Comparison of methods of alert acknowledgement by critical care clinicians in the ICU setting

Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework

Alerts are a common functionality of clinical decision support systems (CDSSs). Although they have proven to be useful in clinical practice, the alert burden can lead to alert fatigue and significantly reduce their usability and acceptance. Based on a literature review, we propose a unified framework consisting of a set of meaningful timestamps that allows the use of state-of-the-art measures for alert burden, such as alert dwell time, alert think time, and response time. In addition, it can be used to investigate other measures that could be relevant as regards dealing with this problem. Furthermore, we provide a case study concerning three different types of alerts to which the framework was successfully applied. We consider that our framework can easily be adapted to other CDSSs and that it could be useful for dealing with alert burden measurement thus contributing to its appropriate management.

Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review

Background

Sepsis is a significant cause of morbidity and mortality worldwide. Early detection of sepsis followed promptly by treatment initiation improves patient outcomes and saves lives. Hospitals are increasingly using computerized clinical decision support (CCDS) systems for the rapid identification of adult patients with sepsis.

Objective

This scoping review aims to systematically describe studies reporting on the use and evaluation of CCDS systems for the early detection of adult inpatients with sepsis.

Methods

The protocol for this scoping review was previously published. A total of 10 electronic databases (MEDLINE, Embase, CINAHL, the Cochrane database, LILACS [Latin American and Caribbean Health Sciences Literature], Scopus, Web of Science, OpenGrey, ClinicalTrials.gov, and PQDT [ProQuest Dissertations and Theses]) were comprehensively searched using terms for sepsis, CCDS, and detection to identify relevant studies. Title, abstract, and full-text screening were performed by 2 independent reviewers using predefined eligibility criteria. Data charting was performed by 1 reviewer with a second reviewer checking a random sample of studies. Any disagreements were discussed with input from a third reviewer. In this review, we present the results for adult inpatients, including studies that do not specify patient age.

Results

A search of the electronic databases retrieved 12,139 studies following duplicate removal. We identified 124 studies for inclusion after title, abstract, full-text screening, and hand searching were complete. Nearly all studies (121/124, 97.6%) were published after 2009. Half of the studies were journal articles (65/124, 52.4%), and the remainder were conference abstracts (54/124, 43.5%) and theses (5/124, 4%). Most studies used a single cohort (54/124, 43.5%) or before-after (42/124, 33.9%) approach. Across all 124 included studies, patient outcomes were the most frequently reported outcomes (107/124, 86.3%), followed by sepsis treatment and management (75/124, 60.5%), CCDS usability (14/124, 11.3%), and cost outcomes (9/124, 7.3%). For sepsis identification, the systemic inflammatory response syndrome criteria were the most commonly used, alone (50/124, 40.3%), combined with organ dysfunction (28/124, 22.6%), or combined with other criteria (23/124, 18.5%). Over half of the CCDS systems (68/124, 54.8%) were implemented alongside other sepsis-related interventions.

Conclusions

The current body of literature investigating the implementation of CCDS systems for the early detection of adult inpatients with sepsis is extremely diverse. There is substantial variability in study design, CCDS criteria and characteristics, and outcomes measured across the identified literature. Future research on CCDS system usability, cost, and impact on sepsis morbidity is needed.

International Registered Report Identifier (IRRID)

RR2-10.2196/24899

Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model

BACKGROUND

Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. The study aimed to develop a neural network model by using a genetic algorithm (GA) for the prediction of mortality in patients with ARDS.

METHODS

This was a secondary analysis of two multicenter randomized controlled trials conducted in forty-four hospitals that are members of the National Heart, Lung, and Blood Institute, founded to create an acute respiratory distress syndrome Clinical Trials Network. Model training and validation were performed using the SAILS and OMEGA studies, respectively. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction. This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort.

RESULTS

A total number of 1,071 ARDS patients were included for analysis. The GA search identified seven important variables, which were age, AIDS, leukemia, metastatic tumor, hepatic failure, lowest albumin, and FiO₂. A representative neural network model was constructed using the forward selection procedure. The area under the curve (AUC) of the neural network model evaluated with the validation cohort was 0.821 (95% CI [0.753-0.888]), which was greater than the APACHE III score (0.665; 95% CI [0.590-0.739]; p = 0.002 by Delong's test) and logistic regression model, albeit not statistically significant (0.743; 95% CI [0.669-0.817], p = 0.130 by Delong's test).

CONCLUSIONS

The study developed a neural network model using a GA, which outperformed conventional scoring systems for the prediction of mortality in ARDS patients.

Semiautomated System for Nonurgent, Clinically Significant Pathology Results

BACKGROUND

Failure of timely test result follow-up has consequences including delayed diagnosis and treatment, added costs, and potential patient harm. Closed-loop communication is key to ensure clinically significant test results (CSTRs) are acknowledged and acted upon appropriately. A previous implementation of the Alert Notification of Critical Results (ANCR) system to facilitate closed-loop communication of imaging CSTRs yielded improved communication of critical radiology results and enhanced adherence to institutional CSTR policies.

OBJECTIVE

This article extends the ANCR application to pathology and evaluates its impact on closed-loop communication of new malignancies, a common and important type of pathology CSTR.

MATERIALS AND METHODS

This Institutional Review Board-approved study was performed at a 150-bed community, academically affiliated hospital. ANCR was adapted for pathology CSTRs. Natural language processing was used on 30,774 pathology reports 13 months pre- and 13 months postintervention, identifying 5,595 reports with malignancies. Electronic health records were reviewed for documented acknowledgment for a random sample of reports. Percent of reports with documented acknowledgment within 15 days assessed institutional policy adherence. Time to acknowledgment was compared pre- versus postintervention and postintervention with and without ANCR alerts. Pathologists were surveyed regarding ANCR use and satisfaction.

RESULTS

Acknowledgment within 15 days was documented for 98 of 107 (91.6%) pre- and 89 of 103 (86.4%) postintervention reports (p = 0.2294). Median time to acknowledgment was 7 days (interquartile range [IQR], 3, 11) preintervention and 6 days (IQR, 2, 10) postintervention (p = 0.5083). Postintervention, median time to acknowledgment was 2 days (IQR, 1, 6) for reports with ANCR alerts versus 6 days (IQR, 2.75, 9) for reports without alerts (p = 0.0351). ANCR alerts were sent on 15 of 103 (15%) postintervention reports. All pathologists reported that the ANCR system positively impacted their workflow; 75% (three-fourths) felt that the ANCR system improved efficiency of communicating CSTRs.

CONCLUSION

ANCR expansion to facilitate closed-loop communication of pathology CSTRs was favorably perceived and associated with significant improved time to documented acknowledgment for new malignancies. The rate of adherence to institutional policy did not improve.

Analysis of a new best-practice advisory on time to initiation of antibiotics in surgical intensive care unit patients with septic shock

Background

Early administration of antibiotics in septic shock is associated with decreased mortality. Promptly identifying sepsis and eliciting a response are necessary to reduce time to antibiotic administration.

Methods

A best-practice advisory was introduced in the surgical intensive care unit to identify patients with septic shock and promote timely action. The best-practice advisory is triggered by blood culture orders and vasopressor administration within 24 h. The nurse or provider who triggers the alert may send an automatic notification to the intensive care unit resident, clinical pharmacist, and charge nurse, prompting bedside response and closer evaluation. Patients who met best-practice advisory criteria in the surgical intensive care unit from May 2016 through March 2017 were included. Outcomes included changes in antibiotics within 24 h, response to best-practice advisory, and time-to-antibiotics. Time-to-antibiotics was compared between a retrospective pre-intervention period and a six-month prospective post-intervention period defined by launch of the new best-practice advisory in September 2016. Data were analyzed by chi square, Mann-Whitney U, and Kruskal-Wallis.

Results

During the first six months of best-practice advisory implementation, 191 alerts were triggered by 97 unique patients. Alert notification was transmitted in 79 best-practice advisories (41%), with pharmacist bedside response in 53 (67%). New antibiotics were started within 24 h following 83 best-practice advisories (43%). There was a trend toward decreased time-to-antibiotics following implementation of the best-practice advisory (7.4 vs. 4.2 h, p = 0.057). Compared to the entire cohort, time-to-antibiotics was shorter when the team was notified and when a pharmacist responded to the bedside (4.2 vs. 1.6 vs. 1.2 hours).

Conclusions

A new best-practice advisory has been effective at eliciting a rapid response and reducing the time-to-antibiotics in surgical intensive care unit patients with septic shock. Team notification and pharmacist response are associated with decreased time-to-antibiotics.