Eliminate bottlenecks in public data access to improve healthcare
A doctor attends to a patient in a ward. PHOTO | FILE
What you need to know:
I cannot speak for other sectors, but healthcare and medicine in particular is a science and heavy user of data.
There are bottlenecks in collecting the data and aggregating it into Big Data. As a result it leads to numerous desegregated datasets.
The Kenya Open Data Initiative (Kodi) adopted a few years ago was meant to make government information available to the public in an easier and convenient manner.
The state-backed project seeks to open up grey areas in public access to government information by easing the modes of availability.
What started with gusto and great enthusiasm in the data community seems to have shrivelled down, perhaps as a result of a changing effort or maybe dwindling support of the initiative.
Previous obstacles to the initiative seem to have been addressed by the legal backing the freedom of information bill framework subsequently ingrained in a 2013 Act of Parliament offered.
That said not as much user demand as envisaged and desired has been achieved. A couple of weeks ago at a round table gathering bringing data enthusiasts together, a few ideas were shared on how to re-ignite the fire and get more data out there.
There is consensus that the average educated Kenyan is a consumer of statistics and data by extension, with the youth and professionals in particular being regular users of the same on any given day.
The manner and content of interest in this information is what varies from geographical location, occupation, social interests and political affiliations.
A starting point then for those of us seeking to inform and influence public policy ought to be by asking, “What is your source of data for Kenyan statistics and how can we package and avail it in a palatable and easily consumed way?”
I cannot speak for other sectors, but healthcare and medicine in particular is a science and heavy user of data.
Unfortunately, in the Kenyan context most of it is external. In a retrospective sampling of lecture material from a local medical school citing epidemiology of various conditions, 95 per cent of lecturers cited data from the US as the primary reference. Never mind that this is a premier Kenyan university.
This is a rather unfortunate scenario because Kenyan doctors have been going to work for the last 40 years or so.
The issue, it seems, is that there are bottlenecks in collecting the data and aggregating it into Big Data. As a result it leads to numerous desegregated datasets.
Public healthcare still has cumbersome and manual data collection.
Techie Mbugua Njihia in his column a few weeks ago wrote about the opportunities that exist in local content creation and the challenges hampering us in achieving the same.
For those interested in healthcare data both policymakers and ordinary users, the Kodi and the government in particular also need to acknowledge the existing hiccups and act on the same.
Palatable formats
But is data evangelism relevant or useful at all? Amongst those feeling marginalised, forgotten or seeking to air grievances Big Data offers overviews for trends, contrast and forecasting all vital for evidenced arguments.
While collaborations with traditional news and information delivery channels are good, we also need to take a look at how to inculcate the habits of both data generation and consumption amongst our scholars and civil servants in particular.
The next wave of data enthusiasts and mainstream journalists need to work together with data visualisers to disseminate data in palatable formats.
