Evidence based on average patient data, which occupies most of our present day information databases, does not fulfil the needs of individual patient-centred health care. In spite of the unprecedented expansion in medical information we still do not have the types of information required to allow us to tailor optimal care for a given individual patient. As our current information is chiefly provided in disconnected silos, we need an information system that can seamlessly integrate different types of information to meet diverse user group needs. Groups of certain individual medical learners namely patients, medical students and health professionals share the patient's need to increasingly interact with and seek knowledge and solutions offered by others (individual medical learners) who have the lived experiences that they would benefit to access and learn from. A web-based user-driven learning solution may be a stepping-stone to address the present problem of information oversupply in medicine that mostly remains underutilized, as it doesn't meet the needs of the individual patient and health professional user. The key to its success would be to relax central control and make local trust and strategic health workers feel more engaged in the project such that it is truly user-driven.
BACKGROUND AND RATIONALE: Evidence based medicine is the present backbone of rational and objective, modern medical problem solving and is a meeting ground for quantitative and qualitative researchers alike as it culminates into applying the fruits of clinical research to the individual patient. A systematic enquiry into the evolving paradigms in EBM is a need of the hour.
AIMS AND METHODS: A qualitative enquiry examining the impact of different methodologies in EBM and their role in generating meaning interpretable at individual levels.
RESULTS: Present day outcome based research deals less with patients as individuals than as populations. Evidence based medicine struggles to apply the fruits of population based research to individuals who are often not as predictable as linear quantitative research would like them to be. The present EBM literature neglects a lot of events it doesn't believe to be statistically significant and perhaps here is an area that needs to be improved on - it assumes that because associations are demonstrated between interventions and outcomes in RCTs/meta-analysis, these associations are linear and causal in the real world. While they may be demonstrated repeatedly in highly controlled environments, in the real 'uncontrolled' world of clinical practice with real people, their validity breaks down.
CONCLUSIONS: One needs to make the EBM standard model patient-individual (a projection of collective patient event data) resemble the real human individual patient so that optimal EBM individual data that matches our query can be easily and quickly spotted from the dense jungle of information that has grown over the years. This hints at rethinking our entire research methodology and modifying it to suit the needs of the individual patient.