Criteria for the selection of complementary private health insurance: the experience of a large organisation in Iran.

BACKGROUND: Expenses related to employee's health benefit packages are rising. Hence, organisations are looking for complementary health financing arrangements to provide more financial protection for employees. This study aims to develop criteria to choose the most appropriate complementary health insurance company based on the experience of a large organisation in Iran. METHODS: This study was conducted in 2021 in Iran, in the Foundation of Martyrs and Veterans Affairs to find as many applicable criteria as possible. To develop a comprehensive list of criteria, we used triangulation in data sources, including review of relevant national and international documents, in-depth interviews of key informants, focus group discussion, and examining similar but unpublished checklists used by other organisations in Iran. The list of criteria was prioritised during focus group discussions. We used the best-worst method as a multi-criteria decision making method and a qualitative consensus among the key informants to value the importance of each of the finalised criteria. FINDINGS: Out of 85 criteria, we selected 28 criteria to choose an insurer for implementing complementary private health insurance. The finalised criteria were fell into six domains: (i) Previous experience of the applicants; (ii) Communication with clients; (iii) Financial status; (iv) Health care providers' network; (v) Technical infrastructure and workforce; (vi) and Process of reviewing claims and reimbursement. CONCLUSION: We propose a quantitative decision-making checklist to choose the best complimentary private health insurance provider. We invite colleagues to utilise, adapt, modify, or develop these criteria to suit their organisational needs. This checklist can be applied in any low- and middle-income country where the industry of complementary health insurance is blooming.

Evaluation of the efficacy of amplitude-integrated electroencephalography in the screening of newborns with metabolic disorder admitted to the NICU.

BACKGROUND: Neonate patients with metabolic disorder show encephalopathy and seizures that may lead to morbidity and mortality. Thus rapid detection and treatment of these patients is necessary. Although Amplitude-integrated electroencephalography (aEEG) has been used for more than a decade in the evaluation of infants with encephalopathy but has not been used in the assessment of neonates suffering from metabolic disorders. In this study, we tried to determine the efficacy of aEEG as an easily available diagnostic tool in the diagnosis of neonates with metabolic diseases. METHODS: All cases which admitted to the Neonatal Intensive Care Unit (NICU) of the Children's Medical Center during a one-year period were enrolled. aEEG recordings were obtained by installing 4 electrodes on the infant's head by a trained nurse and aEEG was recorded for at least 24 h with a description of the whole tracing. Clinical information, final outcome and questionnaires, including patient information: symptoms of the disease, gender, age, duration of hospitalization and the type of the metabolic disease were recorded in details. The obtained data was analyzed with the Spss24 software. RESULTS: Only 3 (two girl and one boy) out of 29 aEEGs recordings were abnormal; other patients showed normal aEEGs. The most common clinical and neurological manifestations were seizure (34.5%), hypotonia (31%), and mortality rate was 10.3%. There was no significant correlation between aEEG findings and gender, age, type of disease, laboratory tests findings and positive family history. CONCLUSIONS: Although it has been shown that EEG has a diagnostic value in metabolic diseases, there has been no study on the efficacy of aEEG to evaluate neonates with metabolic diseases. But good accessibility and easy of working with aEEG, promote a tendency to use this procedure as screening tool for metabolic diseases. The current study about aEEG monitoring in these patients, while limited, can be used as a pilot study for further research on this topic. Therefore, a correct judgment in this field requires administration of aEEG on a larger population of neonates with metabolic diseases.