An electronic order set for acute myocardial infarction is associated with improved patient outcomes through better adherence to clinical practice guidelines

Ballesca, Manuel; LaGuardia, Juan Carlos; Lee, Philip C.; Hwang, Andrew M.; Park, David K.; Gardner, Marla N.; Turk, Benjamin J.; Kipnis, Patricia; Escobar, Gabriel J.

doi:10.1002/jhm.2149

Cited by 24 publications

(22 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On an absolute scale, it is interesting that manually produced content like order sets continues to demonstrate improvements in care 21 , 23 , 24 , 49 despite what we have found to be a low “accuracy” of recommendations. Table 2 indicates that initial inpatient care on average involves a few order sets (3.0), with a preference for general order sets with a large number of suggested orders (> 100), resulting in higher recall (43%) but low precision (11%).…”

Section: Discussionmentioning

confidence: 78%

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

Chen

Goldstein

Asch

et al. 2016

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

Objective: Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. Materials and Methods: The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Results: Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% (P < 10−20) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., “critical care,” “pneumonia,” “neurologic evaluation”). Discussion: Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Conclusion: Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support.

show abstract

Section: Discussionmentioning

confidence: 78%

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

Chen

Goldstein

Asch

et al. 2016

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

show abstract

“…For example, an electronic order set for acute myocardial infarction management was associated with better guidelines compliance and improved clinical outcomes when compared with patients treated with individual ("a la carte") orders. 26 The efficacy of opt-out versus opt-in modes of preference indication have been explored in other areas of healthcare utilization research. For example, rates of organ transplantation are higher in countries with opt-out preference indication for organ donation (presumed consent) than in countries with opt-in preference indication (explicit consent).…”

Section: Discussionmentioning

confidence: 99%

Impact of Increased Early Statin Administration on Ischemic Stroke Outcomes: A Multicenter Electronic Medical Record Intervention

Flint

Conell

Klingman

et al. 2016

JAHA

View full text Add to dashboard Cite

BackgroundStatin administration early in ischemic stroke may influence outcomes. Our aim was to determine the clinical impact of increasing statin administration early in ischemic stroke hospitalization.Methods and ResultsThis is a retrospective analysis of a multicenter electronic medical record (EMR) intervention to increase early statin administration in ischemic stroke across all 20 hospitals of an integrated healthcare delivery system. A stroke EMR order set was modified from an “opt‐in” to “opt‐out” mode of statin ordering. Outcomes were mortality by 90 days, discharge disposition, and increase in stroke severity. We examined the relationship between intervention and outcome using autoregressive integrated moving average (ARIMA) time‐series modeling. The EMR intervention increased both overall in‐hospital statin administration (from 87.2% to 90.7%, P<0.001) and early statin administration (from 16.9% to 26.3%, P<0.001). ARIMA models showed a small increase in the rate of survival (difference in probability [P diff]=0.02, P=0.016) and discharge to home or rehabilitation facility (P diff=0.04, P=0.034) associated with the intervention. The increase in statin administration <8 hours was associated with much larger increases in survival (P diff=0.17, P=0.033) and rate of discharge to home or rehabilitation (P diff=0.29, P=0.011), as well as a decreased rate of neurological deterioration in‐hospital (P diff=−0.14, P=0.026).ConclusionsA simple EMR change increased early statin administration in ischemic stroke and was associated with improved clinical outcomes. This is, to our knowledge, the first EMR intervention study to show that a modification of an electronic order set resulted in improved clinical outcomes.

show abstract

“…Other work suggests that CDSSs are more effective if adapted based on human factor analysis [31]. While there has been significant backlash regarding the benefits of the EHR, we know that certain aspects of it are effective -these include well-designed assistive order sets and condition-specific decision support tools [18,32]. We also know that clinicians familiar with using the EHR are receptive to CDSSs through multiple means [33].…”

Section: Discussionmentioning

confidence: 99%

Optimizing Clinical Decision Support in the Electronic Health Record

Vemula¹,

Chettipally²,

Kene³

et al. 2016

Appl Clin Inform

View full text Add to dashboard Cite

SummaryObjective: Adoption of clinical decision support (CDS) tools by clinicians is often limited by workflow barriers. We sought to assess characteristics associated with clinician use of an electronic health record-embedded clinical decision support system (CDSS). Methods: In a prospective study on emergency department (ED) activation of a CDSS tool across 14 hospitals between 9/1/14 to 4/30/15, the CDSS was deployed at 10 active sites with an on-site champion, education sessions, iterative feedback, and up to 3 gift cards/clinician as an incentive. The tool was also deployed at 4 passive sites that received only an introductory educational session. Activation of the CDSS -which calculated the Pulmonary Embolism Severity Index (PESI) score and provided guidance -and associated clinical data were collected prospectively. We used multivariable logistic regression with random effects at provider/facility levels to assess the association between activation of the CDSS tool and characteristics at: 1) patient level (PESI score), 2) provider level (demographics and clinical load at time of activation opportunity), and 3) facility level (active vs. passive site, facility ED volume, and ED acuity at time of activation opportunity). Results: Out of 662 eligible patient encounters, the CDSS was activated in 55%: active sites: 68% (346/512); passive sites 13% (20/150). In bivariate analysis, active sites had an increase in activation rates based on the number of prior gift cards the physician had received (96% if 3 prior cards versus 60% if 0, p<0.0001). At passive sites, physicians < age 40 had higher rates of activation (p=0.03). In multivariable analysis, active site status, low ED volume at the time of diagnosis and PESI scores I or II (compared to III or higher) were associated with higher likelihood of CDSS activation. Conclusions: Performing on-site tool promotion significantly increased odds of CDSS activation. Optimizing CDSS adoption requires active education. Research Article Background and SignificanceThe goal of knowledge translation (KT) is to close the gap between proven science and real-time care delivery. Historically, the adoption of new evidence into clinical practice has lagged a decade or more -with variable uptake across physicians and settings [1,2]. While KT, and implementation science more generally, are relatively new fields of emphasis and study, previous work has identified barriers to effective KT as well as best practices for implementation [3].For example, Davidoff has stressed the importance of concentrating not only on the diffusion of new validated techniques and treatments into practice but also on the "undiffusion" of previous practice -especially amongst those physicians who may be prone to suffering from inertia when it comes to changing their practice [4]. Michie has described a behavior change wheel framework for identifying well-suited interventions (e.g., education, environmental context, etc.) to combat barriers to implementation of knowledge translation tools [5]. Diner has de...

show abstract

An electronic order set for acute myocardial infarction is associated with improved patient outcomes through better adherence to clinical practice guidelines

Abstract: BACKGROUND: Adherence to evidence-based recommendations for acute myocardial infarction (AMI) remains unsatisfactory.

Cited by 24 publications

References 37 publications

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

Impact of Increased Early Statin Administration on Ischemic Stroke Outcomes: A Multicenter Electronic Medical Record Intervention

Optimizing Clinical Decision Support in the Electronic Health Record

Contact Info

Product

Resources

About