Alexander Reid and the prevention of future deaths
We begin with a sad and cautionary case study. In June 2021, Alexander Reid, a 28-year-old man, suffered a fatal cerebral venous sinus thrombosis secondary to COVID-19 vaccine-induced immune thrombotic thrombocytopenia.1 His case offers an important learning point.
In April 2021, the Joint Committee on Vaccination and Immunisation had published a statement advising against the use of the AstraZeneca COVID-19 vaccine for adults aged <30 years who did not have any underlying health conditions that put them at a higher risk of severe COVID-19 disease.2 However, those who had already received this vaccine as their first dose were advised to receive it as their second dose. Reid received his second dose of the vaccine on 18 May 2021, and died 6 weeks later.
Regardless of how safe or risky the vaccine was in general, Reid, as it turned out, was not eligible to receive the AstraZeneca COVID-19 vaccine at the time when he received it. Our cautionary note relates not to vaccine safety but to the clerical error carried forward by automated systems in primary health care.
Reid received his first dose of the Oxford AstraZeneca vaccine in March 2021, earlier than his age alone would have entitled him to, because of an error made in his GP records 17 years earlier in 2004. The clinician in 2004 had entered his height as 145 cm and his weight as 145 kg, giving a body mass index (BMI) of 69 kg/m2 for a boy aged only 11 years. This erroneous BMI was never corrected and resulted in Reid being classed as a vulnerable person and being incorrectly invited to receive a COVID-19 vaccination earlier than he should have.
The prevention of future deaths report calls for primary care IT systems with the ability to ‘challenge’ grossly abnormal values from being entered into a patient’s medical records.1 We strongly agree with such a recommendation, as this is a common problem universally encountered by medical practices.
Research into errors in clinical data
In 2021, the same year as Reid’s death, we delivered a programme at a GP surgery in London offering virtual group clinics (VGCs) to people living with obesity and a BMI >35 kg/m2. During the assessment of patients meeting the programme’s inclusion criteria, we identified several patients with an apparently elevated BMI who in fact had a normal-range weight; historical errors appeared to have been made during the entry of height and weight data into the electronic medical records (EMRs). Such instances had not been challenged by the IT system at the time of data entry. Conversely, it appeared likely that some patients were inappropriately excluded from invitation to the programme due to falsely normal BMIs being recorded. We wonder what our colleagues might find if they audited this kind of data for accuracy.
The use of IT and EMRs in health care brings many potential benefits including improving patient safety, efficiency, and accuracy of data, but it can also create new opportunities for errors.3 This can result in harm to patients, as well as affect the validity of clinical research,4 the ability to determine operational performance, and financial loss due to underpayment.5
Incorrect recording of weight (or height) in EMRs is potentially an easy mistake to make, requiring only an extra digit or a decimal point in the wrong place to have a significant impact on the final value. The resultant effect for patients extends beyond their classification of disease vulnerability and risk to include safe medication dose calculations, particularly in paediatric, oncology, and critical care settings. A 2019 study (using data from 2015– 2017) on manual transcription errors by healthcare personnel performing point-of-care laboratory tests showed that 3.7% of manual entries were erroneous and 0.5% potentially dangerous, a finding consistent with similar studies in the field.6
Errors in non-clinical data can also have the potential to impact patient care. For example, our VGC programme identified several patients with a BMI >35 kg/m2 who did not receive the SMS invitation to the programme because of inaccurate contact details on the GP record. Such errors can either be due to incorrect data entry by GP practice staff or inaccurate data provision by patients during the registration process. Despite accurate patient contact details being essential for communicating with patients in urgent and routine health matters, their erroneous entry in EMRs is regularly encountered across healthcare organisations. One study reported that of 1136 patients who gave their phone numbers during an emergency department visit, only 42.1% could be contacted a week later and almost 28% of the numbers were either wrong or disconnected.7
Solutions and conclusions
We suggest three solutions for reducing data entry errors in cases such as the ones mentioned in this article:
Automating data entry by using direct data transfer from medical devices to EMRs.
Data validation and verification via machine learning. EMRs should be able to assess and highlight if an entry is likely to be erroneous, whether that is because it is significantly different to a previous reading for the same patient or whether it is generally an unexpected reading in the population as a whole (Such as a BMI of 69).
Having certain fields that must be filled in and with a certain number of digits – for example, telephone numbers.
We welcome a discussion of the potential for automation to amplify human error, of our proposed solutions, and of other strategies to prevent the problem or harms arising from it.
In a data-driven healthcare system, the accuracy of clinical and non-clinical values, as well as diagnostic coding entered into EMRs, is crucial for delivering appropriate and timely care. Developing ‘live’ IT safety-netting systems to challenge data entry at the time of input is essential to reduce the negative impact of human error on morbidity and mortality. This approach not only mitigates risks but also enhances the potential of data-rich EMRs to improve health outcomes for individuals living with or at risk of disease.
Footnotes
Competing interests Amrit Lamba reports honoraria from AstraZeneca, Boehringer Ingelheim, Janssen, Eli Lilly, NAPP, Novo Nordisk, and Sanofi, and grants from Boehringer Ingelheim, NAPP, and Novo Nordisk outside the submitted work. Saud Jukaku has declared no competing interests.
This article was first posted on BJGP Life on 30 Apr 2025; https://bjgplife.com/clerical
- © British Journal of General Practice 2025
