• Written by Sonali Subhadarsini, Product Manager

A typical Monday morning in a hospital's operating theatre suite, and the shift supervisor is already dealing with the fallout from a delayed procedure, trying to reschedule the day's cases while juggling the availability of surgeons, nurses, and anaesthetists, all of whom have strict union-mandated break times and shift limits, which makes it difficult to find a spare 30 minutes to fit in an emergency appendectomy, and the supervisor knows that every delay will have a ripple effect on the rest of the day's schedule, and potentially even on the next day's, because some of these patients are coming in from other hospitals, and their transport has to be arranged, and their families have to be informed, and so on.

Challenges in healthcare providers

The healthcare sector presents a unique set of challenges when it comes to implementing AI productivity solutions, particularly in areas such as scheduling and utilisation, supply consumption variability, and documentation burden, where the shift supervisor's task is complicated by the need to balance the needs of multiple stakeholders, including patients, clinicians, and administrators, and to do so in a way that is both efficient and respectful of the strict clinical and administrative protocols that govern hospital operations, and that also takes into account the physical constraints of the hospital's layout, and the availability of equipment and supplies, which can be a major challenge, especially in hospitals with older infrastructure, or those that are located in areas with limited access to resources.

For instance, a category manager in a hospital's procurement department might be tasked with reducing supply consumption variability, which could involve analysing data on usage patterns, identifying areas of waste, and implementing more efficient procurement and inventory management systems, but this would require access to accurate and up-to-date data, which can be difficult to obtain, especially in hospitals with legacy systems, or those that are still using paper-based records, and it would also require the category manager to work closely with clinicians and other stakeholders to understand their needs and preferences, and to identify areas where changes could be made without compromising patient care.

Difference between time saved and time redeployed

One of the key differences between time saved and time redeployed is that the former refers to the reduction in time spent on a particular task, while the latter refers to the ability to use that saved time to perform other tasks or activities, and in the context of healthcare providers, this distinction is particularly important, because time saved on administrative tasks can be redeployed to patient care, which can have a direct impact on health outcomes, and this is where AI productivity solutions can make a real difference, by automating routine tasks, and freeing up staff to focus on more complex and high-value activities, such as patient care and clinical decision-making.

However, this requires a nuanced understanding of how time is currently being used, and how it could be better utilised, which is why many healthcare providers are turning to solutions like AsscherAi, which can help them to analyse their data and identify areas where time could be saved, and redeployed to better effect.

Self-reported time savings

Self-reported time savings are often used as a metric to evaluate the effectiveness of AI productivity solutions, but they can be misleading, because they are based on subjective estimates, rather than objective measurements, and they may not take into account the complexity of the tasks being performed, or the variability in the way that time is used, and this is particularly true in healthcare providers, where tasks are often highly complex, and require a high degree of nuance and judgment, and where the stakes are high, and the consequences of error can be severe.

Furthermore, self-reported time savings may overstate the effect of AI productivity solutions, because they are based on the perceptions of individual users, rather than on objective data, and they may not account for the fact that time saved in one area may be offset by increased time spent in another area, and this is why it is so important to use objective metrics, and to carefully evaluate the impact of AI productivity solutions, to ensure that they are having the desired effect, and that they are not introducing unintended consequences, such as increased workload, or decreased job satisfaction.

Tasks worth removing first

When it comes to implementing AI productivity solutions in healthcare providers, it is often helpful to start by identifying tasks that are repetitive, routine, and prone to error, such as data entry, or document scanning, and to automate these tasks, in order to free up staff to focus on more complex and high-value activities, and this can be particularly effective in areas such as clinical documentation, where the use of AI-powered tools can help to reduce the burden of paperwork, and to improve the accuracy and completeness of patient records, and it can also help to reduce the risk of errors, and to improve patient safety.

For example, a doctor might use an AI-powered tool to generate a list of potential diagnoses based on a patient's symptoms, and medical history, and this could help to save time, and to improve the accuracy of diagnoses, and it could also help to reduce the risk of errors, and to improve patient outcomes.

Limitations of AI productivity solutions

While AI productivity solutions can be highly effective in certain contexts, they are not a panacea, and they may not be suitable for all tasks, or all organisations, and this is particularly true in areas such as healthcare, where tasks are often highly complex, and require a high degree of nuance and judgment, and where the stakes are high, and the consequences of error can be severe, and in these cases, it may be more effective to use other approaches, such as process redesign, or staff training, to improve productivity, and to reduce the risk of errors.

Furthermore, AI productivity solutions may not be effective in situations where there is a high degree of variability, or uncertainty, such as in emergency departments, or intensive care units, where the situation is constantly changing, and where staff need to be able to respond quickly, and flexibly, to changing circumstances, and in these cases, it may be more effective to use other approaches, such as agile management, or flexible staffing, to improve productivity, and to reduce the risk of errors.