Synthetic opioids are the most common drugs involved in drug-involved overdose mortalities in the U.S. The Center for Disease Control and Prevention reported that in 2018, about 70% of all drug overdose deaths involved opioids and 67% of all opioid-involved deaths were accounted for by synthetic opioids. In this study, we investigated the spread of synthetic opioids between 2013 and 2020 in the U.S. We analyzed the relationship between the spatiotemporal pattern of synthetic opioid-involved deaths and another key opioid, heroin, and compared patterns of deaths involving these two types of drugs during this period. Spatial connections and human mobility between counties were incorporated into a graph convolutional neural network model to represent and analyze the spread of synthetic opioid-involved deaths in the context of previous heroin-involved death patterns.

David Oh, the Republican candidate for mayor of Philadelphia, shared how years of failed city policies have eliminated police officers' power in Kensington. WARNING: This story contains graphic images. PHILADELPHIA -- David Oh is frustrated with widespread, open-air drug use and high crime in the Kensington neighborhood. That's why the mayoral nominee has formed a plan aiming to clean up the streets and to save and protect its residents, helpless to stop addicts from stumbling through the streets in a stupor. "If we get rid of Kensington Avenue as a place that exists in this region, the better off people will be," Oh, a Republican, said.

An automated process based on computer algorithms that can read text from medical examiners' death certificates can substantially speed up data collection of overdose deaths – which in turn can ensure a more rapid public health response time than the system currently used, new UCLA research finds. The analysis, to be published Aug. 8 in the peer-reviewed JAMA Network Open, used tools from artificial intelligence to rapidly identify substances that caused overdose deaths. The overdose crisis in America is the number one cause of death in young adults, but we don't know the actual number of overdose deaths until months after the fact. We also don't know the number of overdoses in our communities, as rapidly released data is only available at the state level, at best. We need systems that get this data out fast and at a local level so public health can respond.

Opioid overdose rates have increased in the United States over the past decade and reflect a major public health crisis. Modeling and prediction of drug and opioid hotspots, where a high percentage of events fall in a small percentage of space-time, could help better focus limited social and health services. In this work we present a spatial-temporal point process model for drug overdose clustering. The data input into the model comes from two heterogeneous sources: 1) high volume emergency medical calls for service (EMS) records containing location and time, but no information on the type of non-fatal overdose and 2) fatal overdose toxicology reports from the coroner containing location and high-dimensional information from the toxicology screen on the drugs present at the time of death. We first use non-negative matrix factorization to cluster toxicology reports into drug overdose categories and we then develop an EM algorithm for integrating the two heterogeneous data sets, where the mark corresponding to overdose category is inferred for the EMS data and the high volume EMS data is used to more accurately predict drug overdose death hotspots. We apply the algorithm to drug overdose data from Indianapolis, showing that the point process defined on the integrated data outperforms point processes that use only homogeneous EMS (AUC improvement .72 to .8) or coroner data (AUC improvement .81 to .85).We also investigate the extent to which overdoses are contagious, as a function of the type of overdose, while controlling for exogenous fluctuations in the background rate that might also contribute to clustering. We find that drug and opioid overdose deaths exhibit significant excitation, with branching ratio ranging from .72 to .98.

Far more people in the US may have died from opioids in the past two decades than previously reported, according to a new analysis of unclassified drug deaths carried out using machine-learning algorithms. Elaine Hill and her colleagues at the University of Rochester, New York, were examining data on drug overdose deaths when they realised that 22 per cent of such cases reported between 1999 and 2016 were listed on death certificates as overdoses without specifying the substance involved. "We found that remarkable, given the scale of the issue," says team member Andrew Boslett. The team tried to estimate what percentage of these unclassified deaths were due to opioids by analysing the coroners' and medical reports from opioid overdoses and unclassified overdoses. First, the researchers used machine-learning algorithms to analyse deaths that had been recorded as being due to opioid overdose.

Opioid overdose rates have reached an epidemic level and state-level policy innovations have followed suit in an effort to prevent overdose deaths. State-level drug law is a set of policies that may reinforce or undermine each other, and analysts have a limited set of tools for handling the policy collinearity using statistical methods. This paper uses a machine learning method called hierarchical clustering to empirically generate "policy bundles" by grouping states with similar sets of policies in force at a given time together for analysis in a 50-state, 10-year interrupted time series regression with drug overdose deaths as the dependent variable. Policy clusters were generated from 138 binomial variables observed by state and year from the Prescription Drug Abuse Policy System. Clustering reduced the policies to a set of 10 bundles. The approach allows for ranking of the relative effect of different bundles and is a tool to recommend those most likely to succeed. This study shows that a set of policies balancing Medication Assisted Treatment, Naloxone Access, Good Samaritan Laws, Medication Assisted Treatment, Prescription Drug Monitoring Programs and legalization of medical marijuana leads to a reduced number of overdose deaths, but not until its second year in force.

We find ourselves on the cusp of some very interesting dynamics in 2019 from a healthcare technology and innovation perspective. Advances in artificial intelligence (AI) including machine learning, natural language processing (NLP) and robotics have shown incredible promise in terms of automating repetitive manual tasks as well as improving decision making. Leaders in the healthcare industry--as well as technology vendors--are witnessing this first hand and are planning to integrate AI with next generation analytics platforms to empower executives, clinicians and analysts with unprecedented actionable insights from the board room to the point of care. Here are some key trends enabled by AI that are empowering stakeholders across the healthcare continuum, from policy makers and executives, to physicians and patients. AI and analytics integrated with electronic health records (EHRs) will enable superior care delivery and personalized care at a lower cost.

In the United States, an average of 135 people die per day as a result of opioid-induced overdose, according to the National Institute on Drug Abuse. A November 2017 report issued by the Council of Economic Advisers estimates that the total economic cost of the opioid crisis was $504 billion in 2015. This likely underestimates the total cost, given the difficulty of quantifying social impact on people suffering from opioid use disorder, as well as the financial hardships of family members and communities affected by addiction and fatal overdose. The opioid epidemic is a national health crisis that requires intervention by state, local, and national policymakers. To reduce the prevalence of opioid-induced mortality, stakeholders need access to more opportune data that will drive evidence-based policy recommendations and patient-centric treatment pathways.

As the opioid crisis continues to wreak havoc on the nation's health and productivity, an Indianapolis-based startup called hc1 is applying artificial intelligence to a vast array of datasets in an attempt to uncover insights aimed at decreasing opioid misuse, abuse, and addiction. Brad Bostic, CEO of hc1, describes his venture as a healthcare relationship management company, a term he coined in 2011, the same year he started the company. With the rapid growth of cloud storage technologies, Bostic founded hc1 to harness the abundance of siloed data at both the patient and provider levels and create holistic consumer profiles that could span providers, and thus improve care. So far, hc1 has amassed 90 million HIPAA-compliant consumer profiles and has more than 1,000 customers that subscribe to its customer-relationship and data-parsing services. The current state of American healthcare, Bostic says, is "impersonal and appalling."

We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan enables early detection of emerging patterns in spatio-temporal data, accounting for both the non-iid nature of the data and the fact that detecting subtle patterns requires integration of information across multiple spatial areas and multiple time steps. We apply this approach to 17 years of county-aggregated data for monthly opioid overdose deaths in the New York City metropolitan area, showing clear advantages in the utility of discovered patterns as compared to typical anomaly detection approaches. To detect and characterize emerging overdose patterns that differentially affect a subpopulation of the data, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved), we apply the Multidimensional Tensor Scan to 8 years of case-level overdose data from Allegheny County, PA. We discover previously unidentified overdose patterns which reveal unusual demographic clusters, show impacts of drug legislation, and demonstrate potential for early detection and targeted intervention. These approaches to early detection of overdose patterns can inform prevention and response efforts, as well as understanding the effects of policy changes.