scholarly journals Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study

10.2196/15794 ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. e15794 ◽  
Author(s):  
David Chartash ◽  
Hyung Paek ◽  
James D Dziura ◽  
Bill K Ross ◽  
Daniel P Nogee ◽  
...  
Keyword(s):  
Health Care ◽  
Emergency Department ◽  
Health Care Systems ◽  
External Validation ◽  
Opioid Use Disorder ◽  
Health Record ◽  
Opioid Use ◽  
Internal Validation ◽  
Care Systems ◽  
Emergency Department Patients

Background Deploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department. Objective This study aimed to derive and validate an electronic health record–based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard. Methods A two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative). Results Cohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978). Conclusions This phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.

scholarly journals Validation of an Electronic Health Record–Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems

2020 ◽  
Vol 3 (3) ◽  
pp. e201262 ◽  
Author(s):  
Yuval Barak-Corren ◽  
Victor M. Castro ◽  
Matthew K. Nock ◽  
Kenneth D. Mandl ◽  
Emily M. Madsen ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Health Care Systems ◽  
Health Record ◽  
Prediction Modeling ◽  
Modeling Approach ◽  
Care Systems ◽  
Multiple Health

scholarly journals Trends in Emergency Department Visits and Hospital Admissions in Health Care Systems in 5 States in the First Months of the COVID-19 Pandemic in the US

2020 ◽  
Vol 180 (10) ◽  
pp. 1328 ◽  
Author(s):  
Molly M. Jeffery ◽  
Gail D’Onofrio ◽  
Hyung Paek ◽  
Timothy F. Platts-Mills ◽  
William E. Soares ◽  
...  
Keyword(s):  
Health Care ◽  
Emergency Department ◽  
Hospital Admissions ◽  
Health Care Systems ◽  
Emergency Department Visits ◽  
The Us ◽  
Care Systems

scholarly journals An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study

2016 ◽  
Vol 23 (6) ◽  
pp. 1060-1067 ◽  
Author(s):  
Victor W Zhong ◽  
Jihad S Obeid ◽  
Jean B Craig ◽  
Emily R Pfaff ◽  
Joan Thomas ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Diabetes Type ◽  
Health Record ◽  
Childhood Diabetes ◽  
Electronic Health Record Data ◽  
Efficient Approach ◽  
Care Systems ◽  
Electronic Health

Abstract Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data. Materials and Methods Presumptive diabetes cases <20 years of age from 2 large independent health care systems were identified as those having ≥1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified. Results We developed a stepwise surveillance approach using billing code–based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with “other” type were also correctly classified. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods. Conclusion EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed.

scholarly journals Potential uses of naltrexone in emergency department patients with opioid use disorder

2019 ◽  
Vol 57 (9) ◽  
pp. 753-759
Author(s):  
Evan Stuart Bradley ◽  
David Liss ◽  
Stephanie Pepper Carreiro ◽  
David Eric Brush ◽  
Kavita Babu
Keyword(s):  
Emergency Department ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Emergency Department Patients

Emergency Medicine Pharmacy Practice

2011 ◽  
Vol 24 (2) ◽  
pp. 135-145 ◽  
Author(s):  
Maria I. Rudis ◽  
Ryan J. Attwood
Keyword(s):  
Health Care ◽  
Emergency Department ◽  
Emergency Medicine ◽  
Clinical Pharmacist ◽  
Health Care Systems ◽  
Pharmacy Practice ◽  
Health Care Team ◽  
The Past ◽  
Care Systems

Emergency medicine (EM) pharmacy practice has existed for over 30 years. In recent years, however, the specialty has grown significantly. A large number of health care systems have either a dedicated EM pharmacist or other clinical pharmacist presence in the Emergency department (ED). Over the past decade, the role of the EM pharmacist as a critical member of the health care team has expanded significantly and many innovative practices have evolved throughout the country. There is also some heterogeneity between different EM pharmacy practice sites. This article reviews the history and general concepts of EM pharmacy practice as well as illustrate some of the established benefits of an EM pharmacist.

Effects of an antipsychotic restriction policy in a veteran population

2013 ◽  
Vol 3 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Nicole Cupples ◽  
Cynthia A. Mascarenas ◽  
Troy A. Moore
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Emergency Department ◽  
Index Date ◽  
Hemoglobin A1c ◽  
Health Care Systems ◽  
Psychotropic Medications ◽  
Emergency Department Visits ◽  
Care Systems ◽  
Secondary Outcomes

Introduction: Recent trials have failed to demonstrate differences in efficacy between first generation antipsychotics (FGAs) and second generation antipsychotics (SGAs). To reduce costs, many health care systems have restricted the availability of SGAs through use of prior authorizations. Restrictions for the off-label use of SGAs and the use of dual-antipsychotic therapy have also been implemented in many health care systems. At the South Texas Veterans Health Care System (STVHCS), a restricted drug request (RDR) method has been implemented to manage costs and improve patient safety. Risperidone, due to its lower cost and equal efficacy, is the first-line option of SGAs. If one wishes to prescribe an SGA other than risperidone, an RDR is submitted and reviewed by Veterans Integrated Service Network (VISN) pharmacists. Since the introduction of these policies at the STVHCS, the impact of the RDR has not been assessed. Rationale: The primary aim of this study was to determine the effects of the RDR policy on the care of STVHCS veterans as evidenced by changes in hospitalization rates of veterans with a denied request for an SGA due to initial criterion failure. Secondary outcomes included: impact of antipsychotic RDR denial on mental health as evidenced by changes in no-shows and cancellations for follow-up psychiatric appointments, psychiatric emergency department visits, presence of suicidal ideation, change in weight, hemoglobin A1c, number of psychotropic medications prescribed, and extrapyramidal symptoms. Methods: A retrospective chart review of veterans denied an initial SGA request was conducted from 3 months prior to denial to 3 months post request denial (index date). Data collected included: patient demographics, indication for SGA request, reason for SGA denial, length of time for request evaluation, number of psychiatric hospitalizations, number of no-shows and cancellations for mental health appointments, number of psychiatric emergency department visits, number of reports of suicidal ideation or attempts, weight, hemoglobin A1c lab results, presence of extrapyramidal symptoms, and number of prescribed psychotropic medications. The health care utilization data collected pre- and post-index date, were compared. Results were analyzed using Fisher's Exact, 2-tailed standardized t-tests, and descriptive statistics appropriately matched to data type. Results: Results for both primary and secondary outcomes were not statistically significant. No differences were found in the number of veterans hospitalized pre- versus post-index date [0/33 (0%) versus 2/33 (6%), p=0.492.] The most requested indication for an SGA was PTSD [22/33 (66.7%)] and the most frequently denied SGA was quetiapine [16/33 (48.5%)]. Conclusions: Although outcomes were not statistically significant, several valuable conclusions were drawn from this research. Positive outcomes from a RDR policy were seen by the limitations placed on inappropriate medication prescribing. Also, it was observed that the number of approvals for SGAs was almost three times higher than denials. A subsequent finding from this research is the apparent lack of metabolic monitoring for veterans prescribed SGAs. Further research on these observations, as well as conducting a pharmacoeconomic analysis on the RDR policy, would also be beneficial information for health care providers.

scholarly journals Seeking Care for Hyperglycemia in the Emergency Department: Through the Eyes of the Patient

2020 ◽  
Author(s):  
Justin W. Yan ◽  
Dimah Azzam ◽  
Melanie P. Columbus ◽  
Kristine Van Aarsen ◽  
Selina L. Liu ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Emergency Department ◽  
Hospital Admission ◽  
Health Care Systems ◽  
Primary Care Providers ◽  
Urgent Care ◽  
Care Providers ◽  
Health Care Settings ◽  
Care Systems

Health care systems often provide a range of options of care for patients with illnesses who do not require hospital admission. For individuals with diabetes, these options may include primary care providers, specialized diabetes clinics, and urgent care and walk-in clinics. We explored the reasons why patients choose the Emergency Department over other health care settings when seeking care for hyperglycemia.

Naloxone provision to emergency department patients recognized as high-risk for opioid use disorder

2020 ◽  
Author(s):  
Bennett H. Lane ◽  
Michael S. Lyons ◽  
Uwe Stolz ◽  
Rachel M. Ancona ◽  
Richard J. Ryan ◽  
...  
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Emergency Department Patients

scholarly journals Differences, Opportunities, and Strategies in Drug Alert Optimization—Experiences of Two Different Integrated Health Care Systems

2019 ◽  
Vol 10 (05) ◽  
pp. 777-782 ◽  
Author(s):  
Salim M. Saiyed ◽  
Katherine R. Davis ◽  
David C. Kaelber
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Systematic Approach ◽  
Drug Information ◽  
Health Care Systems ◽  
Clinical Decision ◽  
Health Record ◽  
Alarm Fatigue ◽  
Care Systems ◽  
Guidelines And Recommendations

Abstract Background Concerns about the number of automated medication alerts issued within the electronic health record (EHR), and the subsequent potential for alarm fatigue, led us to examine strategies and methods to optimize the configuration of our drug alerts. Objectives This article reports on comprehensive drug alerting rates and develops strategies across two different health care systems to reduce the number of drug alerts. Methods Standardized reports compared drug alert rates between the two systems, among 13 categories of drug alerts. Both health care systems made modifications to the out-of-box alerts available from their EHR and drug information vendors, focusing on system-wide approaches, when relevant, while performing more drug-specific changes when necessary. Results Drug alerting rates even after initial optimization were 38 alerts and 51 alerts per 100 drug orders, respectively. Eight principles were identified and developed to reflect the themes in the implementation and optimization of drug alerting. Conclusion A team-based, systematic approach to optimizing drug-alerting strategies can reduce the number of drug alerts, but alert rates still remain high. In addition to strategic principles, additional tactical guidelines and recommendations need to be developed to enhance out-of-the-box clinical decision support for drug alerts.

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