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£7 billion and the failure to deliver electronic health records

Ami Banerjee
Last edited 14th September 2012

£7 billion has been spent on developing electronic medical records within the NHS since 2002, part of the ambitious £11.4bn NHS IT project. It turns out that £7 billion is a common figure in NHS stats. It could buy you GP services in England for a year. £7 billion is spent annually on NHS services in Kent, Surrey and Sussex. It is the amount that stroke costs the NHS per year. The NHS would save £7 billion if it used generic or unbranded drugs instead of branded medicines. The NHS Institute reckons it could save £7 billion per year by implementing six scaleable interventions across hospitals, general practice and community health services.

But the important one is that between 2003 and 2006, the government spent the same amount on private consultancy firms to advise on health policy and services, and a lot of that money was spent on IT.

I qualified as a doctor in 2002, and it still beggars belief that little has changed when I go to see a hospital patient admitted in the middle of the night and ask for the old notes. The old notes are requested by the ward clerk who looks them up on a computer, often predating the machines Bill Gates cut his teeth programming on. Any amount of time from hours to sometimes days can elapse before these important documents are located. I think every patient should have the opportunity to see the sometimes prehistoric conditions old notes are kept in.

For the third time, the National Audit Office has found little progress and doubts whether the deadline of 2016 (already 6 years late) will be met, but even though electronic health records are the norm in primary care settings, hospitals are not getting there quickly, despite many improvements, including electronic image viewers, electronic discharge letters and prescriptions.

There is no question that electronic health records can be a game-changer in healthcare, whether in hospitals or general practice; whether in medical emergencies or in care of chronic diseases, but we are not close to bringing this to all NHS patients yet. Interestingly, e-health record systems for more than 10 million patients do not seem to work. Perhaps we can take some consolation that our friends across the pond have not yet decided how keen they are on e-health records either.

Understanding clinical risk scores in 4 days. Day 4: Treatment

Ami Banerjee
Last edited 25th April 2011

Clinical trials tell us about the effects of certain drugs in certain patients in certain clinical situations. Arguably the most practical use of risk scores is to help in deciding which patients are most at risk of disease or have disease requiring most urgent treatment. In other words, risk scores can help answer the question, “Who needs treatment?”

An example we have used before is the CHADS2 score
for risk of stroke in patients with atrial fibrillation. Patients with a CHADS2 score of 2 or more should get warfarin therapy. Risk scores used in this way are easy to incorporate into treatment guidelines and are therefore translated into clinical practice fairly well.

However, we have already seen that risk scores are underused in clinical practice. In cardiovascular disease, to some extent, there are too many risk scores. These scores differ in terms of population, predictors and outcomes, which may not fit with those used by clinicians. Ideally, risk scores should be formulated, tried and tested in populations which have not received any treatment, but often patients in these populations have been treated, partially or fully and this is poorly accounted for by most rules.

Refining and re-defining of risk scores can lead to profound impact on who gets treatment, which can lead to a bigger impact on healthcare resources. For example, the CHADS2 system may not fully exclude risk of stroke in patients with a CHADS2 score of <2. Therefore, the CHA(2)DS(2)-VASc score was formulated to better define the patients with atrial fibrillation who truly are at low risk of stroke and therefore do not need warfarin. However, use of the CHA(2)DS(2)-VASc system does mean that more patients will go on warfarin treatment than with the CHADS2 system.

Another example is the impact of adding social deprivation to cardiovascular risk scores. One such score which incorporates the social gradient in cardiovascular disease is the ASSIGN score. Patients with an ASSIGN risk of 20% or more receive statin treatment. One study calculated that if the ASSIGN risk score was implemented, where a person lives would affect the decision to initiate statin treatment in the case of 15.7% of the population (aged 30-74 years), corresponding to 4.6 million in the UK.

The upshot is that risk scores are very versatile tools which can be used to guide diagnosis, prediction, prognosis and treatment in clinical practice, if the populations in which the scores were formulated and tested match your patient population, and you recognise their limitations.

Understanding clinical risk scores in 4 days. Day 3: Prognosis

Ami Banerjee
Last edited 23rd April 2011

Closely related and interlinked to prediction is the idea of prognosis. Literally meaning “foreseeing” in Greek, prognosis attempts to quantify the likely outcome over time for a particular patient in a particular clinical setting. For example, in terms of risk prediction, a CHADS2 score of 2 or more in a patient with AF tells us that he or she is at a large enough risk of stroke to warrant treatment with warfarin to thin the blood. 18.2% of patients with a maximum of CHADS2 score of 6 will have had a stroke at one year: this is prognostic information. Therefore, the same score can be used as both a predictive and a prognostic tool.

One of the most enduring and widely used prognostic scores in clinical practice, is the Apgar score, used to predict outcomes in newborns by a score of 1 to 10. As noted in Apgar’s seminal paper, scores of 0-3 indicate poor prognosis for newborn babies.

The prognostic information doctors and patients are most commonly interested in is survival, especially in the case of cancer. The challenge is individualising risk from epidemiologic data to a particular patient.

As in the previous 2 blogs about diagnosis and prognosis, scores are more relevant and more likely to be used in clinical practice when they are tied in a decision-making strategy, i.e. there is no point in prognostic information (other than telling the patient what their outlook is likely to be), unless the management of the patient is altered. This is nicely summarised in this 10-year old JAMA editorial:

“Medicine is an action-oriented profession in which clinicians want to relieve suffering, rather than just watch its course. Regrettably, most prognostic indices are not accompanied by decision thresholds that convert level of risk into degree of action.”

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