STORY INLINE POST
If you’ve followed the healthcare space at all in the last few years, one theme has probably stood out: Virtual care is the future.
Spurred by the pandemic, healthcare organizations large and small have scrambled to implement telehealth programs, with many needing to overhaul their tech stack, operational protocols and staff in the process.
At the same time, a slew of new digital health companies have emerged, offering on-demand telehealth services covering everything from obesity to diabetes, OCD to ADHD.
That’s great news overall. It has never been easier to get the care you need, wherever you are.
But there’s still a big blind spot in virtual care: Quality assurance (QA).
Healthcare organizations have done a great job of getting patients to buy into virtual care, at scheduling and billing, but the longer I work in the space, the clearer it becomes that next to no one is actually reviewing telehealth interactions to see if they meet an organization’s quality standards.
The best most teams can do is a handful of QA audits per month or the occasional patient satisfaction survey, leaving 90-something percent of clinician-patient interactions unreviewed.
This not only puts organizations at risk of huge reputational damage or compliance violations, but also neglects patient experience and safety, especially for patients seeking help for mental health or substance abuse. This risk is even bigger considering how many digital health companies employ contract or part-time staff in a remote setting that leaves little room for oversight.
Without QA, by the time most organizations get wind of a problem, it may already be too late.
But the days of “no news is good news” may soon be ending. Advancements in AI and conversation intelligence now make it possible to QA 100% of telehealth calls in real time.
Why hasn’t QA been a priority in telehealth?
Most virtual care leaders would tell you they wish they could do more QA, but it’s simply not realistic. And, historically, they’ve been right.
Until recently, telehealth QA has been a huge time sink, requiring dedicated QA teams — or, more frequently, clinical team leads — to manually review hours’ worth of call recordings or transcripts just to get a sense of where one team member stood.
You’d then have to distill all of that information, create a report and provide staff feedback or training to ensure quality standards were upheld. You’d also have to track performance trends over weeks or months to see if things were improving or staying on track, requiring even more time and resources.
As you might guess, this has been untenable for the majority of organizations. Teams are already stretched thin and pressured to see as many patients as possible. Where would they find the space to do any meaningful QA?
Many virtual care leaders may feel they have no realistic choice other than to hire and train talented staff and hope for the best. And that works out fine — until it doesn’t.
How AI Can Help
One of the key advantages of telehealth for QA purposes is that it makes it easy to capture every clinician-patient interaction. You don’t need to rely on a clinician’s notes or patient records to get a sense of how a visit went — you can simply listen to a recording or read a transcript and see what happened moment by moment.
As we’ve seen, however, analyzing those interactions at scale and taking action based on them has been the hard part.
This is where AI can be a game-changer.
Healthcare organizations can train an AI on their best practices and automate the QA process. The AI can analyze a telehealth call in real time based on a custom scoring rubric and detect whether a clinician has followed an organization’s necessary compliance protocols or other required steps. The AI can also provide a QA score or real-time feedback to help clinicians stay on track.
This not only gives clinicians a clear target to aim for (such as getting an A+ on every call), but also reinforces training and best practices to ensure consistent care quality.
After the call, managers can also use AI to generate QA reports that stretch across multiple staff members and hundreds of calls. This allows teams to see where their organization stands overall or evaluate individual staff member performance over time and build tailored performance reviews or coaching and training plans.
This means that AI not only gives organizations better insight into telehealth call quality and patient experience, but also helps them use those insights to level up their team. Considering the challenge of feedback in telehealth, this could be a huge leap forward even for the few organizations that were already able to do some QA.
It’s no longer a fraction of calls being reviewed, it’s every call. It’s not a once-a-quarter performance review, it’s a real-time feedback loop. Instead of waiting weeks for feedback on a call they barely remember — one call out of hundreds — clinicians get feedback in the moment on every call.
Now that AI has made comprehensive call review and feedback possible, telehealth QA should no longer be seen as a “nice to have.” Instead, I’d argue QA on every call should be our new minimum standard — a must-have for healthcare organizations serious about patient experience, compliance and staff development.
Bottom line: No more excuses
It’s easy to fall into change-resistant thinking — “this is the way we’ve always done things.”
It wasn’t so long ago that people thought the best way of getting a ride was to stand on the curb, lean into oncoming traffic and hope a cab would stop. Now that rideshare apps are here, it seems so silly. Why would we ever go back to the way things used to be?
When it comes to healthcare, the stakes are simply too high. If AI makes it possible to automate QA on every telehealth call, how can we look the other way? What could be more important than ensuring the best experience possible for patients?