• Written by Sonali Subhadarsini, Product Manager

A typical Monday morning for a healthcare operations team begins with a flurry of activity, as shift supervisors scramble to review the previous day's events, category managers assess supplier performance, and project managers track budget consumption. Amidst this chaos, many questions arise that currently wait on a report, such as the status of ongoing projects, the performance of suppliers, and the consumption of budgets.

The operations questions that currently wait on a report

These questions often revolve around the operational aspects of healthcare delivery, including patient flow, resource allocation, and supply chain management. For instance, a shift supervisor may need to know the current occupancy rate of the hospital, while a category manager may want to compare the prices of different medical equipment suppliers. Unfortunately, the answers to these questions are often buried in reports that are only generated on a weekly or monthly basis, leaving operations teams to make decisions based on outdated information.

By having access to real-time data, operations teams can ask more nuanced questions, such as what is the current wait time for patients in the emergency department, or which suppliers have the best track record for on-time delivery. This enables them to make more informed decisions, respond quickly to changing circumstances, and ultimately improve the quality of care delivered to patients.

The data behind them: process and exception records, project status, budget consumption, supplier performance

The data that underlies these questions is diverse and complex, encompassing process and exception records, project status, budget consumption, and supplier performance. For example, a project manager may want to track the progress of a construction project, including the status of permits, inspections, and payments. This requires access to a range of data sources, including project management software, financial systems, and external databases.

Similarly, a category manager may need to analyze the performance of different suppliers, including their pricing, delivery times, and product quality. This involves reviewing data from procurement systems, contract management software, and supplier performance metrics. By integrating these disparate data sources, operations teams can gain a more comprehensive understanding of their operations and make more effective decisions.

Sector constraints: scheduling and utilisation and supply consumption variability

Healthcare operations are subject to a unique set of constraints, including scheduling and utilisation variability, as well as supply consumption variability. For instance, the demand for hospital services can fluctuate significantly depending on the time of day, day of the week, and season. This requires operations teams to be highly adaptable and responsive to changing circumstances.

Additionally, the consumption of supplies, such as medical equipment and pharmaceuticals, can vary significantly depending on the type of patients being treated and the procedures being performed. This necessitates careful planning and management of inventory levels, as well as close monitoring of supplier performance. By taking these constraints into account, operations teams can optimize their operations and improve patient outcomes.

What improvement looks like: on-time delivery, exception rate, expediting cost

Improvement in healthcare operations can take many forms, including on-time delivery of services, reduction in exception rates, and minimization of expediting costs. For example, a hospital may aim to reduce the wait time for patients in the emergency department, or to increase the percentage of patients who receive their medications on time. By tracking these metrics and analyzing the underlying data, operations teams can identify areas for improvement and implement targeted interventions.

By leveraging AI for operations, healthcare providers can streamline their operations and improve patient outcomes. This can involve using natural language processing to analyze large datasets, or machine learning algorithms to identify patterns and predict outcomes. Whatever the approach, the goal is to provide operations teams with the insights and information they need to make better decisions and drive improvement.

What this does not do, or when it is the wrong choice

While AI for operations can be a powerful tool for healthcare providers, it is not a panacea for all operational challenges. There are certain situations where it may not be the best fit, such as when the data is incomplete or inaccurate, or when the operational processes are highly complex or nuanced. In these cases, other approaches, such as process mapping or lean management, may be more effective.

Additionally, AI for operations is not a replacement for human judgment and expertise. Rather, it is a tool that can be used to support and augment the decision-making process. Operations teams should carefully consider their needs and goals before implementing an AI solution, and should be prepared to invest time and resources in ensuring that the solution is properly integrated and utilized. For more information on how to get started with AI for operations, please contact us.