Search Results (5)

Objective: This study intends to evaluate simulated experiences provided to pharmacy students that directly compare the perspective of patients managing chronic disease states through traditional means without compliance aids to those using compliance aids, such as continuous glucose monitors (CGMs) and other devices. Methods: This simulation was conducted with third-year pharmacy students enrolled in the ambulatory care elective course at the University of Florida College of Pharmacy. It was designed to simulate a patient responsible for self-administering an array of medications for multiple chronic diseases that the students are likely to encounter during clinical practice. For the first week, students were tasked with adhering to a complex medication schedule from their associated pill bottles without the use of compliance aids (pill organizers, alarms, etc.) and checking their blood glucose twice daily using a traditional glucometer. In the second week, students continued the role of the patient; however, they were provided with compliance aids and encouraged to set alarms and use CGMs. Using a questionnaire developed based on the traditional Likert scale model, the students were able to quantify their experiences in a way that allowed the investigators to observe any changes. Results: Regarding the overall implications of this experience, most participants (>80%) agreed that this project increased their understanding of the value of compliance aids and devices and encouraged them to not only incorporate them into their future patient care plans but also advocate for accessibility to improve health outcomes. Conclusion: Students who completed this experience reported better adherence to chronic disease state control using compliance aids and, in turn, the applicability of the use of compliance aids in managing those with complex medication regimens. This simulation may encourage future pharmacists to incorporate compliance aids with their patients to improve health outcomes. Full article

Patient compliance with prescribed medication regimens is critical for maintaining health and managing disease and illness. To encourage patient compliance, multiple aids, like automatic pill dispensers, pill organizers, and various reminder applications, have been developed to help people adhere to their medication regimens. However, when utilizing these aids, the user or patient must manually enter their medication information and schedule. This process is time-consuming and often prone to error. For example, elderly patients may have difficulty reading medication information on the bottle due to decreased eyesight, leading them to enter medication information incorrectly. This study explored methods for extracting pertinent information from cylindrically distorted prescription drug labels using Machine Learning and Computer Vision techniques. This study found that Deep Convolutional Neural Networks (DCNN) performed better than other techniques in identifying label key points under different lighting conditions and various backgrounds. This method achieved a percentage of Correct Key points PCK @ 0.03 of 97%. These key points were then used to correct the cylindrical distortion. Next, the multiple dewarped label images were stitched together and processed by an Optical Character Recognition (OCR) engine. Pertinent information, such as patient name, drug name, drug strength, and directions of use, were extracted from the recognized text using Natural Language Processing (NLP) techniques. The system created in this study can be used to improve patient health and compliance by creating an accurate medication schedule. Full article

A common problem for healthcare providers is accurately tracking patients’ adherence to medication and providing real-time feedback on the management of their medication regimen. This is a particular problem for eye drop medications, as the current commercially available monitors focus on measuring adherence to pills, and not to eye drops. This work presents an intelligent bottle sleeve that slides onto a prescription eye drop medication bottle. The intelligent sleeve is capable of detecting eye drop use, measuring fluid level, and sending use information to a healthcare team to facilitate intervention. The electronics embedded into the sleeve measure fluid level, dropper orientation, the state of the dropper top (on/off), and rates of angular motion during an application. The sleeve was tested with ten patients (age ≥65) and successfully identified and timestamped 94% of use events. On-board processing enabled event detection and the measurement of fluid levels at a 0.4 mL resolution. These data were communicated to the healthcare team using Bluetooth and Wi-Fi in real-time, enabling rapid feedback to the subject. The healthcare team can therefore monitor a log of medication use behavior to make informed decisions on treatment or support for the patient. Full article

Medication non-adherence is a prevalent, complex problem. Failure to follow medication schedules may lead to major health complications, including death. Proper medication adherence is thus required in order to gain the greatest possible drug benefit during a patient’s treatment. Interventions have been proven to improve medication adherence if deviations are detected. This review focuses on recent advances in the field of technology-based medication adherence approaches and pays particular attention to their technical monitoring aspects. The taxonomy space of this review spans multiple techniques including sensor systems, proximity sensing, vision systems, and combinations of these. As each technique has unique advantages and limitations, this work describes their trade-offs in accuracy, energy consumption, acceptability and user’s comfort, and user authentication. Full article

Ubiquitous intelligence of Internet of Things (IoT) objects and new sensors provide innovative solutions for a variety of health issues. Unintentional child poisoning represents an increasingly important health issue worldwide, partially because of an increase in the use of drugs and food supplements. Although child-resistant bottle caps have probably saved many lives, they are not foolproof and do not provide warnings for parents and caregivers when children try to access the bottles. In this paper we present a design, implementation, and feasibility analysis of an intelligent “safe pill bottle” that can identify when a child is trying to open a bottle and then generate an immediate warning to deter a child from opening the bottle and send alerts to parents/guardians. The bottle controller uses capacitive sensing to identify the class of user. We present the results of pilot testing with eight adults and eight children using neural networks (NN). With 474 bottle-opening events, our NN had 96.4% accuracy of predicting whether the user was a child or an adult. Preliminary results demonstrate that smart pill bottles may be an effective tool to prevent unintentional child poisoning. Full article