Using Failure Mode, Effect and Criticality Analysis to improve safety in the cancer treatment prescription and administration process.

BACKGROUND: Administering cancer drugs is a high-risk process, and mistakes can have fatal consequences. Failure Mode, Effect and Criticality Analysis (FMECA) is a widely recognized method for identifying and preventing potential risks, applied in various settings, including healthcare. The aim of this study was to recognize potential failures in cancer treatment prescription and administration, with a view to enabling the adoption of measures to prevent them. METHODS: This study consists of a FMECA. A team of resident doctors in public health at the University of Padua examined the cancer chemotherapy process with the support of a multidisciplinary team from the Veneto Institute of Oncology (an acknowledged comprehensive cancer center), and two other provincial hospitals. A diagram was drafted to illustrate 9 different phases of chemotherapy, from the adoption of a treatment plan to its administration, and to identify all possible failure modes. Criticality was ascertained by rating severity, frequency and likelihood of a failure being detected, using adapted versions of already published scales. Safety strategies were identified and summarized. RESULTS: Twenty-two failure modes came to light, distributed over the various phases of the cancer treatment process, and seven of them were classified as high risk. All phases of the cancer chemotherapy process were defined as potentially critical and at least one action was identified for a single high-risk failure mode. To reduce the likelihood of the cause, or to improve the chances of a failure mode being detected, a total of 10 recommendations have been identified. CONCLUSIONS: FMECA can be useful for identifying potential failures in a process considered to be at high risk. Safety strategies were devised for each high-risk failure mode identified.

Adherence to Therapy, Physical and Mental Quality of Life in Patients with Multiple Sclerosis.

Ensuring multiple sclerosis (MS) patients' adherence to therapy is often challenging, but it is crucial to their survival and health-related quality of life (HRQoL). The aim of the present study was to outline connections between adherence, physical and mental HRQoL, levels of psychological readiness to engage in a treatment, levels of social support, anthropometric, socio-demographic and clinical factors in patients suffering from MS. This cross-sectional study involved a sample of 237 Italian MS patients. A survey was conducted with a structured self-administered online questionnaire using validated measures of quality of life, adherence to therapy and anthropometric, socio-demographic, psychological and clinical variables. A path analysis was used to test the overall structure of the associations between the variables. The pathway indicates a positive association between mental health index and a stronger degree of engagement and being or having been in a long-term relationship. Physical health index was positively associated with age, having an occupation, and having a specific form of MS. Having had relapses in the previous year raised the odds of better adherence to therapy, while an increase in Body Mass Index (BMI) reduced them. Our findings could help in the management of MS patients, promoting behavioral interventions that take the psychological and socio-demographic peculiarities of each patient into account with a view to improving their adherence to therapy.

Health care service use and costs for a cohort of high-needs elderly diabetic patients.

AIMS: To describe the impact of diabetes comorbidities on the health care services use and costs of a cohort of elderly patients with diabetes and high health care needs (HHCN), based on real-world data. METHODS: We focused on a cohort of diabetic patients with HHCN belonging to Resource Utilization Bands 4 and 5 according to the Adjusted Clinical Group (ACG) system. Their comorbidities were assessed using the clinical diagnoses that the ACG system assigns to single patients by combining different information flows. Regression models were applied to analyze the associations between comorbidities and health care service use or costs, adjusting for age and sex. RESULTS: Our analyses showed that all health care service usage measures (e.g. access to emergency care; number of outpatient visits) and the total annual costs and pharmacy costs are associated significantly with comorbidity class. Instead, no differences in hospitalization rates by comorbidity class were revealed. CONCLUSION: The association between a larger number of comorbidities and higher total health care service usage and costs was seen mainly for primary care services. This underscores the need to strengthen primary care for today's aging and multimorbid population.