive. A further bonus was assigned when performance targets are achieved. Conclusions The quality care evaluation in a clinical use-case scenario demonstrated how the empowerment of the GPs through the use of the platform (integrating the proposed DSS), along with the economic incentives, may speed up the improvement of care.

Discovering the Type 2 Diabetes in Electronic Health Records Using the Sparse Balanced Support Vector Machine.

The diagnosis of type 2 diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patient's follow-up. Recent years have witnessed an increasing amount of available electronic health record (EHR) data and machine learning (ML) techniques have been considerably evolving. However, managing and modeling this amount of information may lead to several challenges, such as overfitting, model interpretability, and computational cost. Starting from these motivations, we introduced an ML method called sparse balanced support vector machine (SB-SVM) for discovering T2D in a novel collected EHR dataset (named Federazione Italiana Medici di Medicina Generale dataset). In particular, among all the EHR features related to exemptions, examination, and drug prescriptions, we have selected only those collected before T2D diagnosis from an uniform age group of subjects. We demonstrated the reliability of the introduced approach with respect to other ML and deep learning approaches widely employed in the state-of-the-art for solving this task. Results evidence that the SB-SVM overcomes the other state-of-the-art competitors providing the best compromise between predictive performance and computation time. Additionally, the induced sparsity allows to increase the model interpretability, while implicitly managing high-dimensional data and the usual unbalanced class distribution.

Sharing health data among general practitioners: The Nu.Sa. project.

Today, e-health has entered the everyday work flow in the form of a variety of healthcare providers. General practitioners (GPs) are the largest category in the public sanitary service, with about 60,000 GPs throughout Italy. Here, we present the Nu.Sa. project, operating in Italy, which has established one of the first GP healthcare information systems based on heterogeneous data sources. This system connects all providers and provides full access to clinical and health-related data. This goal is achieved through a novel technological infrastructure for data sharing based on interoperability specifications recognised at the national level for messages transmitted from GP providers to the central domain. All data standards are publicly available and subjected to continuous improvement. Currently, the system manages more than 5,000 GPs with about 5,500,000 patients in total, with 4,700,000 pharmacological e-prescriptions and 1,700,000 e-prescriptions for laboratory exams per month. Hence, the Nu.Sa. healthcare system that has the capacity to gather standardised data from 16 different form of GP software, connecting patients, GPs, healthcare organisations, and healthcare professionals across a large and heterogeneous territory through the implementation of data standards with a strong focus on cybersecurity. Results show that the application of this scenario at a national level, with novel metrics on the architecture's scalability and the software's usability, affect the sanitary system and on GPs' professional activities.

[Human Papillomavirus infection and vaccination: knowledge and attitudes of Italian general practitioners]. / Infezione da Papillomavirus umano e vaccinazione: conoscenze e ruolo dei medici di medicina generale.

OBJECTIVE: To assess General Pratictioner's (GPs) knowledge and attitudes about HPV infection and prevention. DESIGN AND PARTICIPANTS: A semi-structured survey was conducted from November to December 2013 among Italian GPs. Descriptive and univariate analyses were carried out. MAIN OUTCOME MEASURES AND RESULTS: 938 GPs were included in the study. 15% participated in continuing medical education courses focusing on HPV. GPs identified as HPV transmission routes: sexual (100%), cutaneous (15%), transplacental (13%), haematic (9%) and by air (2%); they considered HPV-related diseases: cervical (98%), vulvar and vaginal (42%), anal (39%), penile (38%) and oral (38%) cancer, genital warts (79%) and respiratory papillomatosis (12%). They knew HPV vaccination is to prevent HPV-associated cancer (60%), in particular cervical cancer (35%), genital warts (3%) and sexually transmitted diseases (2%). A total of 73% were aware of the existence of both available vaccines, 69% believed that immunization target population should be females before initiation of sexual activity, 87% knew the age of vaccine administration. No significant difference in knowledge was retrieved by age, gender, level of education or region of origin. CONCLUSION: We report a lack of knowledge on HPV infection and vaccination in GPs. GPs have a key role in the Italian health system. Although 12-year old patients are a small percentage of their patients, it is of fundamental importance to promote medical education and training among GPs in order to meet HPV coverage targets and control HPV-associated diseases.