How Social Media Can Help Tackle Depression

Robin Williams’ death last year left the world stunned. How can a man who had so much ‘going’ for him take his own life? How can a man who made millions laugh could not bring himself out of a seemingly ‘bad mood’? Williams was suffering from depression, a condition so debilitating that if left unchecked could lead an otherwise healthy person to commit suicide. The incident is a wake-up call for the healthcare community as it is predicted by WHO that depression is projected to become the second leading contributor to the global burden of disease by 2020. The event has certainly brought a lot of attention on the issue but I believe a lot more needs to be done. In this post I talk about how social media can play a role in addressing the condition.

A model created by Eric Horovitz , Data Scientist at Microsoft Research can detect and predict whether an individual will have depression based on his/her Twitter feed. Koko, a social network created for people with depression assists users to crowdsource help to combat negative thinking, one of the major symptoms of depression. While these are excellent initiatives, social media can be further leveraged. Here are some use cases that highlight how social media can augment traditional methods of tackling the condition:

  • Campaigns to educate: The stigma attached with mental illnesses has made it hard for people to talk about it openly. Creating awareness and establishing platforms to educate general public is the first step towards removing the stigma. Since social media has enormous reach it can be used to launch campaigns highlighting the myths, treatment options and help centres related with depression. For e.g. Twitter campaigns can be organized to encourage people including patients, family members, friends, doctors, psychologists to talk about their experiences. This open dialogue can not only help remove stigma but can also lead to previously unknown insights to deal with the condition.
  • Identify ideal treatment: According to WebMD there are multiple ways to treat depression. The choice of treatment depends on a number of factors such as type, stress, early life experiences, genetics etc. The perfect fit can be tailored if all the patient data is available. Medical reports, patient reported data has been traditionally available but the missing piece was patient behavioural data. Social platforms such as Koko would enable access to patient behavioural data that could deliver meaningful insights to create an ideal treatment plan.
  • Recruit patients for clinical trials: Despite massive efforts, CROs have failed to meet the patient recruitment target. One of the major barriers is that patients generally consider clinical trials risky and therefore do not enrol in them. With the advent and use of social media and the rise of e-Patients and e-caregivers we can easily overcome the barrier. This group of e-patients and e-caregivers are a new informed breed who is well aware of the risks/rewards of trails. They are 60% more likely to participate in trials and therefore social media websites can be a great tool to support trial recruitment.
  • New source for patient research: Patient blogs are a rich source of information that is not available elsewhere. Mining patient blogs (with the permission of the owner) can help discover unique behavioural patterns which could be a major boon to comparative effectiveness research and for public health.


In words of Robin Williams “No matter what people tell you, words and ideas can change the world”. What is your idea? How can social media change the way we deal with depression?

Healthcare Analytics to Check Skyrocketing Costs

Annual healthcare spending in the US is estimated to be around $3 Trillion, nearly 18% of the country’s GDP.  According to a report by the Institute of Medicine, the country lost $750 billion due to inefficiencies in the system. The report states that the primary areas of wasteful expenditure are unnecessary services, improper care delivery, excess administrative costs, inflated prices, prevention failures and fraud. While there are multiple measures being taken to control these inefficiencies, healthcare analytics is certainly one of the cogs in the wheel.

Healthcare industry so far has not utilized analytics the way other data heavy industries such as retail and banking have. This could partly be due to legacy systems, unavailability of data, data privacy issues and lack of incentives. Given that the data generated and collected through the introduction of electronic health records, health insurance exchanges, and social media portals is on a rise, the healthcare analytics sector is ripe for change. Moreover, the move from pay-for-service model to pay-for-performance, focus on wellness to prevention and universal coverage clearly makes a strong case to implement analytics in the healthcare space.

Some of the use cases where healthcare analytics can deliver value are as follows:


  • Fraud Analysis: Large amounts of claims data can be analysed to using fraud detection models. Predictive models can detect suspicious claims by providers which can then be further investigated.
  • Incentive Design Analytics: Customer records can be analysed to predict health issues before hand. The payers then can promote preventive measures by linking premiums to the use of such preventive measures


  • Evidence-Based Medicine: Doctors can use EMR, EHR, financial, operational, clinical, and genomic data. This will eventually decrease the cost by reducing readmission rates, wrong diagnoses and efficient care.

Pharma Companies:

  • Research & Development: R&D can benefit from analysing clinical data which can provide insights to accurately design trials which in turn would provide data points to efficiently design clinical trials, leaner R&D pipeline, improve time to market drugs hence reducing costs in the drug discovery process.

Although big data offers a very promising proposition to tackle the rising healthcare costs, there are certain challenges that need to be addressed.  One of the aspects that need special attention is patient data security and privacy. With a large number of regulations in place, data analytics applications would also be required to comply with the rules of the game. Additionally, all the stakeholders in the healthcare space will have to foster an open culture of trust to allow data sharing & data-integration to reap the full potential of analytics solutions.