In recent years, the healthcare industry has seen explosive growth in the use of artificial intelligence (AI) and it doesn’t seem this trend shows signs of slowing down any time soon. In fact, recent market analysis predicts a compound annual growth rate (CAGR) of 46.2% in the coming years, with potential spending on healthcare AI reaching $67.4 billion by 2027.
What is AI? Simply put, “AI” is a blanket term for a wide assortment of technologies that allow machines to perform cognitive functions, such as writing, reading or analyzing data at a speed and accuracy that humans can’t match.
In healthcare, AI has had many applications such as reading CT scans, diagnosing skin cancer, scheduling nursing work rotations and managing electronic health records (EHR). However, one particularly exciting development is the use of AI to operate microgrids, which can transform how healthcare organizations distribute their power needs.
Predictive AI In Healthcare
Predictive AI is a data analytics approach that can anticipate future outcomes for an organization. In healthcare, predictive AI has had a broad array of applications, including:
- Health Insurance. In this instance, predictive modeling means accurate calculations of insurance costs for specific individuals, based on parameters such as age, case history, gender, regional location and/or tracked health habits.
- Medical Imaging. Radiologic prognostic software has shown AI can identify potential problem areas with a higher degree of accuracy in lung screenings and breast cancer diagnostics than human radiologists.
- Palliative Care. Basing predictive models on electronic health records (EHR) allows doctors to make informed health risk forecasts for patients with life-threatening diseases.
- Mental Health. Predictive analytics utilize EHR to help identify, flag and implement proactive solutions for patients identified as at-risk for mental health issues.
Beyond clinical and administrative uses, predictive AI also has value in the use of microgrids, which are clean, renewable energy resources. Breakthroughs in AI technology make microgrids affordable, appealing and accessible for healthcare organizations.
Why Use AI and Microgrids?
Microgrids are a step forward from traditional power grids, which are interconnected power elements such as transformers, power lines, substations, etc. Often, the area served by a standard grid is enormous, with power supplied at a great distance from the generating source.
By contrast, a microgrid is a resident energy grid that offers local control capability, which means it can be disconnected from the main, traditionally run grid and operate independently. Because it’s capable of isolating from the (usually) remote central power plant, microgrids are resilient and capable of delivering services that are more flexible as well as competitive. Microgrids also provide low-cost, efficient, clean energy that supports improved stability in the regional electric grid and gives many health organizations the opportunity to access affordable, alternative power sources. What’s more, predictive AI helps microgrids operate more effectively and efficiently. Here are three of the prominent issues with microgrids that AI has solved:
Keeping The Power On 24/7
Hospitals and other healthcare organizations need energy to provide around-the-clock services to patients, so any loss of power can severely disrupt clinical operations. With an AI-fueled microgrid system, power companies can locate, diagnose and fix many problems in real-time as well as avoid downtimes. A network of microgrids with integrated AI is capable of sorting through and absorbing massive amounts of data, which it can use to study the power grid, determine where failures are happening in addition to reinforce spots at risk of shutting down. The ability to predict where the outage may occur means repairs can happen before customers—including healthcare providers—are left without power.
Processing Large Volumes Of Data In Real-Time
The data required for a microgrid to function at maximum efficiency is overwhelming. While this complex data management and analysis are far beyond the capacity of a human to handle, it’s not outside the ability of AI. In fact, AI can conduct real-time data processing to generate predictive models for such variables as weather, electricity supply or demand as well as synchronized control of the microgrid and end-user devices.
Maximizing Microgrid Performance and Affordability
An AI-based controller can utilize up-to-date market data and grid load to maximize microgrid performance. For example, here are three specific issues that may benefit from an AI-based system:
- Improved battery health maintenance. AI forecasting combined with predictive analytics can smooth the transition between solar or wind power generation, the microgrid and end-use energy devices. This enhances the performance of battery banks.
- Extension of battery life. AI control of renewable energy generation, such as roof-top solar panels, can synchronize energy transfers between the grid and end-user devices, preventing power spikes or drops that shorten battery life.
- Inverter/battery optimization. Using real-time access to grid load and market data, an AI-equipped controller can regulate the distribution of stored electricity to the grid where this feature is available.
Powering The Future of Healthcare
Predictive AI and microgrids are on the cusp of reshaping healthcare. However, for AI solutions and microgrids to be successful in propelling the industry forward, healthcare organizations must be willing to invest in data management. Constant innovation to improve analytic frameworks, platforms and systems means more effective AI tools will be coming online in the years moving forward.
So, what does the future hold? The innovative applications of predictive AI have already started to transform the delivery and administrative components of healthcare. As more healthcare organizations adopt AI-powered microgrids, new, clean and renewable energy opportunities are on the horizon.