We need to talk about AI

James Chen founded DrFirst and is the CEO and chairman. He is a seasoned entrepreneur with experience starting and running tech companies.

From the Jetsons to the Terminator, we’ve enjoyed a cinematic look at the future where artificial intelligence (AI) reaches human-like consciousness and takes on everything from cleaning our homes to doing all kinds of jobs we’d rather not do. to do. Is it any wonder that healthcare leaders are becoming increasingly disillusioned with artificial intelligence, with overly high expectations like this?

Reclaiming the story

As architects and advocates of AI technology, it’s time we reclaim the story and restore lost trust. Business and technology leaders can’t rewrite TV and movie scripts, but we can – and do – tell the story of AI’s potential every day, whether we realize it or not. And people listen in ways we might not expect.

Investors read between the lines of quarterly earnings for evidence that AI’s impact has loftier goals than saving time and money. Customers are turning away from tired clichés about AI and machine learning and want proof of results that matter and ROI.

If we choose our words carefully, we can strongly and positively influence conversations about AI and decisions about how to use it.

In healthcare technology, some of us have decided to talk less about the amazing things machine learning should do in the future and more about the practical tasks AI performs every day. We are working towards a more deliberate use of our language, eliminating buzzwords and technical jargon in favor of plain language that defines our digital solutions through the improvements in patient outcomes they achieve.

Past over-promises leave many executives wondering if AI will ever prove it can transform healthcare, when in fact it already does. Millions of small tasks that were once a heavy burden on caregivers are now performed safely, reliably and accurately – words we don’t use lightly, but can say with confidence because we have clinical results to back them up.

To have meaningful conversations about artificial intelligence beyond buzzwords and clichés, such as “AI-powered,” we need to focus on these kinds of practical use cases. I’m talking about examples where AI and machine learning prove that they can transfer data from different systems, even filling in the blanks so that the data can be used to trigger security alerts – not just stored in a digital file cabinet.

Real talk about AI and the free text mess

Prescription instructions are an example of how what seems like a small thing can have a huge impact on the entire healthcare system. Medicines are ubiquitous, with 69% of doctor’s office visits and 80% of emergency room visits with drug treatment. That means millions of electronic prescribing instructions are shared every month between different systems using different terminologies. Most people assume that this data transfer is done completely electronically, without human intervention. But that is not the case.

For every written e-prescription, there are multiple drug databases that play a role in how the prescription instructions are written and sent electronically. This means that a single, general prescription instruction – take one tablet by mouth once a day – de 832 common permutations, including “1QD” and “tk 1 t po qd.”

Due to disparate systems and different vocabulary, data is usually dumped in chunks of “free text” rather than parsed into useful fields. As a result, clinicians read and manually enter data over and over, which is tedious, mind-numbing and can cause keyboard errors. This is more common than you may think. A recent study found that pharmacy workers need to edit manually 83.8% e-prescription instructions.

Again, it is important that we reframe the perception that digitization and artificial intelligence created inefficiencies when they were intended to solve it. For AI to deliver on its promise of patient safety and healthcare provider efficiency, healthcare systems must have a clearly defined implementation strategy that makes shared data truly interoperable in a practical sense.

Reducing boredom to reduce clinician burnout and staff shortages

Burnout is prevalent today among caregivers who face the pressures of out-of-hours work, excessive administrative tasks and chaotic workplaces. The pandemic has only exacerbated this pattern and is now affecting about 30% of all clinicians: doctors, pharmacists, nurses and others.

This is where AI can (and does) make the difference.

Collecting and processing data, including regulations, are among the repetitive tasks highly feasible for AI-driven automation, according to a groundbreaking study by McKinsey. In pioneering artificial intelligence to improve healthcare workflows, my own company finds that certain data entry, data exchange, and data synchronization tasks are ripe for optimization.

When implemented, this optimization means that doctors, pharmacists and other healthcare professionals don’t waste precious time typing and transcribing data – time they should be spending on patients. And we are talking about a lot of time; healthcare professionals spend hundreds of thousands of hours on data entry rather than interacting with patients. As staff shortages and clinician burnout continue to endanger healthcare systems, AI can make an extraordinary difference to everyday work.

The way forward

We can achieve macro results with AI in healthcare by focusing on seemingly “small” things like prescription data entry that, when combined with other forms of data management, have proven to save lives, time and money. Let’s lead healthcare AI into these areas, where it can finally deliver on its promises. It may not be the next big blockbuster in theater, but it’s a realistic story that we can all get behind.

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