How Does Predictive Hiring Work in Healthcare?

Predictive hiring refers to strategies that leverage technology, including artificial intelligence (AI), to streamline and optimize the hiring process by predicting candidates’ potential for success. These strategies can be used to forecast staffing needs, automatically screen large volumes of applicants, and identify best-fit candidates. This newer approach represents a move away from traditional hiring methods, and is made possible by increasing availability of digital data coupled with the analytical capabilities of AI.

Hiring managers are responsible for ensuring their facilities are staffed appropriately so that patients receive the care they deserve. If you’re in this position, it’s important to know about emerging methods that may give your organization an edge over others.

Here’s a detailed breakdown of how predictive analytics can guide the hiring process, along with examples that demonstrate how it differs from traditional methods. Learn how your organization can implement prediction-based tactics and techniques to improve the processes you have in place to onboard better staff, faster.

The Importance of Optimized Hiring Strategies in Healthcare

Filling job vacancies with high-quality staff is vital to the functioning of the team and the wellbeing of patients. The healthcare labor market is competitive, and turnover rates remain high. Given the aging population, retiring baby boomers, and a dearth of replacement healthcare workers entering the field, this picture may sharpen in the near future — creating even higher competition for top talent.

Because recruiting practices are so crucial, it’s important to stay up to date on current best practices, and understand how innovative approaches can be put to work.

How Does Predictive Hiring Work?

In general, predictive hiring blends technology, such as AI-powered software, with human decision making to meet hiring objectives. However, some healthcare leaders believe that effective hiring could be accomplished without human involvement, based purely on concise data.

The hiring objectives are defined by the hiring organization, and may involve key metrics such as:

• Time to hire
• Quality of hire
• Predicted tenure
• Job satisfaction
• Job performance
• Patient outcomes (for example, number of patient falls on a unit)

Examples of Hiring With Predictive AI in Healthcare

To grasp the ways predictive analytics can reshape the hiring process, it’s helpful to contrast traditional hiring methods with predictive methods. Here’s an example of each that will give you a better understanding of their differences.

Example of a Hiring Process Without Predictive Tactics

Without the integration of assistive technology, the hiring cycle for an intensive care unit (ICU) may take four months, and include the following steps:

1. A nurse quits.
2. The hiring team posts the open position.
3. The position is listed for eight weeks, resulting in a high volume of applications.
4. The hiring team manually screens resumes to eliminate unqualified candidates.
5. A first round of interviews occurs.
6. Top applicants progress to a second round of interviews.
7. The hiring team makes the final decision and the applicant is onboarded.

During this time, the facility incurs high labor costs related to overtime pay for existing staff.

Example of a Hiring Process With Predictive Tactics

In an effort to reduce high overtime costs in the future, leadership may identify the objective: reduce time to hire by 50%. The hiring team could revise their process to incorporate data-driven predictions. Their new process could include the following steps:

1. The hiring team uses software to analyze large volumes of historical data. They uncover two key metrics linked to high-performing and long-lasting nursing staff on the unit: BSN credentials and at least two years of experience in a high-stress, critical care environment.
2. The hiring team learns how to use new healthcare staffing technologies designed to apply predictive analytics to the hiring process. They collaborate with a third-party expert to ensure that candidate screening will favor applicants who meet their unit-specific education and experience criteria.
3. A nurse quits.
4. The open position is posted and applications are automatically screened against the customized criteria.
5. The target number of qualified applicants is reached within four weeks.
6. The first and second rounds of interviews occur.
7. The hiring team makes the final decision and the nurse is onboarded.
8. An automated process continues to collect data that can be used in the future.

With this revised process in place, the time to hire could be reduced to two months and overtime labor costs may be successfully cut.

Review of the Examples: How Can Data Enhance Hiring Decisions?

In the above prediction-based hiring example, the process retained some human elements, including in-person interviews and the final decision to onboard. It was proactive, tackling hiring challenges before the nurse quit.

Ultimately, data was used to enhance the hiring decision by leveraging historical records so that there were evidence-based criteria in place for who would progress to interviews. The data-gathering process continued after the applicant was onboarded so that valuable information garnered through the experience could be used to inform future decisions.

The Pros and Cons of Predictive AI in Healthcare Hiring

Using predictions in the hiring process is not a new concept. Hiring managers have always used information about candidates’ experiences and skills to come to decisions about who would contribute best to their organizations.

What’s new about predictive strategies is the technology involved. AI-driven software is capable of processing incredibly large amounts of data quickly, allowing for insights that weren’t possible before. The advantages and disadvantages of these strategies are related to the technology involved.