There’s a lot at stake in the decisions that social workers have to make when they care for people — and Dr. Besa Bauta keeps this in mind when her teams are designing the data products that care providers use in the field.
As Chief Data Officer at MercyFirst, a New York-based social service nonprofit, Besa explains how her teams use design and design thinking to create useful decision support applications that lead to improved clinician-client interactions, health and well-being outcomes, and better decision making.
In addition to her work at MercyFirst, Besa currently serves as an adjunct assistant professor at New York University’s Silver School of Social Work where she teaches public health, social science theories and mental/behavioral health. On today’s episode, Besa and I talked about how MercyFirst’s focus on user experience improves its delivery of care and the challenges Besa and her team have encountered in driving adoption of new technology.
In total, we covered:
We're not a technology company, ...so, for us, it’s about finding the right partners that understand our use cases and who are also willing to work alongside us to actually develop something that our end-users — our physicians, for example — are able to use in their interaction with a patient. - Besa
No one wants to have a different type of application every other week, month, or year. We want to have a solution that grows with the organization. - Besa on the importance of creating a product that is sustainable over time
If we think about data as largely about providing decision support or decision intelligence, how do you measure that it's designed to do a good job? What's the KPI for choosing good KPIs? - Brian
Earlier on, engaging with the key stakeholders is really important. You're going to have important gatekeepers, who are going to say, ‘No, no, no,’ — the naysayers. I start with the naysayers first — the harder nuts to crack — and say, ‘How can this improve your process or your service?’ If I could win them over, the rest is cake. Well, almost. Not all the time. - Besa
Failure is how some orgs learn about just how much design matters. At some point, they realize that data science, engineering, and technical work doesn't count if no human will use that app, model, product, or dashboard when it rolls out. -Brian
Besa: It was a dud. [laugh].
Brian: —yeah, if it doesn’t get used, it doesn't matter
What my team has done is create workgroups with our vendors and others to sort of shift developmental timelines [...] and change what needs to go into development and production first—and then ensure there's a tiered approach to meet [everyone’s] needs because we work as a collective. It’s not just one healthcare organization: there are many health and social service organizations on the same boat. - Besa
It's really important to think about the human in the middle of this entire process. Sometimes products get developed without really thinking, ‘is this going to improve the way I do things? Is it going to improve anything?’ … The more personalized a product is,the better it is and the greater the adoption. - Besa
122 - Listener Questions Answered: Conducting Effective Discovery for Data Products with Brian T. O’Neill
121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill
120 - The Portfolio Mindset: Data Product Management and Design with Nadiem von Heydebrand (Part 2)
119 - Skills vs. Roles: Data Product Management and Design with Nadiem von Heydebrand (Part 1)
118 - Attracting Talent and Landing a Role in Data Product Management with Kyle Winterbottom
117 - Phil Harvey, Co-Author of “Data: A Guide to Humans,” on the Non-Technical Skills Needed to Produce Valuable AI Solutions
116 - 10 Reasons Your Customers Don’t Make Time for Your Data Product Initiatives + A Big Update on the Data Product Leadership Community (DPLC)
115 - Applying a Product and UX-Driven Approach to Building Stuart’s Data Platform with Osian Jones
114 - Designing Anti-Biasing and Explainability Tools for Data Scientists Creating ML Models with Josh Noble
113 - Turning the Weather into an Indispensable Data Product for Businesses with Cole Swain, VP Product at tomorrow.io
112 - Solving for Common Pitfalls When Developing a Data Strategy featuring Samir Sharma, CEO of datazuum
111 - Designing and Monetizing Data Products Like a Startup with Yuval Gonczarowski
110 - CDO Spotlight: The Value and Journey of Implementing a Data Product Mindset with Sebastian Klapdor of Vista
109 - The Role of Product Management and Design in Turning ML/AI into a Valuable Business with Bob Mason from Argon Ventures
108 - Google Cloud’s Bruno Aziza on What Makes a Good Customer-Obsessed Data Product Manager
107 - Tom Davenport on Data Product Management and the Impact of a Product Orientation on Enterprise Data Science and ML Initiatives
106 - Ideaflow: Applying the Practice of Design and Innovation to Internal Data Products w/ Jeremy Utley
105 - Defining “Data Product” the Producty Way and the Non-technical Skills ML/AI Product Managers Need
104 - Surfacing the Unarticulated Needs of Users and Stakeholders through Effective Listening
103 - Helping Pediatric Cardiac Surgeons Make Better Decisions with ML featuring Eugenio Zuccarelli of MIT Media Lab
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