Using Electronic Health Records to Characterize Prescription Patterns: Focus on Antidepressants in Nonpsychiatric Outpatient Settings

Objective To characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data. Methods A retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured “medication–diagnosis” pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments. Results Of 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as “on-label/evidence-based uses.” Depression diagnoses were associated with 67 233 (48%) prescriptions in this study, while pain diagnoses were slightly less common (35%). Manual chart review of “off-label use” prescriptions revealed that on-label/evidence-based diagnoses of depression (39%), anxiety (25%), insomnia (13%), mood disorders (7%), and neuropathic pain (5%) were frequently cited as prescription indication despite lacking ICD-9/10 documentation. Conclusions The results indicate that antidepressants may be prescribed for off-label uses, by PCPs and nonpsychiatry specialists, less frequently than believed. This study also points to the fact that there are a number of off-label uses that are efficacious and widely accepted by expert clinical opinion but have not been included in drug compendia. Despite the fact that diagnosis codes in the outpatient setting are notoriously inaccurate, our approach demonstrates that the correct codes are often documented in a patient’s recent diagnosis history. Examining both structured and unstructured data will help to further validate findings. Routinely collected clinical data in EHRs can serve as an important resource for future studies in investigating prescribing behaviors in outpatient clinics

Traffic and Related Self-Driven Many-Particle Systems

Since the subject of traffic dynamics has captured the interest of physicists, many astonishing effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by so-called ``phantom traffic jams'', although they all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction of the traffic volume cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize in lanes, while similar systems are ``freezing by heating''? Why do self-organizing systems tend to reach an optimal state? Why do panicking pedestrians produce dangerous deadlocks? All these questions have been answered by applying and extending methods from statistical physics and non-linear dynamics to self-driven many-particle systems. This review article on traffic introduces (i) empirically data, facts, and observations, (ii) the main approaches to pedestrian, highway, and city traffic, (iii) microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts like a general modelling framework for self-driven many-particle systems, including spin systems. Subjects such as the optimization of traffic flows and relations to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are discussed as well.Comment: A shortened version of this article will appear in Reviews of Modern Physics, an extended one as a book. The 63 figures were omitted because of storage capacity. For related work see http://www.helbing.org

A review of cognitive therapy in acute medical settings. Part I: therapy model and assessment

Introduction: Although cognitive therapy (CT) has established outpatient utility, there is no integrative framework for using CT in acute medical settings where most psychosomatic medicine (P-M) clinicians practice. Biopsychosocial complexity challenges P-M clinicians who want to use CT as the a priori psychotherapeutic modality. For example, how should clinicians modify the data gathering and formulation process to support CT in acute settings? Method: Narrative review methodology is used to describe the framework for a CT informed interview, formulation, and assessment in acute medical settings. Because this review is aimed largely at P-M trainees and educators, exemplary dialogues model the approach (specific CT strategies for common P-M scenarios appear in the companion article.) Results: Structured data gathering needs to be tailored by focusing on cognitive processes informed by the cognitive hypothesis. Agenda setting, Socratic questioning, and adaptations to the mental state examination are necessary. Specific attention is paid to the CT formulation, Folkman's Cognitive Coping Model, self-report measures, data-driven evaluations, and collaboration (e.g., sharing the formulation with the patient.) Integrative CT-psychopharmacological approaches and the importance of empathy are emphasized. Significance of results: The value of implementing psychotherapy in parallel with data gathering because of time urgency is advocated, but this is a significant departure from usual outpatient approaches in which psychotherapy follows evaluation. This conceptual approach offers a novel integrative framework for using CT in acute medical settings, but future challenges include demonstrating clinical outcomes and training P-M clinicians so as to demonstrate fidelity

Using Electronic Health Records to Characterize Prescription Patterns: Focus on Antidepressants in Nonpsychiatric Outpatient Settings

Objective To characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data. Methods A retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured “medication–diagnosis” pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments. Results Of 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as “on-label/evidence-based uses.” Depression diagnoses were associated with 67 233 (48%) prescriptions in this study, while pain diagnoses were slightly less common (35%). Manual chart review of “off-label use” prescriptions revealed that on-label/evidence-based diagnoses of depression (39%), anxiety (25%), insomnia (13%), mood disorders (7%), and neuropathic pain (5%) were frequently cited as prescription indication despite lacking ICD-9/10 documentation. Conclusions The results indicate that antidepressants may be prescribed for off-label uses, by PCPs and nonpsychiatry specialists, less frequently than believed. This study also points to the fact that there are a number of off-label uses that are efficacious and widely accepted by expert clinical opinion but have not been included in drug compendia. Despite the fact that diagnosis codes in the outpatient setting are notoriously inaccurate, our approach demonstrates that the correct codes are often documented in a patient’s recent diagnosis history. Examining both structured and unstructured data will help to further validate findings. Routinely collected clinical data in EHRs can serve as an important resource for future studies in investigating prescribing behaviors in outpatient clinics

A review of cognitive therapy in acute medical settings. Part II: Strategies and complexities

Objective: Cognitive therapy (CT) has considerable utility for psychosomatic medicine (PM) in acute medical settings but, to date, no such cohesive adaptation has been developed. Part I delineated a CT model for acute medical settings focusing on assessment and formulation. In Part II, we review how CT can be applied to common PM clinical challenges. A pragmatic approach is helpful because this review targets PM trainees and educators. Methods: Narrative review is used to discuss the application of CT strategies to common challenges in acute medical settings. Treatment complexities and limitations associated with the PM setting are detailed. Exemplary dialogues are used to model techniques. Result: We present CT approaches to eight common scenarios: (1) distressed or hopeless patients; (2) patients expressing pivotal distorted cognitions/images; (3) patients who catastrophize; (4) patients who benefit from distraction and activation strategies; (5) panic and anxiety; (6) suicidal patients; (7) patients who are stuck and helpless; (8) inhibited patients. Limitations are discussed. Significance of results: A CT informed PM assessment, formulation and early intervention with specific techniques offers a novel integrative framework for psychotherapy with the acutely medically ill. Future efforts should focus on dissemination, education of fellows and building research efficacy data