MSP-Driven Talent Pools & AI-Powered Skill Matching: A Predictive Model for Clinical Trial Staffing

The Clinical Research Workforce Crisis

Traditional staffing methods are struggling to keep pace with the growing demand for qualified clinical research professionals. Among patient-facing clinical research professionals (CRPs), turnover can be as high as 61% in places.¹

A study from the Association of Clinical Research Professionals (ACRP) found that for experienced clinical research associates (CRAs), turnover has reached 32%, largely driven by burnout.² There are seven open positions for every experienced clinical research coordinator seeking employment, 10 for every nurse and 35 for every regulatory affairs specialist.¹

High turnover and a declining investigator pool cause inefficiencies, disrupt workflows, delay trials, increase costs, and undermine patient and sponsor confidence.

Managed service provider (MSP)-driven talent pools, combined with AI-powered skill matching and forecasting, can help stabilize this situation.

A Proposed Predictive MSP-AI Framework

The goal of this framework is to improve continuity and quality of staffing at clinical research sites. If implemented, it could reduce time-to-fill, improve candidate fit and enhance regulatory compliance. The core elements are:

●     MSP-driven talent pools to provide a foundation for stability.

●     AI-powered skill matching for precision and efficiency.

●     Workforce forecasting to accurately predict staffing demand.

When used together, these three elements can play a defining role in reducing the clinical research workforce crisis.

A one-month delay in a Phase III clinical trial can cost a pharmaceutical company millions of dollars. By ensuring that trials are fully staffed with qualified professionals, MSPs and AI can help prevent these delays and accelerate the drug development process.

To understand this framework, it’s essential to know what MSPs do and what AI-powered skill matching involves.

What Is an MSP Recruiter?

An MSP recruiter manages talent pools — a curated group of prescreened, qualified contingent workers. For biotech and pharmaceutical firms, this means having readily available professionals with the skills and experience needed for various roles, such as CRAs, data managers, study nurses, regulatory specialists and project managers.

The Benefits of MSP Staffing

MSPs that specialize in clinical trial staffing typically have rigorous screening processes in place to ensure that all pool members have the necessary certifications and a proven track record in this field. This reduces the time required for internal HR teams to source, screen and onboard new staff.

Because the pool is prescreened, organizations can quickly fill open positions, minimizing time-to-hire and delays in trial timelines. Regulatory compliance and quality can be improved because pool members will be up to date on the latest requirements and best practices.

MSPs also offer scalability and flexibility to meet fluctuating trial needs, matching the typical clinical trial periods of high versus low activity.

Challenges and Limitations of MSP Staffing

MSPs that serve multiple sectors may lack the specialist candidates that clinical research requires. Choosing the wrong one could result in increased compliance risks and lower-quality staff. Organizations must carefully select a partner that aligns with their values and has a strong track record in clinical research.

One concern is the cost of using an MSP. However, organizations should weigh the expense against the potential savings from reduced time to hire, improved compliance and increased efficiency.

What Is AI-Powered Skill Matching?

How can AI be used in clinical trials? Much of the research so far has focused on the promise of AI in patient recruitment and retention. However, the potential of using it in trial staffing has been largely overlooked. This framework suggests harnessing AI to help further widen the staffing pool while driving up the quality of candidates.

AI-powered skill matching involves using algorithms and machine learning techniques to analyze candidate profiles and match them with the specific requirements of a clinical trial role. This happens by leveraging established taxonomies to identify skill and attribute archetypes, enabling algorithms to find candidates more accurately.³

This is particularly important for clinical research staffing due to the lack of awareness of it as a career option. As the ACRP notes in its 2025 white paper, skills-based hiring will be essential to resolve this crisis, including harnessing expertise from nontraditional entry routes into the profession. The paper notes that hiring processes can be archaic, with an overreliance on experience and academic degrees, overlooking candidates who have taken career-specific courses or who have transferable skills from other professions.⁴

Because AI skill matching goes far beyond simple keyword matching to understand context, assess job fit and even predict candidate performance, it can play a key role in overcoming these issues. Whether used by MSPs or individual organizations, the power of AI can significantly impact clinical trial recruitment.

The Benefits of AI-Powered Skill Matching

The first and most obvious benefit is speed — AI can quickly scan thousands of resumes and applications, identifying candidates much faster than recruiters manually reviewing them. AI can also improve applicant fit and reduce the risk of poor hires. Because it analyzes deeper data points, such as licensing history, shift preferences, soft skills and past performance, this type of skill matching can predict whether someone will be a long-term fit or not.

AI platforms can automate initial candidate screening through smart questionnaires or video analysis and automatically schedule interviews with qualified candidates, freeing up recruiters to focus on more strategic tasks.

Skill matching can help MSPs identify and recruit candidates with highly specialized skills that are in short supply. By analyzing job descriptions and applicant profiles, AI can identify the specific abilities needed and then search for people who have them.

How AI Skill Matching Can Overcome Barriers to Entry

The ACRP white paper underlines that the trial workforce must reflect the community it serves. It notes that research from 2021 uncovered a strong correlation between diverse clinical trial staffing and the successful recruitment of diverse patient cohorts.

However, with only 22% of staff understanding the importance of diversity, the paper further notes that “Site hiring criteria — which focus on fulfilling urgent needs rather than an intentional model of workforce development — generally don’t reflect this imperative.”

When properly developed, AI-powered skill matching can provide a bridge to help eliminate bias in recruitment.

Challenges and Limitations of AI-Powered Skill Matching

While AI can be taught to reduce bias, it is only ever as strong as the data it is trained on. It is crucial to ensure that AI systems are designed and audited responsibly to level the playing field for qualified candidates who might otherwise be overlooked.

There is also a concern regarding overreliance on AI. Human recruiters must still play a critical role in the process, bringing emotional intelligence and cultural awareness to candidate evaluation.

The Importance of Workforce Forecasting

The MSP-AI framework can be used to improve workforce forecasting by:

●     Analyzing historical data on trial volume, complexity and staffing levels.

●     Predicting future demand based on industry trends and clinical trial pipelines.

●     Identifying potential skill gaps and proactively recruiting talent.

●     Modeling different staffing scenarios and preparing for potential disruptions.

Forecasting can be tailored to specific trial characteristics, such as phase, complexity or geography. For example, AI algorithms can be trained on data from previous trials to identify the staffing requirements for different types of studies. A phase 3 trial with a large patient population and complex endpoints will likely require more staff than a phase 1 trial with a smaller group.

Decentralized trials often involve remote monitoring, home health care visits and other activities that require staff to be located near patients. Regulatory concerns have been raised about patient safety in such trials, highlighting the need for consistent local staffing.⁵ MSPs with AI-powered skill matching can quickly identify and deploy professionals in specific geographic areas, ensuring that decentralized trials are properly staffed.

MSPs are already known for their ability to scale staffing up or down based on trial needs. By layering AI on top of this, MSPs can gain even greater precision in forecasting demand. For example, AI algorithms can analyze historical data on enrollment rates, patient demographics and protocol complexity to predict the number of staff needed at each stage of a trial.

Implementing the MSP-AI Predictive Framework

Drawing on a framework proposed by You et al in 2025, a four-phase approach can guide the implementation of AI-powered skill matching in clinical trial staffing.⁶ As an overview, these phases would be as follows:

MSPs play a vital role in this process by providing expertise in both staffing and AI, helping organizations navigate the technical and ethical challenges involved. Sites, sponsors and contract research organizations (CROs) must collaborate to implement such a framework.

Measuring Success — KPIs to Watch

The key performance indicators that can be used to measure the success of this MSP-AI framework include:

●     Time to fill.

●     Candidate quality, measured by performance reviews and retention rates.

●     Staff turnover numbers.

●     Trial enrollment rates.

●     Data quality metrics.

●     Regulatory compliance metrics.

●     Site satisfaction scores.

Summarizing the Benefits of the MSP-AI Framework for Clinical Research Site Staffing

Across the board, from initial staff recruitment to better long-term outcomes, the MSP-AI framework has crucial benefits.

Addressing the Workforce Crisis — A Path Forward With MSPs and AI

Clinical research sites face a growing crisis of workforce instability, with high turnover and skill gaps threatening the efficiency and integrity of trials. A predictive framework, combining MSP-driven talent pools with AI-powered skills matching and workforce forecasting, offers a powerful solution. It can widen your access to qualified professionals, strengthen candidate fit and enable proactive planning.

Sponsors and CROs must prioritize investment in innovative staffing solutions, such as the proposed MSP-AI framework, to support their clinical research sites. Embracing this approach creates a more stable, skilled and diverse clinical research workforce, accelerating the development of lifesaving therapies and improving patient outcomes for years to come.

Sources:

1. Freel S, et al. Now is the time to fix the clinical research workforce crisis. Clinical Trials. 2023 Jun 2;20(5):457–462. https://doi.org/10.1177/17407745231177885

2. Parrish, G. CRA turnover within contract research organizations post-COVID-19: A cross-sectional study. Clinical Researcher. 2025 Feb 14;39(1).

3. Corewood Care. Exploring the 4 most common types of caregivers.

4. The Association of Clinical Research Professionals. Addressing the clinical research workforce crisis: A call for collective action.

5. De Jong A, et al. Opportunities and challenges for decentralized clinical trials: European regulators’ perspective. Clinical Pharmacology and Therapeutics. 2022 Apr 30;112(2):344–352. https://doi.org/10.1002/cpt.2628

6. You, J, et al. Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications. NPJ Digital Medicine. 2025 8(107). https://doi.org/10.1038/s41746-025-01506-4