Emergency department risk model: timely identification of patients for outpatient care coordination.

OBJECTIVE: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patients for CoCM to prevent frequent ED utilization remains unclear. This study aimed to develop and validate a risk identification model to proactively detect patients with MDD in CoCM at high risk of frequent (≥ 3) ED visits. STUDY DESIGN: This retrospective cohort study utilized electronic health records from Mayo Clinic's primary care system to develop and validate a machine learning-based risk identification model. The model predicts the likelihood of frequent ED visits among patients with MDD within a 12-month period. METHODS: Data were collected from Mayo Clinic's primary care system between May 1, 2006, and December 19, 2018. Risk identification models were developed and validated using machine learning classifiers to estimate frequent ED visit risks over 12 months. The Shapley Additive Explanations model identified variables driving frequent ED visits. RESULTS: The patient population had a mean (SD) age of 39.78 (16.66) years, with 30.3% being male and 6.1% experiencing frequent ED visits. The best-performing algorithm (elastic-net logistic regression) achieved an area under the curve of 0.79 (95% CI, 0.74-0.84), a sensitivity of 0.71 (95% CI, 0.57-0.82), and a specificity of 0.76 (95% CI, 0.64-0.85) in the development data set. In the validation data set, the best-performing algorithm (random forest) achieved an area under the curve of 0.79, a sensitivity of 0.83, and a specificity of 0.61. Significant variables included male gender, prior frequent ED visits, high Patient Health Questionnaire-9 score, low education level, unemployment, and use of multiple medications. CONCLUSIONS: The risk identification model has potential for clinical application in triaging primary care patients with MDD in CoCM, aiming to reduce future ED utilization.

Portal Message Language Use Prior to Suicide, Suicide Attempts, and Hospitalization for Depression.

Introduction: Previous research suggests patients may be willing to communicate serious psychiatric concerns through patient portals. Methods: Retrospective chart review of portal messages sent by patients who had an emergency department (ED) visit or hospitalization for depression, self-harm, or suicidality or had a completed suicide (cases) was reviewed for content that was suggestive of depression or self-harm and language indicating emotional distress. Comparison with a randomly selected group (controls) was performed. Results: During the study period 420 messages were sent by 149 patients within 30 days of death by suicide, ED visit, and/or hospitalization related to depression, suicidality, or suicide attempt. Thirteen patients died by suicide but only 23% (3 of 13) sent one or more portal messages within 30 days before their death. None mentioned thoughts of self-harm. There were 271 messages sent by patients who were hospitalized, 142 messages by those who presented to the ED, and 56 messages patients who attempted suicide. Patient messages from cases were more likely than messages from controls to convey a depressed mood (17.1% vs. 3.1%, odds ratio 6.5; 95% confidence interval 3.6-11.9, p < 0.0001), thoughts of suicide or self-harm (4.8% vs. 0% p < 0.0001), or have a distressed tone (24.0% vs. 1.7%, odds ratio 18.7; 95% confidence interval 8.6-41, p < 0.0001). Conclusions: Patient portal messages from patients with subsequent hospitalizations for depression and suicidality do report thoughts of depression, distress, and thoughts of self-harm. However, portal use before completed suicide was not helpful at identifying at-risk patients although total numbers were small.

Adaptations of an Integrated Behavioral Health Program During COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has consistently been described as an "unprecedented" global health crisis. While the focus has been primarily on the medical and economic impact of the pandemic, psychological sequelae are anticipated. Primary care is the main point of access for mental health care in the United States, making it the ideal locale to provide psychological services for a larger proportion of the population than traditional mental health care settings. The aim of this paper is to describe how our multi-state, multi-site integrated primary care program adapted and applied cognitive behavioral therapy in the context of COVID-19. Access to mental health care was disrupted despite burgeoning mental health concerns, necessitating novel approaches to providing care. A stepped-care approach was implemented within our primary care practice, which consisted of a combination of low-intensity, high-yield stress management and resiliency building resources and cognitive behavioral therapy that were delivered flexibly based on patient preference, technological capabilities, state ordinances, insurance coverage, and institutional policies. The lessons learned from this experience can inform other integrated primary care clinics in responding to the current and future pandemics.

A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression.

BACKGROUND: Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research. OBJECTIVE: This study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries. METHODS: The systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions. RESULTS: Using a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use. CONCLUSIONS: The systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18366.

Depression and Anxiety Among Patients on Chronic Opioid Therapy.

BACKGROUND: Chronic noncancer pain (CNCP) and chronic opioid therapy (COT) commonly coexist with comorbid depression and anxiety. We investigated the prevalence of depression and anxiety and their correlates at the time of controlled substance agreement (CSA) enrollment among patients with CNCP and a history of depression or anxiety on COT. METHODS: Retrospective analysis of 1066 patients in a Midwest primary care practice enrolled in CSAs for COT between May 9, 2013, and August 15, 2016. Patients with self-reported symptoms or a clinical history of depression or anxiety were screened at CSA enrollment using the Patient Health Questionnaire-9 item scale and the Generalized Anxiety Disorder-7 item scale. RESULTS: The percentage of patients screening positive for depression and anxiety at CSA enrollment was 15.4% and 14.4%, respectively. Patients screening positive for depression or anxiety were more likely to be younger, unmarried, unemployed, and live alone compared to patients not screening positive. Patients screening positive for depression or anxiety were more likely to smoke cigarettes and report concern from friends or relatives regarding alcohol consumption. Compared to patients screening negative, patients screening positive for depression had higher odds of receiving opioid doses of ≥50 morphine milligram equivalents per day (adjusted odds ratio: 1.62; 95% confidence interval: 1.01-2.58). CONCLUSION: Anxiety and depression are prevalent at enrollment in CSAs among patients receiving COT. Future research is needed to determine whether recognition of anxiety and depression leads to improved management and outcomes for this population.

The Role of Integrated Care in Managing Chronic Pain.

Chronic pain affects up to 20% of the population and costs as much as $635 billion per year in the United States alone. The management of chronic pain is fragmented among medical providers of varying specialties, and evidence-based treatments are often not readily available. Psychiatric comorbidity, which compounds chronic pain treatment, is common. Further complicating the problem are the challenges created by opioid medications, the use of which has increased dramatically in recent decades. Integrated-care psychiatrists are uniquely situated to help navigate this complex landscape and help primary care providers and patients access effective treatments. This article summarizes a number of evidence-based treatments for chronic pain and suggests ways in which an integrated-care psychiatrist may incorporate them into practice.