Imagine you’re a business owner with a suggestion box in your store. Every week you gather those cards from the box and read through them. And week after week, two out of every three cards have the same complaint.
What would you do? Your knee-jerk reaction might be to remove the suggestion box. But more likely, you’d get to work fixing that persistent issue. Because you know that otherwise the problem is only going to grow.
For hospitals and other healthcare providers, this “suggestion box” moment has arrived. When two-thirds of poll respondents are saying they wish their healthcare provider took more time to understand them, the message should be loud and clear: The patient experience has a chronic problem – and healthcare providers need to act.
But where will that additional time to understand their patients come from? And how will that better understanding happen? Here’s how technology – and specifically AI – can play a role.
The diagnosis: Simple digitization is not enough
Digitization was supposed to help.
But while parts of the system digitized, underlying processes didn’t adapt. We have EHRs and EMRs instead of paper charts, but physicians are still checking that data manually.
Patients are still inputting their information over and over, and growing frustrated by the redundancy. Worse, they’re not seeing that data being used for anything.
Meanwhile, hospitals and healthcare providers are being squeezed by insurers that demand all of that detailed information when considering claims.
Then there’s the issue of the doctor’s visits themselves. Looking to maintain profitability, hospitals want doctors to see as many patients as possible.
But a doctor who goes beyond a brief appointment window by asking too many questions can’t see as many patients in a day. And lost appointments mean lost revenue. So those appointments get compacted, with less time for patients and physicians to interact.
The result of all of this standardization and clock-watching? Patients lose trust in the system and feel like they’re nothing but a number.
The prescription: AI as an addition
Changing that narrative to make patients feel more understood by the healthcare system means making the process of getting checked out and treated more personable and engaging. This is where AI can help doctors, freeing up time for them to converse more thoroughly with patients and providing them with more insights to draw upon in their care plans.
The goal is to focus AI on augmenting doctors’ capabilities to allow them to build the human connection that patients want. Here are two practical ways to do this:
1. Summarizing patient notes
The problem: The doctor has only a few minutes to glance over the notes on a patient before entering the exam room. But when the patient’s health issues or medical history are more complex, that quick recap becomes a bigger ask. Things can get overlooked or the review can steal precious minutes from an already short visit.
The AI assist: AI can help prepare and summarize these pre-visit notes. By providing a quick history of everything from new medications to recent specialist visits, the doctor can have a better understanding of everything that’s going on before they walk into the room.
The result: The doctor has more time during the appointment to actually work with and talk to the patient instead of reviewing notes. The patient feels better heard and better understood.
2. Making the most of wearable data
The problem: More and more patients are using wearable devices to pay closer attention to their health. These rings and smartwatches track everything from blood sugar to heart rate to caloric intake, but this information is rarely shared with or used by their healthcare provider.
The AI assist: AI can process wearable data to show trends and patterns that a doctor may not have time to work out on their own. For example, AI may be able to pinpoint that a patient’s glucose spikes at regular intervals, such as days and times when that patient goes into the office, where they’re confronted with donuts and colleagues’ suggestions of takeout.
The result: Rather than relying on a patient’s stories about eating better or exercising, the doctor now has the data to better understand what’s going on – and to ask the right questions.
In both of these scenarios, the interactions between doctor and patient are more personalized because data is being used in a way that’s actually helpful. Doctors appreciate and adopt the technology because it makes their work easier. And patients enjoy a more satisfying experience.
Another key point: In both of these cases, AI isn’t doing the doctor’s work; it’s simply providing the insights and analysis that makes it easier for the doctor to do their own work. In this way, AI helps everyone do what they do best – the tech crunches the numbers and illuminates the patterns, and the doctor uses that information to interact with and treat the patient.
The care plan: Confronting AI challenges
While AI holds tremendous possibilities for healthcare providers looking to better know and understand their patients, bringing the tech onboard isn’t as simple as flipping a switch. There are several challenges to confront, including:
Provider size: Larger hospital systems generally have bigger budgets, patient populations, and appetites to experiment more extensively with AI to find efficiencies of scale. Smaller systems and rural hospitals, on the other hand, have to be more careful with their tech spending, making large-scale AI investments an overreach.
Availability of personalized data: For AI to be a worthwhile solution, providers need to have specific data for their context, solution, or use case to feed into the tool. Building on the above challenge, larger systems can usually validate their data sets, but smaller hospitals probably need help.
Ability to customize: Customization is often the difference between AI projects that succeed and those that fail. In fact, that was a major takeaway of a recent headline-grabbing MIT study: while 95 percent of AI investments failed overall, a majority of those were doomed by their reliance on generic tools and their inability to address the very specific needs of the organization.
The message is clear: A customized AI tool working with good data tends to lead to a reliable output, whereas a generic AI tool tends to produce a generic output. With the latter, you’re no better off than you were before the investment.
AI can enhance the patient experience
Digitization has certainly cut down on the paperwork glut in healthcare. But if that’s the extent of technology’s impact on the patient experience, it’s clearly not enough.
So how is AI any different? It’s another stab at applying technology in healthcare, but with a real chance to address the key area where patients feel their hospitals and providers are coming up short – truly seeing and knowing them.
By freeing up doctors’ time and making their lives easier, AI gives them the chance to do less busywork and more human work, which is so needed under times of distress. And that’s the heart of the patient experience.
Photo: malerapaso, Getty Images
Luiz Cieslak is an SVP at CI&T a global digital specialist. CI&T’s Life Sciences and Healthcare team partners with pharmaceutical companies, consumer healthcare firms, and medical device manufacturers to create better experiences for patients and healthcare professionals.
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