Realizing the benefits of open government data: Journalists’ coverage of the NHS winter crisis, 2016–17

Abstract

In the current literature on open government data (OGD) ecosystems, academics have paid little attention to the way data journalists realize the benefits of publicly-available data. To address this, in this study I use content analysis (n = 65), thematic analysis, and interviews with journalists (n = 5) to examine how the news media used data in their reporting during the NHS (National Health Service, UK) winter crisis of 2016–2017. The findings show that journalists to some extent realized the benefits of the OGD through the adoption of the population problem data-frame to criticize the government. But they were limited in their use of more informative and critical data-frames, even though they accessed the available data and had the relevant data skills. This was a product of the reification and naturalization of certain health data that rendered other data-informed news stories very hard to articulate. These findings suggest that data themselves – rather than just the motives of data producers – can limit the benefits that can be realized by data users.

Keywords:

• Communication
• ecosystem
• data
• framing
• governance

Notes

1 https://digital.nhs.uk/data-and-information

2 Whilst it is more common for framing to be studied with qualitative methodologies, there has been a rise in the use of quantitative methodologies since the 1990s (Matthes 2009).

3 Here Stoneman (2015) is quoting David Higgerson of the Daily Mirror’s data team.

4 These data were leaked to the press before their “official release” by NHS Digital. The Conservative government argued that the necessary technical checks of the data (e.g. double entry of records) had not been completed. For them, this meant the data could not underpin criticisms of its health policy.

5 Figure 1 provides a typology of the different numbers found in news media coverage. This article focuses on statistics (descriptive, inferential and inferential predictive) and complex classifications, indexes or rankings that are derived from these types of statistics. This means the article did not look at measurements (complex or simple), quantitative targets, pledges or precedents and non-metrological numbers (including numerical phrases and names).

6 J2 interview transcript.

7 J2 interview transcript.

8 J2 interview transcript.

9 J1 interview transcript.

10 J3 interview transcript.

11 J4 interview transcript.

12 J5 interview transcript.

13 J1 interview transcript.

14 J1 interview transcript.

15 Here Levay, Jönsson, and Huzzard (2020) draw on Chua’s (1995) research on Australian healthcare system.