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Guessing is easy. Verifying is better, and in hearing aid fittings it can be the difference between “good enough” and genuinely clear speech. We sit down with Madison Levine, BC-HIS and Dr. Dave Fabry to unpack what real ear measurement actually looks like in a busy clinic, starting with a simple question: when should you run REM, at the first fitting or later?

We share a first-fit workflow that’s built for speed: prep the room, connect devices ahead of time, set expectations the moment the patient sits down, and run verification before anything else steals the clock. Then we zoom out to the bigger “why” behind probe microphone measures, including how REM helps confirm audibility at the eardrum regardless of prescriptive targets, proprietary algorithms, or fitting software defaults.

Dr. Fabry also lays out a practical verification protocol: multiple input levels, automated REM to match targets efficiently, and the often-missed safety-and-performance checks like MPO sweeps and LDL/UCL so comfort is protected without throwing away dynamic range. We end with the uncomfortable question: if the evidence is strong, why isn’t real ear measurement universal, and what can clinicians do to remove the time, cost, and confusion barriers?

If you care about hearing aid verification, audiology best practices, and better patient outcomes, hit play, then subscribe, share with a colleague, and leave a review. What’s the biggest obstacle keeping REM consistent in your workflow?

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If you’ve ever wondered why two “good” hearing aid fittings can feel wildly different to a patient, the answer usually isn’t the device, it’s the process. I’m joined by Madison Levine, BC-HIS and owner of Levine Hearing in Charlotte, and Dr. Dave Fabry, Chief Hearing Health Officer at Starkey, for a practical conversation about best practices in hearing health care that actually improve patient outcomes.

We get specific about what belongs in a modern best-practice toolkit: strong case history and counseling, real ear measurement (REM) for verification, and speech-in-noise testing (like QuickSIN) that matches how people struggle in the real world. Dave explains his REM goals (smooth real ear aided response, three input levels, MPO sweeps, LDL/UCL) and why “hitting target” is a starting line, not the finish. Madison shares how her clinic bakes verification and outcome measures into the workflow without slowing the day down, and how data logging turns follow-ups into smarter, calmer conversations.

We also dig into innovation, including how immersive sound simulation with systems like Inventis Symphonia can help demonstrate noise features, personalize settings, and validate a patient’s experience. Along the way we touch on the ear-brain connection, motivation for first-time users, and what to implement tomorrow if you can only change one thing.

Subscribe for more conversations on hearing loss, hearing aids, audiology best practices, and better communication, then share this episode with a colleague and leave a review with your biggest takeaway.

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Healthcare creates a paradox: we collect endless clinical data, publish important research, and still watch patients wait years for proven ideas to become routine care. We sit down with Donna Murray, PhD, to explain how Learning Health Networks (LHNs) are designed to fix that by connecting patients, families, clinicians, and researchers into a shared system that learns quickly and improves care faster.

We walk through what an LHN is, why the Institute of Medicine’s vision of a learning health system matters, and how networks scale the concept across multiple organizations. Donna breaks down the core problem LHNs tackle: silos. Clinicians are on the ground delivering care, researchers are producing findings, and the bridge between them is often weak. The result is slow translation, uneven implementation, and missed opportunities to focus on the barriers patients and families say are most urgent.

From there, we get practical. We talk about “data in once” and why returning insights back to providers in near real time changes clinical decision making. When outcomes can be aggregated across sites, the network can identify which interventions work best for specific subpopulations, learn from high-performing clinics, and spot patients who are not improving even when guidelines are followed. We also connect the dots to audiology and hearing care, where evidence-based practice has to compete with pseudoscience and rapid-fire health claims online.

If you care about real-world evidence, quality improvement, faster adoption of best practices, and patient-centered healthcare innovation, this conversation will give you a clear framework and a hopeful path forward. Subscribe for more, share this with a clinician or researcher who cares about closing the gap, and leave a review. What’s one healthcare change you wish could spread in months instead of years?

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