Intelligent Medication Vending Machine for Enhanced Adherence

In the field of healthcare, non-adherence to medications is still a big issue, which greatly influences the patients' outcomes and the efficacy of the prescribed medications. This paper proposes a smart solution to ensure patients adhere to health practitioners prescribed medication regimen, the Intelligent Medication Vending Machine. The one proposed here has a fairly friendly UI, especially when entering prescription schedules and doses. It also arranges the spatial storage of pills and releases them at appropriate cycles throughout the day. A detailed reminder system incorporated into the system provides timely alarms at the scheduled time and 15 minutes to the appointment at the clinic. In this case, the dissemination of the results is handled via phone and email to the corresponding guardians if the patient has not ingested a medication within 15 minutes of the set time. Moreover, it monitors the quantity of medication stock and sends email alert to the guardians a week before stock exhaustion and followed by the reminder three days to the stock exhaustion and this ensures rapid restocking. By means of the convenient and intuitive GUI, the users can edit prescription details, dosage information, and schedules swiftly. This device is expected to increase the usage of drugs significantly by patients and in the process, improve the patient's condition, therefore, reducing health costs.

Authors

Institutions

Publication Details

Published
2024-11-12
DOI
https://doi.org/10.1109/iccda64887.2024.10867355
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intelligent Medication Vending Machine for Enhanced Adherence

C Linda Hepsiba, K Sowndarya
Artificial Intelligence in Healthcare
article

Intelligent Medication Vending Machine for Enhanced Adherence

C Linda Hepsiba, K Sowndarya
article en

Abstract

In the field of healthcare, non-adherence to medications is still a big issue, which greatly influences the patients' outcomes and the efficacy of the prescribed medications. This paper proposes a smart solution to ensure patients adhere to health practitioners prescribed medication regimen, the Intelligent Medication Vending Machine. The one proposed here has a fairly friendly UI, especially when entering prescription schedules and doses. It also arranges the spatial storage of pills and releases them at appropriate cycles throughout the day. A detailed reminder system incorporated into the system provides timely alarms at the scheduled time and 15 minutes to the appointment at the clinic. In this case, the dissemination of the results is handled via phone and email to the corresponding guardians if the patient has not ingested a medication within 15 minutes of the set time. Moreover, it monitors the quantity of medication stock and sends email alert to the guardians a week before stock exhaustion and followed by the reminder three days to the stock exhaustion and this ensures rapid restocking. By means of the convenient and intuitive GUI, the users can edit prescription details, dosage information, and schedules swiftly. This device is expected to increase the usage of drugs significantly by patients and in the process, improve the patient's condition, therefore, reducing health costs.

Bharathidasan University (IN)
Openalex Percentile: Top 63%
Artificial Intelligence in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Intelligent Medication Vending Machine for Enhanced Adherence — C Linda Hepsiba, K Sowndarya · (2024) | TGRS Research Map | TGRS