AI Is Coming For Your Job in the best possible way - Chewing It Over with Caelum Trott of Preve

Preve.co | Discount code Chew26What if AI in physiotherapy could do more than write your notes?In this episode of Chewing It Over, Jack speaks with physiotherapist, clinic owner and Preev co-founder Calum Trott about using AI to tackle one of private MSK practice's biggest challenges: patients dropping out before completing their planned care.Preev has been designed around more than clinician efficiency. It aims to turn information from the consultation into a clear patient treatment plan, helping patients understand where they're going, what happens next and why continuing their care matters.Jack and Calum discuss the problem with traditional metrics such as patient visit average, why expectation management matters, and whether clinics should instead be measuring how successfully patients complete the individual treatment plan actually recommended to them.They also explore:Why AI scribes are only the beginningPatient drop-off and self-dischargeCreating personalised treatment plansImproving communication between appointmentsReducing administrative burden for cliniciansPatient retention versus simply increasing appointment numbersThe limitations of PVA/PBA as a clinic metricMeasuring planned completion ratesUsing AI to identify patients falling off their recovery pathwayEarly results from clinics using PreevWhere AI-supported MSK care could go nextCalum shares early examples of clinics reporting significant improvements in rebooking among patients receiving treatment plans, including one clinic where average visits per case reportedly increased from around 4.5 to 7 following implementation.The bigger question is whether healthcare AI should simply make existing work faster—or help us deliver a fundamentally better patient experience.🧠 5 clinical/professional takeawaysAI doesn't have to stop at documentation. The more interesting opportunity may be using information already captured during consultations to improve what happens between appointments.Expectation management is part of treatment. A patient who understands the proposed journey, expected number of appointments and purpose of each stage is better equipped to engage with their care.PVA/PBA can be a crude metric. Seven appointments might represent excellent care for one presentation and incomplete care for another. The number alone lacks clinical context.Planned completion could be a better measure. Comparing the clinician's recommended plan with what the patient actually completes creates a more individualised measure of engagement.Efficiency and patient experience are connected. Asking clinicians to produce detailed plans and follow up every disengaged patient manually creates more work. The opportunity for AI is to make better care easier to deliver consistently—not simply to make notes faster.