Table 1.

Practical applications of big data analytics and artificial intelligence (AI) applications relevant to opium use disorder (OUD) treatment services.

Domain, and big data analytics and AI approaches			Examples
Population-level epidemiology and needs assessment
	Contextualizing opioid overdose events with visual representations of neighborhood-built environment conditions [51]	Geospatial data, Google Street View images, nonemergency “311” service requests, and US Census data were used as indicators to produce a high-resolution spatial-temporal analysis, indicating that OUD is influenced by social and neighborhood determinants such as depressing or insecure living environments, poverty, and health issues to inform health policies and guide responses to the opioid crisis.
	Mapping opioid overdose events against health care access and socioeconomic factors	Spatiotemporal patterns and maps using color to show variation in aggregates of geographical data created from opioid overdose–related emergency department visits, geolocation, data on socioecological factors such as health behaviors, health care, social and economic factors, and physical environment identified that emergency room visit rates were significantly associated with the changes in health care factors (ie, access to care and quality of care) and socioeconomic factors (ie, levels of education, employment, income, family and social support, and community safety) [61].
	Using social media data to identify trends in opioid use [62-64]	Statistical techniques applied to social media data may provide close to real-time, county-level estimates of overdose mortality, a basis to inform prevention and treatment decisions.
	Social media analysis and digital phenotyping [65]	Other studies have identified the possibility of detecting a “phenotype” of social media use by text analysis in conditions such as schizophrenia, which may allow new opportunities to support early illness or relapse detection in the community.
	Using social media to provide targeted interventions for OUD	The social media platform X (formerly known as Twitter) offers a feasible approach to identify some people who use opioids, making it a possible arena to disseminate evidence-based content and facilitate linkage to treatment and harm reduction services [66].
Integrated treatment approaches.
	Identifying intervention touchpoints for people with OUD across sectors and services	Data linkage studies, for example, between the records of major health and social agencies and the use of machine learning predictive models, have the potential to identify key intervention (touch) points to provide health or social care, treatment for OUD, or harm reduction interventions and can improve understanding of clinical trajectories for people with OUD [67,68].
	Generative AI and chatbots	Chatbots that offer support, accountability, and some forms of psychotherapy. Preliminary studies show encouraging application in addiction treatment [69-71]
Clinical decision-making
	Development of clinical decision support algorithms for OUD treatment	Combining evidence-based tools, expert consensus with electronic health care data, and monitoring tools to develop a screening measure, symptom tracking measure, and clinical decision support algorithm necessary to implement measurement-based care for OUD with buprenorphine in primary care [72].
	Stratifying overdose risk	NarxCare is a proprietary analytic tool that analyses state-mandated prescription databases in the United States to calculate a risk score for possible overdose deaths, which is displayed in the patient’s electronic medical record [28].
Tailoring interventions and personalized approaches; personalized medicine or treatment
	Predictive analytics to identify high-risk periods for people with OUD and link these with appropriate interventions	Linking ecological momentary assessment data (where participants are prompted by a smartphone app to self-report on various factors such as sleep, stress, pain, craving, and mood), ambulatory physiological assessment using mobile sensors or smartwatches and social media data, and using deep neural networks for predictive analysis may be useful to identify people at high risk of cravings or withdrawal symptoms to receiving dynamic dose adjustments of medications for OUD (eg, methadone and buprenorphine) to improve treatment retention [54].
Drug discovery
	—a	Drug discovery models using generative AI may drive forward potential brain-targeting biological therapeutics for people who use drugs [73].
Performance and quality benchmarking
	Identifying service level characteristics associated with disengagement from OUD treatment	Predicting premature discontinuation of OUD treatment, using a supervised machine learning approach for analysis of millions of treatment episodes to identify predictors of treatment discontinuation: the most influential risk factors include characteristics of service setting, geographic region, primary source of payment, referral source, employment status, and delays to entering treatment [56].

^aNot applicable.