HALO

UX / Fall 2016

This project was developed in collaboration with 3 other students for my Human Computer Interaction class at SCAD. I served as project manager, and was responsible for the interface, prototype, process book, service models and research analysis. Throughout the 10 week duration of the project I learned about how the healthcare industry operates, and how to unpack the complexity of it all through interaction design and systems thinking.

Tools used: Origami, Sketch, Invision, Illustrator, Indesign, Photoshop, Javascript, pen and paper, Human brain


Introduction

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For this project we were given the freedom to create our own prompt. With ambition in our hearts, we decided to tackle a problem related to healthcare. The goal of the course was to use the UX process of divergent and convergent thinking coupled with iteration to synthesize a solution to the problem we had chosen.

The team

Andrew Sibert

Project manager

Delivered:

Wireframes, Interface, prototypes, process book, Service models, Research analysis, User tesing

Nolan Canady

UX Engineer

Delivered:

Chatbot, wireframes, research

Rizwan Zaki

UX Designer

Delivered:

Persona creation, Interface, research

Kyler Emig

UX Designer

Delivered:

Wireframes, User testing, research

The problem

Three out of four Americans will die prematurely from a condition that is largely related to lifestyle or habit.

How might we?

How might we utilize emerging technologies such as artificial intelligence and telemedicine to help prevent these conditions before they occur?

Our process

Research

We conducted thorough secondary research to form a basis for the questions we needed to ask. We then used various user-centered research methods such as contexual inquiry, data affinitzation, and cultural probes to figure out what the problem actually was that we needed to tackle.

Divergence

We took our insights from the research we conducted and used divergent thinking to come up with as many solutions to the problem as possible.

Iteration

We took our best concept and iterated. We tested our concept with focus groups, performed user tests and coded the data to get a clear picture of what worked and what didn't.

Implementation

Once we analyzed the findings from our user-tests, we developed and prototyped a mockup of our user-facing product. We also developed business models and service packages to suggest potential ways to implement the solution. To demonstrate our conversational interface, we built a chatbot with API.AI.


Research

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Secondary research

For secondary research we reviewed articles, statistics, and medical journals in order to better understand our problem and create a basis for our primary research. We became particularly interested in Habit formation as a topic to explore further. We took the data we had collected, extrapolated what we deemed relevant to our research goal, and performed three rounds of affinitization. Afterwards, we had two solid research questions we knew we needed to explore further.

"We reviewed articles, statistics, and medical journals in order to better understand our problem and create a basis for our primary research."

First, we needed to understand the reasoning behind people not seeing their doctor as often as they should.

Second, we needed to uncover what our user groups current health related habits were, and the trigger-reward mechanism driving the behaviours.

Expert interviews

We needed to understand what obstacles the doctors face on a day-to-day basis, and what issues they percieve to be systemic. We interviewed Dr. Andrew Alspaugh, Professor of Medicine at Duke University and Dr. Bill Ando, Director of Movement Disorders at Houston Methodist Institute of Neurological Disorders.

From this we learned that a major issue is the sheer amount of time primary-care doctors spend on paperwork and documentation of patients. The quality of patient care is drastically reduced by this.

In order to understand what technologies were being utilized in the healthcare industry, we interviewed Hannah Moyers, a product designer at IBM, and Qian Yang, PHD researcher at Carnegie Mellon. From this we learned that technologies like IBM Watson currently don't touch the people being served as much as they could. We also learned how to optimize our interfaces for machine learning algorithms through responsive and adaptable design

"We also learned how to optimize our interfaces for machine learning algorithms through responsive and adaptable design"

Contextual inquiry

We conducted contextual inquiry studies to understand how and why patients use the systems they do. This helped us frame the problem that we needed to address. After observing our users in the clinic and ER, we mapped our users journey according to the data we collected and identified painpoints in the experience. We realized that the majority of the painpoints occured before they arrived at the clinic or ER.

Cultural probes

We developed and released several digital and physical cultural probes out into the wild in order to amplify the quantity and quality of qualitative data we collected on our users. We released our probes to 10 different people of diverse backgrounds and age groups. We needed to understand the daily behaviours of our users.

Audit

We performed an extensive audit of heatlhcare technology companies products. primarly, we focused our audit on the user flows of telemedicine applications. This was helpful in identifying what features existed and why. We then used sticky notes to write down the features we had identified, and performed a feature combination session to help identify untapped market space.

Competitive analysis

After completing our competitive analysis, we realized that not many telemedicine companies focus on augmenting existing patient-doctor relationships.


Insight

From our research, we learned that in order to create lasting healthy habits we needed to focus on facilitating an ongoing and personal relationship with a user’s healthcare provider.
We needed to explore solutions that would maximize the frequency and quality of engagement with a user’s health specialist.

A.

We knew from our audit, the system would need to be simple yet adaptable. We looked towards AI, telemedicine, and wearable technology for opportunities to innovate.

B.

We developed personas in order to give everyone on the team the same focus moving forward.


Primary persona

Small business owner
Motivators

A good education for his kids
Spending time with his family
Succeeding at his business.

Behaviors

Running every morning
Hardees after work
Tennis on the weekends

Served persona

Suffers from Osteoporosis
Motivators

Improving her mobility
Visiting her relatives often
Participation in the community

Behaviors

Writing short stories
Doctor visits
Pottery enthusiast

Provider persona

Primary care doctor
Motivators

Improving his patients health
Work in a positive environment
Reducing overhead cost

Behaviors

Avid reader
Golf player
Owns a small private practice.


Divergence

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Concepts we explored

Kiosk system

This system’s goal was to expedite the process of an in-person primary care checkup and automate patient documentation and the preliminary triage process.

Home appliance

This concepts goal was to create a miniaturized pseudo-clinic for the home. We wanted to create a simplified primary care checkup system that could be completed through an IOT device in the home, monitored by a physician over wi-fi.

Telemedicine app

The final concept we looked at was based in telemedicine. We wanted to explore ways in which we could use machine learning and wearable technology to dramatically enhance the effectiveness of a telemedicine app.

We realized after mapping the full journey of a user visiting their primary care doctor that the critical pain points which we we’re trying to solve occurred before the user arrived at the clinic, and the solution was out of the scope of our problem area.

We realized through our focus groups and desktop walkthroughs that while we could potentially increase the quality of care delivered, the accessibility, and frequency would be driven downwards by having an expensive physical device in the home.

We decided to pursue this concept further with additional wireframes, user journeys, storyboards, prototypes and desktop walkthroughs.

Telemedecine

The remote diagnosis and treatment of patients by means of telecommunications technology.

Iteration

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Wireframes

Version 1 prototype

We wanted to allow doctors and patients to communicate with greater frequency.

We also wanted our solution to be dynamic and adaptable to fit our users widely variant needs. To avoid having the doctors become overloaded with information, the product needed to be a hybrid between a doctor and a chatbot.

We took inspiration from conversational interfaces such as  Operator and  Facebook messenger.

We knew these systems could deliver a wide variety of content within one single interface, and do so in a way that is intuitive and responds dynamically to user input.

We framed our interface around a messaging state and cards. Each card would be dynamic and adapt to the users need. By defining preset parameters for cards like symptoms, it would be able to organize the information in a way that enhances the algorithms prediction ability, which in turn enhances the doctors ability to treat his patient.


Cards

One of our main features in the initial prototype was the card. User’s could send cards to the doctor they had been connected with, and recieve cards back from the doctor. These cards could be lab test requests, prescription refills/questions, and video/phone call requests.

Send a card
Lessons
Group messages

Lessons

Habit cards allowed the doctor to “teach” users how to deal with or actively prevent chronic conditions. We took inspiration from the app  Streaks, and wanted to create an addictive and simple to-do list for your health, curated by your doctor.

Groups

From our research and competitive analysis of the company  Omada Health we learned that group reinforcement is an essential part of forming healthy habits. We attempted to facilitate group reinforcement in our product by implementing a group chat feature. Users could send challenges to their friends or family members in the group they created, and receive achievement notifications when their friends had completed a lesson goal.


User-testing

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Focus group

We performed several focus groups with users from our persona demographics. In our focus groups we asked users about our concepts, and showed them our initial prototype. The feedback was instrumental in our iteration. Users felt that telemedicine was taking away from the personal relationship of a local doctor.

Desktop walkthroughs

We used desktop walkthroughs to concept and test the non-tangible artifacts of our concept. We involved real users in our walkthroughs to get feedback on the service model, and to work through the potential obstacles in the service we foresaw. This was extremely helpful in clarifying the needs of the doctor. From this, we learned that doctors and groups could experience an information overload from the potential overuse of the system.

User test results

Solution

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Halo

A communication tool for doctors and their patients, enhanced through artificial intelligence.

Features

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Home screen

Features

Daily reminders

Daily reminders is a doctor-curated health to-do list. Doctors can see what tasks patients have been completing and use that data to improve patient care. Doctors who know their patients best can create daily activity list’s for their patients. This allows doctors to give and receive real-time feedback on habits that are important in preventing or managing chronic conditions.

Appointments

Appointments allows users to set up physical appointments, or video and voice call appointments with their local physician directly through the app. They can also see when their next appointment is, and have it sync to their favorite calendar app.

Chat

Chat is where the AI comes into play. Halo is a chatbot we prototyped that lives within the applications messaging interface. Halo serves as the doctors virtual assistant. Halo can take appointment requests, take questions to pass along to the doctor, and give you information related to appointments or prescriptions. Most importantly, it serves as a direct line of communication with your doctor, that is mediated through the AI to prevent an overload of information on the doctor’s side.

Symptoms

This feature allows Halo to predict health problems for the user. By using the human body and a simple tagging system for symptoms, users can quickly tell their doctor how they are feeling in a way that can be quickly understood and analyzed by a machine learning algorithm and the doctor. Over time as doctors use this information to better treat their patients, Users would have an additional incentive to keep self-reporting symptoms that could be indicative of more serious health conditions.

Symptoms screen
Prescriptions screen

prescriptions

The prescriptions feature allows users to manage and verify their prescriptions. Users can use this to track shipping notifications for their medicine, see how much they have left, and scan directly from the app to verify their prescriptions at local pharmacies. Users also have the option to receive reminders for when they need to take their medicine.

Side menu

The side menu contains the apps and hardware integration menus. This is where users can sync their various apps and health devices. The data from the devices and apps connected is fed to the Halo system and can be utilized by the doctor.

Side menu
App integrations
Connect hardware
Chatbot prototype

Chatbot prototype

In addition to prototyping in Origami for our final deliverable, we also developed a chatbot prototype using  API.AI. This helped serve as a demonstration tool for how our digital assistant Halo might function within chat.

Take-aways

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I learned an incredible amount from this project. One of the most important lessons I learned was how to design within a plethora of constraints. There are few industries that rival healthcare in terms of complexity. Uncovering the complexity of the system, and identifying the constraints we needed to consider was a daunting, yet an important task.

Creating a methodical process I could follow to identify these constraints proved extremely valuable.

The second most important lesson I learned is about team collaboration. Coming at a problem from extremely different viewpoints can often yield amazing results. However, at times it can be stressful. Valuing the time and opinion of my team members was instrumental in the project's success, and in my role as project manager.

Considering diverse perspectives yields amazing results.

If I were to take this project further, I would love to design the doctor facing application. The doctors adoption of the product plays a crucial role in the success of the product. I would also create a metric to measure the product's success, and I would test the system's effectiveness in the real world.

If you're interested in this project and want to hear more, or just want to chat, feel free to contact me!