Priority strategic directions in adolescent health in Iran based on the WHO's Global Accelerated Action for the Health of Adolescents

OBJECTIVES

In line with the World Health Organization's Global Accelerated Action for the Health of Adolescents (AA-HA!) guidance, the goal of the current research was to identify critical strategies for adolescents' health and to determine the role and distribution of responsibilities among the leading players in the field of adolescent health in Iran.

METHODS

The current qualitative and applied study is part of the Ministry of Health and Medical Education's "Adolescent, Youth and School Health" plan to develop the "National Adolescent Health Plan Document" in 2020. First, stakeholder analysis was done, then a pool of nationally appropriate strategies was selected from the list of priority strategies recommended by the WHO in the AA-HA! through several group sessions. After that, the experts selected priority strategies based on the criteria of feasibility, acceptability, effectiveness, guaranteed resources, coordination with other plans and temporal priority, scoring, and executive priorities. Eventually, the priority strategies were assigned to different players/stakeholders in the field over several sessions bearing in mind the methods of implementation and the target groups.

RESULTS

The experts identified 58 priority strategies/actions for adolescent health under the seven priority areas of positive development, sexual protection, reproductive health, mental health, substance abuse, self-harm, violence, unintentional injury, communicable and non-communicable diseases, nutrition, and physical activity.

CONCLUSIONS

The highest identified priority areas were in the areas of vaccination; special health care package for service providers; training and education to promote health literacy and self-care, life skills, sexual awareness, and prevention/protection against violence; community-based mental health services, planning for adolescents' spare time, substance use prevention.

Detecting medical prescriptions suspected of fraud using an unsupervised data mining algorithm

Nowadays, health insurance companies face various types of fraud, like phantom billing, up-coding, and identity theft. Detecting such frauds is thus of vital importance to reduce and eliminate corresponding financial losses. We used an unsupervised data mining algorithm and implemented an outlier detection model to assist the experts in detecting medical prescriptions suspected of fraud. The implementation ran medicine code, patients' sex, and patients' age variables through three successive screening steps. The proposed model is capable of detecting 25% to 100% of cases violating the standards for some medicines that are not supposed to be prescribed at the same time in one single prescription. This model can also detect medical prescriptions suspected of fraud with a sensitivity of 62.16%, specificity of 55.11%, and accuracy of 57.2%. This paper shows that data mining can help detecting potential fraud cases in medical prescriptions more quickly and accurately than by the manual inspection as well as reducing the number of medical prescriptions to be checked which will result in reducing investigators heavy workload. The results of the proposed model can also help policymakers to plan for fighting against fraudulent activities. Graphical Abstract Detecting Medical Prescriptions Suspected of Fraud Using an Unsupervised Data Mining Algorithm.