public health

Key to successful adoption of Internet-based health records is how much a patient places trust that data will be properly secured from inadvertent leakage.

In an attempt to reduce costs and improve quality, digital health records are permeating health systems all over the world. Internet-based access to them creates new opportunities for access and sharing—while at the same time causing nightmares to many patients: medical data floating around freely within the clouds, unprotected from strangers, being abused to target and discriminate people without their knowledge? Individuals often have little knowledge about the actual risks, and single instances of breaches are exaggerated in the media. Key to successful adoption of Internet-based health records is, however, how much a patient places trust in the technology: trust that data will be properly secured from inadvertent leakage, and trust that it will not be accessed by unauthorised strangers. Situated in this context, my own research has taken a closer look at the structural and institutional factors influencing patient trust in Internet-based health records. Utilising a survey and interviews, the research has looked specifically at Germany—a very suitable environment for this question given its wide range of actors in the health system, and often being referred to as a “hard-line privacy country”. Germany has struggled for years with the introduction of smart cards linked to centralised Electronic Health Records, not only changing its design features over several iterations, but also battling negative press coverage about data security. The first element to this question of patient trust is the “who”: that is, does it make a difference whether the health record is maintained by either a medical or a non-medical entity, and whether the entity is public or private? I found that patients clearly expressed a higher trust in medical operators, evidence of a certain “halo effect” surrounding medical professionals and organisations driven by patient faith in their good intentions. This overrode the concern that medical operators might be less adept at securing the data than (for example) most non-medical IT firms. The distinction between public and private operators is…

While traditional surveillance systems will remain the pillars of public health, online media monitoring has added an important early-warning function, with social media bringing additional benefits to epidemic intelligence.

Communication of risk in any public health emergency is a complex task for healthcare agencies; a task made more challenging when citizens are bombarded with online information. Mexico City, 2009. Image by Eneas.

Ed: Could you briefly outline your study? Patty: We investigated the role of Twitter during the 2009 swine flu pandemics from two perspectives. Firstly, we demonstrated the role of the social network to detect an upcoming spike in an epidemic before the official surveillance systems—up to week in the UK and up to 2-3 weeks in the US—by investigating users who “self-diagnosed” themselves posting tweets such as “I have flu/swine flu.” Secondly, we illustrated how online resources reporting the WHO declaration of “pandemics” on 11 June 2009 were propagated through Twitter during the 24 hours after the official announcement [1,2,3]. Ed: Disease control agencies already routinely follow media sources; are public health agencies  aware of social media as another valuable source of information? Patty:  Social media are providing an invaluable real-time data signal complementing well-established epidemic intelligence (EI) systems monitoring online media, such as MedISys and GPHIN. While traditional surveillance systems will remain the pillars of public health, online media monitoring has added an important early-warning function, with social media bringing additional benefits to epidemic intelligence: virtually real-time information available in the public domain that is contributed by users themselves, thus not relying on the editorial policies of media agencies. Public health agencies (such as the European Centre for Disease Prevention and Control) are interested in social media early warning systems, but more research is required to develop robust social media monitoring solutions that are ready to be integrated with agencies’ EI services. Ed: How difficult is this data to process? E.g.: is this a full sample, processed in real-time? Patty:  No, obtaining all Twitter search query results is not possible. In our 2009 pilot study we were accessing data from Twitter using a search API interface querying the database every minute (the number of results was limited to 100 tweets). Currently, only 1% of the ‘Firehose’ (massive real-time stream of all public tweets) is made available using the streaming API. The searches have…