IntusCare is a healthtech startup with a data and analytics software. As a product designer, I am heavily involved in the discovery phase of the product, from gathering data and user research to designing high fidelity prototypes. Individual projects I’m involved in include: leading design related conversations, standardizing the style guide, and future visions of our product.
IntusCare is used by healthcare workers and admin at Program of All-Inclusive Care for the Elderly (PACE) organizations. A PACE organization is a comprehensive healthcare model designed to support and meet the diverse needs of older adults who qualify for nursing home care but prefer to live in their communities. PACE participants often qualify for both Medicare and Medicaid, with PACE bearing complete financial responsibility. Not only are PACE organizations fixated on reducing and preventing hospitalizations, as they are often the costliest expense, but [being hospitalized itself is also detrimental](https://www.jeffersonhealth.org/your-health/living-well/the-mental-and-physical-effects-of-a-hospital-stay-on-seniors#:~:text=Elderly patients are more susceptible,of cognitive decline for seniors.) to elderly patient health.
Sticker I designed for our company last year
While falls may not be life-threatening in most stages of life, they pose a significant risk to the well-being of elderly adults, potentially leading them to the hospital due to their weaker constitution. PACE must document all falls to ensure comprehensive care and effectively address health complications.
The data from the documentation is often manually analyzed for monthly fall meetings, which involves a painstakingly long process of sorting through raw data and tallying up statistics for individual patients.
The Patient Falls Tab can be filtered by a date range so that users can look into specific periods where falls were a concern.
Interviews with internal team members experienced in PACE provided us with background context on what fall statistics might be useful, considering the organizational structure and their meetings. We learned that actions taken regarding falls typically occur in group meetings where multiple members of the interdisciplinary care team are present.
<aside> ❓ How might we make falls data presented in IDT morning meetings and monthly falls meetings actionable so that healthcare workers can make more informed care decisions?
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As PACE organizations often operate with slight variations from one another, we attended various meetings of different organizations. We observed that when new falls were discussed, there was minimal reference to the contexts of previous falls, except for highlighting that the individual is a frequent faller.
In our subsequent debrief interviews, we inquired about the potential value of a person's historical fall context. We confirmed a list of trend data that would assist them in making a more comprehensive assessment of a patient's care:
To validate our assumptions regarding the value and usability of our idea, we conducted concierge tests with two separate organizations. Before each meeting, we were provided with a list of patients to be discussed and created interactive Figma prototypes for the falls tab of each patient. We manually configured the data to eliminate the need for development time.
The response was overwhelmingly positive. The data facilitated immediate root cause analysis, with clinicians brainstorming interventions based on learning, for instance, that a patient frequently fell in the bathtub.
The feature brought patterns to the forefront, enabling the evaluation of new falls not merely as isolated events but as clear participants in the causes and effects shaping a patient's well-being journey.
The Patient Falls Tab is our most used feature. It boasts a clear workflow and clear patterns of usage during monthly meetings can be identified across multiple organizations.
More importantly, since its release, our customer fall rate went down by 9%, then another 3%, then another 17% over the three quarters post-launch, equivalent to 1,904 fewer falls. An estimated $18M has been saved across our customer base.
The sidebar: Analytics tab summarizes trends seen through the main four fall metrics and indicates fall risk factors. The Falls sidebar tab provides a view of the patient’s falls as a chronological list, just as clinicians might in their EMR.
The mockups in preparation for concierge testing
Collaborating with the lead product manager, we formulated a vision for what the product might evolve into three years from now.
Hospitalizations represent the primary concern for PACE organizations, posing the greatest risk to both patient health and health plan utilization. The information entered into patient Electronic Medical Records (EMR) and databases does not facilitate the early detection of spikes and changes in condition before a critical event occurs. Consequently, some hospitalizations occur unnecessarily, with warning signals being triggered but not easily discernible within the text-dense EMRs.
<aside> ❓ How can we help our users identify and prevent avoidable hospitalizations?
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The “no-fi” flow we created, delineating a path the user would take in relation to the regular meetings of the day and how the usefulness of an insight is relative to the context it exists in.
We first identified different methods of hospitalization prevention:
We also recognized the different scales at which the staff at PACE worked:
The warning signals on the left side focuses on proactive prevention
A new timeline design for patient focus view, that allows deeper analysis of the changes that happened before hospitalization.
A synthesis view for hospitalizations in the population. This chart allows the user to dig deeper into data and identify where the greatest issues lie.
Allowing our users to dig further into data is a great priority for us. Based on our research, we learned that understanding population-level trends is helpful, but it is sometimes difficult to act on that data without being able to identify individual actors.
Through internal and external expert feedback, we learned that some metrics might be useful as a representation over time, while those same metrics may be preferred as a count or percentage in other scenarios.
The previous version of this feature had a much narrower database of metrics and lacked the infrastructure to flexibly navigate a wider range of data visualizations. As the lead designer, I focused a lot of my time on designing the “add new metric” feature so that the directory of new information was not overwhelming while not hiding any of the new features that give users a lot more freedom with how they explored and analyzed their data. While designing, it was important to think of future expansions of this feature:
and I designed “add new metric” and the rest of the feature with these questions in mind so that we could make space for scoping.
An example custom dashboard showcasing the different types of visualizations available: percentage, count, breakdown by categories, and trend over time.
By clicking into any of the metric cards, you can see more details. Cards with graphs also allow you to click into a bar to see specifically who contributes to that time range/category.
“add new metric” button on the main page leads to this modal that previews your selection and creates one metric at a time.