One of the first steps of my research was to discover and read the relevant literature in open data journalism. Also, I had to identify and catalogue the barriers journalists encounter when using open data. For that, I focused on papers describing open data use cases in journalism. In most publications, the problems were related to the data quantity or quality, which was something anticipated. Interestingly, I encountered a paper where the data were of high quality and sufficient quantity, but still, journalists were struggling to create compelling stories around them. So let’s dive into this particular case study, the coverage of the NHS winter crisis. 

The NHS winter crisis was the period between December 2016 to February 2017 when the United Kindom healthcare system was experiencing significant pressure. The media covered this crisis extensively; therefore, many journalistic articles were published during that period, an excellent opportunity for examining open data usage in journalism.

To make a long story short (or a 12-page paper in 2 paragraphs), the journalist had to pick between two indicators, the four-hour-wait target and the delayed transfers of care. 

The four-hour-wait target is a metric introduced by the Labor government in 2002 and states that 95% of the patients attending an A&E department must receive care within 4 hours.

The delayed transfers of care or “bed-blocking” refers to the patients that are e ready to be discharged from a hospital but are unable to leave due to issues in arranging their ongoing care or support outside the hospital. This situation creates a severe problem for hospitals since they lack the beds to serve new patients.

According to the paper, the delayed transfers of care results could produce significant insight into the problem of the NHS during that period; however, the indicator that most journalists decided to use was the four-hour wait target.

While reading the paper, I found that quite peculiar; why would someone intentionally use an “inferior” data indicator? For me, the indicator that can provide better insights is superior, although for a journalist, the ideal metric is the one that can have a higher impact on the audience. But why the four-hour-wait target has a better impact on the audience?

First, the four-hour-wait target was well known to the audience since it was introduced in the public sphere through political discussions, and afterwards, it became a metric for the NHS. On the other hand, bed-blocking is an internal metric of the health system, and the majority of the citizens are unaware of it. This also created another problem; patients knew when the four-hour-wait targe was breached but were unaware of any delays in their hospital discharge. Journalists could not find anyone to interview on that matter, and since they could not provide a compelling story with the data, they decided not to use this metric.

I ponder upon their decision; they could publish results about the healthcare system’s deep problems, and they could have educated the audience about this unknown metric, but without a compelling story, even the best results will be alienating for the audience. Our society revolves around stories, from ancient myths to modern news articles, this is how humans empathise and understand each other’s problems, and while policymakers and scientists find reports based on data interesting, most people see them as dull. 

In conclusion, the quality and the quantity of the data are only some of the important factors; we also have to keep in mind the special need of the data users. In this case, an important value for the data is the knowledge of the domain by the public and the ability to construct a captivating story around them.