Abstract:
The Drug Abuse Monitoring System (DAMS) is a computerized information system, which
collects information on persons arrested for drug offences and persons seeking treatment for
drug abuse. Purposes of the DAMS are to determine the incidence, prevalence and characteristics
of drug users, monitor the trends and patterns in drug use, determine and describe the at risk
groups in the population and evaluate the existing treatment and rehabilitation programmes.
Information is collected from drug law enforcement agencies and drug treatment agencies on
a monthly basis and produce report quarterly and by annually for policy making. The National
Dangerous Drugs Control Board (NDDCB) was designated the principal national institution
charged with the formulation and review of a national policy relating to prevention and control
of the abuse of dangerous drugs and the treatment and rehabilitation of drug abusers and was
given power to make evidence based recommendations to eradicate drug menace using this
database. The main objective of this paper was to analyze improvement and effectiveness of
the DAMS after moving from manual data entering phase to web application phase. The DAMS
was established in 1989 and it was updated as web application in 2015. This situation analysis
has done considering the time period of 2012-2015 as manual data entering system and 2015 –
June, 2018 as web application. According to the DAMS system data during the period of 2005-
2015 NDDCB has identified the issues which are data repeating, data entering delays, system
crash/failures. The data reporting percentages in 2012 and 2015 were 14% and 35%
respectively. Data reporting percentages in 2016 and 2017 were 38% and 44% respectively.
During the period of January to June 2018, 37% of drug related arrest records have been
reported to the system. This analysis illustrated that common advantage of implementing web
application for users and administrator which are user friendly appearance; reduce the paper
cost, timely reporting data and minimizing data repeating errors.
