TiER1 Performance This article originally appeared in The Data & Measurement Issue of Performance Matters magazine. Download a digital copy of the magazine to read more on this topic.
How can data help inform decisions that support strategy activation? Learn how two organizations leveraged data insights to improve patient experience and help leaders become more inclusive and proficient in making data-driven talent decisions. Plus, explore how Generative Artificial Intelligence (GAI) creates new content from datasets.
“We need to improve our patient experience. Can you help?”
Moments that matter are the foundation of meaningful experiences. At an ambulatory services organization within a large healthcare system, the vision is for the organization to be a place where everyone feels safe, appreciated, and taken care of—so that, ultimately, the organization’s people can provide an exceptional world-class patient experience for others. To help bring that vision to life, TiER1 partnered with this organization to drive measurable changes across its ways of working and outcomes in the leader and associate experience, starting first with ambulatory care centers.
We conducted three workshops with approximately 30 operational leaders across nine ambulatory care centers. Based on the science of healthy and high-performing teams, these workshops helped company leaders build on the “bright spots” of the leader and associate experience, as well as surfaced opportunities or challenges associated with patient experience data. Because patient experience scores are a lagging indicator, we also looked at leading indicators throughout the work to provide meaningful insight into progress toward desired results, as well as identify results early enough to course-correct as needed.
Examples of leading indicators:
Outside the workshops, we facilitated fieldwork and guiding sessions to help site leaders identify a key moment to redesign. The work included listening to staff needs and pain points relative to two key patient experience drivers; reviewing patient and associate experience data; identifying the moments that matter that need to be intentionally redesigned at their site; and empowering a core team of staff to develop and test new ways of working. With support from TiER1, site leaders were able to determine which leading and lagging indicators they would use to gain insight into the impact of the newly designed moment at their site.
Clare Jeong, 2023 Excellence in Learning & Performance Award recipient, is a talent management professional who is helping leaders become inclusive and more proficient in making data-driven decisions when it comes to talent.
Here’s how:
As a passionate advocate for DEI, Clare seamlessly integrated a DEI lens to talent review by building key spreadsheet dashboards that highlighted succession and DEI metrics via a customized dashboard for leaders to support talent discussions. This was critical as it enabled leaders to see key talent gaps as part of their calibration discussions. As a result of Clare’s work, leaders are now being trained on how to leverage the dashboard and DEI talent metrics to have more meaningful talent conversations and create a more effective talent practice. With this newfound training, Clare is helping leaders prioritize inclusiveness and become more proficient in making data-informed talent decisions.
Clare is passionate about supporting growth, curating a diverse and inclusive work environment, and creating innovative solutions to large-scale global problems that better support the business. She’s also a skilled project manager with change management and cross-functional collaboration experience. (Fun fact: Clare played a lead and strategic role in standing up the talent and performance practice at a multinational mass media and entertainment conglomerate—from the manual design of talent tools to providing strategic analysis for the talent practice.)
Clare’s work makes an impact on the organization’s culture by ensuring that talent processes are equitable and sustainable — using data and measurement as a tool.
Generative artificial intelligence (GAI) has become ubiquitous in the technology industry.
For years, AI has been integrated into business operations to identify patterns and trends, while enhancing customer experiences; tailoring content on enterprise platforms; enhancing product design; and navigating supply chain hurdles. GAI advances AI architecture through its ability to learn relationships between data elements and then reassemble the data into new content based on prompts. “Content” may include text, images, code, analysis, and much more.
The power of GAI is its predictive ability to generate new content from existing datasets. Each new advance in GAI modeling sees a monumental step forward—and the advances are accelerating even amid the navigation of ethical implications of bias and copyright, legal considerations, and security concerns.
So, how do leaders strategically navigate this shift in the power of data without creating chaos in their organizations? To do so, first consider these three areas:
Understand where you are in the cycle. This can range from “we have a small team adept at navigating GAI” to “we’ve already identified our areas of potential impact, data pools, and role needs.” Focus on what is practical and applicable now to create a near-term AI integration roadmap. In addition, consider how this maps back to your overarching strategy and organizational priorities. If you are early in the process, mapping your current data infrastructure and data quality might be great starting points.
Engage employees authentically. Helping employees see what AI-enhanced performance can look like is a critical first step to leveraging it from a data and measurement perspective. Be proactive in helping people find their place in it and to adopt a new personal narrative when needed. Mapping skills and talent, as well as future role impacts, can be another space in which you can build buy-in, and shift mindsets to see possibility while also gaining data on current AI skill capacity.
Build trust-rich teams to explore options together. Developing AI agility—the capacity to shape and reshape the organization as it navigates uncharted territory—will be critical. Experimental pilots, sharing success stories, as well as lessons learned, can all be used to build trust. However, the agility will also need to be balanced with use case prioritization (how will we focus our efforts) and return on investment measurements (how do we know what’s working). Finding ways to lift up monitoring and evaluation data to effectively and transparently identify potential biases, errors, and unexpected impacts will also be a necessity for building trustworthy GAI solutions.
When it comes to how we leverage data, GAI is a definite game-changer. Being strategic in building a data and measurement strategy around its adoption in our organizations will go a long way in turning it into a competitive advantage for our organizations and people.