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Why general practitioners need a better way to measure demand

Learn how measuring and applying data-on-demand flows across your primary care practice can draw out new ways of supporting patients and reducing time to care.

By Peter Milmer, MD | August 2023

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I recently published an article on the importance of the personal connections that take place within healthcare. In our pursuit of efficiency, we must never lose sight of the deep human interactions that go to the heart of what general practice is about.

On the flip side, we won’t be able to adequately meet these basic human responsibilities unless we get on top of the demand and capacity challenges all primary care services face. Population health management is a good way of squaring the circle.

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Demand for services vs activity measures

First, though, we need to be a little more precise about how we define what demand really is. In simple terms, demand is all about a patient or a professional colleague first perceiving a need for something and then requesting it from the organisation

In other words, demand shows every single request for the practice to provide a formal service. It can arrive digitally or by phone, paper or someone walking through front door. At the point that the practice does work on these requests, it then becomes activity

This is an important distinction because too often NHS organisations use activity measures as a proxy for demand. What a patient actually asked for — rather than what they received by way of service delivery — isn’t something we routinely code for. 

The problem, as we’ll see, is that measuring the activity generated by a request doesn’t necessarily tell you whether that work was necessary or desirable or led to the right outcome. And this can sometimes take us in the wrong direction.

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The drivers of demand

So I decided to capture the volume and nature of all incoming demands — raw requests for help — as accurately as I could across my practice to give us a more exact picture.

When you do this analysis, you find a single demand contact often ripples out to numerous extra pieces of work across the organisation and sometimes beyond. And by understanding the full contours of demand, you can do 3 things:

  1. Make predictions on what types of demands are going to come into your practice, how they're likely to affect different teams and how you can manage them more effectively.  
  2. Understand the intricacies of patient behaviour in a more sophisticated way by developing patient clusters segmented not by pathology or age but by the type of demand-stimulating actions they take.
  3. Start to track and measure the way demand flows across the system. This teases out the way activity being carried out (or not) elsewhere connects to the demand experienced at a practice level.

Let me give you some examples of what this means in the real world. This will illustrate why measuring demand rather than activity matters so much and how it can lead you to some simple but often game-changing actions.

Reducing downstream demand linked to prescribing

The first example relates to prescribing. Imagine you get a patient telephone query about a prescription. You might review the dosage and check how long they've been taking it, but most likely you wouldn’t book the patient for an appointment.

Often this wouldn’t register in any activity data as most practices don't have the ability to link phone calls to the patients that made them. 

However, we did do this analysis in a practice and immediately found that about 30% of their inbound calls related to prescribing. This felt counterintuitive as the practice had a digital system for requesting prescriptions. The problem was quite easy to spot:

  • The digital system didn't send a response to the patient saying the request had been received.
  • As a result, people were requesting prescriptions digitally, then phoning the practice to see if the request had been received.

When the digital system was modified so that patients got a message back, those calls stopped entering the practice system, reducing the corresponding activity.

A more sophisticated analysis of the same data then helped us understand why certain patients chose that moment to call about their prescription and what decisions in their care pathway led to it. When we did this, we found:

  • There was a clear link between the practitioner that had previously seen the patient and the likelihood of a subsequent demand query. 
  • The patients most likely to make these demands were 20 to 40 years old and not already on repeat prescriptions.
  • They were phoning 24 to 28 days after the consultation. Most had been prescribed analgesics or antidepressants. 

The solution here wasn’t signposting patients to a community pharmacy or employing more in-house support. But either might have been our reflex response if we’d just looked at activity levels showing a spike in prescribing-related calls.

Instead, we worked with the specific prescribers and created a standard message for any patient prescribed these drugs for the first time. 

Online consultations and predictive modelling

A second example relates to online consultations. We wanted to explore whether this technology improved efficiency and reduced demand in practice. And, if so, how do we maximise the benefits?

We sought to reduce to a minimum the decision-making time for both reception and GPs. So, we spent time segmenting our patients and analysing their eConsult data and how this manifested in terms of demand across the practice workforce.

Out of this, we created a pretty accurate predictive modelling system that helped us understand what a patient’s most likely need was based on their known behaviours. A big part of this was understanding how to use continuity in digital care with a part-time workforce.

For some patients, this meant the receptionist could book appointments with a specific GP directly because they knew the conversion rates from eConsult to face-to-face appointments were so high.

Indeed, in some cases, our analysis showed we were going to see them up to 70% of the time. Interestingly, this had more to do with the patient than their presenting complaint. This saved us triage work overall. 

Wider system perspective on demand

A few other examples show how you can also make connections with what’s going on across the wider system. The first relates to A&E attendance, where we found that 20% of patients attending our local A&E contacted the practice within 24 hours of a visit.  

Importantly, much of this demand was related to administrative queries, fit notes, prescription changes  and so on — intelligence that would have been lost had we focused solely on measuring activity.

This deeper insight enabled us to have better conversations with our system partners about how we manage patient demand across different parts of the health service.

Another example relates to mental health. Like many PCNs, we use ARRS funding to provide mental health appointments into which member practices can book patients. We wanted to test the theory that referring a patient to the PCN would mean less activity for practice GPs. 

When we crunched the numbers, we found this wasn’t always the case. For complex mental health patients, referral to a PCN team made no difference to the referring GP’s resulting activity. There was no demand reduction at all. They still had to do as much work as before.

Where it did make a difference was for patients who hadn’t yet been diagnosed. In part, this is because there tends to be a lot of follow-up activity after a diagnosis that a dedicated PCN service can absorb.

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What you can do to meet rising demands

I’ve drawn a few conclusions from all of this.

First, we need to rethink the way we measure demand at all levels, up to and including national policy:

  • Relying on measuring activity as a proxy doesn’t help you get to the root of what is driving demand.
  •  In fact, as we’ve seen, this may take you in a direction that adds demand unnecessarily.

Second, we need a more forensic study of demand:

  • This can help you create a more personalised and proactive way of reshaping your care pathway and showing where you need to prioritise and build capacity.
  • By using intelligent software, it should be possible to create 'smart systems' that capture demand at the point of entry and advise practice staff and clinicians on the most effective way to manage this.
  • This intelligent software is one of the exciting areas of development I’m working on with Optum.

Third, we’re not powerless in the face of rising demand:

  • We can change a significant proportion — a double-digit percentage — of the demand our practices receive.
  • By recording and interrogating patient demand data, we can create better solutions, helping patients get where they need to be more quickly.
  • With fewer demand contact points, we can free up essential time to help us care for our patients with the level of excellence, dignity and humanity to which we all aspire.

Discover how solutions from Optum can help your organisation.

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About Dr. Peter Milmer

A GP in Devon and a product specialist at Optum, Peter has helped develop primary care data visualisation tools to inform actionable change.

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This article was prepared by Peter Milmer in a personal capacity. The views, thoughts and opinions expressed by the author of this piece belong to the author and do not purport to represent the views, thoughts and opinions of Optum.