Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study.

Background: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state).

Methods: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA.

Dosing profiles of concurrent opioid and benzodiazepine use associated with overdose risk among US Medicare beneficiaries: group-based multi-trajectory models.

Background And Aims: One-third of opioid (OPI) overdose deaths involve concurrent benzodiazepine (BZD) use. Little is known about concurrent opioid and benzodiazepine use (OPI-BZD) most associated with overdose risk. We aimed to examine associations between OPI-BZD dose and duration trajectories, and subsequent OPI or BZD overdose in US Medicare.

Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples.

Predicting Mortality Risk After a Hospital or Emergency Department Visit for Nonfatal Opioid Overdose.

Background: Survivors of opioid overdose have substantially increased mortality risk, although this risk is not evenly distributed across individuals. No study has focused on predicting an individual's risk of death after a nonfatal opioid overdose.

Objective: To predict risk of death after a nonfatal opioid overdose.

Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study.

Objective: To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions.

Methods: This prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling ≥1 opioid prescriptions from 2011-2016. We randomly divided beneficiaries into training, testing, and validation samples.

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.

Importance: Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk.

Objective: To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription.

Design, Setting, And Participants: A prognostic study was conducted between September 1, 2017, and December 31, 2018.

Exploring family and social context through the electronic health record: Physicians' experiences.

Introduction: There is ample evidence that social and familial context significantly impacts health. However, family and social history templates typically used in clinical practice exclude prompts to explore important contextual information, such as family dynamics, health beliefs, housing, and neighborhood environment.

Method: At the Residency Program in Social Medicine at Montefiore Medical Center/Albert Einstein College of Medicine in Bronx, NY, we developed and piloted an expanded family and social information (FSI) template in our electronic health record (EHR) system.

Myelographic evaluation of cervical spondylosis: patient tolerance and complications.

Study Design: Retrospective chart review of documented adverse events in 637 consecutive patients after computed tomogram myelography and follow-up interview of the most recent 100 of these patients.

Objectives: This study assessed documented prevalence of adverse events after diagnostic myelography in cervical spondylotic patients and compared with perceived adverse events and satisfaction in a subset of the same cohort of patients.

Summary Of Background Data: There are some data that suggest complimentary benefits of myelography to magnetic resonance imaging.