“Evidence-based medicine is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients”
Dr. David Sackett, Original founder of EBM
Even with the most rigorous conducted study and robust statistical results, it is vital that before applying such findings, the clinician considers the specific context in which it will be applied.
For any given conclusion or recommendation, it must be appreciated that the usefulness or benefit of a test or treatment will have been conducted within a particular population of patients.
It is entirely possible that the external validity of the study may be completely altered by a different population of patients – such as the one in front of you.
To further add to the complexity, the studied population may have been over-represented by certain sub-groups which distort the findings and give the impression that the results could be generalised to all other members.
Another way to paraphrase this would be:
- The Fallacy of composition – ‘What is true of the studied group, is equally true for everyone else’
- The Fallacy of division – ‘What is true of the studied group, is equally true for each individual in that studied group’
Here are some possible factors.
The studied population were:
- Sicker (more likely to have disease? higher likelihood of tests being positive? more co-morbidities? failed conventional therapies? different risk:benefit to treatment? more likely to suffer complications from treatment? died/withdrew before study ended?)
- Healthier (vice versa)
- More compliant (higher success of treatment?)
- Less compliant (vice versa)
- Treated in a system with special expertise (higher chance of successful intervention?)
- Treated in a system with general expertise (vice versa)
- Had greater access to health resources and followup (closer monitoring? greater chance of having issues and complications addressed?)
- Had Less access to health resources and followup (vice versa)
The enrolment / selection process can significantly alter these factors:
- Well established health networks vs Limited health networks
- Developed vs Developing world
- Metropolitan vs Peripheral centre
- Specialist patient vs Primary care patient
- Hospital patient vs ambulatory/community patient
- High SE class vs Low SE class
So whenever you are tempted to implement a new idea you need to consider:
- The composition of the studied population and the context of your patient
- Are there alternative factors that may have lead to the observed results (a good knowledge of the social determinants of disease, aetiology, pathophysiology, pathology and therapeutics helps)
- If this is applicable or not in your patient
- Most importantly, does your patient want it?
‘Best evidence’ is not best for all so that ‘best practise’ leads to ‘inappropriate practise’