NHS Waiting List Estimation Methodology

Goal of the Wait Time Estimation Project

With the outbreak of COVID19, it was necessary to maximize the availability of intensive care units. In order to do so NHS and private facilities have been asked to reduce or to postpone elective surgeries. Further necessary was the reduction of none urgent diagnostic work in order to free healthcare workers and reduce spread of the virus through those facilities. The NHS has stopped to publish and record performance data for the time being.

For patients with conditions unrelated to COVID19, this situation has caused an unexpected disruption. In order to estimate the impact on the healthcare system as such and from an individual level, Medbelle started this project internally.

We are not attempting to forecast individual waiting times - since these are dependent on many more factors like the patient's history, decisions, their specific condition, the surgeon's therapy plan and the availability of capacity.

The goal is to provide an overview using the best available data, which patients can take into account when making decisions

Basic Hypothesis

In order to make predictions about the future, we had to make some assumptions. We list the hypotheses in order of importance for the results.

Same need as last year Hypothesis 1: The general (non-COVID19 related) need for healthcare is very comparable month to month with the previous year. In March 2020 and March 2019 a comparable number of patients were referred to specialists. For example in March 2020 a similar number of cancers were detected and a similar amount of ACL injuries occurred and a similar number of heart attacks happened as in March 2019. Therefore, non-Covid19 related healthcare has been stable. We do not account for changed medical needs from COVID19 patients, like a possible increased need for Kidney transplants due to COVID19 survivors.

Another aspect of this Hypothesis is that some medical needs may be hidden at this time. Reports show that many patients did choose not to consult with their GP at this time. Therefore some necessary treatments are being delayed and not accounted for in the system until the patients actually consult their GPs.

Same capacity as last year Hypothesis: If COVID19 had not happened, the capacity of the healthcare system en large as well as on the provider level would have been and will become again comparable to the same period from the last year. That is to say, that at some point in the future, the capacity of the system for non-COVID19 related conditions will be comparable again with the capacity before the COVID19 disruption.

All specialities are the same Hypothesis: We assume that the capacity limitation hits every speciality the same. This does clearly not reflect the reality, as the limitations are more severe for certain procedures (like joint replacement) than others (like cancer related surgeries).

ait time independent chance of treatment Hypothesis: We assume that the distribution of treatments across the waiting times is independent of capacity if the condition is not urgent. Urgent treatments will require, by definition, shorter wait times. Our hypothesis is that all treatments in March 2019 that had a waiting time shorter than 2 weeks were done on urgent conditions. We assume that the number of urgent treatments is similar in March 2020, and that they are carried out regardless of capacity. We assume that none urgent treatments are carried out depending on the capacity independent of the waiting time of the patient. Which would mean that a patient, which has been waiting longer has the same probability of receiving treatment as a patient which is waiting a shorter time. This has no influence on the number of patients on the waiting list but has an influence on the average wait time of patients on the waiting list.

All weeks are the same and a month has 4 weeks Hypothesis: We assume that newly referred patients enter equally distributed across the 4 weeks of a month. We do not take into account dates such as bank holidays when calculating wait times.

Calculations

Total completed pathways for admitted patients

We take the number of completed pathways for admitted patients of the healthcare system from the same period one year before and multiply it with the assumed capacity for the time period of 2020. That is for February 2020 100%, for March 2020 90%, for the first half of April 90%, for the second half of April 30%, May 30%, June 30%, first half of July 30% and second half July 90%. In the online version of this model, you can adjust these numbers yourself.

Completed Pathways For Non-Admitted Patients

We take the number of completed pathways for non-admitted patients of the healthcare system from the same period one year before and multiply it with the assumed capacity for the time period of 2020. That is for February 2020 100%, for March 2020 95%, for the first half of April 95%, for the second half of April 70%, May 70%, June 70%, first half of July 70% and second half July 90%. In the online version of this model, you can adjust these numbers yourself.

Incomplete Pathways

We add the number of new patients to the existing number of patients with incomplete pathways and subtracted the number of completed pathways in that month.

Patients waiting for DTA / Admission

We add the delta between the admission from last year and this year to the number of patients waiting for admission from the previous month.

Wait times

We move all patients every month 4 weeks further and fill the first 4 weeks with new patients. We adjust depending on the calculated capacity. We determine the week in which more than 50%, 92%, or 95% of patients have been waiting less respectively by summing the weeks and comparing with the total amount.

Sources