In many sectors, automation is seen as a threat due to the potential for job losses. By contrast, automation is seen as an opportunity in healthcare, as a way to address pressures including staff shortages, increasing demand and workloads, reduced budget, skills shortages, and decreased consultation times. Automation may address these pressures in primary care, while also reconfiguring the work of staff roles and changing the patient-doctor relationship.

In the interview below, Matt Willis discusses a project, funded by The Health Foundation, which looks at opportunities and challenges to automation in NHS England general practice services. While the main goal of the project is to classify work tasks and then calculate the probability that each task will be automated, Matt is currently conducting ethnographic fieldwork in primary care sites to understand the work practices of surgery staff and clinicians.

Since the first automated pill counting machine was introduced in 1970 the role of the pharmacist has expanded to where they now perform more patient consultations, consult with primary care physicians, and require greater technical skill (including a Pharm.D degree). While this provides one clear way in which a medical profession has responded to automation, the research team is now looking at how automation will reconfigure other professions in primary care, and how it will shape its technical and digital infrastructures.

We caught up with Matt Willis to explore the implications of automation in primary care.

Ed.: One finding from an analysis by Frey and Osborne is that most healthcare occupations (that involve things like social intelligence, caring etc.) show a remarkably low probability for computerisation. But what sorts of things could be automated, despite that?

Matt: While providing care is the most important work that happens in primary care, there are many tasks that support that care. Many of those tasks are highly structured and repetitive, ideal things we can automate. There is an incredible amount of what I call “letter work” that occurs in primary care. It’s tasks like responding to requests for information from secondary care, an information request from a medical supplier, processing a trusted assessment, and so on.

There is also generating the letters that are sent to other parts of the NHS — and letters are also triaged at the beginning of each day depending on the urgency of the request. Medical coding is another task that can be automated as well as medication orders and renewal. All of these tasks require someone working with paper or digital text documents and gathering information according to a set of criteria. Often surgeries are overwhelmed with paperwork, so automation is a potential way to make a dent in the way information is processed.

Ed.: I suppose that the increasing digitisation of sensors and data capture (e.g. digital thermometers) and patient records actually helps in this: i.e. automation sounds like the obvious next step in an increasingly digital environment? But is it really as simple as that?

Matt: Well, it’s never as simple as you think it’s going to be. The commonality of data originating in a digital format usually does make data easier to work with, manipulate, analyze, and make actionable. Even when information is entirely digital there can be barriers of interoperability between systems. Automation could even be automating the use of data from one system to the next. There are also social and policy barriers to the use of digital data for automation. Think back to the recent care.data debacle that was supposed to centralize much of the NHS data from disparate silos.

Ed.: So will automation of these tasks be driven by government / within the NHS, or by industry / the market? i.e. is there already a market for automating aspects of healthcare?

Matt: Oh yes, I think it will be a variety of those forces you mention. There is already partial automation in many little ways all over NHS. Automation of messages and notifications, blood pressure cuffs, and other medical devices. Automation is not entirely new to healthcare. The pharmacist is an exemplar health profession to look at if we want to see how automation has changed the tasks of a profession for decades. Many of the electronic health record providers in the UK have different workflow automation features or let clinicians develop workflow efficiency protocols that may automate things in specific ways.

Ed.: You say that one of the bottlenecks to automating healthcare is lack of detailed knowledge of the sorts of tasks that could actually be automated. Is this what you’re working on now?

Matt: Absolutely. The data from labour statistics is self-reported and many of the occupations were lumped together meaning all receptionists in different sectors are just listed under receptionist. One early finding I have that I have been thinking about is how a receptionist in the healthcare sector is different in their information work than a receptionist’s counterpart in another sector. I see this with occupations across health, that there are unique features that differentiate health occupations from similar occupations. This begs the need to tease out those details in the data.

Additionally, we need to understand the use of technologies in primary care and what tasks those technologies perform. One of the most important links I am trying to understand is that between the tasks of people and the tasks of technologies. I am working on not only understanding the opportunities and challenges of automation in primary care but also what are the precursors that exist that may support the implementation of automation.

Ed.: When I started in journals publishing I went to the post room every day to mail out hardcopy proofs to authors. Now everything I do is electronic. I’m not really aware of when the shift happened, or what I do with the time freed up (blog, I suppose..). Do you think it will be similarly difficult in healthcare to pin-point a moment when “things got automated”?

Matt: Well, often times with technology and the change of social practices it’s rarely something that happens overnight. You probably started to gradually send out less and less paper manuscripts over a period of time. It’s the frog sitting in a pot where the heat is slowly turned up. There is a theory that technological change comes in swarm patterns — meaning it’s not one technological change that upends everything, but the advent of numerous technologies that start to create big change.

For example, one of the many reasons that the application of automation technologies is increasing is the swarming of prior technologies like “big data” sets, advances in machine vision, machine learning, machine pattern recognition, mobile robotics, the proliferation of sensors, and further development of autonomous technologies. These kinds of things drive big advances forward.

Ed.: I don’t know if people in the publishing house I worked in lost their jobs when things like post rooms and tea trolleys got replaced by email and coffee machines — or were simply moved to different types of jobs. Do you think people will “lose their jobs“ as automation spreads through the health sector, or will it just drive a shift to people doing something else instead?

Matt: One of the justifications in the project is that in many sectors automation is seen as a threat, however, automation is seen as an opportunity in healthcare. This is in great part due to the current state of the NHS and that the smart and appropriate application of automation technologies can be a force multiplier, particularly in primary care.

I see it as not that people will be put out of jobs, but that you’ll be less likely to have to work 12 hours when you should be working 8 and to not have a pile of documents stacking up that you are three months behind in processing. The demand for healthcare is increasing, the population is aging, and people live longer. One of the ways to keep up with this trend is to implement automation technologies that support healthcare workers and management.

I think we are a long ways away from the science fiction future where a patient lays in an entirely automated medical pod that scans them and administers whatever drug, treatment, procedure, or surgery they need. A person’s tasks and the allocation of work will shift in part due to technology. But that has been happening for decades. There is also a longstanding debate about if technology creates more jobs in the long term than it destroys. It’s likely that in healthcare we will see new occupational roles, job titles, and tasks emerge that are in part automation related. Also, that tasks like filing paperwork or writing a letter will seem barbaric when a computer can, through little time and effort, do that for you.

Matthew Willis was talking to blog editor David Sutcliffe.

The authors surveyed 19,000 men and women from nationally representative samples from the UK, the United States, Canada and Germany, with over half saying they had played internet games recently. Out of the total sample, 1% of young adults (18-24 year olds) and 0.5% of the general population (aged 18 or older) reported symptoms linking play to possible addictive behaviour — less than half of recently reported rates for gambling.

They warn that researchers studying the potential “darker sides” of Internet-based games must be cautious. Extrapolating from their data, as many as a million American adults might meet the proposed DSM-5 criteria for addiction to online games — representing a large cohort of people struggling with what could be clinically dysregulated behavior. However, because the authors found no evidence supporting a clear link to clinical outcomes, they warn that more evidence for clinical and behavioral effects is needed before concluding that this is a legitimate candidate for inclusion in future revisions of the DSM. If adopted, Internet gaming disorder would vie for limited therapeutic resources with a range of serious psychiatric disorders.

Read the full article: Andrew K. Przybylski, Netta Weinstein, Kou Murayama (2016) Internet Gaming Disorder: Investigating the Clinical Relevance of a New Phenomenon. American Journal of Psychiatry. Published online: November 04, 2016.

We caught up with Andy to explore the broader implications of the study:

Ed.: Is “gaming addiction” or “Internet addition” really a thing? e.g. is it something dreamed up by politicians / media people, or is it something that has been discussed and reported by psychiatrists and GPs on the ground?

Andy: Although internet addiction started as a joke about the pathologizing of everyday behaviours, popular fears have put it on the map for policymakers and researchers. In other words, thinking about potential disorders linked to the internet, gaming, and technology have taken on a life of their own.

Ed.: Two years ago the APA identified “a critical need for good research to look into whether internet gamers run a risk of becoming addicted” and asked how such an addiction might be diagnosed properly (i.e. using a checklist of symptoms). What other work or discussion has come out of that call?

Andy: In recent years two groups of researchers have emerged, one arguing there is an international consensus about the potential disorder based on the checklist, the second arguing that it is problematic to pathologize internet gaming. This second group says we don’t understand enough about gaming to know if it’s any different from other hobbies, like being a sports fan. They’re concerned that it could lead other activities to be classified as pathological. Our study set out to test if the checklist approach works, a rigorous test of the APA call for research using the symptoms proposed.

Ed.: Do fears (whether founded or not) of addiction overlap at all with fears of violent video games perhaps altering players’ behaviour? Or are they very clearly discussed and understood as very separate issues?

Andy: Although the fears do converge, the evidence does not. There is a general view that some people might be more liable to be influenced by the addictive or violent aspects of gaming but this remains an untested assumption. In both areas the quality of the evidence base needs critical improvement before the work is valuable for policymakers and mental health professionals.

Ed.: And what’s the broad landscape like in this area – i.e. who are the main players, stakeholders, and pressure points?

Andy: In addition to the American Psychiatric Association (DSM-5), the World Health Organisation is considering formalising Gaming Disorder as a potential mental health issue in the next revision of the International Classifications of Disease (ICD) tool. There is a movement among researchers (myself included based on this research) to urge caution rushing to create new behavioural addition based on gaming for the ICD-11. It is likely that including gaming addiction will do more harm than good by confusing an already complex and under developed research area.

Ed.: And lastly: asking the researcher – do we have enough data and analysis to be able to discuss this sensibly and scientifically? What would a “definitive answer” to this question look like to you — and is it achievable?

Andy: The most important thing to understand about this research area is that there is very little high quality evidence. Generally speaking there are two kinds of empirical studies in the social and clinical sciences, exploratory studies and confirmatory ones. Most of the evidence about gaming addiction to date is exploratory, that is the analyses reported represent what ‘sticks to the wall’ after the data is collected. This isn’t a good evidence for health policy.

Our studies represent the first confirmatory research on gaming addiction. We pre-registered how we were going to collect and analyse our data before we saw it. We collected large representative samples and tested a priori hypotheses. This makes a big difference in the kinds of inferences you can draw and the value of the work to policymakers. We hope our work represents the first of many studies on technology effects that put open data, open code, and a pre-registered analysis plans at the centre of science in this area. Until the research field adopts these high standards we will not have accurate definitive answers about Internet Gaming Disorder.

Read the full article: Andrew K. Przybylski, Netta Weinstein, Kou Murayama (2016) Internet Gaming Disorder: Investigating the Clinical Relevance of a New Phenomenon. American Journal of Psychiatry. Published online: November 04, 2016.

Andy was talking to David Sutcliffe, Managing Editor of the Policy blog.

Online support groups are one of the major ways in which the Internet has fundamentally changed how people experience health and health care. They provide a platform for health discussions formerly restricted by time and place, enable individuals to connect with others in similar situations, and facilitate open, anonymous communication.

Previous studies have identified that individuals primarily obtain two kinds of support from online support groups: informational (for example, advice on treatments, medication, symptom relief, and diet) and emotional (for example, receiving encouragement, being told they are in others’ prayers, receiving “hugs”, or being told that they are not alone). However, existing research has been limited as it has often used hand-coded qualitative approaches to contrast both forms of support, thereby only examining relatively few posts (<1,000) for one or two conditions.

In contrast, our research employed a machine-learning approach suitable for uncovering patterns in “big data”. Using this method a computer (which initially has no knowledge of online support groups) is given examples of informational and emotional posts (2,000 examples in our study). It then “learns” what words are associated with each category (emotional: prayers, sorry, hugs, glad, thoughts, deal, welcome, thank, god, loved, strength, alone, support, wonderful, sending; informational: effects, started, weight, blood, eating, drink, dose, night, recently, taking, side, using, twice, meal). The computer then uses this knowledge to assess new posts, and decide whether they contain more emotional or informational support.

With this approach we were able to determine the emotional or informational content of 40,000 posts across 14 different health conditions (breast cancer, prostate cancer, lung cancer, depression, schizophrenia, Alzheimer’s disease, multiple sclerosis, cystic fibrosis, fibromyalgia, heart failure, diabetes type 2, irritable bowel syndrome, asthma, and chronic obstructive pulmonary disease) on the international support group forum Dailystrength.org.

Our research revealed a slight overall tendency towards emotional posts (58% of posts were emotionally oriented). Across all diseases, those who write more also tend to write more emotional posts—we assume that as people become more involved and build relationships with other users they tend to provide more emotional support, instead of simply providing information in one-off interactions. At the same time, we also observed that older people write more informational posts. This may be explained by the fact that older people more generally use the Internet to find information, that they become experts in their chronic conditions over time, and that with increasing age health conditions may have less emotional impact as they are relatively more expected.

The demographic prevalence of the condition may also be enmeshed with the disease-related tendency to write informational or emotional posts. Our analysis suggests that content differs across the 14 conditions: mental health or brain-related conditions (such as depression, schizophrenia, and Alzheimer’s disease) feature more emotionally oriented posts, with around 80% of posts primarily containing emotional support. In contrast, nonterminal physical conditions (such as irritable bowel syndrome, diabetes, asthma) rather focus on informational support, with around 70% of posts providing advice about symptoms, treatments, and medication.

Finally, there was no gender difference across conditions with respect to the amount of posts that were informational versus emotional. That said, prostate cancer forums are oriented towards informational support, whereas breast cancer forums feature more emotional support. Apart from the generally different nature of both conditions, one explanation may lie in the nature of single-gender versus mixed-gender groups: an earlier meta-study found that women write more emotional content than men when talking among others of the same gender – but interestingly, in mixed-gender discussions, these differences nearly disappeared.

Our research helped to identify factors that determine whether online content is informational or emotional, and demonstrated how posts differ across conditions. In addition to theoretical insights about patient needs, this research will help practitioners to better understand the role of online support groups for different patients, and to provide advice to patients about the value of online support.

The results also suggest that online support groups should be integrated into the digital health strategies of the UK and other nations. At present the UK plan for “Personalised Health and Care 2020” is centred around digital services provided within the health system, and does not yet reflect the value of person-generated health data from online support groups to patients. Our research substantiates that it would benefit from considering the instrumental role that online support groups can play in the healthcare process.

Read the full paper: Deetjen, U. and J. A. Powell (2016) Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis. Journal of the American Medical Informatics Association. http://dx.doi.org/10.1093/jamia/ocv190

Ulrike Deetjen (née Rauer) is a doctoral student at the Oxford Internet Institute researching the influence of the Internet on healthcare provision and health outcomes.

Dementia affects about 44 million individuals, a number that is expected to nearly double by 2030 and triple by 2050. With an estimated annual cost of USD 604 billion, dementia represents a major economic burden for both industrial and developing countries, as well as a significant physical and emotional burden on individuals, family members and caregivers. There is currently no cure for dementia or a reliable way to slow its progress, and the G8 health ministers have set the goal of finding a cure or disease-modifying therapy by 2025. However, the underlying mechanisms are complex, and influenced by a range of genetic and environmental influences that may have no immediately apparent connection to brain health.

Of course medical research relies on access to large amounts of data, including clinical, genetic and imaging datasets. Making these widely available across research groups helps reduce data collection efforts, increases the statistical power of studies and makes data accessible to more researchers. This is particularly important from a global perspective: Swedish researchers say, for example, that they are sitting on a goldmine of excellent longitudinal and linked data on a variety of medical conditions including dementia, but that they have too few researchers to exploit its potential. Other countries will have many researchers, and less data.

‘Big data’ adds new sources of data and ways of analysing them to the repertoire of traditional medical research data. This can include (non-medical) data from online patient platforms, shop loyalty cards, and mobile phones — made available, for example, through Apple’s ResearchKit, just announced last week. As dementia is believed to be influenced by a wide range of social, environmental and lifestyle-related factors (such as diet, smoking, fitness training, and people’s social networks), and this behavioural data has the potential to improve early diagnosis, as well as allow retrospective insights into events in the years leading up to a diagnosis. For example, data on changes in shopping habits (accessible through loyalty cards) may provide an early indication of dementia.

However, there are many challenges to using and sharing big data for dementia research. The technology hurdles can largely be overcome, but there are also deep-seated issues around the management of data collection, analysis and sharing, as well as underlying people-related challenges in relation to skills, incentives, and mindsets. Change will only happen if we tackle these challenges at all levels jointly.

As data are combined from different research teams, institutions and nations — or even from non-medical sources — new access models will need to be developed that make data widely available to researchers while protecting the privacy and other interests of the data originator. Establishing robust and flexible core data standards that make data more sharable by design can lower barriers for data sharing, and help avoid researchers expending time and effort trying to establish the conditions of their use.

At the same time, we need policies that protect citizens against undue exploitation of their data. Consent needs to be understood by individuals — including the complex and far-reaching implications of providing genetic information — and should provide effective enforcement mechanisms to protect them against data misuse. Privacy concerns about digital, highly sensitive data are important and should not be de-emphasised as a subordinate goal to advancing dementia research. Beyond releasing data in a protected environments, allowing people to voluntarily “donate data”, and making consent understandable and enforceable, we also need governance mechanisms that safeguard appropriate data use for a wide range of purposes. This is particularly important as the significance of data changes with its context of use, and data will never be fully anonymisable.

We also need a favourable ecosystem with stable and beneficial legal frameworks, and links between academic researchers and private organisations for exchange of data and expertise. Legislation needs to account of the growing importance of global research communities in terms of funding and making best use of human and data resources. Also important is sustainable funding for data infrastructures, as well as an understanding that funders can have considerable influence on how research data, in particular, are made available. One of the most fundamental challenges in terms of data sharing is that there are relatively few incentives or career rewards that accrue to data creators and curators, so ways to recognise the value of shared data must be built into the research system.

In terms of skills, we need more health-/bioinformatics talent, as well as collaboration with those disciplines researching factors “below the neck”, such as cardiovascular or metabolic diseases, as scientists increasingly find that these may be associated with dementia to a larger extent than previously thought. Linking in engineers, physicists or innovative private sector organisations may prove fruitful for tapping into new skill sets to separate the signal from the noise in big data approaches.

In summary, everyone involved needs to adopt a mindset of responsible data sharing, collaborative effort, and a long-term commitment to building two-way connections between basic science, clinical care and the healthcare in everyday life. Fully capturing the health-related potential of big data requires “out of the box” thinking in terms of how to profit from the huge amounts of data being generated routinely across all facets of our everyday lives. This sort of data offers ways for individuals to become involved, by actively donating their data to research efforts, participating in consumer-led research, or engaging as citizen scientists. Empowering people to be active contributors to science may help alleviate the common feeling of helplessness faced by those whose lives are affected by dementia.

Of course, to do this we need to develop a culture that promotes trust between the people providing the data and those capturing and using it, as well as an ongoing dialogue about new ethical questions raised by collection and use of big data. Technical, legal and consent-related mechanisms to protect individual’s sensitive biomedical and lifestyle-related data against misuse may not always be sufficient, as the recent Nuffield Council on Bioethics report has argued. For example, we need a discussion around the direct and indirect benefits to participants of engaging in research, when it is appropriate for data collected for one purpose to be put to others, and to what extent individuals can make decisions particularly on genetic data, which may have more far-reaching consequences for their own and their family members’ professional and personal lives if health conditions, for example, can be predicted by others (such as employers and insurance companies).

Policymakers and the international community have an integral leadership role to play in informing and driving the public debate on responsible use and sharing of medical data, as well as in supporting the process through funding, incentivising collaboration between public and private stakeholders, creating data sharing incentives (for example, via taxation), and ensuring stability of research and legal frameworks.

Dementia is a disease that concerns all nations in the developed and developing world, and just as diseases have no respect for national boundaries, neither should research into dementia (and the data infrastructures that support it) be seen as a purely national or regional priority. The high personal, societal and economic importance of improving the prevention, diagnosis, treatment and cure of dementia worldwide should provide a strong incentive for establishing robust and safe mechanisms for data sharing.

Read the full report: Deetjen, U., E. T. Meyer and R. Schroeder (2015) Big Data for Advancing Dementia Research. Paris, France: OECD Publishing.

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 much more blurry in patients’ perception. However, there was a sense among the interviewees that a stronger concern about misuse was related to a preference for public entities who would “not intentionally give data to others”, while data theft concerns resulted in a preference for private operators – as opposed to public institutions who might just “shrug their shoulders and finger-point at subordinate levels”.

Equally important to the question of “who” is managing the data may be the “how”: that is, is the patient’s ability to access and control their health-record content perceived as trust enhancing? While the general finding of this research is that having the opportunity to both access and control their records helps to build patient trust, an often overlooked (and discomforting) factor is that easy access for the patient may also mean easy access for the rest of the family. In the words of one interviewee: “For example, you have Alzheimer’s disease or dementia. You don’t want everyone around you to know. They will say ‘show us your health record online’, and then talk to doctors about you – just going over your head.” Nevertheless, for most people I surveyed, having access and control of records was perceived as trust enhancing.

At the same time, a striking survey finding is how greater access and control of records can be less trust-enhancing for those with lower Internet experience, confidence, and breadth of use: as one older interviewee put it – “I am sceptical because I am not good at these Internet things. My husband can help me, but somehow it is not really worth this effort.” The quote reveals one of the facets of digital divides, and additionally highlights the relevance of life-stage in the discussion. Older participants see the benefits of sharing data (if it means avoiding unnecessary repetition of routine examinations) and are less concerned about outsider access, while younger people are more apprehensive of the risk of medical data falling into the wrong hands. An older participant summarised this very effectively: “If I was 30 years younger and at the beginning of my professional career or my family life, it would be causing more concern for me than now”. Finally, this reinforces the importance of legal regulations and security audits ensuring a general level of protection – even if the patient chooses not to be (or cannot be) directly involved in the management of their data.

Interestingly, the research also uncovered what is known as the certainty trough: not only are those with low online affinity highly suspicious of Internet-based health records – the experts are as well! The more different activities a user engaged in, the higher the suspicion of Internet-based health records. This confirms the notion that with more knowledge and more intense engagement with the Internet, we tend to become more aware of the risks – and lose trust in the technology and what the protections might actually be worth.

Finally, it is clear that the “who” and the “how” are interrelated, as a low degree of trust goes hand in hand with a desire for control. For a generally less trustworthy operator, access to records is not sufficient to inspire patient trust. While access improves knowledge and may allow for legal steps to change what is stored online, few people make use of this possibility; only direct control of what is stored online helps to compensate for a general suspicion about the operator. It is noteworthy here that there is a discrepancy between how much importance people place on having control, and how much they actually use it, but in the end, trust is a subjective concept that doesn’t necessarily reflect actual privacy and security.

The results of this research provide valuable insights for the further development of Internet-based health records. In short: to gain patient trust, the operator should ideally be of a medical nature and should allow the patients to get involved in how their health records are maintained. Moreover, policy initiatives designed to increase the Internet and health literacy of the public are crucial in reaching all parts of the population, as is an underlying legal and regulatory framework within which any Internet-based health record should be embedded.

Read the full paper: Rauer, Ulrike (2012) Patient Trust in Internet-based Health Records: An Analysis Across Operator Types and Levels of Patient Involvement in Germany. Policy and Internet 4 (2).

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? Eg: 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 to be performed in real-time as historical Twitter data are normally available only through paid services. Twitter analytics methods are diverse; in our study, we used frequency calculations, developed algorithms for geo-location, automatic spam and duplication detection, and applied time series and cross-correlation with surveillance data [1,2,3].

Ed: What’s the relationship between traditional and social media in terms of diffusion of health information? Do you have a sense that one may be driving the other?

Patty: This is a fundamental question. “Does media coverage of certain topic causes buzz on social media or does social media discussion causes media frenzy?” This was particularly important to investigate for the 2009 swine flu pandemic, which experienced unprecedented media interest. While it could be assumed that disease cases preceded media coverage, or that media discussion sparked public interest causing Twitter debate, neither proved to be the case in our experiment. On some days, media coverage for flu was higher, and on others Twitter discussion was higher; but peaks seemed synchronized – happening on the same days.

Ed: In terms of communicating accurate information, does the Internet make the job easier or more difficult for health authorities?

Patty: The communication of risk in any public health emergencies is a complex task for government and healthcare agencies; this task is made more challenging when citizens are bombarded with online information, from a variety of sources that vary in accuracy. This has become even more challenging with the increase in users accessing health-related information on their mobile phones (17% in 2010 and 31% in 2012, according to the US Pew Internet study).

Our findings from analyzing Twitter reaction to online media coverage of the WHO declaration of swine flu as a “pandemic” (stage 6) on 11 June 2009, which unquestionably was the most media-covered event during the 2009 epidemic, indicated that Twitter does favour reputable sources (such as the BBC, which was by far the most popular) but also that bogus information can still leak into the network.

Ed: What differences do you see between traditional and social media, in terms of eg bias / error rate of public health-related information?

Patty: Fully understanding quality of media coverage of health topics such as the 2009 swine flu pandemics in terms of bias and medical accuracy would require a qualitative study (for example, one conducted by Duncan in the EU [4]). However, the main role of social media, in particular Twitter due to the 140 character limit, is to disseminate media coverage by propagating links rather than creating primary health information about a particular event. In our study around 65% of tweets analysed contained a link.

Ed: Google flu trends (which monitors user search terms to estimate worldwide flu activity) has been around a couple of years: where is that going? And how useful is it?

Patty: Search companies such as Google have demonstrated that online search queries for keywords relating to flu and its symptoms can serve as a proxy for the number of individuals who are sick (Google Flu Trends), however, in 2013 the system “drastically overestimated peak flu levels”, as reported by Nature. Most importantly, however, unlike Twitter, Google search queries remain proprietary and are therefore not useful for research or the construction of non-commercial applications.

Ed: What are implications of social media monitoring for countries that may want to suppress information about potential pandemics?

Patty: The importance of event-based surveillance and monitoring social media for epidemic intelligence is of particular importance in countries with sub-optimal surveillance systems and those lacking the capacity for outbreak preparedness and response. Secondly, the role of user-generated information on social media is also of particular importance in counties with limited freedom of press or those that actively try to suppress information about potential outbreaks.

Ed: Would it be possible with this data to follow spread geographically, ie from point sources, or is population movement too complex to allow this sort of modelling?

Patty: Spatio-temporal modelling is technically possible as tweets are time-stamped and there is a support for geo-tagging. However, the location of all tweets can’t be precisely identified; however, early warning systems will improve in accuracy as geo-tagging of user generated content becomes widespread. Mathematical modelling of the spread of diseases and population movements are very topical research challenges (undertaken by, for example, by Colliza et al. [5]) but modelling social media user behaviour during health emergencies to provide a robust baseline for early disease detection remains a challenge.

Ed: A strength of monitoring social media is that it follows what people do already (eg search / Tweet / update statuses). Are there any mobile / SNS apps to support collection of epidemic health data? eg a sort of ‘how are you feeling now’ app?

Patty: The strength of early warning systems using social media is exactly in the ability to piggy-back on existing users’ behaviour rather than having to recruit participants. However, there are a growing number of participatory surveillance systems that ask users to provide their symptoms (web-based such as Flusurvey in the UK, and “Flu Near You” in the US that also exists as a mobile app). While interest in self-reporting systems is growing, challenges include their reliability, user recruitment and long-term retention, and integration with public health services; these remain open research questions for the future. There is also a potential for public health services to use social media two-ways – by providing information over the networks rather than only collect user-generated content. Social media could be used for providing evidence-based advice and personalized health information directly to affected citizens where they need it and when they need it, thus effectively engaging them in active management of their health.

References

[1.] M Szomszor, P Kostkova, C St Louis: Twitter Informatics: Tracking and Understanding Public Reaction during the 2009 Swine Flu Pandemics, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology 2011, WI-IAT, Vol. 1, pp.320-323.

[2.]  Szomszor, M., Kostkova, P., de Quincey, E. (2010). #swineflu: Twitter Predicts Swine Flu Outbreak in 2009. M Szomszor, P Kostkova (Eds.): ehealth 2010, Springer Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering LNICST 69, pages 18-26, 2011.

[3.] Ed de Quincey, Patty Kostkova Early Warning and Outbreak Detection Using Social Networking Websites: the Potential of Twitter, P Kostkova (Ed.): ehealth 2009, Springer Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering LNICST 27, pages 21-24, 2010.

[4.] B Duncan. How the Media reported the first day of the pandemic H1N1) 2009: Results of EU-wide Media Analysis. Eurosurveillance, Vol 14, Issue 30, July 2009

[5.] Colizza V, Barrat A, Barthelemy M, Valleron AJ, Vespignani A (2007) Modeling the worldwide spread of pandemic influenza: Baseline case an containment interventions. PloS Med 4(1): e13. doi:10.1371/journal. pmed.0040013

Further information on this project and related activities, can be found at: BMJ-funded scientific film: http://www.youtube.com/watch?v=_JNogEk-pnM ; Can Twitter predict disease outbreaks? http://www.bmj.com/content/344/bmj.e2353 ; 1st International Workshop on Public Health in the Digital Age: Social Media, Crowdsourcing and Participatory Systems (PHDA 2013): http://www.digitalhealth.ws/ ; Social networks and big data meet public health @ WWW 2013: http://www2013.org/2013/04/25/social-networks-and-big-data-meet-public-health/

Patty Kostkova was talking to blog editor David Sutcliffe.

Dr Patty Kostkova is a Principal Research Associate in eHealth at the Department of Computer Science, University College London (UCL) and held a Research Scientist post at the ISI Foundation in Italy. Until 2012, she was the Head of the City eHealth Research Centre (CeRC) at City University, London, a thriving multidisciplinary research centre with expertise in computer science, information science and public health. In recent years, she was appointed a consultant at WHO responsible for the design and development of information systems for international surveillance.

Researchers who were instrumental in this project include Ed de Quincey, Martin Szomszor and Connie St Louis.

Emergency contraception has long been heralded as a “second chance” for women to prevent pregnancy after unprotected intercourse. However, the commercial promotion and use of ECPs has been a highly contentious issue in the United States, a fact that has had a significant impact on legislative action and accessibility. Due to their limited window of effectiveness and given that people do not tend to obtain them until the moment when they are needed urgently, it is essential for people to be able to find accurate information about ECPs as quickly as possible.

Our study investigated empirically how over 200 young college students (18-19 years old) at two college campuses in the Midwestern United States searched for and evaluated information about emergency contraception. They were given the hypothetical scenario: “You are at home in the middle of summer. A friend calls you frantically on a Friday at midnight. The condom broke while she was with her boyfriend. What can she do to prevent pregnancy? Remember, neither of you is on campus. She lives in South Bend, Indiana.” All of the students had considerable experience with using the Internet.

Worryingly, a third of the participants, after looking for information online, were unable to conclude that the friend should seek out ECPs. Less than half gave what we consider the ideal response: to have the friend purchase ECPs over the counter at a pharmacy. Some participants suggested such solutions as “wait it out,” “adoption,” “visit a gynecologist” (in the incorrect location), and purchasing another condom. Three percent of respondents came to no conclusion at all.

While adolescents often claim to be confident in searching for information online, they are often unsystematic in their search and few students made a concerted effort to verify information they found during their search. The presence of a dot-org domain name was sometimes cited as a measure of credibility: “Cause it’s like a government issued kind of website,” noted one participant. While it’s encouraging that students are aware of different top-level domain names, it’s alarming that their knowledge of what they signify can be wrong: dot-org sites are not sanctioned any more than are dot-com sites and thus should not be considered a signal of credibility.

Another student assumed that “the main website” for the morning after pill was morningafterpill.org, which happens to be sponsored by the American Life League, a pro-life organization. The website includes articles with titles such as “Emergency Contraception: the Truth, the Whole Truth, and Nothing but the Truth,” as well as advocacy by medical professionals matching the perspectives of the American Life League. This demonstrates the way in which people and organizations with a particular agenda can publicize any type of information – in this case erroneous health information – to the public.

Overall, the findings suggest that despite information theoretically available on the Web about emergency contraception, even young adults with considerable online experiences may not be able to find it in a time of need. Many respondents were uncertain of how to begin looking for information; some did not immediately consider the Internet as a primary source for it. An important policy implication of this study is that it is problematic to assume that just because content exists online, it is easily within the reach of all users. In particular, it is a mistake to think that just because young people grew up with digital media, they are universally savvy with finding and evaluating Web content.

Given the importance of finding credible and accurate health-related content, it is important to understand the strategies people use to find information so that obstacles can be addressed – rather than taking such know-how for granted, educational institutions should think about incorporating related content into their curricula. Additionally, related services should be available at establishments such as public libraries available to those not enrolled in school.

In some cases particular search terms determined whether people found the right information: providers of content about emergency contraception need to be aware of this. The study also raises questions about search engine practices. While search engine companies seem to take pride in letting their algorithms sort out the ranking of search results, is it ideal or responsible to leave content important to people’s health in the hands of automated processes that are open to manipulation?

Algorithms themselves are not neutral – they include lots of decisions taken by their creators – yet the idea of “algorithm literacy” is not a topic taken up in educational curricula or public conversations.

Is this aim of achieving user empowerment realistic? In their examination of health queries submitted to the NHS Direct online enquiry service, John Powell and Sharon Boden find that while patient empowerment does occur in the use of online health services, it is constrained and context dependent. Policymakers wishing to promote greater choice and control among health system users should therefore take account of the limits to empowerment as well as barriers to participation. The Dutch government’s online public national health and care portal similarly aims to facilitate consumer decision-making behavior and increasing transparency and accountability to improve quality of care and functioning of health markets. Interestingly, Hans Ossebaard, Lisette van Gemert-Pijnen and Erwin Seydel find the influence of the Dutch portal on choice behavior, awareness, and empowerment of users to actually be small.

The Internet is often discussed in terms of empowering (or even endangering) patients through broadening of access to medical and health-related information, but there is evidence that concerns about serious negative effects of using the Internet for health information may be ill-founded. The cancer patients in the study by Alison Chapple, Julie Evans and Sue Ziebland gave few examples of harm from using the Internet or of damage caused to their relationships with health professionals. While policy makers have tended to focus on regulating the factual content of online information, in this study it was actually the consequences of stumbling on factually correct (but unwelcome) information that most concerned the patients and families; good practice guidelines for health information may therefore need to pay more attention to website design and user routing, as well as to the accuracy of content.

Policy makers and health professionals should also acknowledge the often highly individual strategies people use to access health information online, and understand how these practices are shaped by technology — the study by Astrid Mager found that the way people collected and evaluated online information about chronic diseases was shaped by search engines as much as by their individual medical preferences.

Many people still lack the necessary skills to navigate online content effectively. Eszter Hargittai and Heather Young examined the experiences a diverse group of young adults looking for information about emergency contraception online, finding that the majority of the study group could not identify the most efficient way of acquiring emergency contraception in a time of need. Given the increasing trend for people to turn to the Internet for health information, users must possess the necessary skills to make effective and efficient use of it; an important component of this may concern educational efforts to help people better navigate the Web. Improving general e-Health literacy is one of several recommendations by Maria De Jesus and Chenyang Xiao, who examined how Hispanic adults in the United States search for health information online. They report a striking language divide, with English proficiency of the user largely predicting online health information-seeking behavior.

Lastly, but no less importantly, is the policy challenge of addressing the issue of patient trust. The study by Ulrike Rauer on the structural and institutional factors that influence patient trust in Internet-based health records found that while patients typically considered medical operators to be more trustworthy than non-medical ones, there was no evidence of a “public–private” divide; patients perceived physicians and private health insurance providers to be more trustworthy than the government and corporations. Patient involvement in terms of access and control over their records was also found to be trust enhancing.

A lack of policy measures is a common barrier to success of eHealth initiatives; it is therefore essential that we develop measures that facilitate the adoption of initiatives and that demonstrate their success through improvement in services and the health status of the population. The articles presented in this special issue of Policy & Internet provide the sort of evidence-based insight that is urgently needed to help shape these policy measures. The empirical research and perspectives gathered here will make a valuable contribution to future efforts in this area.