Home Care (HC) service consists of providing care to patients in their homes. During the last decade, the HC service industry experienced significant growth in many European countries. This growth stems from several factors, such as governmental pressure to reduce healthcare costs, demographic changes related to population ageing, social changes, an increase in the number of patients that suffer from chronic illnesses, and the development of new home-based services and technologies. This study proposes a framework that will enable HC service providers to better understand HC operations and their management. The study identifies the main processes and decisions that relate to the field of HC operations management. Hence, an IDEF0 (Integrated Definition for Function Modelling) activity-based model describes the most relevant clinical, logistical and organisational processes associated with HC operations. A hierarchical framework for operations management decisions is also proposed. This analysis is derived from data that was collected by nine HC service providers, which are located in France and Italy, and focuses on the manner in which operations are run, as well as associated constraints, inputs and outputs. The most challenging research areas in the field of HC operations management are also discussed.

Home Care (HC) services provide complex and coordinated medical and paramedical care to patients at their homes. As health care services move into the home setting, the need for developing innovative approaches that improve the efficiency of home care organizations increases. We first conduct a literature review of investigations dealing with operation planning within the area of home care management. We then address a particular issue dealing with the planning of operations related to chemotherapy at home as it is an emergent problem in the French context. Our interest is focused on issues specific to the anti-cancer drug supply chain. We identify various models that can be developed and analyze one of them.

The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.

Introduction: About half of hospital readmissions can be avoided with preventive interventions.Developing decision support tools for identification of patients' emergency readmission risk is an important area of research. Because, it remains unclear how to design features and develop predictive models that can adjust continuously to a fast-changing healthcare system and population characteristics. The objective of this study was to develop a generic ensemble Bayesian risk model of emergency readmission. Methods:We produced a decision support tool that predicts risk of emergency readmission using 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 approaches, and it was robust and stable with high precision. Moreover, the framework and the Bayesian model allow the model to continuously adjust it to new significant features, different population characteristics and changes in the system.

Comorbidity in patients, along with attendant operations and complications, is associated with reduced long-term survival probability and an increased need for healthcare facilities. This study proposes a user-friendly toolkit to design an adjusted case-mix model of the risk of comorbidity for use by the public for its incremental development. The proposed model, Temporal Comorbidity-Adjusted Risk of Emergency Readmission (T-CARER), introduces a generic method for generating a pool of features from re-categorised and temporal features to create a customised comorbidity risk index.Research on emergency admission has shown that demographics, temporal dimensions, length of stay, and time between admissions can noticeably improve statistical measures related to comorbidities. The model proposed in this study, T-CARER, incorporates temporal aspects, medical procedures, demographics, admission details,