Brookdale Senior Living in Tennessee agreed to make corporate governance reforms and pay $1.9 million in attorneys' fees and expenses as part of a settlement in a lawsuit regarding its staffing algorithm.

The settlement was approved by Judge Aleta A. Trauger of the U.S. District Court for the Middle District of Tennessee.

The lawsuit, brought by shareholder Patricia Templin in 2021, claimed that Brookdale's board members and executives breached their fiduciary duties by using a staffing algorithm at the corporate level, which allegedly led to chronic understaffing in individual communities.

The plaintiff accuses the company of "intentional understaffing," which resulted in breaches of residency agreements and caused harm to residents by failing to provide the level of care that was advertised and promised. It also alleged executives were excessively and unjustly compensated.

Brookdale denied these allegations, stating that the board and executives operated in good faith and in the best interests of the company and its shareholders, and explained that settling was intended to avoid further litigation costs and disruptions. The exact details of the required reforms have not been disclosed.

An additional lawsuit from 2020 involving Brookdale's staffing algorithm was put on hold after the Templin settlement was announced. In that case, shareholder Brian Davis accused leadership of misconduct and deliberately underestimating staffing needs to meet financial targets. The case was initially halted by a federal judge on procedural grounds but could be dismissed entirely if the Templin settlement is finalized.

Brookdale has faced other criticisms and lawsuits regarding the quality of its services and how those services were represented publicly. In spring 2024, The Washington Post examined Brookdale's staffing algorithm and suggested the system underestimated needed staffing levels for its residents. Brookdale disputed this depiction, saying the algorithm was designed to find best practices rather than to limit staffing or control care costs.

Lawsuits on similar grounds have arisen before; notably, a 2017 case in California alleged elder abuse and violations of the Americans with Disabilities Act due to chronic understaffing, although only certain claims in that case were granted class action status by a judge in 2023.

Source: https://www.mcknightsseniorliving.com/news/brookdale-to-implement-reforms-pay-1-9-million-in-attorneys-fees-to-settle-staffing-algorithm-lawsuit/

Commentary

Using algorithms to determine staffing or hiring levels in healthcare and other workplaces can introduce significant legal and operational risks. If these algorithms consistently understaff or filter candidates in ways that disadvantage protected groups, organizations may face both negligence liability and employment discrimination claims.

As the above matter illustrates, risks can occur from negligence based on understaffing. Data shows that healthcare facilities operating below recommended staffing levels experience increased rates of adverse outcomes, such as medical errors, hospital-acquired infections, patient falls, and higher mortality rates.

For example, every additional patient assigned to a nurse increases the risk of errors and poor outcomes substantially. Chronic understaffing leads to increased workloads, staff burnout, and diminished patient safety, creating direct causation in negligence lawsuits if harm can be traced back to staffing shortfalls.

Algorithms can also pose employment law risks, especially the possibility of disparate impact. If an algorithm disproportionately screens out candidates from protected classes (such as by using biased data or proxies closely correlated with race, sex, or age), this can violate equal employment opportunity laws. Disparate impact can occur even when the algorithm's design is ostensibly neutral. Algorithms that optimize only for cost, tenure, or turnover risk may inadvertently replicate historical inequities or systematically favor certain groups.

To prevent these risks, organizations should implement the following safeguards:

• Regularly audit staffing algorithms for both accuracy and unintended bias using diverse datasets and independent assessors.
• Ensure staffing algorithms incorporate qualitative as well as quantitative considerations, such as required skill sets or situational acuity, not just headcounts.
• Maintain adequate resources and flexibility for manual overrides where clinical judgment or operational needs dictate algorithmic exceptions.
• Monitor workforce diversity metrics and validate that automated processes do not adversely affect protected groups.
• Provide transparency about algorithmic decision-making and communicate changes to staff so they understand how staffing levels are set.
• Offer ongoing training and support for all staff to improve retention and mitigate turnover, because high turnover rates can amplify deficiencies stemming from algorithmic staffing.
• Consult with legal and HR experts to ensure hiring tools fully comply with anti-discrimination laws.
• Solicit feedback from frontline staff to spot mismatches between algorithmic recommendations and real-world needs, then adapt accordingly.

These prevention strategies help mitigate the liability risks from negligence and other unlawful employment practices.

Outside Sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC11471616/; https://pubmed.ncbi.nlm.nih.gov/35768898/; https://www.cms.gov/newsroom/fact-sheets/medicare-and-medicaid-programs-minimum-staffing-standards-long-term-care-facilities-and-medicaid-0; https://www.cdc.gov/niosh/learning/safetyculturehc/module-4/5.html