DIMS

DIMS: Bangladesh's Leading Offline Mobile Drug Index

DIMS is the premier offline mobile drug index application for Bangladesh, providing healthcare and pharmaceutical professionals with instant access to crucial clinical drug information. Developed by ITmedicus, DIMS offers the most comprehensive, advanced, and up-to-date resource on available drugs, featuring over 28,000+ brand names and 2228+ generics.

Key Features and Benefits:

1. Detailed Drug Information: Access comprehensive details including indications, dosage, administration, contraindications, side effects, precautions, warnings, FDA pregnancy category, therapeutic class, pack size, and price.
2. Versatile Search Options: Search for drugs by brand name, generic name, or medical condition.
3. Organized Drug Listings: Browse drugs alphabetically by brand or generic name, or explore listings organized by therapeutic class or condition.
4. Personalized Features: Bookmark your frequently accessed brand names for quick retrieval.
5. Expanded Resources: Stay informed with information on international medical events, national and international guidelines, herbal brands, mini Rx, and practice updates.
6. Advanced Search Capabilities: Refine your searches using various categories for precise results.
7. Disease Information: Access detailed information on various diseases.
8. User Feedback: Share valuable suggestions, advice, and comments directly through the app.

Disclaimer:

DIMS serves as a reference aid and educational tool only. It is not intended for medical advice, diagnosis, or treatment and should not replace professional judgment. The information provided supplements, but does not substitute for, the expertise of healthcare professionals in patient care. While DIMS utilizes reliable data sources, the publisher and developers are not liable for inaccuracies or omissions. Licensed medical professionals retain sole responsibility for all medical judgments, diagnoses, and treatments. Use of this app signifies your acknowledgment and acceptance of potential inaccuracies within the data.