Recruiting in healthcare already has several baseline challenges as it is, like finding and hiring top candidates from a pool that can feel limited. And despite the best intentions of attracting and bringing in the best people, no matter who they are, these recruiting efforts can be further hampered by an insidious enemy: bias.
Sometimes hiring bias is obvious (with candidates excluded because of particular personal or physical characteristics), but more often it’s an unconscious bias that is keeping healthcare organizations from diversifying and broadening their recruiting. Race, gender, and family status are all blatant sources of hiring bias, but it can also be something as innocuous as seeking out candidates from a particular school, or unconsciously leaning toward particular types of names on a resume. You (and your organization) may not even notice that you’re tipping the scales in favor of a specific group, but it still happens sometimes, due to human nature and personal biases that we may not even realize we have.
This is where technology can help. Because AI is about as impartial as it gets, it can be used to save us from ourselves. For healthcare organizations, that means using AI to refine recruiting and hiring processes so that you’re setting the higher level objectives while taking out some of the early, lower-level personal decisions that can be rife with bias.
Using AI for more diverse healthcare recruiting
Because the healthcare industry is growing so rapidly, candidates are likely to come from all corners. But are your processes ready to take advantage of that wide range of talent sources? Because AI platforms are based solely on data, and not personal discernment, you can use it to cast a wide net in the healthcare talent pool.
AI platforms will screen candidates based on the criteria you set. If you’re concerned about bias entering into the process, you can have it exclude factors that are known to lead to hiring bias danger zones.
For example, studies have repeatedly shown that candidates with strongly minority-identified names still receive between 30 and 50 percent fewer callbacks and interview offers than their ''white''-sounding counterparts. That’s a huge problem, especially when the healthcare industry is attracting people from all backgrounds and walks of life. Using AI to screen applicants would allow you to exclude things like name, location, and school names and focus on more substantive qualifications and keywords instead.
AI helps ensure EEOC compliance
Some AI platforms are able to take candidate and employee data and check them against set metrics, like EEOC guidelines and percentages. If you’re looking for a blind, more compliant hiring process for your healthcare organization, all of this can be done on the back end, without any concern that your (human) reviewers are subjecting candidates to their own personal biases.
AI improves in-person hiring practices
The interview is potentially the most bias-affected part of the recruiting and hiring process. After all, it’s likely when you meet and speak with a candidate for the first time, kicking off all sorts of possible unconscious biases. AI can’t yet replace the need for face-to-face interviews, but you can use it to supplement your process and cut down on the opportunities for bias. Some AI programs can generate a set list of interview questions, ensuring that you’re giving each candidate the same shake. With data-driven questions rather than personally-driven ones, recruiters and hiring managers are less likely to stray into hiring bias danger zones.
Using AI for interviews (in addition to initial screening) isn’t meant to limit your discretion and decisionmaking when it comes to hiring. Rather, it’s used to create a standardized environment for all candidates, helping you to find the best talent for your organization. Candidates are measured against metrics, not against personal opinions, giving you a more data-fied approach to hiring.
As the healthcare industry grows and the talent pool grows more selective, it’s important to make sure that you’re sourcing and bringing in the best possible candidates. Moving to a data-based, AI-driven approach can help you manage even the least visible biases and ensure that you’re not dismissing great candidates before they even get through your door.