Overview
Goal
Discover the patient experience and use of digital technology at the Heart Failure Clinic and Inpatient Unit at a hospital in Northwest Baltimore. Ideate solutions or opportunities for technology adoption across the continuum of care. Ideally, this work could be extrapolated to generally understand the patient experience with chronic disease. Work with local startup, emocha, to create a pilot for a new use case; using their medication adherence application for patient adoption in heart failure treatment.
Why this Project?
I jumped at the opportunity to work on healthcare innovation. My personal health journey has been a tough patient experience and I wanted to help improve the experience for others. It was exciting to be in a clinical setting where I had in-the-field learnings directly from patients and clinicians.
Lessons Learned
• Researching a vulnerable population requires extreme caution for ethical approaches to design. Mainly, protecting patient confidentiality was the primary concern, so no private or identifiable information was collected during research. Secondly, removing bias from our process, like not including quantitative data that could include racist skews or ensuring that our questions were at a third grade reading level and comprehensible to all our patients.
• Limiting factor: we could only talk to patients that arrived to the clinic. An entire population of patients is left out of this analysis simply because they could not show up. Furthermore, scheduling focus groups with this population was challenging because most patients only used phones (some cell phones), rather than email or electronic/digital methods.
What Patients Need
Problem
What is the patient experience with Heart Failure and what digital technologies do they use to manage their medications and condition?
Many Heart Failure patients in Baltimore City are from vulnerable populations– zip codes that have life expectancy 10 years younger than the national average and almost a 20 year gap between poor and rich neighborhoods in Baltimore — they suffer from poverty and lack of resources like transportation, access to healthcare, low literacy and e-literacy rates, and access to healthy food. How does this affect their experience?
Discovery
Between November 2017 and January 2018, interviews and observation with:
• 17 patients with various classes of Heart Failure
• 4 patient family members
• 7 clinicians
• 50 total hours spent in contextual inquiry, interviews, and focus group
• 90 patients filling out survey
Survey
The team created a survey to get feedback from patients to learn about their use, comfort, and access to technology to guide our research and decisions in creating a new use case for the emocha app to improve medication adherence for patients with heart failure.
Patient Personas
I created 10 patient personas based on aggregated details from interviews, observation, and quantitative and qualitative data analysis from workflow development and various patient experience survey and regional health data. The personas had many categories, especially focusing on:
• Heart Failure class
• Digital technology use
• Readmissions to hospital
• Medication adherence
• Insurance type
An example of a patient persona
Patient Experience Map
Major themes emerged based on patient needs, grouped in 3 segments: Patient Engagement, Gaps in Care, and Communication across the continuum of care. We focused the journey mostly on the patient actions and feelings, though provided physician experience insight for a more well-rounded understanding of the communication between both end-users.
Major User Takeaways
• Use of technology and e-literacy ranged from full distrust of any tools to competent smartphone and tablet users. Patients’ use of technology usually depended on 2 main factors: age and family or caregiver support.
• Younger patients (35–60 years) were more likely to feel comfortable using smartphones, tablets, or computers, especially relating to their medical information and managing their disease.
• Older patients (65 years and older) were less likely to trust technology or be willing to use the tool as a way to manage their disease.
• Most patients had a younger family member helping them with care (a child or younger sibling), and those caregivers utilized smartphone, tablets, or computers.
• Less than half of the family caregivers mentioned using applications to manage care; almost all caregivers agreed that digital tools would improve the caregiving experience.
• The younger patient group affirmed that they would be willing to adopt various digital technologies, like phone or tablet applications, to improve medication adherence and treatment management. All patients agreed that they would like more options for interacting with the care team.
Creating a New Use Case
Based on the personas, we knew that a group of patients would be open to using technology, specifically a phone app. emocha and the hospital had an agreement to establish a pilot to test the new use case to determine if we could scale the project.
emocha's previous use cases focused on improving medication adherence using video technology and human engagement by leveraging a CDC-endorsed model called Directly Observed Therapy (DOT) where healthcare workers watch patients take every dose of medication, monitor side effects, and provide critical support - in opioids, TB, hep C, and HIV and PrEP.
Role
On a daily basis, I managed communication with emocha and the hospital, coordinated our clinical research time, implemented user research, detailed workflows, synthesized data to provide insights and guidance requirements for emocha's product designer, and facilitated project meetings.
Goal
To develop an emocha heart failure product that demonstrates a measurable impact on medication adherence while having high patient and staff satisfaction indicators. If successful, the goal will be to scale this and other applications of the tool across LifeBridge for other chronic health medication adherence needs.
Research Design
2 Month Design/Prototype, 3 Month Enrollment and Study, 1 Month Adjustments
Randomized study design: ~ 20 patient to emocha, 20 patients standard of care. Clinic visit at 30,60, and 90 days for medication reconciliation, assessment of adherence and medication errors
Adapting the Use Case
Below is an image of emocha's experience overview that we would have to adapt for the new use case. The major points of adaption focused on:
• Updating the list to be specifically for heart failure
• Updating the medication list to include up to 10 different medications
• Adding time frames for the medication list (breakdown for evening and morning)
• Adding checklist to medications list to select specific medications being taken
• Generally updating the sizing on the UI to be easier to see for older population using the app (average age of 68)
Measures
We outlined 4 main hypothesis to establish objectives and key results for the pilot:
• Hypothesis 1: CHF patients using emocha will show an increase in medication adherence as compared to the control group.
• Hypothesis 2: CHF patients using emocha will make less errors in taking medications as compared to their adherence history and as compared to the control group.
• Hypothesis 3: CHF patients using emocha believed it helped them adhere to medication regiments.
• Hypothesis 4: Staff using emocha believed it helped them manage patients with low medication adherence.
Timeline
I coordinated the research and emocha teams to ensure that everyone was working according to our launch timeline (example provided below). We met weekly to discuss updated and progress along with working meetings.
Unfortunately, I cannot share more deliverables than the information displayed. My contract with the system ended a week after our official launch of the pilot because we ran out of funding. As I wrapped up my time on the project, we had 5 users enrolled and actively using the application!
As part of this research, I contributed to a plan for digital medicine testing. Unique features of digital medicine technology lead to both challenges and opportunities for testing and validation. Little guidance exists to help a health system decide whether to undertake a pilot test of new technology, move right to full-scale adoption or start somewhere in between.
