The objective of this article was to survey available intimate partner violence (IPV) treatment studies with (a) randomized case assignment, and (b) at least 20 participants per group. Studies were classified into 4 categories according to primary treatment focus: perpetrator, victim, couples, or child-witness interventions. The results suggest that extant interventions have limited effect on repeat violence, with most treatments reporting minimal benefit above arrest alone. There is a lack of research evidence for the effectiveness of the most common treatments provided for victims and perpetrators of IPV, including the Duluth model for perpetrators and shelter-advocacy approaches for victims. Rates of recidivism in most perpetrator-and partner-focused treatments are approximately 30% within 6 months, regardless of intervention strategy used. Couples treatment approaches that simultaneously address problems with substance abuse and aggression yield the lowest recidivism rates, and manualized child trauma treatments are effective in reducing child symptoms secondary to IPV. This review shows the benefit of integrating empirically validated substance abuse and trauma treatments into IPV interventions and highlights the need for more work in this area.

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ObjectiveTo compare the effectiveness of multiple artificial intelligence (AI) models with unweighted Opioid Risk Tool (ORT) in opioid use disorder (OUD) prediction.Materials and MethodsThis is a retrospective cohort study of deidentified claims data from 2009 to 2020. The study cohort includes 474,208 patients. Cases are prescription opioid users with at least one diagnosis of OUD or at least one prescription for buprenorphine or methadone. Controls are prescription opioid users with no OUD diagnoses or buprenorphine or methadone prescriptions. Cases and controls are matched based on age, sex, opioid use duration and longitudinal data availability. OUD prediction performance of logistic regression (LR), random forest (RF), XGBoost, long short-term memory (LSTM), transformer, our proposed AI model for OUD prediction (MUPOD), and the unweighted ORT were assessed using accuracy, precision, recall, F1-score and AUC.ResultsData includes 474,208 patients; 269,748 were females with an average age of 56.78 years. On 100 randomly selected test sets including 47,396 patients, MUPOD can predict OUD more efficiently (AUC=0.742±0.021) compared to LR (AUC=0.651±0.025), RF (AUC=0.679±0.026), XGBoost (AUC=0.690±0.027), LSTM (AUC=0.706±0.026), transformer (AUC=0.725±0.024) as well as the unweighted ORT model (AUC=0.559±0.025).DiscussionOUD is a leading cause of death in the United States. AI can be harnessed with available claims data to produce automated OUD prediction tools. We compared the effectiveness of AI models for OUD prediction and showed that AI can predict OUD more effectively than the unweighted ORT tool.ConclusionEmbedding AI algorithms into clinical care may assist clinicians in risk stratification and management of patients receiving opioid therapy.

Background and Objectives-Forty-nine out of 50 states have implemented Prescription Drug Monitoring Programs (PDMPs) to monitor controlled substance (CS) prescribing. PDMPs change health care provider behavior, but few studies have examined changes in CS prescription by health care provider type.Methods-Aggregated yearly data, including number of CS prescriptions, doses, and doses per prescription by health care provider type (physician, APRN, and dentist) for each year from 2011-2017 was provided by the state PDMP, Kentucky All Schedule Prescription Electronic Reporting System (KASPER).Results-Physicians and dentists showed a trend of decreasing prescriptions and doses for Schedule II opioids from 2012 to 2017. APRNs showed a substantive increase in the number of doses and prescriptions, with increases remaining when controlling for number of providers. Physicians increased doses and prescriptions of Schedule II stimulants, but by a smaller magnitude than APRN increases in stimulants. Dentists showed decreases in Schedule II stimulants prescribed. Similar trends, but more modest in magnitude, were observed for Schedule III-IV.Discussion and Conclusions-Although monitoring and continuing education requirements are similar across all providers in Kentucky, differences in prescription trends for Schedule II opioids and stimulants were noted for physicians, APRNs and dentists.

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Problem Suicides are now the second leading cause of death among teenagers and young adults, 10–24. Many people who die by suicide visit a healthcare provider in the months before their death. Unfortunately, many healthcare clinicians do not routinely screen for mental health concerns such as suicide risk even though the American Academy of Pediatrics recommends screening adolescents for suicide risk. Methods The Ask Suicide‐Screening Questions (aSQ), a four‐question screening instrument, was administered by nurses to all patients, 12 years and older, admitted to the general pediatric wards of a tertiary Children's Hospital. Nursing feedback and comfort levels were assessed before and after the 6‐week pilot program. Findings During the 6 weeks, 152 eligible children were admitted to the general pediatric wards and 67 were screened using the ASQ; 3/67 had a nonacute “positive” screen and received a further psychiatric assessment. Conclusions This pilot quality improvement initiative showed that suicide screening is feasible and acceptable to patients and families in a general pediatric inpatient setting. However, nurses would benefit from further teaching and training around asking suicide screening questions.