Big data for bipolar disorder

Abstract

The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions (Preprint)

“…However, there is a possibility that predictive models may increase inequalities for vulnerable groups [47], particularly when commercial interests are at play. Psychiatric profiling will identify some as being at high risk, which may shape self-perception [55] and beliefs about an individual.…”

“…Other issues include data ownership and the direction of any benefits created, considering that the data is from users, and what happens to data if an organisation goes out of business or is purchased [47]. Pentland et al [53] argue that participants should have ownership over their own data, by which they mean that app users should maintain the rights of possession, use, and disposal, with some limitations on the right to disclose data about others in one's network.…”

“…Our results revealed a degree of uncertainty about the purpose of the study, suggesting that many participants took part without necessarily feeling informed about the reasons for the study. We therefore recommend using more robust measures for informed consent, considering the opacity of data analysis techniques and purposes [47], and engage users with informative feedback derived from their data.…”

“…through the identification of markers for risk [46]. De-identification may be used to protect the privacy of individuals, as this data is potentially left uncovered by privacy laws in some national regulatory settings [47]. On the other hand, de-identification may remove information that it is important to maintain the usefulness of the data, depending on context and purpose for use [48].…”

Big Data in mental health: a challenging fragmented future

“…Neurosciences were not out of this wave of – somewhat grandiose – expectations over the Big Data potential, given the increasing need to bridge the gap between brain structure/function and behavior. This complexity requires a comprehensive, holistic perspective in order to fully understand the course of an illness. However, one of the most important limitations in brain research is that it has so far yielded partial, diverse and not generalizable results, which can hardly be directly transferred into clinical practice.…”