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Problems We Need To Address Before Healthcare AI Becomes A Thing

Posted on September 7, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Just about everybody who’s anybody in health IT is paying close attention to the emergence of healthcare AI, and the hype cycle is in full swing. It’d be easier to tell you what proposals I haven’t seen for healthcare AI use than those I have.

Of course, just because a technology is hot and people are going crazy over it doesn’t mean they’re wrong about its potential. Enthusiasm doesn’t equal irrational exuberance. That being said, it doesn’t hurt to check in on the realities of healthcare AI adoption. Here are some issues I’m seeing surface over and over again, below.

The black box

It’s hard to argue that healthcare AI can make good “decisions” when presented with the right data in the right volume. In fact, it can make them at lightning speed, taking details into account which might not have seemed important to human eyes. And on a high level, that’s exactly what it’s supposed to do.

The problem with this, though, is that this process may end up bypassing physicians. As things stand, healthcare AI technology is seldom designed to show how it reached its conclusions, and it may be due to completely unexpected factors. If clinical teams want to know how the artificial intelligence engine drew a conclusion, they may have to ask their IT department to dig into the system and find out. Such a lack of transparency won’t work over the long term.

Workflow

Many healthcare organizations have tweaked their EHR workflow into near-perfect shape over time. Clinicians are largely satisfied with work patterns and patient throughput is reasonable. Documentation processes seem to be in shape. Does it make sense to throw an AI monkeywrench into the mix? The answer definitely isn’t an unqualified yes.

In some situations, it may make sense for a provider to run a limited test of AI technology aimed at solving a specific problem, such as assisting radiologists with breast cancer scan interpretations. Taking this approach may create less workflow disruption. However, even a smaller test may call for a big investment of time and effort, as there aren’t exactly a ton of best practices available yet for optimizing AI implementations, so workflow adjustments might not get enough attention. This is no small concern.

Data

Before an AI can do anything, it needs to chew on a lot of relevant clinical data. In theory, this shouldn’t be an issue, as most organizations have all of the digital data they need.  If you need millions of care datapoints or several thousand images, they’re likely to be available. The thing is, they may not be as usable as one might hope.

While healthcare providers may have an embarrassment of data on hand, much of it is difficult to filter and mine. For example, while researchers and some isolated providers are using natural language processing to dig up useful information, critics point out that until more healthcare info is indexed and tagged there’s only so much it can do. It may take a new generation of data processing and indexing tech to prepare the data before we have the right data to feed an AI.

These are just a few practical issues likely to arise as providers begin to use AI technologies; I’m sure there are many others you might be able to name. While I have little doubt we can work our way through such issues, they aren’t trivial, and it could take a while before we have standardized approaches in place for addressing them. In the meantime, it’s probably a good idea to experiment with AI projects and prepare for the day when it becomes more practical.

Appointment Scheduling Site Zocdoc Connects With Epic

Posted on May 25, 2016 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

In a bid to capture hospital and health system business, appointment scheduling site Zocdoc announced that its customers can now connect the site to their Epic EMRs via an API. The updated Zocdoc platform targets the partners’ joint customers, which include Yale New Haven Health, NYU Langone Medical Center, Inova Health System and Hartford HealthCare. And I’ll admit it – I’m intrigued.

Typically, I don’t write stories about vendors other than the top EMR players. And on the surface, the deal may not appear very interesting. But the truth is, this partnership may turn out to offer a new model for digital health relationships. If nothing else, it’s a shrewd move.

Historically, Zocdoc has focused on connecting medical practices to patients. Physicians list their appointment schedule and biographical data on the site, as well as their specialty. Patients, who join for free, can search the site for doctors, see when their chosen physician’s next available appointment is and reserve a time of their choosing. If patients provide insurance information, they are only shown doctors who take their insurance.

As a patient, I find this to be pretty nifty. Particularly if you manage chronic conditions, it’s great be able to set timely medical appointments without making a bunch of phone calls. There are some glitches (for example, it appears that doctors often don’t get the drug list I entered), but when I report problems, the site’s customer service team does an excellent job of patching things up. So all told, it’s a very useful and consumer-friendly site.

That being said, there are probably limits to how much money Zocdoc can make this way. My guess is that onboarding doctors is somewhat costly, and that the site can’t charge enough to generate a high profit margin. After all, medical practices are not known for their lavish marketing spending.

On the other hand, working with health systems and hospitals solves both the onboarding problem and the margin problem. If a health system or hospital goes with Zocdoc, they’re likely to bring a high volume of physicians to the table, and what’s more, they are likely to train those doctors on the platform. Also, hospitals and health systems have larger marketing budgets than medical practices, and if they see Zocdoc as offering a real competitive advantage, they’ll probably pay more than physicians.

Now, it appears that Zocdoc had already attracted some health systems and hospitals to the table prior to the Epic linkage. But if it wants to be a major player in the enterprise space, connecting the service to Epic matters. Health systems and hospitals are desperate to connect disparate systems, and they’re more likely to do deals with partners that work with their mission-critical EMR.

To be fair, this approach may not stick. While connecting an EMR to Zocdoc’s systems may help health systems and hospitals build patient loyalty, appointment records don’t add anything to the patient’s clinical picture. So we’re not talking about the invention of the light bulb here.

Still, I could see other ancillary service vendors, particularly web-based vendors, following in Zocdoc’s footsteps if they can. As health systems and hospitals work to provide value-based healthcare, they’ll be less and less tolerant of complexity, and an Epic connection may simplify things. All told, Zocdoc’s deal is driven by an idea whose time has come.