The Physio Matters Podcast

In this episode of Chewing It Over, Jack is joined by Laura Bevan, Head of Communications at Physio Matters, for a complete guide to the inaugural MSK Awards — and, remarkably, Laura's first ever podcast appearance.
Taking place on 27 November in Birmingham, the MSK Awards have been created as a dedicated celebration of excellence across the MSK community. Rather than another conference or educational event, the evening is designed to recognise the clinicians, leaders and teams making a genuine difference to patients and colleagues — including those whose work often happens quietly behind the scenes.
Jack and Laura discuss the thinking behind the awards, the categories, nominations and judging process, as well as what attendees can expect from the evening itself. The ambition is to create something that combines a prestigious awards ceremony with a genuinely enjoyable end-of-year celebration: dinner, entertainment, networking and a proper party with the wider MSK community.
They also reveal that two-time Olympic triathlon champion Alistair Brownlee will join the event as its celebrity guest speaker. Rather than simply making an appearance, Brownlee will be interviewed about his experiences of injury, rehabilitation and working with MSK professionals throughout an extraordinary elite sporting career.
With leading MSK clinicians and educators attending alongside teams from across the profession, the awards are intended to become an annual opportunity to recognise the breadth of work happening across MSK healthcare.
The message is simple: our profession spends plenty of time discussing how it can improve. Sometimes it should also celebrate what it's already doing brilliantly.

dataplayer.io
In this episode of Chewing It Over, Jack speaks with Elona Bregasi and Xhesika Tusha from Dataplayer about why many clinic owners are effectively “flying blind” when it comes to understanding the performance of their businesses.
Clinicians are trained to deliver healthcare, not necessarily to run data-driven organisations. As a result, many practices rely heavily on intuition, revenue figures and diary occupancy when making business decisions. The problem is that a busy clinic can still have significant weaknesses hidden beneath those headline numbers.
Elona and Xhesika highlight patient retention as a particularly important example. A clinic may have a full diary because it continually attracts new patients, while simultaneously losing existing patients earlier than expected. Without measuring retention, cancellation rates, utilisation and future appointment coverage, owners can miss these underlying problems.
The conversation also explores another growing challenge: having too much data. Information may be scattered across practice management, accounting and booking systems, while clinic owners increasingly experiment with uploading spreadsheets into generic AI tools. Without a consistent data model or appropriate expertise, this can create misleading conclusions and potentially introduce concerns around sensitive patient and business information.
Dataplayer aims to bring these different sources together, turning raw operational information into dashboards and actionable insights tailored to each clinic.
Ultimately, the discussion argues that good clinic data isn't about collecting everything. It's about identifying measurable, actionable metrics that help owners understand what's actually happening—and using them to make better decisions about patients, clinicians and the future of their business.

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.

In this episode of Chewing It Over, Jack speaks with Luke Kellaway about his MSKMag article, The Biggest Wins in the NHS Are Cheap, exploring whether healthcare innovation has become too focused on expensive technology at the expense of simpler solutions that already work.
Luke highlights the enormous burden musculoskeletal conditions place on the NHS and argues that relatively inexpensive initiatives can produce substantial improvements in waiting lists, patient outcomes and service efficiency. Yet these less glamorous interventions can struggle to attract the attention given to AI and digital health pilots.
The conversation questions what healthcare actually means by efficiency. Saving clinicians time with technology sounds valuable, but if that additional capacity simply allows more patients to enter an already constrained pathway, it may increase spending rather than reduce it. Improving one part of a system also achieves little if patients simply reach the next bottleneck faster.
Jack and Luke discuss the importance of examining and redesigning clinical workflows before adding technology. Rather than assuming AI will fix inefficient systems, organisations should first understand where delays occur, streamline existing processes and then identify where technology genuinely adds value.
They also explore the challenges surrounding healthcare AI, procurement and medical-device regulation. As increasingly sophisticated technology enters clinical practice, both managers and clinicians need enough digital literacy to question how these systems work, whether they are appropriately regulated and what they mean for patient safety.
Ultimately, the discussion makes the case for pragmatic innovation: empower clinicians, improve pathways, scale interventions that already work and introduce technology where it solves a clearly defined problem—not simply because it is new.

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