Modernizing healthcare staffing through data stack: An overview of Florence's challenges and strategies from Coalesce 2023

"Florence was really born of a problem. There's a staffing crisis, tons of demand…but it is definitely under-resourced, and it has tons of inefficiencies.”

- Monica Youn, Chief Analytics Officer at Florence

Team members of Florence, Monica Youn, chief analytics officer, and Daniel Ferguson, data engineer, discuss implementing data analytics at Florence—a marketplace that connects healthcare professionals with healthcare organizations.

Leveraging technology to reduce inefficiencies in healthcare staffing

The team at Florence faced considerable challenges in modernizing within an outdated and inefficient healthcare staffing industry. To tackle this, they used data and technology to streamline processes and remove inefficiencies.

Monica explains the traditional method of temp staffing involved tedious phone calls and manual agency coordination. Florence's innovative approach "with data and technology" aimed to transform this outdated industry practice. Daniel elaborates on the importance of using technology to automate processes, stating, "We do our best to shift work away from people and onto machines as much as we can."

The team emphasizes the importance of iterative deliveries and maintaining speed-to-value by making the most of existing resources. They also highlight the importance of having the right architecture, understanding the tools at their disposal, and being able to identify which tool can solve a specific problem.

Overcoming cultural and technical challenges in data usage

Florence had to overcome significant cultural and technical challenges to effectively implement its data-driven solutions in an industry where data was considered a luxury rather than a necessity. They used a variety of strategies, including targeted training sessions, designating Looker champions, and hiring staff who could bridge the gap between data and business.

Monica outlines the initial challenges, including a negative attitude towards complicated products, a lack of data literacy, ambiguous field definitions, and a low level of trust in data. To combat this, the team provided in-person Looker training sessions, designated Looker champions in each office, and hired staff who could understand both data and business.

On the technical front, they adopted tools that were accessible to everyone on the team, used continuous integration (CI) checks, and relied on automated quality control methods. "Combined, these three things give us a huge amount of confidence in the quality of the changes we're shipping," says Daniel.

Improving transparency and engagement through data visualization

By leveraging data visualization tools, the Florence team was able to improve transparency, increase stakeholder engagement, and build trust in the data. They achieved this through iterative dashboard developments, embedding all necessary information onto each dashboard, and using certification stickers to signify source-truth data.

Monica illustrates how they iteratively developed a dashboard for operations and sales teams to track their commissions and targets, which significantly improved engagement. They also embedded all necessary information onto each dashboard, including direct links to their ticketing board for new feature or bug requests and training session recordings.

Daniel further emphasizes the importance of understanding the tools at hand to maximize their potential. By learning all the features and functionalities of their tools, they were able to effectively manage their data and deliver high-quality results. "Learn your tools. They're great," he affirms.

Monica and Daniel's key insights

• The healthcare temp staffing industry is outdated, and Florence aims to remediate that problem with technology
• There was a significant challenge with data culture within Florence due to a negative attitude towards complicated technology and data
• The company faced technical difficulties because its data stack was not built for scale
• They achieved full adoption of their data tools by delivering in pieces, being on the ground with their users, and hiring people who could speak data and business
• They achieved speed-to-value by making the most of the tools they already had and critically evaluating their tools
• They strived for flexibility with the right architecture, keeping it simple and understanding the urgency of the challenge versus the impact of the tool