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
022 - Creating a Trusted Data Science Team That Is Indispensable to the Business with
021 - Turning Complex Cloud IT Data Into Useful Decision Support Info with John Purcell of
020 - How Human-Centered Design Increases Engagement with Data Science Initiatives
019 - The Non-Technical (Human!) Challenges that Can Impede Great Data Science Solutions
018 - The Business Value of Showing the “Why” in AI Models with Jana Eggers (CEO, Naralogics)
017 - John Cutler on Productizing Storytelling Measuring What Matters & Analytics Product Management
016 - Farming with Data: How Advanced Analytics are Transforming the Agriculture Industry with Dinu Ajikutra
015 – Opportunities and Challenges When Designing IoT Analytics Experiences for the Industrial & Manufacturing Industries with CEO Bill Bither
014 - How Worthington Industries Makes Predictive Analytics Useful from the Steel Mill Floor to the Corner Office with Dr. Stephen Bartos
013 - Paul Mattal (Dir. of Network Systems, Akamai) on designing decision support tools and analytics services for the largest CDN on the web
012 - Dr. Andrey Sharapov (Data Scientist, Lidl) on explainable AI and demystifying predictions from machine learning models for better user experienc...
011 - Gadi Oren (VP Product, LogicMonitor) on analytics for monitoring applications and looking at declarative analytics as “opinions”
010 - Carl Hoffman (CEO, Basis Technology) on text analytics, NLP, entity resolution, and why exact match search is stupid
009 - Nancy Hensley (Chief Digital Officer, IBM Analytics) on the role of design and UX in modernizing analytics tools as old as 50 years
008 - Dr. Puneet Batra (Assoc. Director, Machine Learning at Broad Institute of MIT and Harvard) on aligning data science with biz objectives, user re...
007 -Jim Psota (CTO & Co-Founder, Panjiva/S&P Global) on designing a meaningful SAAS analytics product for the global supply chain
006 - Julien Benatar (PM for Pandora's data service, Next Big Sound) on analytics for musicians, record labels and performing artists
005 - Jason Krantz (Dir. of Biz Analytics/Insights, Weil-McClain) on centering analytics around internal customers
004 - Vinay Seth Mohta (CEO, Manifold) on Lean AI and machine learning for enterprise data products
003 - Mark Madsen (Global Architecture Lead, Teradata Consulting) on the common interests of analytics software architecture and product design
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