The world is seeing a global shift towards artificial intelligence (AI) in the healthcare industry. Part of this stems from the healthcare industry’s transition towards a cloud environment for data management; with the cloud, data is now available on a real-time scale for further analysis. But rather than rely on staff to meticulously comb through data, artificial intelligence enables a much efficient—and in many cases, much more accurate—process.
As AI's capabilities increase, everything from internal operations to medical records benefits from integrating predictive modeling, automatic report generation, and other artificial intelligence features. Let's take a look at four specific use cases for AI in healthcare:
Whether a hospital or individual clinic, healthcare operations continue to be a complicated and multifaceted series of processes. From internal operations such as HR to dealing with insurance claims to taking in patient data from other providers, data is always flowing both inward and outward for healthcare operations. Decades ago, this was a lot of physical paper and phone calls. In recent times, it streamlined into emails and files, and in the past few years, much of the healthcare industry has pushed towards cloud databases and custom applications.
Today, artificial intelligence can push the boundaries of this even further to smooth operations across the board for healthcare industries. For example, HR departments can use artificial intelligence to crunch employee information and provide insights for real-time actionable decisions. Finance departments can identify expenses and cost trends while handling invoicing. For patients, prior authorizations and eligibility can be automated to reduce manual labor. Supply chain management can also be handled by AI to identify potential blocks and gaps.
Healthcare patients are mired in all sorts of paperwork, from intake forms to follow-up data. This is particularly true in the COVID-19 era, when prescreening questions are critical to providing safe and effective healthcare. As the healthcare industry shifts towards a cloud model, data is now collected in real time, but artificial intelligence allows this to be much more than simple displays of forms.
With artificial intelligence, medical teams can get updates, analysis, and reports automatically generated, saving them time while also highlighting preventative care issues to bring up with patients during their appointments. This enables a more proactive and thorough approach to healthcare while reducing the workload on staff.
The finances of a healthcare organization go beyond the typical needs of a company. With regulatory needs, patient confidentiality, and the different requirements of various insurance companies, moving towards a unified cloud-based system is a step towards significantly reducing churn while improving accuracy. When these cloud applications implement artificial intelligence, things can get further streamlined.
By using artificial intelligence for financial needs and operations, a healthcare organization can benefit in the following ways:
Resource management has always been a critical part of a healthcare organization, for both hospitals and individual clinics. This has never been more visible than during the COVID-19 era, when resource usage and availability hit extreme circumstances. For these instances, resources covered a wide range of topics, from staff to vaccines to tools and supplies. Moving this data to the cloud marked a significant step forward for the industry, creating a consolidated single source of truth to make decisions. However, implementing artificial intelligence has proven to be just as significant.
Using artificial intelligence and machine learning in healthcare has created a number of data management benefits. By applying these tools to real-time data, reports and metrics on resource usage can be auto-generated, significantly saving on both process time and reaction time. Predictive modeling on both micro and macro scales also ensure a better balance of resource usage, as well as identifying situations and seasons when organizations will need to scale up. With data-driven predictive modeling, organizations can plan ahead, ensuring that their communities receive better care.