PSY206_F24 - #5: Zoey, Celesity, Jaymee, Tracie

Introduction

The goal of our project is to aid elderly adults, those aged 75 and older, in remembering to take their medications using AI-assisted reminder apps.

To achieve our goal, our first task was to determine how we can get elderly adults to trust AI. Elderly adults are less likely to be familiar or proficient with the technology of today, even though they can greatly benefit from its use. In order to promote trust, we have to build understanding. To do this, we compiled class material to describe the differences between human- and AI-cognition.

Next, we decided to test out three different medication reminder apps ourselves and evaluate them based on their features, ease-of-use, and effectiveness. The three apps we chose to test were Dosecast, Medisafe, and Everydose. Each differed in certain ways, but all were effective at sending out accurate reminders.

Finally, in order to present this information in an approachable way, we focused on the power of storytelling. Much of what we have learned in class about attention, perception, and memory supports the idea that storytelling is an effective and psychologically-privileged way of relaying information. For our project, we combined storytelling with AI-generated images and an infographic (see right) to help convey our message to our intended audience.

Information and Data Collection

ZCJT - Phase 1

Story Scaffolding

Phase 1 of our project provides a historical overview of the connection between computation and human cognition. Then, we discuss the cultural vs. scientific story of AI and how they often don't align, leading to misplaced fear and misunderstanding. In reality, computation and human cognition are good at very different things! If we combine the strengths of both, we can offset the weaknesses.

Phase 2 Assignment

Story Detailing

Phase 2 of our project dives further into the differences between human and artificial intelligence. We outline our experiment and provide qualitative data about each of the medication apps we tried: Dosecast, Medisafe, and Everydose. We discuss the benefits of offloading the cognitive load of medication adherence onto an AI-supported app, which can handle the task with more efficiency and accuracy.

Phase 3

Story Embellishing

Phase 3 of our project explains how human memory is aided by techniques such as retrieval practice, using a variety of cues, and attaching meaning to memory context. All of these factors support the use of storytelling to help people understand and remember scientific concepts or facts. We use this information to inform our own story script with regards to our project goal and intended population.

AI-Assisted Medication Apps

Dosecast

Dosecast is a medication management app offering a free version with basic features and a paid subscription for enhanced functionality. The free version includes reliable notifications, flexible scheduling, and customizable dosing. Setting up can be time-consuming, as medications must be entered manually and certain features, like a searchable drug database and accurate drug types, are only available in the paid version. Despite the setup challenges, the reminder notifications are effective, providing options to take, skip or postpone a dose.

Medisafe

Medisafe is a user-friendly, feature-rich app that simplifies medication adherence. It offers personalized reminders for medications, missed dose alerts, and a setup process that auto-populates details like dosage, frequency, and timing, making it quick and easy to use. A calendar also displays the history of doses to ensure users stay on track. The app enables users to add caregivers or "Medfriends" who receive notifications if a dose is missed to foster a support system. Additional tools include a health diary, symptom tracker, appointment manager, and weekly adherence reports. The app allows prescriptions to be imported by doctors and includes a Medisafe card that offers significant savings.

Everydose

Everydose is a free, easy-to-use medication management app designed to improve adherence by connecting patients, providers, and pharmacies. Users can quickly set up their profiles and input medication details like dosage, form, and frequency. Key features include a detailed medication list, a calendar with color-coded adherence tracking, and customizable reminders with follow-up alerts for missed doses. The app's AI assistant, Maxwell, offers drug information, including interactions, side effects, and precautions. The app also offers Rx savings to help users find medication discounts.

AI and Working Memory: Enhancing Mrs. Johnson's Medication Routine

Mrs. Johnson, a 75-year-old retiree, had been struggling to manage her multiple prescriptions for various health conditions. Despite her best efforts, remembering to take each medication at the right time had become a daunting task. Her days were filled with reminders scribbled on sticky notes, alarms set on her clock, and occasional calls from her family to check if she had taken her medications. However, this system was far from foolproof, and Mrs. Johnson often found herself unsure if she had taken her morning or evening doses. This uncertainty caused significant stress and anxiety, affecting her overall well-being.

One day, Mrs. Johnson's healthcare provider, Dr. Smith, suggested using a medication management app to help her stay on track. Dr. Smith explained that these apps could send reminders, track medication intake, and even provide warnings about potential drug interactions. Together, they decided to use an app like MediSafe or Dosecast, which are highly regarded for their user-friendly interfaces and comprehensive features.

Dr. Smith and Mrs. Johnson sat down to input each of her medications into the app, including the dosage, frequency, and any special instructions. This interactive process helped Mrs. Johnson understand her medication regimen better and reinforced her memory through the generation effect – the act of generating material rather than passively receiving it enhances learning and retention. They also set up a weekly pill box schedule within the app, which helped in organizing her medications visually and created a routine that aided in schema formation and cues. This routine made it easier for Mrs. Johnson to remember when to take her medications.

The app was configured to send reminders at the designated times, using a multi-sensory approach to ensure Mrs. Johnson was reminded in a way that was hard to ignore. Visual notifications appeared on the screen, auditory chimes sounded, and kinesthetic buzzes from the phone all worked together to remind her. The app also logged each time Mrs. Johnson took her medication, providing a record of her adherence and allowing her to see her progress and stay motivated. Additionally, the app provided warnings about potential drug interactions and sent reminders when it was time to refill her prescriptions, eliminating the worry of running out of medication.

As Mrs. Johnson continued to use the app, she found it increasingly easier to remember her medications. The routine established by the app's reminders and the visual cues from the pill box helped solidify her memory. Her medication adherence improved significantly, and she no longer worried about missing doses or taking the wrong medications. The peace of mind that came with knowing she was taking her medications correctly reduced her stress and anxiety levels. As a result of improved adherence, Mrs. Johnson's health outcomes began to improve; her conditions were better managed, and she experienced fewer complications.

Mrs. Johnson's story highlights the transformative power of medication management apps for elderly individuals. By leveraging these tools, she was able to overcome the challenges of medication forgetfulness and take a more active role in her health management. The combination of routine, multi-sensory reminders, and the generation effect made it easier for her to adhere to her medication regimen, leading to better health outcomes and an improved quality of life.

Meet the Authors

Jaymee (she/her)

Hi! My name is Jaymee Morrone and I am a senior majoring in Social Work with a minor in Disability Studies. Working on this project has expanded my thinking about the potential challenges and benefits of using AI in a healthcare setting. This has been interesting to me as someone who is going into a helping profession. I hope to use this knowledge to think critically about human-AI interactions in the future.

Tracie (they/she)

Heyo! My name is Tracie Sun and I'm a senior majoring in Psychology and Kinesiology. I was happy to learn while working on this project that AI has already been experimented with to help improve medication adherence for older adults. As someone who wants to work as a geriatric physical therapist, part of my role is to help reassure patients and explain topics simply. This inspired me to explain certain topics through storytelling.

Celesity

Aloha! My name is Celesity Kim, and I am a sophomore majoring in Public Health with a minor in Psychology. Working on this project transformed my perspective on the role of AI in healthcare and its accessibility. As someone aspiring to enter the healthcare field, this topic deeply resonated with me. The insights I gained will empower me to leverage AI effectively to enhance healthcare outcomes in the future.

Zoey

Aloha! My name is Zoey Yoshikawa and I am a sophomore majoring in Psychology on the pre-Physician Assistant path. This project has increased my understanding on AI, not only in healthcare, but in general. It was a great topic to work on because I plan to go to PA school after undergrad. The new things I have learned about AI will resonate with me and I hope to teach others around my about AIʻs benefits rather than the disadvantages.

References

Babel, A., Taneja, R., Malvestiti, F. M., Monaco, A., & Donde, S. (2021). Artificial intelligence solutions to increase medication adherence in patients with non-communicable diseases. Frontiers in Digital Health, 3. doi.org/10.3389/fdgth.2021.669869

Labovitz, D. L., Shafner, L., Gil, M. R., Virmani D., & Hanina, A. (2017). Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke, 48(5). doi.org/10.1161/STROKEAHA.116.016281

Punnapurath, S., Vijayakumar, P., Platty, P. L., Krishna, S., &Thomas, T. (2021). A study of medication compliance in geriatric patients with chronic illness. Journal of Family Medicine and Primary Care, 10(4), 1644-1648. doi.org/10.4103/jfmpc.jfmpc_1302_20

Page updated

Report abuse