Tackling ongoing staffing burnout rates through AI-enabled precision oncology
The emergence of COVID-19 resulted in staffing challenges that have continued to have a sustained, negative impact on clinical trial workflows. In fact, 76% of healthcare professionals have recently cited feelings of burnout, leading to a turnover rate twice as high as before the pandemic and increased levels of vacant positions across clinical research sites.
With recent fears around profitability, many hospitals and institutions have adjusted or decreased their workforce in particular areas, with some re-positioning back-office employees to handle patient-facing roles. In addition, many laid-off healthcare workers decided to pursue different careers and chose not to return to the same field, leaving a talent gap that needs to be filled.
These challenges pertaining to burnt-out healthcare employees have resulted in major clinical trial workflow issues, including the impact of patients’ willingness to participate in trials, and their ability to enrol. However, one solution that can help mitigate current feelings of burnout is through the integration of precision technologies like artificial intelligence (AI). Through the integration of precision technologies, healthcare providers can reduce excessive workloads, increase productivity, and ultimately help eliminate the burnout many professionals are currently experiencing.
The impact of burnout on clinical trials
Each staff member involved in a clinical trial is essential to its success. If workers are lacking the mental and/or physical capacity to complete tasks and are leaving their trials, it can lead to a significant blow to the quality of trial feasibility procedures, ultimately impacting the patients’ enrollment processes. For example, since the COVID-19 pandemic, there has been a 20% drop in trial accrual rates, as mentioned in a panel held by the Society for Immunotherapy for Cancer (SITC).
Physicians and staff have a significant responsibility to maintain the quality of a trial through important procedures, including patient chart analysis, to grasp key insights that will have an impact on patient care decisions. For example, electronic medical records (EMRs) typically do not contain consequential trial details, such as the identification of pertinent patient biomarkers. Instead, the staff is relied upon to identify this type of pertinent information. Staff members are a crucial aspect of maintaining trial workflow efficiency and identifying key trial data. Therefore, it is essential to provide them with solutions that help increase the efficiency of their workloads and help reduce feelings of burnout and fatigue. Doing so will result in increased trial productivity, accuracy, and efficiency.
Leveraging key precision oncology solutions, like AI-enabled software, staff and physicians can automate tedious manual procedures contributing to feelings of burnout, allowing for greater time to focus on training and developing their skills to receive even higher quality workloads.
The benefits of modern technology relieving burnt out employees
- Increased patient engagement – The Journal of Health Policy indicates that the patient-facing portion of clinical trials is most affected by turnover rates. Citing heavy workloads as the main issue, workers continue to quit and look for other jobs. This lag within the patient-facing sector appears to be one of the main reasons trials fall behind and even why they are terminated. By leveraging AI-enabled tools to cut down the time spent on tedious manual tasks from multiple hours to just minutes, employee feelings of burnout are alleviated and hundreds of hours become available for physicians and staff to spend greater time with face-to-face activities. This includes activities such as engaging with and caring for patients, which has proven a crucial part of clinical trial timeliness and success.
- Elevated prioritisation of high-level activities – As stated in the National Library of Medicine, a few factors as to why many healthcare workers leave their positions is related to a lack of motivation, exhaustion, and reduced personal accomplishment. These factors can lead to staff leaving their position and causing major financial impacts, resulting in thousands of dollars lost for each employee who quits.
- By automating clinical trial workflows, staff can both optimise their time spent on projects, and enhance their overall quality of work. Most importantly, staff members have more energy, time, and motivation to tackle projects they are most interested in within the trial, such as spending additional hours at the bench or writing post-trial case studies. This reallocation of time is a major factor among clinical research sites in improving employee retention rates.
- Enhanced patient-matching – A data-driven, automated approach for matching patients to intricate clinical trial protocols will result in an exponentially more efficient and quicker process, especially as staff members face limited resources and budgets to address the needs of their region’s patient population. Healthcare organisations leveraging tools driven by artificial intelligence (AI) will have the capability to accelerate the analysis of structured and unstructured data and extract relevant information at any moment in time. This access will produce a rise in confidence among physicians and their staff to accurately retrieve, analyse, and utilise patient data.
Ongoing burnout rates within the healthcare industry continue to impact the quality and effectiveness of clinical trials, but can be eased through the integration of precision technology solutions such as AI. While these technological tools may not be the sole solution to solving the burnout crisis, they certainly help relieve the added exhaustion and stress of conducting tedious, routine manual procedures, which account for many of the hours that staff members hope to leverage for other, higher-level activities. Adopting modern solutions to tackle continued problems will help healthcare professionals get one step closer to providing the work environment needed to run efficient, productive, and high-quality clinical trials.
About the author
Marie E. Lamont is the global head of RWE Data Strategy, Access & Enablement at IQVIA and general manager at Inteliquet, a patient-matching clinical trial software company. She is the former president of the patient services business at Dohmen Life Science Services (DLSS), which was subsequently sold and is now part of EVERSANA. Prior to DLSS, Marie was global head of business strategy and commercial operations for rare disease at Sanofi Genzyme.