Healthcare is on the brink of revolution, owing to the rapid industrialisation of medicine. We are not talking about 'global challenges', but far from meeting them. Healthcare is very splintered across the geography that it would be impractical to implement "technological advances" without giving due cognisance to socio-cultural issues. I find it surprising, because investors' … Continue reading Healthcare Innovation: Tackling it head-on
This post was "inspired" by an editorial from Scientific American (and I am riding its coattails) because I needed someone to call out the broken process. The essay does make some generalisations, however. Yet, it is still relevant because we, as scientists (and clinicians) owe it to our patients who look up to us. We … Continue reading Good science, Bad Science: Why don’t we get a “cure” for cancer?
Reason for inclusion: An accurate assessment of treatment-related complications is missing in follow-ups. Practise changing statement: Physician assessment+ Patient report outcomes on a continuous basis will help more to define the extent of problems. We also need more quantitative measures. Page 1 specific information before the design of any particular research study. Patient-specific information may … Continue reading Is physician-reported data better than patient-reported outcomes? (Example of prostate cancer)
(The highlights appear in standard text as bulleted lists while my comments appear as block quotes). Page 1 In those early days, hospital registrars would source paper charts to abstract tumor cases into hospital registries. During the annual Call for Data, years of completed cases were submitted to the NCDB via mailing floppy disks. This … Continue reading National Cancer Database: The Past, Present, and Future of the Cancer Registry and Its Efforts to Improve the Quality of Cancer Care
Incorporating the Artificial Intelligence in healthcare has several issues. I stumbled on an excellent post in HBR that lists the key deficiency of AI- it is a black box. For all its deficiencies, AI and machine learning will never explain a process of "inclusion and exclusion" or why it came to a decision the way … Continue reading #AI #ML in healthcare: Will the twain ever meet?