Networking and data sharing reduces hospitalization cost of heart failure: the experience of <scp>GISC</scp> study

Pisanò, Franco; Lorenzoni, Giulia; Sabato, Stefano Santo; Soriani, Nicola; Narraci, Ottavio; Accogli, Michele; Rosato, Carlo; Paolis, Paola De; Folino, Franco; Buja, Gianfranco; Tona, Francesco; Baldi, Ileana; Iliceto, Sabino; Gregori, Darío

doi:10.1111/jep.12255

Cited by 7 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data analyzed in the present study have been derived from the GISC study. It is an ongoing project, and it takes place in the region of Puglia, Southern Italy [15]. Patients with a diagnosis of HF are enrolled in a long-term assistance program that includes the adoption of an online platform for data-sharing between general practitioners and cardiologists working in hospitals and community health districts.…”

Section: Methodsmentioning

confidence: 99%

“…The present study aims to compare the performance of several MLTs in the prediction of hospitalization among patients with HF, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study [15]. We compared the following algorithms: LR, generalized linear model net (GLMN), classification and regression tree (CART), logitboost (LB), AB, RF, SVM, and neural network (NN).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

Lorenzoni

Sabato²,

Lanera

et al. 2019

JCM

Self Cite

View full text Add to dashboard Cite

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

Lorenzoni

Sabato²,

Lanera

et al. 2019

JCM

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the beginning of EHR implementation, there has been a clear interest in assessing and analyzing which were the economic impacts of introducing these technologies [80][81][82][83][84]. Most of the research carried out so far has been focused on the analyses in different areas such as:…”

Section: Analysis Of Economic Benefitmentioning

confidence: 99%

Secondary Use and Analysis of Big Data Collected for Patient Care

et al. 2017

View full text Add to dashboard Cite

SummaryObjectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on "Data mining and Big Data analytics" focuses on the literature published during the last two years, covering the timeframe since the working group's last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.

show abstract

“…[7][8][9][10] In the case of heart failure, faster communication between patients and medical staff improved patient satisfaction and reduce hospitalized costs. [11][12][13] However, little research has been conducted on a consultation service model between the medical staff of different institutions (Supplementary Table S2, Fig. S1).…”

Section: Qualitative Literature Reviewmentioning

confidence: 99%

Development of Health-RESPECT: An Integrated Service Model for Older Long-Term Care Hospital/Nursing Home Patients Using Information and Communication Technology

Choi

Kim

Lim

et al. 2020

Ann Geriatr Med Res

View full text Add to dashboard Cite

Background: Korea, as one of the fastest-aging countries worldwide, requires an improved healthcare service model for older adults. We evaluated the current healthcare system and developed a service model based on information and communication technologies (ICT) for use in older patients in long-term care facilities (LTCF). Methods: We conducted a qualitative literature review, focus group interviews (FGIs), and structured survey to identify the current technology use and status of healthcare systems. We then developed a web-based platform with necessary, high-priority, and usable content for the care of older patients in LTCF. Results: We reviewed 60 (23 hypertension, 18 diabetes, and 19 heart failure) articles on iinformation and communication technologies (ICT)-based disease management for clinical effectiveness and improved patient satisfaction. FGIs and structured surveys were used to evaluate the inconvenience in patient and medical information transfer between hospitals and cost and time required for its process. Accordingly, we confirmed the unmet need for an ICT-based service model for management, monitoring, and consultation among older patients and developed the Health-RESPECT (integrated caRE Systems for elderly PatiEnts using iCT), a service platform for older patients residing in LTCF. The medical information exchange system was used to transfer medical information. Health-RESPECT includes an established algorithm for evidence-based comprehensive geriatric assessment and customized management; chronic disease management; management of potentially inappropriate medications; rehabilitation; and consultation and videoconferencing. Conclusion: This study identified the current status and unmet needs of healthcare systems for older adults. We developed an ICT-based system to manage older institutionalized patients. However, the Health-RESPECT service model requires further validation.

show abstract

Networking and data sharing reduces hospitalization cost of heart failure: the experience of GISC study

Abstract: We found out that the intervention was a cost-saving strategy for follow-up of the patients suffering from HF at 6 and 12 months after the enrolment compared with hospitalizations' cost before the recruitment.

Cited by 7 publications

References 25 publications

Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

Secondary Use and Analysis of Big Data Collected for Patient Care

Development of Health-RESPECT: An Integrated Service Model for Older Long-Term Care Hospital/Nursing Home Patients Using Information and Communication Technology

Contact Info

Product

Resources

About