How AI is streamlining health care recruitment

AI is streamlining health care recruiting: ©Andrey Popov - stock.adobe.com

Every industry faces challenges in talent recruitment, but health care staffing is arguably in a class by itself. Factors such as personnel shortages, urgent job openings, large resume volumes and the need to fill highly specialized roles add multiple hurdles in the hiring process.

Today, as a result, it takes an average of 125 days to fill primary care physician positions, up to 135 for specialists, and from 59 to 109 days to recruit experienced RNs. Six additional months may be needed to complete licensing and credential requirements.

Artificial intelligence is streamlining the front end of the process, yielding benefits that include both accelerating and improving job-candidate matching. Instead of spending 8 to 12 hours using conventional keyword-based technologies to find good fits for specific nursing positions, for example, AI makes it possible to identify and notify both recruiters and candidates of suitable matches in less than an hour.

In addition, AI platforms can automatically rank candidates based on various criteria and adjust recommendations as new information is gleaned from candidate and recruiter input. This further improves the match quality, reduces recruitment costs, increases the likelihood of offer acceptance, and adds to the ability to attract top talent.

One agency’s experience

Consider the case of a large health care staffing agency tasked with finding nurses to fill full-time, temporary and time-sharing assignments as well as emergency shift coverage for scores of hospitals and health care providers around the country.

With more than 100,000 applicants competing for thousands of new job postings every day, traditional keyword-dependent matching strategies like manual resume parsing and Boolean searches were both time-consuming and insufficiently precise in zeroing in on the best candidates. Recruiters typically spent a full workday or more to identify a group of candidates for a given position – sometimes 20 or 25 – and then perform a manual qualification assessment to narrow the list to a smaller number of recommendations. Even then, human error, inconsistencies in search criteria, and the inability to recognize context beyond keywords risked missing or overlooking qualified jobseekers.

Today the agency uses a fully automated, AI-powered system leveraging natural language processing, machine learning and predictive modeling to expedite and optimize the resume parsing process. Now:

1. As soon as a new position is posted by any of the dozens of health care facilities served by the agency, an Al algorithm automatically begins searching the agency’s applicant tracking system as well as hundreds of talent pooling websites.
2. Candidates that best match the requirements are then scored based on an intelligent analysis of multiple attributes, including how closely each potential hire fits the job requirements as well as likely applicant interest. If a provider is looking for a travel nurse, for example, the algorithm considers factors such as each match’s preferred location, pay rate and job type to weed out those likely to reject job offers.
3. The system then produces a shortlist of top candidates that includes explanations for each choice and score, facilitating assessment of each match.
4. Over time, machine learning increases accuracy of the algorithm by incorporating new information such as reasons why candidates accept or decline offers, why recruiters reject the system’s recommendations for a particular role, and success predictions for interviews and background checks. That feedback further fine-tunes future searches.
5. Both candidates and recruiters are notified of the top matches within 30-60 minutes of the initial posting, all with no recruiter intervention required.

The platform has improved job matching accuracy by as much as five times compared to traditional methods. It also has slashed the time required to search for the best candidates for open positions from hours to minutes.

For the hospital or health care facility, that means an opportunity to shorten the time to hire by connecting with top talent quickly and efficiently. For the staffing agency, which faces competition from hundreds of competing services and sometimes has only a few hours to fill shifts for sick or absent employees, it means reduced administrative costs as well as a potential increase in placement fees. It’s a classic case of the adage that time is money.

Beyond job-candidate matching

Recruiting physicians is considerably more difficult than filling nursing positions, with a much smaller talent pool for highly specialized roles and a need for additional information to enable in-depth candidate evaluation. In this case, AI can provide a more sophisticated resume matching process as well as visual “knowledge graphs” that map relationships between the physician under consideration, his or her employment history, and his or her professional network. Both functions combine to assist hospitals and medical practices in identifying the best fit for their needs.

AI can also improve health care recruitment in a variety of other areas. The list includes:

• Interview call recording analysis that extracts key information from recruiter-candidate conversations and produces reader-friendly summaries, eliminating the time required for manual review and providing key insights for recruitment.
• Automated credentialing thatdigitizes and organizes credential records as well as automatically verifying certifications and licenses with relevant boards and organizations, reducing credentialing time by 60%.
• Pay rate recommendations that help health care organizations remain competitive in an ever-changing market by recommending competitive pay rates based on market trends and candidate expectations, particularly for temporary staffing businesses.
• Demand forecasting that helps organizations plan proactively to avoid staffing shortages or surpluses by analyzing fluctuating demand for health care practitioners.

As AI continues to evolve, new applications will undoubtedly emerge to further help the industry navigate staffing challenges. Meanwhile, AI-driven solutions are already proving their value for improving job-candidate matching as well as increasing efficiency and productivity in other areas. By embracing these technologies, organizations can not only streamline their hiring processes but also ensure that the right talent is in place to meet the demands of modern health care.

Denesh Mani is Head of Solutions at Blackstraw.ai, an AI consulting and implementation firm with a global team of hundreds of AI engineering professionals who serve clients in a wide range of industries including CPG, retail services, market research, healthcare staffing, manufacturing, logistics and utilities.