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Karim Keshavjee

Your characterization of Big Data is accurate. We don't really have Big Data in Canadian Healthcare at the moment. I think Big Data in healthcare would look something like an EHR data set that included all data on all patients being generated in physician practices, hospitals, specialist offices, laboratory and diagnostic imaging facilities, genomic data, remote monitoring data and all the transactions that go with patient care, such as lab orders, prescriptions, dispenses and lab results. If all of this data from across the country were being updated in real-time, then we'd have Big Data.

In the Canadian context, we are very far from anything like that on any level of scale.

I will say that within the CPCSSN project (, we now have a national EMR database that is standardized. We have developed processes to extract data from 12 different EMRs from across the country and put them into a standard database. We have also developed algorithms to clean up that data for about 8 different diseases (we clean up the diagnoses, medications, vital signs, lab results, referrals, risk factors, etc).

We have been extracting and processing data on a quarterly basis for the last 3 years, continuously improving the quality of the data extraction and cleaning processes. We are now up to 380 physicians participating in CPCSSN and we have over 450,000 anonymized patient records in the database.

We recently presented our findings at the eHealth Conference and will be presenting at NAPCRG and Family Medicine Forum this year.

We are realizing how important standardization is and we're learning that we can do a lot of data cleaning behind the scenes and take the pressure off physicians in terms of forcing them to pick items off lists for data entry. Pick lists are time consuming to use and if not designed correctly can cause their own sets of errors, especially in fast paced environments. Forcing users to standardize their data is not a panacea for our data woes. It shifts the blame of dirty data onto people whose role is not data entry, but patient care. If we force standardization of data, we may inadvertently impact patient care. Benefits seldom come without harms.

Derek Ritz

I'm very curious about Karim's comment: "Forcing users to standardize their data is not a panacea for our data woes. It shifts the blame of dirty data onto people whose role is not data entry, but patient care. If we force standardization of data, we may inadvertently impact patient care. Benefits seldom come without harms."

The "shift the blame" part of this comment seems to imply that if a clinician thinks the right thought, but records the wrong thing, he or she should be blameless regarding the "dirty data" because their role is patient care, not data entry. I purposefully used the term clinician in my sentence, above. Is the assertion that such responsibility for accurate data applies to others (nurses, for example), but ought not to apply to physicians? I've known Karim a long time and don't believe that's what he's trying to say... but that impression can be taken from the comment.

Healthcare is a knowledge industry. I would say that we ARE now realizing how important standardization is; and we are seeing we DO have to force users to standardize their data. I can also say that I have been a colleague of both Alan and Karim, for many years now, at eHealth standards fora where the rest of the physician community was grossly under-represented. I'm sad to say, when it comes to both BIG data and SMALL data, we are reaping what we've sown... or perhaps not reaping what we didn't sow.

The old exhortation might need to be rewritten for the eHealth era: "physician, inform thyself."

Karim Keshavjee

Forcing people to anything is the old way. Google doesn't ask me to standardize data entry for giving me access to good map functionality. I can put in an address pretty much any old way and it'll show me how to get there without asking me to 'standardize' my entry to fit their pre-concieved norms.

If they can do that for a service that is free, we should be able to do it for a health care system that collectively costs us $200 Billion each year.

You obviously did not attend my session on the CPCSSN project at eHealth. If you had, you would know that we've moved the state of the art on data cleaning and you would know exactly what I'm talking about and wouldn't be misinterpreting what I wrote.

Our findings are that we can clean up data using sophisticated algorithms BETTER than humans can. Why would you ask a human to do something that a computer can do better? Isn't that the essence of health informatics --to harness the power of technology for improvement of health?

Jane Curry

One of my major disappointments in e-health is that the potential convergence of a number of different technologies that would reduce the data capture burden and promote data standardization has not been realized. Pick lists for coded data has always been a "programmer easy but end user hard" kind of solution. A combination of structured templates, voice recognition and controlled terminologies supported by a semantic infrastructure can theoretically produce more effective standardized data collection. However, it would take a great deal of collaboration among informatics professionals, clinician specialty leaders, technical specialists in both middleware and user interface design to work out the bugs. This level of collaboration is too knowledge intensive for any single organization to undertake and has not received the kind of investment it would take to be successful. Maybe when the pressure on the current health-system gets great enough the willingness to invest and collaborate will emerge. In the meantime the old saw about data analysis will hold true - garbage in = garbage out.

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