Abstract: Background: The objectives of this study were to
assess patient’s knowledge of appropriate sublingual glyceryl
trinitrate (GTN) use as well as to investigate how patients commonly
store and carry their sublingual GTN tablets. Methodology: This was
a cross-sectional survey, using a validated researcher-administered
questionnaire. The study involved cardiac patients receiving
sublingual GTN attending the outpatient and inpatient departments of
Taiping Hospital, a non-academic public care hospital. The minimum
calculated sample size was 92, but 100 patients were conveniently
sampled. Respondents were interviewed on 3 areas, including
demographic data, knowledge and use of sublingual GTN. Eight
items were used to calculate each subject’s knowledge score and six
items were used to calculate use score. Results: Of the 96 patients
who consented to participate, majority (96.9%) were well aware of
the indication of sublingual GTN. With regards to the mechanism of
action of sublingual GTN, 73 (76%) patients did not know how the
medication works. Majority of the patients (66.7%) knew about the
proper storage of the tablet. In relation to the maximum number of
sublingual GTN tablets that can be taken during each angina episode,
36.5% did not know that up to 3 tablets of sublingual GTN can be
taken during each episode of angina. Fifty four (56.2%) patients were
not aware that they need to replace sublingual GTN every 8 weeks
after receiving the tablets. Majority (69.8%) of the patients
demonstrated lack of knowledge with regards to the use of sublingual
GTN as prevention of chest pain. Conclusion: Overall, patients’
knowledge regarding the self-administration of sublingual GTN is
still inadequate. The findings support the need for more frequent
reinforcement of patient education, especially in the areas of
preventive use, storage and drug stability.
Abstract: Data mining techniques have been used in medical
research for many years and have been known to be effective. In order
to solve such problems as long-waiting time, congestion, and delayed
patient care, faced by emergency departments, this study concentrates
on building a hybrid methodology, combining data mining techniques
such as association rules and classification trees. The methodology is
applied to real-world emergency data collected from a hospital and is
evaluated by comparing with other techniques. The methodology is
expected to help physicians to make a faster and more accurate
classification of chest pain diseases.