Development of a decision support system for the practice of responsible self-medication.

BACKGROUND: Responsible self-medication is an integral part of the health system that consists of community pharmacists counseling patients on treating minor illness using non-prescription medications. Systems for properly managing information can assist disease identification and clinical decision-making. OBJECTIVE: To develop a software program to assist community pharmacists in clinical decision-making regarding selfmedication. SETTING: The study was conducted in northeastern Brazil. METHODS: The study was conducted from February 2012 to January 2014. System development included identifying minor illnesses commonly treated by community pharmacists and creating simulations of community pharmacies using a simulated patient methodology. Clinical pharmacists, production engineering students, professors, and a pharmacist researcher comprised the development group. Five meetings were held to develop the software, and the system was completed in December 2013. MAIN OUTCOME MEASURE: Minor illnesses commonly treated by community pharmacists, and simulated patient methodology. RESULTS: In the first meeting the final list of topics for inclusion in the algorithm indicated the exact questions to be addressed by the community pharmacist to properly manage the complaint. In the second meeting, the discussions in the focus group indicated consensus among pharmacists as to the medications on the list of Groups and Specified Therapeutic Indications of Brazilian Legislation. In the third meeting were defined the parameters to refer patients to the doctor. In the fourth meeting the algorithm was tested using a simulated patient, to observe whether the question order ensures an effective, efficient, and safe decision process for the patient. In the fifth meeting, the algorithm was tested again using a simulated patient with the flu, and all group members agreed upon its final incarnation after refinements to the situations that determined referral to the doctor. CONCLUSION: The software may contribute to identifying health risk situations (potentially unsafe medications based on clinical history, clinical hazards, and past adverse events) requiring medical treatment.

Clustering rfam 10.1: clans, families, and classes.

The Rfam database contains information about non-coding RNAs emphasizing their secondary structures and organizing them into families of homologous RNA genes or functional RNA elements. Recently, a higher order organization of Rfam in terms of the so-called clans was proposed along with its "decimal release". In this proposition, some of the families have been assigned to clans based on experimental and computational data in order to find related families. In the present work we investigate an alternative classification for the RNA families based on tree edit distance. The resulting clustering recovers some of the Rfam clans. The majority of clans, however, are not recovered by the structural clustering. Instead, they get dispersed into larger clusters, which correspond roughly to well-described RNA classes such as snoRNAs, miRNAs, and CRISPRs. In conclusion, a structure-based clustering can contribute to the elucidation of the relationships among the Rfam families beyond the realm of clans and classes.