Leading Health Systems (LHS) have already begun automating some administrative tasks, including revenue cycle, supply chain, and HR support.
But to build on that foundation, especially in response to growing workforce challenges, health systems are accelerating investments in this human-machine dyad for clinical care and operations, too.
Defining Workforce Technologies
Three related but distinct technologies come into play when applying tech innovation to the workforce:
- Robotic Process Automation (RPA): Tools that allow users to configure software bots to automatically perform certain repetitive tasks that don’t require built-in intelligence.?
- Artificial Intelligence (AI): Data-driven simulation of human intelligence by machines; systems gain knowledge from data and experience.
- Machine Learning (ML): A subset of AI that includes a data-driven decision engine to analyze data, learn from it, and make a prediction without being programmed to do so.
While LHS have turned to these technologies to help reduce costs and find efficiency, the tenor of conversations around ROI is now more focused on reducing workforce burden and future needs – rather than overt FTE reductions.
Here are a few use cases of the human-machine dyad in action:
- Elevating HR Functions: Self-service tools and chatbots are increasingly being used to solve basic employee requests, while more strategic support is reserved for HR business support, elevating their roles and helping them operate at top-of-license. One LHS used an AI-supported ticketing system to triage requests and saw a reduction in turnaround time from a couple hours to only 15 minutes.
- Smart Assistants to Support Navigation: During the pandemic, health systems leveraged self-service applications and AI chat bots for patients to register, check-in, and schedule appointments more easily. These tools not only shifted some of the burden away from administrators, but also improved patient engagement. One LHS noted a steady increase in active monthly users after launching their app and month-over-month improvement in patient retention.
- Clinical Documentation: Many physicians are excited about the potential for AI tools, like ambient listening technology, to support their documentation efforts because it frees up capacity to spend more time with patients. One LHS that adopted such a solution found great success reducing turnaround time for notes and saw user satisfaction improve week over week.?Over time, this may be expanded to nursing practice as well, though early adopters have faced some resistance and workflow barriers that will need to be addressed.
As LHS consider where to invest their resources in AI and ML, they must carefully assess many considerations, like:?
- Is this the right process to automate?
- Do we have the necessary governance structure in place??
- Should we build in-house, partner, or buy?
- How are we planning to track ROI (e.g., hours saved, dollars)??
- What’s our expectation and process for scaling?
- Do we have aligned expectations with solution partners?
Want more insights on workforce trends? Check back next week for part 3 of our future workforce series on adopting a flexible workplace culture – or join our community today.
