3 best practices for integrating AI in health care

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Bots are breaking into the most human industry on earth: healthcare. Just look at Microsoft — it recently launched a new division to address the healthcare-AI intersection.

That’s not to say AI will be replacing doctors anytime soon, but the integration of AI into regular healthcare tasks is upon us. From surgical assistance to patient education to imaging analysis, robots are already part of the process — and their roles will only increase over time.

Breaking barriers to achieve smarter healthcare

Of course, not everything is smooth sailing — AI faces challenges in healthcare just as it does in other fields.

Medical providers often lack the sheer volume of information necessary to train an AI tool; when the data does exist, it’s usually behind silos. For AI to make greater strides in healthcare, the industry needs to create common data exchange standards. Tangentially related is privacy: When large batches of data move between these silos, ensuring security becomes challenging.

Healthcare providers take their responsibility for patients’ safety seriously, which means technologies take a while to gain acceptance. Even when the data supports AI, bots still face an uphill battle to win over an industry where the stakes are literally life and death.

As technology improves and regulations adapt, AI will overcome these challenges, but that process takes time. Innovation in healthcare cannot be stopped; it’s only kept for observation until it’s stable.

If you’re a developer eager to make your mark on the medical world, heed the following best practices:

Work to cut back on the grunt work.

If you want to gain approval from the healthcare industry quickly, create technology to automate redundant, low-cognition medical tasks. Those small tasks add up — many doctors spend a whopping 49.2 percent of their time on menial tasks like paperwork instead of focusing on patients. Any tool that reduces that grunt work will be valuable.

For instance, create tools that let AI take over quick interactions between medications or recommend specific tests based on doctor-discovered symptoms, reducing human error. Consider customized patient follow-ups — a task absolutely within AI’s power — to reduce the administrative load on doctors, freeing them to pursue tasks that require their specialized skills.

This concept is already popular in farming. Blue River Technology uses cameras on crop sprayers to identify plants, killing weeds and fertilizing crops. Farmers can customize their sprayers to save on chemicals and time. Medical technology should aim to do the same for doctors — eliminate simple tasks to let them focus on more pressing matters.

Don’t play workflow whack-a-mole.

It’s easy to get so wrapped up in creating a new technology that you gloss over the extra work it will require. Remember, healthcare providers don’t rush to change, so you’ve got to make your tool even more appealing and user-friendly than you would for any other audience.

For example, getting patients to adhere to their discharge plans can be difficult. A chatbot can help them stay on schedule, but if that chatbot begs for daily updates, it’s just creating more work for doctors. A better system would only flag the provider when the patient failed to refill a medication or missed a critical appointment. It’s these kinds of situations you need to consider, taking time to analyze every user touchpoint and whether it’s a relief or just a chore.

As a parallel example, look to the legal industry: Kira Systems is a lawyer’s AI assistant specializing in contract review. The beauty of Kira is its simplicity — it doesn’t require lawyers to waste hours feeding it information. Kira simply checks databases faster than any human could and presents its findings for review, saving time without adding new steps. Similarly, to satisfy medical providers, reduce their workload instead of replacing it with a higher-tech version.

Bottom line: When workflows go awry in any other industry, things get messy. But when that happens in healthcare, individuals’ lives are at risk. User-friendliness matters more here than in any other sector.

Put security — not blind innovation — in the driver’s seat.

Technical challenges in healthcare are second to privacy. The most effective tool will be tossed aside if it can’t secure patient information at every turn.

To prepare an AI for the medical field, you need a staff with the right expertise: a high-level security officer who understands the risks; an attorney who is familiar with the laws in patient privacy and security; and a team of developers and engineers committed to keeping technology updated and hack-proof. That means patching software regularly, establishing clear lines of ownership, encrypting everything, and educating staff on security.

It sounds like a lot of overhead work, but in healthcare, security is king over all other concerns, and again, it’s individuals’ most personal information you’re dealing with. Satisfy those concerns, and your new tool will be one step closer to adoption.

The AI revolution is coming to healthcare. If you want to get involved, now is the time. Consider these tips before you dive in — you’ll be much more equipped to confidently develop something useful for medical providers.

Kevin Yamazakiis the founder and chief executive author of Sidebench, a leading digital product, and venture studio.

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