Historically, the healthcare industry has generated large amounts of data. In the past, the data was stored in hard-copy form. However, with technological advancements, there has been a shift to the digitization of data. Some of the aims behind the digitization of data include reducing costs as well as improving the quality of services in healthcare.
In the field of healthcare, big data analytics brings with it great potential: (1) the most cost-effective treatments can be determined by analysing patient characteristics, costs and outcomes of care; (2) using predictive models based on patient profiles can help identify individuals most likely to benefit from medical interventions; (3) medical procedures can be centralised and, through the use of machine learning or artificial intelligence, can be tailored to each future patient.
Ultimately, big data scientists can “discover associations, understand patterns and trends within the data”, which can potentially lower costs, save lives and improve care.
What is Big Data?
Though the term is relatively new, the process behind “big data” – the collection and storing of large amounts of data and information for analysis – has been around for centuries. Laney’s definition, with a focus on the “volume”, “velocity” and “variety” has become commonplace.
“Volume” refers to the collection of large amounts of data from various sources, while “velocity” refers to dealing with data speedily, and “variety” refers to the various formats the data appears in. Others have included “variability” and “visualization”; however, Raghupathi’s inclusion of “veracity” is very relevant to healthcare. By veracity, practitioners and researchers refer to the credibility of the data and error-free outcomes. Though very difficult to achieve, the aim is to have the data as accurate as possible to provide better solutions.
From patient records to treatment plans, access to healthcare data needs to be accurate, quick and efficient. Yet, it remains very difficult to analyse and use any insights uncovered to make progress in healthcare. When assessing mental health this becomes even more difficult.
Other factors, including the stigma around mental health, requires altering perceptions as well as developing thorough diagnoses and innovative treatments. Societal views have prevented many struggling with their mental health from speaking out, resulting in a lack of data and producing inconsistent data. Fear of the opinion of others, lack of knowledge from both those with mental illnesses and those without, and more, influenced the data available to practitioners and researchers.
This does, however, give an opportunity to explore other avenues such as social media data.
While no report has proved the overall number of people with mental health problems has changed significantly in recent years, the increase in people having suicidal thoughts or choosing to self-harm signifies people’s ability to cope with mental health problems in the United Kingdom (UK) is getting worse.
Though the National Health Service (NHS) is alleged to provide the best health system in the world – in 2013-2014 “over 1.7 million adults accessed NHS services for severe or enduring mental health problems”. As this figure increased over the years – and continues to increase – mental health services are overwhelmed by mounting demands. The shortage of specialist nurses and psychiatrists adds to the fear that the UK will be unable to support those with mental illnesses.
The subsequent posts will provide an overview of how big data can provide thorough diagnoses and innovative treatments for mental health.
By analysing clinical (often referred to as ‘traditional’) mental health data, mental health apps, social media, and the potential of AI, the aim is to show how people struggling with their mental health can be provided higher quality care, or more convenient care, at a lower cost.
For much research to be done and for solutions to be contrived, large amounts of personal data will be required. Thus, it is imperative that a brief evaluation of ethics, privacy and security surrounding advancements in big data and mental health is accounted for.
I emphasise how much of a positive impact big data can have on advancements in mental health research and potential solutions; however, practitioners and researchers should not curtail any laws or code of conducts aimed at protecting the privacy of patients and individuals.
Written by Rodney
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