Who is waiting and for how long? Using OpenSAFELY to understand waiting lists.

Health data have been pivotal to informing the response to the COVID-19 pandemic. They have helped us learn which treatments work best, improve the quality and safety of health care, and identify risk factors for death and disease.

Using the OpenSAFELY secure analytics platform, we recently completed a project using national, patient-level data for people on elective waiting lists – one of the first times these data have been used for research purposes.

Data can help improve care for people on waiting lists

The delivery of health services was hugely impacted by the pandemic and the backlog in elective procedures due to the COVID-19 pandemic has yet to recover. In September 2025, there were 7.4 million people waiting for elective care, compared with 4.6 million immediately pre-pandemic in England, with people also waiting longer before receiving treatment.

Long waits for elective care can be distressing for patients, and are linked to poorer physical, psychological and financial outcomes. The size of the waiting list and length of waits is a top priority for the government. In January 2025, the government published the “Reforming Elective Care for Patients”, implementing a variety of interventions with the goal of meeting the 18-week standard by March 2029.

For elective waiting lists, data can help us understand whether targets are being met, and where action is needed to reduce waiting times. They can identify which populations are disproportionately affected, and the negative consequences of longer wait times.

The NHS publishes regular updates on the number of people on RTT (referral-to-treatment) waiting lists and the time they waited for treatment. People waiting for non-emergency, consultant-led treatment are said to be on an RTT pathway, which is considered ‘completed’ when the patient receives treatment. Common reasons for patients to be on RTT pathway are cataract surgery, joint replacements, and hernia repair. These data are incredibly useful for understanding trends - however, they can’t tell us much about the patient experience.

Waiting List Minimum Data Set

In 2021, we added an excerpt of the Waiting List Minimum Data Set (WLMDS) data to OpenSAFELY, linked by a patient’s NHS number with primary care, Hospital Episode Statistics (HES), and Office of National Statistics (ONS) mortality data. This included both data on completed, and incomplete RTT pathways.

Whenever we add new data to OpenSAFELY, it undergoes “data curation” before it can be made available for wider use. This includes data cleaning and validity checks, and creating metadata, that is documenting the data source and creating a data dictionary describing the available columns. This way, we can be sure that only usable columns are made available for analysis – improving the quality of research. Further, a detailed data dictionary makes sure users know exactly what they can expect from the data, and how they can use it to answer their research questions

How did we use these data?

Many people waiting for treatment live with pain, and require opioid analgesics to treat their symptoms. For the first time, we used these data to explore the impact of waiting times on opioid prescribing patterns for people waiting for orthopaedic procedures (like hip or knee replacement). People with musculoskeletal conditions are commonly prescribed opioids, and longer wait times may lead to greater opioid use, including long-term use. We used these data to describe how opioid prescribing changed before waiting list referral, while on the waiting list, and after treatment. We’ve described our findings in a separate blog.

By securely linking the national waiting list data with the rich clinical records in OpenSAFELY, we can generate evidence on the impact of large waiting lists and long waiting times, as well as who, where and how to target interventions and design policy to improve services and outcomes for the public.