Table 1.

Examples of a wide variety of projects using big data in psychiatry

Description	Primary finding	Number of subjects (n)	Data source	References
Create actuarial suicide risk algorithm to predict suicide in the 12 months after inpatient hospitalization for psychiatric disorder	52.9 % of posthospitalization suicides occurred after the 5 % of hospitalizations with the highest predicted suicide risk	40,820 soldiers hospitalized for psychiatric disorders. 421 predictors	38 army and DOD administrative data	Kessler et al. (2015)
Explore prevalence of substance use disorders (SUD) among psychiatric patients in large university system	24.9 % of patients had SUD; SUD associated with more inpatient and emergency care	40,999 psychiatric patients aged 18–64 years who sought treatment between 2000 and 2010	EMR-based psychiatry registry	Wu et al. (2013)
Ongoing study of cognitive impairment using neuroimaging and genetics	Neuroimaging phenotypes were significantly associated with progression of dementia	808 patients over age 65, including 200 with Alzheimer’s disease	20 derived neuroimaging markers plus 20 SNPs	Weiner et al. (2012)
Examine use of psychotropic drugs by patients without psychiatric diagnosis	58 % of those prescribed a psychiatric medication in 2009 had no psychiatric diagnosis	5,132,789 individuals who received prescription for psychotropic medication	Private medication claims database	Wiechers et al. (2013)
Analyze prescribing of psychotropic drugs by specialty	59 % written by general practitioners, 23 % by psychiatrists, 17 % by other physicians and providers	472 million prescriptions for psychotropic drugs	IMS database of 70 % of US retail pharmacy transactions for 2006–2007	Mark et al. (2009)
Compare risk of dementia in those 55 or older having traumatic (TBI) brain injury versus non-TBI trauma (NTT)	TBI increased risk for dementia over NTT	51,799 patients with trauma, of which 31.5 % had TBI	CA statewide administrative health database of ER and inpatient visits	Gardner et al. (2014)
Use machine learning to predict suicidal behavior text in EMR	Model obtained high specificity but low sensitivity, with PPV of 41 %	250,000 US veterans of Gulf War	Clinical records	Ben-Ari and Hammond (1991)
Investigate association between maternal and paternal age and risk of autism	Both increasing maternal age and increasing paternal age were independently associated with increased risk of autism	7,550,026 single births in CA 1989–2002. 23,311 with autism	Developmental services administrative data, birth certificate data	Grether et al. (2009)
Use natural language processing (NLP) to classify current mood state to identify treatment resistant depression	NLP models better than those relying on billing data alone	127,504 patients with diagnosis of major depression	EMR and billing data from outpatient psychiatry practices affiliated with large hospital	Perlis et al. (2012)
Analyze impact of Medicaid prior authorization for atypical antipsychotics on prevalence of schizophrenia among prison inmates	Prior authorization associated with greater prevalence of mental illness in inmates	16,844 inmates	Nationally representative sample from Census Bureau	Goldman et al. (2014)
Investigate incidence of severe psychiatric disorders following hospital contact for head injury	Increased risk of schizophrenia, depression, bipolar disorder and organic mental disorders following head injuries	113,906 people who had suffered head injuries, and were born between 1977 and 2000	Danish psychiatric central register	Orlovska et al. (2014)
Integrate depression screening, prescription fulfillment and EMR to improve care in primary care (PC)	Integration improved diagnosis and management of depression in PC	61,464 patients in PC in 14 clinical organizations	EMR, plus 4900 PHQ-9 questionnaires, plus fulfillment data for 55 % of patients	Valuck et al. (2012)
Analyze if SSRI/SNRI use prior to admission to ICU increased mortality risk	Increased hospital morality among those in ICU taking SSRI/SNRI before admission	14,709 patients with 2471 taking SSRI/SNRI	Multiparameter Intelligent Monitoring in Intensive Care database (data from EMR)	Ghassemi et al. (2014)
Evaluate safety of antipsychotic (AP) medication use in nursing homes	Dose-dependent increased risks of serious medical events such as myocardial infarction, stroke, infection, hip fracture, within 180 days of initiating AP treatment	83,959 Medicaid eligible residents ≥age 65 who initiated AP use after nursing home admission	Medicare and Medicaid claims from 45 states	Huybrechts et al. (2012)
Evaluate use of EMR to assist with phenotyping in bipolar disorder (BP)	Semiautomated data mining of EHR may assist with phenotyping of patients and controls	52,235 patients with at least one diagnosis of BP or mania, spanning 20 years	EMR, billing and inpatient pharmacy data	Castro et al. (2015)