Med-Con Launches Innovative Remote Monitoring Mobile Platform as Global Pharmaceutical Company Awards New Clinical Trial Contract
MedCon Mobile Smart Label is a digital SaaS solution that has both mobile and web portal components. It’s designed to improve patient adherence, assist clinical teams with remote patient monitoring and help patients to manage and keep track of their clinical study medication. Through the use of a barcode, programmed alerts and push notifications, clinical trial patients are proactively reminded when it is time to take their medication. Utilizing their phone’s built in scanner to scan a barcode label to capture detailed date and time stamped information about when medication has been taken (on-time, late, overdose, under dose) or missed. The patient adherence data is immediately and securely communicated via a secured connection to AWS and then available to the clinical team for monitoring, review and analysis.
MedCon Central Portal:
MedCon Central is the information hub through which patient adherence information flows and clinical trial teams can remotely monitor and track their patient’s medication adherence activity and performance. Through several key reports, this analytical data enables clinicians to clearly see and evaluate both individual patient and or total patient information. For example, they can see if patients have taken their study medication on-time, late, under or over dosed, or if the dose was missed completely. Dosing regimens can be scheduled in various formats such as once-a-day or multiple times a day, weekly, monthly or even yearly. It is also possible to have unscheduled dosing events. Medication titration (with container replacement) can be programmed and performed throughout the trial. Broadcast notifications (one way to patient) can be communicated to individual patients, patient groups, or to all patients in a trial, to inform them of important updates or changes. Finally, predictive analytics capture actual patient adherence versus planned adherence, as well as predicted adherence, and will automatically calculate and display worst to best patient adherence performance as well as patients who are at risk of non-adherence trending.
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