The 21st Century Cures Act and Emergency Medicine - Part 1: Digitally Sharing Notes and Results.

Among the provisions of the 21st Century Cures Act is the mandate for digital sharing of clinician notes and test results through the patient portal of the clinician's electronic health record system. Although there is considerable evidence of the benefit to clinic patients from open notes and minimal apparent additional burden to primary care clinicians, emergency department (ED) note sharing has not been studied. With easier access to notes and results, ED patients may have an enhanced understanding of their visit, findings, and clinician's medical decisionmaking, which may improve adherence to recommendations. Patients may also seek clarifications and request edits to their notes. EDs can develop workflows to address patient concerns without placing new undue burden on clinicians, helping to realize the benefits of sharing notes and test results digitally.

Beyond Getting Rid of Stupid Stuff in the Electronic Health Record (Beyond-GROSS): Protocol for a User-Centered, Mixed-Method Intervention to Improve the Electronic Health Record System.

BACKGROUND: Up to 60% of health care providers experience one or more symptoms of burnout. Perceived clinician burden resulting in burnout arises from factors such as electronic health record (EHR) usability or lack thereof, perceived loss of autonomy, and documentation burden leading to less clinical time with patients. Burnout can have detrimental effects on health care quality and contributes to increased medical errors, decreased patient satisfaction, substance use, workforce attrition, and suicide. OBJECTIVE: This project aims to improve the user-centered design of the EHR by obtaining direct input from clinicians about deficiencies. Fixing identified deficiencies via user-centered design has the potential to improve usability, thereby increasing satisfaction by reducing EHR-induced burnout. METHODS: Quantitative and qualitative data will be obtained from clinician EHR users. The input will be received through a form built in a REDCap database via a link embedded in the home page of the EHR. The REDCap data will be analyzed in 2 main dimensions, based on nature of the input, what section of the EHR is affected, and what is required to fix the issue(s). Identified issues will be escalated to relevant stakeholders responsible for rectifying the problems identified. Data analysis, project evaluation, and lessons learned from the evaluation will be incorporated in a Plan-Do-Study-Act (PDSA) manner every 4-6 weeks. RESULTS: The pilot phase of the study began in October 2020 in the Gastroenterology Division at Mount Sinai Hospital, New York City, NY, which includes 39 physicians and 15 nurses. The pilot is expected to run over a 4-6-month period. The results of the REDCap data analysis will be reported within 1 month of completing the pilot phase. We will analyze the nature of requests received and the impact of rectified issues on the clinician EHR user. We expect that the results will reveal which sections of the EHR have the highest deficiencies while also highlighting issues about workflow difficulties. Perceived impact of the project on provider engagement, patient safety, and workflow efficiency will also be captured by evaluation survey and other qualitative methods where possible. CONCLUSIONS: The project aims to improve user-centered design of the EHR by soliciting direct input from clinician EHR users. The ultimate goal is to improve efficiency, reduce EHR inefficiencies with the possibility of improving staff engagement, and lessen EHR-induced clinician burnout. Our project implementation includes using informatics expertise to achieve the desired state of a learning health system as recommended by the National Academy of Medicine as we facilitate feedback loops and rapid cycles of improvement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25148.

Mobile application to optimize care for ST-segment elevation myocardial infarction patients in a large healthcare system, STEMIcathAID: rationale and design.

Aims: Technological advancements have transformed healthcare. System delays in transferring patients with ST-segment elevation myocardial infarction (STEMI) to a primary percutaneous coronary intervention (PCI) centre are associated with worse clinical outcomes. Our aim was to design and develop a secure mobile application, STEMIcathAID, streamlining communication, and coordination between the STEMI care teams to reduce ischaemia time and improve patient outcomes. Methods and results: The app was designed for transfer of patients with STEMI to a cardiac catheterization laboratory (CCL) from an emergency department (ED) of either a PCI capable or a non-PCI capable hospital. When a suspected STEMI arrives to a non-PCI hospital ED, the ED physician uploads the electrocardiogram and relevant patient information. An instant notification is simultaneously sent to the on-call CCL attending and transfer centre. The attending reviews the information, makes a video call and decides to either accept or reject the transfer. If accepted, on-call CCL team members receive an immediate push notification and begin communicating with the ED team via a HIPAA compliant chat. The app provides live GPS tracking of the ambulance and frequent clinical status updates of the patient. In addition, it allows for screening of STEMI patients in cardiogenic shock. Prior to discharge, important data elements have to be entered to close the case. Conclusion: We developed a novel mobile app to optimize care for STEMI patients and facilitate electronic extraction of relevant performance metrics to improve allocation of resources and reduction of costs.

Correction to: Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits.

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Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits.

BACKGROUND: Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene. RESULTS: We identified LoFs in 433 genes significantly associated with at least one of 10 major CVD traits. Next, we used RNA-sequence data from the STARNET study to validate 115 of the 433 LoF harboring-genes in that their expression levels were concordantly associated with corresponding CVD traits. Together with the documented hepatic lipid-lowering gene, APOC3, the expression levels of six additional liver LoF-genes were positively associated with levels of plasma lipids in STARNET. Candidate LoF-genes were subjected to gene silencing in HepG2 cells with marked overall effects on cellular LDLR, levels of triglycerides and on secreted APOB100 and PCSK9. In addition, we identified novel LoFs in DGAT2 associated with lower plasma cholesterol and glucose levels in BioMe that were also confirmed in STARNET, and showed a selective DGAT2-inhibitor in C57BL/6 mice not only significantly lowered fasting glucose levels but also affected body weight. CONCLUSION: In sum, by integrating genetic and electronic medical record data, and leveraging one of the world's largest human RNA-sequence datasets (STARNET), we identified known and novel CVD-trait related genes that may serve as targets for CVD therapeutics and as such merit further investigation.

Effect of Clinical Decision Support on Appropriateness of Advanced Imaging Use Among Physicians-in-Training.

OBJECTIVE: Clinical decision support (CDS) tools have been shown to reduce inappropriate imaging orders. We hypothesized that CDS may be especially effective for house staff physicians who are prone to overuse of resources. MATERIALS AND METHODS: Our hospital implemented CDS for CT and MRI orders in the emergency department with scores based on the American College of Radiology's Appropriateness Criteria (range, 1-9; higher scores represent more-appropriate orders). Data on CT and MRI orders from April 2013 through June 2016 were categorized as pre-CDS or baseline, post-CDS period 1 (i.e., intervention with active feedback for scores of ≤ 4), and post-CDS period 2 (i.e., intervention with active feedback for scores of ≤ 6). Segmented regression analysis with interrupted time series data estimated changes in scores stratified by house staff and non-house staff. Generalized linear models further estimated the modifying effect of the house staff variable. RESULTS: Mean scores were 6.2, 6.2, and 6.7 in the pre-CDS, post-CDS 1, and post-CDS 2 periods, respectively (p < 0.05). In the segmented regression analysis, mean scores significantly (p < 0.05) increased when comparing pre-CDS versus post-CDS 2 periods for both house staff (baseline increase, 0.41; 95% CI, 0.17-0.64) and non-house staff (baseline increase, 0.58; 95% CI, 0.34-0.81), showing no differences in effect between the cohorts. The generalized linear model showed significantly higher scores, particularly in the post-CDS 2 period compared with the pre-CDS period (0.44 increase in scores; p < 0.05). The house staff variable did not significantly change estimates in the post-CDS 2 period. CONCLUSION: Implementation of active CDS increased overall scores of CT and MRI orders. However, there was no significant difference in effect on scores between house staff and non-house staff.

PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT.

Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6-0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such datadriven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.

Coronary heart disease in the elderly: identifying established and subclinical disease with stress testing.

Although cardiovascular disease is a major cause of mortality, morbidity, and healthcare expense in the United States, diagnosis in elderly patients, especially those who are asymptomatic, can be challenging. Noninvasive testing offers an opportunity to identify these patients, but guidelines reflect uncertainty of the impact of diagnosis on long-term outcomes. We review the role of different forms of noninvasive testing in both symptomatic and asymptomatic patients, as well as patients under consideration for elective surgery.

Adjuvant therapy with methylene blue in the treatment of right ventricular failure after pulmonary embolectomy.

Severe pulmonary embolism often leads to right ventricular failure after surgical embolectomy secondary to ischaemia reperfusion injury and acute lung injury (ALI). Acute right ventricular dysfunction is traditionally treated with inotropes and vasopressors to maintain cardiac output and coronary perfusion as well as selective pulmonary vasodilators to provide right ventricular afterload reduction. We report the first case of utilisation of methylene (MB) in a patient with acute right ventricular failure and vasoplegic shock after surgical pulmonary embolectomy.

Lipid management in the geriatric patient.

Elderly individuals are at higher risk for cardiovascular events, and thus this population stands to gain a greater reduction in events from lipid therapy than younger individuals. Multiple primary and secondary prevention trials have demonstrated that the benefits of statins in geriatric patients are equivalent to, or greater than, those seen in younger patients. Combination therapy with non-statin agents should be considered in patients who do not meet cholesterol goals or who have concomitant hypertriglyceridemia or low levels of high-density lipoprotein cholesterol. Although increased side effects may occur with high-dose statin therapy, careful vigilance of drug interactions and limiting polypharmacy can reduce these effects.

Complex genomic rearrangement in CCS-LacZ transgenic mice.

The cardiac conduction system (CCS)-lacZ insertional mouse mutant strain genetically labels the developing and mature CCS. This pattern of expression is presumed to reflect the site of transgene integration rather than regulatory elements within the transgene proper. We sought to characterize the genomic structure of the integration locus and identify nearby gene(s) that might potentially confer the observed CCS-specific transcription. We found rearrangement of chromosome 7 between regions D1 and E1 with altered transcription of multiple genes in the D1 region. Several lines of evidence suggested that regulatory elements from at least one gene, Slco3A1, influenced CCS-restricted reporter gene expression. In embryonic hearts, Slco3A1 was expressed in a spatial pattern similar to the CCS-lacZ transgene and was similarly neuregulin-responsive. At later stages, however, expression patterns of the transgene and Slco3A1 diverged, suggesting that the Slco3A1 locus may be necessary, but not sufficient to confer CCS-specific transgene expression in the CCS-lacZ line.