CAREO
An IBM Watson Health Patient Experience Project
For this sponsored studio with IBM Watson Health, we looked ahead to a future when patients own and control their own healthcare data via a single, secure, and comprehensive access point. We found and analyzed secondary research, and interviewed patients to identity pain points associated with organizing, managing, and comprehending medical data from various doctors and digital medical portals.
Benchmarking and Matrix Activity
Our initial research began with benchmarking where we investigated existing precedents for design opportunities and limitations. We expanded upon our knowledge and creative limitations by utilizing machine learning (ML) cards to rapid brainstorm ideas around how ML can aid in personalized healthcare.
Interviews, Personas, User Journey Maps
My group and I analyzed pertinent journal publications and interviewed doctors, patients, and healthcare administrators to develop two personas and their corresponding user journey maps. Our personas, Jean and Lora, are two young women who suffer from chronic illnesses. Both women have difficulty navigating their health journey, struggle with making necessary appointments, and often feel underrepresented and powerless.
Further research into the experiences of individuals with chronic illnesses led to quick sketches and rough screens. We investigated how our user might access large sectors of data not normally found together in one medical portal. We imagined a system where our user could book appointments, get diagnostics on conditions that come and go, and easily communicate and navigate between specialists. These rough sketches were transformed into early renditions of key screens.
After we received feedback from Watson Health, we narrowed our concept even further. We focused on one persona and aimed to resolve her pain points of noting symptoms, frustrations with lack of medicinal knowledge, and inabilities to keep track of many specialists and appointments. We refined our key screens into wireframes, jumped into user testing, and based on user feedback, created more hi-fidelity screens.
Our final critique with Watson Health led us to investigate even further, dig deep into more research, and finalize our concept into our final product. Our digital health application, Careo, works to provide patients with chronic conditions more agency in their healthcare, archive their symptoms and appointment data for specialists and non-specialists, and facilitates diligent symptom tracking for improved health and outcomes. It is our answer to this research question:
How might a design harness machine learning capabilities to enable a patient with chronic conditions to translate, prioritize, and analyze their data into a useful and understandable format — prior to a doctor’s visit?
Careo:
collects data via connected devices and user input
it analyzes, translates, prioritizes, and visualizes for a predictive, customizable, responsive, and ever-changing experience
the information is accessible before, during, and after appointments
it utilizes both patient and doctors
Other features include:
Careo users have the option to “zoom into” their data insights to view a more detailed analysis
Careo displays easy-to-read data dashboards for the doctor to view
Careo utilizes natural language processing to transcribe and answer questions during the appointment
For our scenario video, featured to the right, Jean receives a notification from Careo, her health app, reminding her that she has an appointment with a sleep specialist in a few days. She clicks on the notification to review the appointment details and her data insight dashboard.