The role of artificial intelligence (AI) and machine learning (ML) in healthcare has garnered considerable attention in recent years. From clinical documentation to remote patient monitoring, AI/ML technology is finding a foothold in nearly every part of healthcare’s digital transformation.
Originally focused in clinical areas, healthcare’s digital transformation has expanded since the Covid-19 pandemic to include addressing growing operational and workforce challenges. That expansion shows no signs of slowing down, and the global healthcare AI market is expected to be valued at $427.5 billion by 2032 as innovations such as personalized medicine, predictive analytics, and the demand for more efficient, convenient patient care continue to evolve and shape market dynamics.
I find there is exciting near-term potential for an AI/ML-fueled transformation in healthcare operations and, by extension, patient safety and experience. I believe those in healthcare can learn from how other industries have integrated AI/ML and, through strategic implementation, streamline and automate operations to address some of the most pressing pain points they face today.
Pressing Healthcare Challenges Primed For AI/ML Solutions
Today, healthcare organizations and frontline caregivers have become increasingly burdened with managing complex workflows and administrative tasks. From staff schedule creation and conflict resolution to administrative duties like charting, clinicians and nurses often find themselves bogged down by manual, repetitive tasks that consume valuable time and resources. A study conducted by the American Organization for Nursing Leadership (AONL) found 60% to 80% of nurse managers’ time is spent on recruitment, staffing and scheduling.
I see caregivers spending too much time on non-value-added tasks contribute to crippling levels of clinician burnout and an ongoing clinician shortage. In fact, a survey of healthcare IT leaders by my company revealed that workforce challenges are the number one threat faced by healthcare organizations in 2024.
While it’s important to continue to invest in solutions to address clinician burnout and bring more trained caregivers into the field, I believe the fastest way to start addressing workforce challenges is by making their day-to-day easier. McKinsey & Company recently published a report showing that 10% to 20% of nurses’ time during a 12-hour shift is spent on “activities that could be optimized through tech enablement.” The report estimates that by simply reducing the burden of mundane tasks, investments in technology and workflow design could potentially reduce the nursing workforce shortage by as many as 300,000 inpatient nurses.
Learning From Other Industries Improving Workflows With AI/ML
One of the ways in which I’m seeing AI/ML pick up steam in healthcare is by drawing inspiration from other industries that have benefitted from AI/ML technology. For example, the manufacturing industry is using AI/ML capabilities to create more efficient workflows for production and assembly to cut costs. In addition, companies are using AI/ML to automate and streamline operational IT workflows to both improve their current business processes and create more sustainable IT systems.
The healthcare industry can apply these learnings from other industries to similarly optimize workflows. For instance, AI/ML-powered technology can be used to augment supply chain sourcing and inventory management. It can also help health systems manage contracts by detecting auto-renewals for services that may no longer be needed and reducing redundancies, especially following a merger or acquisition when it’s common to end up with duplicate services.
Another specific AI/ML technology from manufacturing that can be adapted for healthcare workflows is predictive maintenance. Similar to how manufacturing plants use AI algorithms to predict equipment failures before they occur, healthcare facilities can leverage predictive analytics to forecast equipment maintenance needs in advance. By implementing predictive maintenance solutions, healthcare organizations can reduce unplanned downtime, minimize disruptions to patient care and extend the lifespan of expensive medical equipment.
However, healthcare leaders must consider several unique challenges when adopting solutions from other industries. Unlike manufacturing, healthcare environments are highly regulated with stringent data privacy and security requirements.
Additionally, healthcare workflows often involve complex decision-making processes and interdependencies among various stakeholders, which may not always align with off-the-shelf solutions designed for other sectors.
Strategically Harnessing AI/ML
As we continue navigating the complexities of the modern healthcare environment, we can expect to see AI’s influence expand as more healthcare organizations look for opportunities to innovate and improve operations. Yet, as organizations embrace the potential of AI-driven solutions, they must remain vigilant in ensuring ethical use, data security and regulatory compliance.
By harnessing the power of AI responsibly and strategically, we can unlock new possibilities for improved healthcare delivery and, ultimately, improve outcomes for patients worldwide.
Whether it’s automating appointment scheduling, optimizing staff scheduling or streamlining billing processes, the right AI/ML-powered tools can help drive efficiency and productivity to pave the way for a more efficient and patient-centered healthcare ecosystem.
In the context of the Industrial Revolution, applying technology to operating mechanisms in each organization is extremely necessary, and is also an inevitable trend to minimize workload while still ensuring efficiency and enhance its competitive position in the market. Furthermore, applying management software into a business will also help build an organization with a clear system, promoting consistency, transparency and accuracy. Tasken eOffice, researched and built by Opus Solution – a business consultant in Vietnam – is an internal work management system as well as the management of automated, online, user-friendly approval processes, allowing businesses to operate more effectively on the path of digital transformation.
Forbes
