Tag Archives: Healthcare

When Waiting is Not Enough

Healthcare is emerging from the immediate crisis response of COVID-19 into a hugely uncertain environment. One of the very few things of which we can be sure is significantly longer waiting times for elective procedures.

The Health Foundation recently published a report drawn from pre-COVID data,[1] which starkly portrayed the challenges around the 18 weeks Referral to Treatment target. The report estimated that the NHS needed to treat an additional 500,000 patients per year for the next four years to restore delivery of the target. Using data from NHS England following the first month of COVID-19 induced elective shutdown, Dr Rob Findlay noted a jump, both in the number of patients waiting over 52 weeks, and the average wait time for patients, which rose to 6 months.[2] These figures are likely to increase further in coming months. The article also noted that very few long-wait patients were treated. Longer wait patients should be de facto low clinical urgency, as it is this that has made them appropriate to wait.

There are two significant decision-making points for the treatment of patients on waiting lists. Clinical urgency, which of course affects those near the start of their waiting time, and being in imminent danger of breaching a waiting time target, which necessarily affects those towards the end. Between these decision-making points at the start and end of the waiting list lie a huge volume of patients with little categorisation or prioritisation.

Herein lies a significant future challenge: as waiting times increase and a growing number of patients breach waiting time targets, how do you ensure that limited elective capacity is targeted towards those with greatest clinical need?

If NHS England and NHS Improvement do not relax waiting time restrictions, maximum wait times will continue to be an important decision-making point. This incentivises providers to make a trade-off and treat longer waiting, but clinically less urgent, patients over short waiting, but clinically more urgent, ones. This would be a difficult position to justify ordinarily but in a time of likely constrained resource, the policy is likely to do far more harm than good.

It is crucially important to use need as the basis for prioritising which patients to treat. A recent literature review described some of the efforts made around the start of the millennium to develop a more systematic and transparent approach to prioritisation based on need. This approach developed from the Western Canada Waiting List Project [3] and the New Zealand Priority Criteria Project.[4] These approaches were rigorously reviewed through a range of academic articles and evaluated well, showing both transparency and consistency of decision making and prioritisation. Importantly, they also carried strong public support when reviewed with focus groups.

These ‘point-count’ systems work by creating a scoring chart for each clinical condition, such as cataract surgery, major joint replacement, coronary bypass graft. However, they have also been successfully used and evaluated for topics such as the use of Magnetic Resonance Imaging (MRI) and children’s mental health. The scoring grid is unique to each clinical condition and developed through consensus discussion with clinicians to balance a range of clinical and social factors. The objective is to prioritise patients for treatment who will gain the most substantial benefit from intervention.

‘Point-count’ systems have translated successfully into several healthcare settings but not to the NHS. Often these types of changes are put in to the ‘too difficult’ category as the resource required to implement them is seen to be greater than the benefit gained. However, we are moving to a different paradigm post COVID-19 where integrated care systems are more accountable to their population and a more objective and transparent decision-making process is desirable.

Think too of the benefits of a shared language of waiting lists. We should not forget that many non-clinical staff are involved in the booking and scheduling of elective patients. A common currency in which objective comparisons can be made on the likely benefit of surgery or intervention across clinical indications and specialties is highly appealing.

One of the most keenly-debated elements of the development of these ‘point-count’ systems was what factors should be considered as part of the scoring criteria. Repeatedly the idea of including some reflection of how long a patient had waited was considered, and strongly rejected. Instead a measure of ‘potential for disease progression’ was included to ensure those, for instance, waiting for a joint replacement procedure, were not constantly usurped by patients with a more acute presentation. However, it guards against the current system of those waiting longest receiving priority at the potential expense of another who would derive greater clinical benefit.

So, as a policy directive there is a clear indication – the maintenance of the current maximum wait times will prioritise many clinically less urgent patients over more urgent cases. It remains to be seen whether the evidence base is substantial enough, and whether there is sufficient appetite within the NHS to revisit some of these clinical prioritisation approaches, but their use should be considered and their implementation would make a fascinating piece of research in the coming years.

Paul Bird, Head of Programme Delivery (Engagement), Richard Lilford, ARC WM Director

With thanks to Prof. Tim Hofer (University of Michigan) for discussion and input.


References:

  1. Charlesworth A, Watt T, Gardner T. Returning NHS waiting times to 18 weeks for routine treatment. The Health Foundation. 2020.
  2. Findlay R. Average waiting time for NHS operations hits six months thanks to covid. Health Serv J. 2020.
  3. Noseworthy TW, McGurran JJ, Hadorn DC, et al. Waiting for scheduled services in Canada: development of priority-setting scoring system. J Eval Clin Pract. 2003; 9(1): 23-31.
  4. Hadorn DC, Holmes AC. The New Zealand priority criteria project. Part 1: Overview. BMJ. 1997; 314: 131.

Walking Through the Digital Door: Video Consultations During COVID-19 and Beyond

The “NHS Long-Term Plan” (2019) is a five-year plan describing how NHS services should be redesigned for the next decade. This plan includes making better use of digital technologies, such as video consultations. While video consultations have potential advantages for patients and hospital systems,[1] they may make patients uncomfortable. If patients do not walk through the ‘digital door’ to attend a video consultation, then potential advantages cannot be realised. Likely the motto of “build it and they will come” is insufficient. Instead, we need to support patients so that they come the first time and return after that. 

What support that patients need is, at least in part, an empirical question that we plan to address in a future study. One way to support attendance may be with the behavioural science principle of ‘defaults’ – people tend to ‘go with the flow’ of pre-set options.[2] Defaults have been used to influence organ donations by adding the word ‘don’t’ to an application, i.e. “If you want to be an organ donor, please check here,” vs. “If you don’t want to be an organ donor, please check here”. In a simulated study, 42% of people opted-in to become organ donors given the original phrasing, and 82% did not opt-out given the second.[3] In other words, the realised organ donation rate nearly doubled by changing the default option. Until April 2020 England had an opt-in system with 38% of people having opted-in to become organ donors. When England’s law changed to an opt-out system in May 2020 the assumed donor rate has increased instantly. Time will tell how many people fill out the form to opt-out, but the present authors suspect the resultant donor rate to remain higher than 38%.

Defaults have been used to influence people’s behaviour in many contexts, e.g. how much money people save for retirement,[4] physicians’ medication use,[5] and purchases of healthy foods.[6] At least three psychological mechanisms are at play: endorsement (believing the proposed default is recommended), endowment (believing the default is normal), and ease (taking up the proposed default is simpler than refusing it).[7,8] Re-framing an invitation to attend an outpatient appointment from ‘in-person’ to ‘video’ creates a new default ‘endorsed’ mode of attendance that is ‘easier’ to accept than refuse. However, if a substantial number of patients refuse an invitation to attend a video consultation, this would suggest that more support is needed to garner people’s acceptance.

An ideal experimental test of the default effect on out-patient appointment attendance would occur in the field setting, similar to our work on influenza vaccination letters.[9] But (without tremendous follow-up efforts) this approach provides a limited ability to explore barriers and facilitators patients believe influence their choices. These beliefs undoubtably influence whether patients attend. To explore how default options and beliefs influence whether patients accept an invitation to attend a video consultation, we will conduct a simulated study with patients from the site Prolific Academic. Prolific Academic contains thousands of people prepared to answer researchers’ questions who can be filtered on criteria such as health status, age, and education. Our research will utilise an online experiment with quantitative and qualitative items. We plan to compare our findings to real hospital data on video consultations before and after COVID-19, which may have provided the impetus for more patients to engage in digital healthcare. 

Conversations with researchers across ARC WM’s themes and with public contributors suggest several barriers and facilitators to the uptake of video consultations. For instance, while the location of in-person consultations was obvious, video consultations require patients to make an additional choice about where they feel comfortable attending. Whether attending from home or work, new privacy concerns arise regarding what other people can overhear across physical and digital space. Our research will show how much such concerns matter to patients, and suggest what additional support should be offered to increase patients’ attendance within their invitation to attend. If COVID-19 hasn’t provided the push that patients need to walk through the digital door, this research will help us understand why. Equally, if it has, we will be better equipped to sustain and expand the shift, and in so doing help realise the NHS Long-Term Plan.

Kelly Ann Schmidtke (Assistant Professor) and Laura Kudrna (Research Fellow)


References:

  1. Greenhalgh T, et al. Virtual Online Consultations: Advantages and Limitations (VOCAL) Study. BMJ Open 2016; 6: e009388. 
  2. Dolan P, et al. Influencing Behaviour: The Mindspace Way. J Econ Psychol. 2012; 33(1): 264-77.
  3. Johnson EJ, Goldstein D. Do Defaults Save Lives? Science. 2003; 302(5649): 1338-9. 
  4. Madrian BC, Shea DF. The Power of Suggestion: Inertia in 401(k) Participation and Savings Behaviour. Q J Econ. 2001; 116(4):1149–87. 
  5. Ansher C, et al. Better Medicine by Default. Med Decis Making. 2014; 34(2):147-58. 
  6. Peters J, et al. Using Healthy Defaults in Walt Disney World Restaurants to Improve Nutritional Choices. J Assoc Consum Res. 2016; 1(1): 92-103.
  7. Jachimowicz JM, et al. When and Why Defaults Influence Decisions: a Meta-Analysis of Default Effects. Behav Public Policy. 2019; 3(2): 159-86. 
  8. Dinner I, et al. Partitioning Default Effects: Why People Choose Not to Choose. J Exp Psychol Appl. 2011; 17(4): 332-41. 
  9. Schmidtke KA, et al. Randomised controlled trial of a theory-based intervention to prompt front-line staff to take up the seasonal influenza vaccine. BMJ Qual Saf. 2020; 29(3): 189-97.