Caregivers in healthcare deal with a considerable amount of complexity: In addition to a high volume of patients, they need to patriciate in and/or coordinate communication between team members and make sure all information is up to date. Their work is fast-paced, and situations can change in seconds.
One way to make these efforts more manageable is by using artificial intelligence. In healthcare, as in all fields, the job of AI is not to replace humans, but rather to perform repetitive, tedious and time-consuming tasks so that people don't have to — freeing time for tasks that require a personal touch. Human judgment should remain the ultimate decision-maker.
AI frees up time
Algorithms and software can help caregivers make predictions, analyze data and simplify processes. In my experience, if one is looking at a list of 50 repetitive tasks, AI can eliminate 45 of them, handing people extra hours for the five most pivotal. Personal care is scarce and valuable, but essential: the more technology can free up this time, the more focus can be on those precious tasks that technology alone can't handle.
It helps patients
These prioritization benefits move down to patients. Efficient use of AI can reduce the costs of healthcare and the time required for treatment, not least because when routines are made more efficient, procedures can be completed faster, which ideally leads to lower expenditures.
AI also supports caregivers in making higher-quality decisions. For these professionals, it can be hard to find a starting point in interpreting data. In MRI imaging, for example, looking through thousands of images is inherently time-consuming and can lead to information being overlooked or misinterpreted. Artificial intelligence can help save time by bringing up the most relevant images, making care more efficient and accurate.
How predictions and patterns can make work easier
Algorithms can also be used for predicting: Software can take the current state of a situation, learn from patterns and make projections, which can be deeply useful. At GE, we use machine learning to forecast census for 14 days at the hospitals we serve, and look at every bed, unit and service in the process. This allows us to make accurate guesses as to conditions for each unit, over each hour, and for two weeks. Such forecasts can predict which parts of a facility will become hotspots, and teams can then determine which caregivers to transfer to each. They also help hospitals accept transfer patients more efficiently: If they receive a call asking whether they can accept an admission in two days, caregivers can give a confident answer, with forecasts in front of them.
The importance of context
We're still in the beginning stages of AI software applied in healthcare, and it needs to be fine-tuned, but users also need to make sure they're employing the technology correctly.
It's up to them to put software in context and use it in a way that's helpful. AI isn't a crutch to be relied upon, but a tool to be wielded. A nurse's job isn't to sit there all day looking at forecasts, and a staffing coordinator doesn't wait all day for staffing forecasts. Whether algorithms are applied to worker allocation or radiology, they must be used in context in order to be helpful.
Think of these systems as akin to software in a phone, which likely includes a compass. When you're looking at the compass directly, it's of marginal use, but when integrated into a map navigating app, it's incredibly helpful. There's an algorithm, and then there's the larger app it's contained in. The same goes for AI in healthcare: It has to be used in the right context for it to reach full potential.
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