We asked Professor Tim Ackland, member of Premo Systems’ Advisory Board, about the use of algorithm pre-employment assessments in modern employment strategies, and the degree of accuracy systems like PREMFA can have. Here’s what he had to say…
How, in your opinion, have pre-employment assessments become ubiquitous in modern employment strategies for both blue and white collar workers?
Many companies acknowledge the benefits of assessing potential employees prior to employment, especially when they receive hundreds of applications for a limited number of positions. This helps the company screen applicants with the right capacities for the job, in contrast to others who may not be suited for the role. In ergonomics parlance, this prevents a mismatch between the worker’s capacities and the physical demands of the job – a circumstance that may increase the worker’s risk of injury when engaged in the role.
There are many drivers for companies to adopt pre-employment assessments, including financial incentives offered by insurers, as well as the efficiencies derived from recruiting and retaining a healthy, productive workforce.
Given the breadth of medical requirements and physical demands of all jobs in industry, pre-employment screening assessments can be used to advantage for both white-collar and blue-collar roles. Furthermore, the pre-employment screening data can be retained for future comparison if required.
What benefits does an algorithm-based system have over a human-based system when it comes to making decisions, and how does this apply to pre-employment assessments?Human-based assessment systems are constrained due to time, cost, convenience, available expertise and potential for bias.
- Time – A large time commitment for each assessment impacts both the potential employee and the assessor.
- Cost – Many assessments raise no red flags in terms of medical or functional risk, yet the full cost of the assessment is still borne by the company.
- Convenience – Scheduling assessments for both the potential employee and assessor can create problems, especially for workers constrained by their current shifts and other circumstances.
- Expertise – Not all assessors have a sufficient wealth of experience to match the potential employee’s medical history and functional capacity with the demands of the proposed job.
- Bias – There always exists the potential for bias in assessment with human-to-human interactions.
These constraints are either eliminated or greatly reduced when using an algorithm-based system; especially with respect to the time lag between assessment and outcome reporting, convenience, assessor expertise, and potential for bias.
In your opinion, do you believe that a system such as PREMFA can replace pre-employment assessments with the same degree of accuracy and minimal risk as the processes that have been used for the last 2-3 decades?
Firstly, it must be understood that pre-employment assessments do not guarantee that a worker will not be injured on the job. Rather, they are a means of preventing any mismatch arising from a potential employee’s medical history / functional capacity and the demands of the proposed job. This serves to reduce the risk of placing workers into roles for which they are not suited.
The PREMFA algorithm has been tested for accuracy and reliability, so may be used with confidence as a triage tool for pre-employment assessments. This triage tool highlights only those individuals who require additional face-to-face, expert assessment in selected aspects of the process. And since the PREMFA assessment is done online, it can be scaled up to accommodate multiple assessments simultaneously – with outcomes communicated to the client with minimal delay.
In this way, PREMFA addresses each of the shortcomings related to current processes, by improving time and cost efficiency as well as convenience for all parties (including HR personnel). Furthermore, the wealth of experience gained over many years of pre-employment assessments has been brought to bear via the computer application, which eliminates the potential for human bias.