Healthcare Data for Everyone: Crafting Stories That Are Simple, Accessible, and Empathetic

Datalabs by Plum is our investigation into the burden and cost of India's health conditions, viewed through the lens of insurance claims and health checkup data. We built it because we believe data should drive better health decisions — for individuals, for companies, and for the industry.

Today, the cost of healthcare — especially for chronic disease — is shrouded in jargon, archaic systems, and speculation. It doesn't help that conversations around these subjects seem intimidating, largely due to an asymmetry in context and information between healthcare systems and the general public.

At Plum, we process over 125,000 health insurance claims, 100,000 telehealth consultations, and 25,000 health checkups every year. This puts us in a unique vantage point, where we have data on the state of the urban corporate Indian's health as well as insights and trends on urban, tier-one hospitalisations. With DataLabs, we aim to bring truth to light, and do it in a way that's simple, inclusive, and accessible to the general public.

In this talk, we will cover four key areas.

Why healthcare data today is a black box and feels inaccessible. This is a two-pronged issue. The first is information asymmetry, and how data around incidence, costs, and treatment is hidden in jargon, claims forms, and scientific reports that are not accessible to the general public. The second is psychological — the general public is averse to talking about healthcare, and refuse to confront the possibility of a health scare. This is largely due to fearmongering, speculation, and intimidating reports and language that's cold and bereft of empathy.

What we're attempting to do with DataLabs. We do not see DataLabs as a dashboard product or an analytics tool, but an initiative to bring truth to light. The guiding belief is that health data should serve three audiences: individuals trying to understand what a diagnosis means for their life and finances, companies trying to design better benefits for their people, and the industry trying to move from reactive to preventive care.

Groundwork and research. There are many things that could go wrong when we're writing stories based on real people and their healthcare experiences. In this section, we talk about the guardrails we incorporate to ensure the privacy of our data, the groundwork to ensure that our narrative does not have ingrained bias, and qualitative research to identify and rectify potential red flags in our stories.

Our guiding principles and editorial framework. This section captures our editorial process, design decisions, and designing for inclusivity. In our editorial process, we decide the story we want to tell from the myriad data points that we have — this is the stage where we decide which talking points to keep, which ones to kill, and which ones might require further work because they might seem overwhelming to the audience. In design decisions, we will then talk about the choices behind the data visualisation (for example — why we chose horizon charts, why use violin charts to show length of stay in a hospital, where tables are better than charts, etc.). And in designing for inclusivity, we talk about the little interactive elements on the charts that help users understand the data, especially for visualisations that are new to them.

We believe this will be useful for data enthusiasts, writers, and journalists sitting at the intersection of data and research.

This talk will be presented with Ganapathi Ramanathan (https://www.linkedin.com/in/ganapathi-ramanathan/)