Since the emergence of the novel corona virus, the front line soldiers during this pandemic are the healthcare professionals because of their direct association with COVID19 patients. In the management of such patients, nurses play a significant role through proper care and preventive measures. Due to its contagious nature, fatality and no proper medicine, it is a risk to the health and life of nurses and has an impact on their psychological health. In the current study we assessed the knowledge, attitude, practices and anxiety levels of nurses who are directly involved in the management of COVID-19 patients. It was an online questionnaire based cross sectional survey targeting only those nurses involved in the management of COVID-19 patients from different hospitals of Karachi, Pakistan. SPSS 21 was used for data analysis. Descriptive analysis, Chi Square and t-tests were applied. P value <0.05 was considered significant. Data of 78 nurses was analyzed. We observed that nurses possess good knowledge about COVID-19, its sources, symptoms and routes of transmission of the Virus etc. The knowledge mean score was calculated 14.67 ±3.36. Health department /Hospital and social media are the main sources of information regarding COVID-19. We investigated that 92.3% of the nurses had mild to very severe anxiety and anxiety levels are significantly higher among females (P<0.05). We concluded that the nurses performing their duties with COVID-19 positive patients have good knowledge and attitude. But their anxiety levels are high. Psychological interventions along with training should be given.

Methadone Flexi Dispensing Service (MFlex) has been officially re-branded from Methadone 1Malaysia Service (M1M) since 2nd January 2019. Patients under MFlex are frequently taking their methadone according to a plan provided by pharmacist at public clinic. From the dose monitoring taken annually, pharmacists can predict critical patients based on high monthly dose increases. However, the current monitoring system is written documentation with total doses that cannot accurately measure addiction levels and slow down the distribution process to appropriate incentives as provided by the government. The main objective of this work is to develop a new data monitoring system by evaluating all factors contributed to the addiction level. Mahalanobis-Taguchi System (MTS) is a method of predicting and diagnosing system performance using multivariate data in order to make quantitative decisions with the construction of a multivariate measurement scale using an analytical method. The results show that the minimum Mahalanobis Distance (MD) for healthy data is 0.2245 while the maximum is 2.3380. The minimum and maximum MD of unhealthy data is 0.6077 and 24.5719 respectively. Thus, parameters of blood, bilirubin, nitrite, specific gravity, leukocytes are considered as significant parameters by considering positive value signal-to-noise ratio (SNR) gain. Graphical user interface (GUI) has been developed for analyzing the normal and abnormal patients in detail. Meanwhile, mobile application has been developed as a decision-making tool to classify that the patients is either normal or abnormal.

Patients under the methadone flexi dispensing (MFlex) program are required to do blood tests like liver function profile. A doctor assesses 3 parameters like Alk phosphatase, ALT (SGPT), and AST (SGOT) to ensure the patient has a liver problem. Consequently, the existing system does not have a stable ecosystem towards classification and optimization. The objective is to apply the Mahalanobis-Taguchi system (MTS) in the MFlex program. The data is collected at Bandar Pekan clinic with 34 parameters. Two types of MTS methods are used like RT-Method and T-Method for classification and optimization respectively. The average Mahalanobis distance (MD) of healthy is 1.00 and unhealthy is 352.58. A positive degree of contribution has only 1 parameter. 15 unknown samples have been diagnosed. Type 2 of 6 modifications has been selected as the best-proposed solution. In conclusion, a pharmacist from Bandar Pekan clinic confirmed that MTS can solve problems in the classification and optimization of MFlex program.