scholarly journals Transfusion Medicine Informatics: Analysis of User Burden from Clinical Decision Support Alerts in Promoting Patient Blood Management

2020 ◽  
Vol 154 (Supplement_1) ◽  
pp. S123-S124
Author(s):  
H C Tsang ◽  
P Mathias ◽  
N Hoffman ◽  
M B Pagano
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Blood Product ◽  
Clinical Decision ◽  
Blood Products ◽  
Blood Management ◽  
Before And After ◽  
Blood Utilization ◽  
Transfusion Ratio ◽  
Major Project

Abstract Introduction/Objective To increase efficiency of blood product ordering and delivery processes and improve appropriateness of orders, a major project to implement clinical decision support (CDS) alerts in the electronic medical record (EMR) was undertaken. A design team was assembled including hospital and laboratory medicine information technology and clinical informatics, transfusion services, nursing and clinical services from medical and surgical specialties. Methods Consensus-derived thresholds in hemoglobin/hematocrit, platelet count, INR, and fibrinogen for red blood cell (RBC), platelet, plasma, and cryoprecipitate blood products CDS alerts were determined. Data from the EMR and laboratory information system were queried from the 12-month period before and after implementation and the data was analyzed. Results During the analysis period, 5813 RBC (avg. monthly = 484), 1040 platelet (avg. monthly = 87), 423 plasma (avg. monthly = 35), and 88 cryoprecipitate (avg. monthly = 7) alerts fired. The average time it took for a user to respond was 5.175 seconds. The total amount of time alerts displayed over 12 months was 5813 seconds (~97 minutes of user time) compared to 56503 blood products transfused. Of active CDS alerts, hemoglobin/RBC alerts fired most often with ~1:5 (31141 RBC units) alert to transfusion ratio and 4% of orders canceled (n=231) when viewing the alert, platelet alerts fired with ~1:15 (15385 platelet units) alert to transfusion ratio and 6% orders canceled (n=66), INR/plasma alerts fired with ~1:21 (8793 plasma units) alert to transfusion ratio and 10% orders canceled (n=41), cryoprecipitate alerts fired with ~1:13 (1184 cryoprecipitate units) alert to transfusion ratio and 10% orders canceled (n=9). Overall monthly blood utilization normalized to 1000 patient discharges did not appear to have statistically significant differences comparing pre- versus post-go-live, except a potentially significant increase in monthly plasma usage at one facility with p = 0.34, although possibly due to an outlier single month of heavy usage. Conclusion Clinical decision support alerts can guide provider ordering with minimal user burden. This resulted in increased safety and quality use of the ordering process, although overall blood utilization did not appear to change significantly.

scholarly journals The use of a clinical decision support tool to assess the risk of QT drug–drug interactions in community pharmacies

2021 ◽  
Vol 12 ◽  
pp. 204209862199609
Author(s):  
Florine A. Berger ◽  
Heleen van der Sijs ◽  
Teun van Gelder ◽  
Patricia M. L. A. van den Bemt
Keyword(s):  
Decision Support ◽  
Drug Interactions ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Community Pharmacies ◽  
Support Tool ◽  
Interacting Agents ◽  
Before And After ◽  
Structured Approach

Introduction: The handling of drug–drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. Methods: An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome. Results: Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49–1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool. Conclusion: The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts. Plain Language Summary The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is difficult to decide whether or not it is safe to proceed treating a patient with combinations of two or more of these QT-prolonging drugs. Recently, a tool was developed that supports the risk management of these QT drug–drug interactions (QT-DDIs). Methods: In this study, we studied the effect of this tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of 3 months were included. Results: Two hundred and forty-four QT-DDIs pre-implementation of the tool and 157 QT-DDIs post-implementation of the tool were included. Pharmacists intervened in 43.0% of the QT-DDIs before the tool was implemented and in 35.7% after implementation of the tool. Substitution of one of the interacting agents was the most frequent intervention. Pharmacists spent less time on handling QT-DDIs when the tool was used. Conclusion: The clinical decision support tool might be a first step to developing a tool to manage QT-DDIs via a structured approach.

scholarly journals Impact of clinical decision support preventing the use of QT-prolonging medications for patients at risk for torsade de pointes

2014 ◽  
Vol 22 (e1) ◽  
pp. e21-e27 ◽  
Author(s):  
Atsushi Sorita ◽  
J Martijn Bos ◽  
Bruce W Morlan ◽  
Robert F Tarrell ◽  
Michael J Ackerman ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Torsade De Pointes ◽  
Clinical Decision ◽  
Medication Administration ◽  
Alert System ◽  
Before And After ◽  
Patients At Risk ◽  
Risk Patients ◽  
Physician Order Entry

Abstract We developed and implemented a ‘CPOE-QT Alert’ system, that is, clinical decision support integrated in the computerized physician order entry system (CPOE), in 2011. The system identifies any attempts to order medications with risk of torsade de pointes (TdP) for patients with a history of significant QT prolongation (QTc ≥500 ms) and alerts the provider entering the order. We assessed its impact by comparing orders and subsequent medication administration before and after activation of the system. We found a significant decrease in the proportion of completed order per ordering attempt after system activation (94% (1293/1379) vs 77% (1888/2453), difference 16.8%; p<0.001). This resulted in a 13.9% reduction in the administration of those medications to patients. A significant decrease was observed across all provider types, educational levels, and specialties. The CPOE-QT Alert system successfully reduced exposure to QT-prolonging medications in high risk patients.

scholarly journals Use of quick sequential organ failure assessment score-based sepsis clinical decision support system may be helpful to predict sepsis development

2021 ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Compliance Rate ◽  
Clinical Decision ◽  
Suspected Infection ◽  
Before And After ◽  
Bundle Compliance ◽  
Resuscitation Bundle

Objectives: A sepsis clinical decision support system (CDSS) can facilitate quicker sepsis detection and treatment and consequently improve outcomes. We developed a qSOFA-based sepsis CDSS and evaluated its impact on compliance with a 3-hour resuscitation bundle for patients with sepsis. Methods: This before-and-after study included consecutive adult patients with suspected infection and qSOFA scores of ≥ 2 at their emergency department (ED) presentation of a tertiary care hospital. Sepsis was defined according to the Sepsis-3 criteria. We evaluated the 3-hour resuscitation bundle compliance rate for control patients from July through August 2016, for patients using the qSOFA-based sepsis CDSS from September through December 2016, and the impact of the system using multivariable logistic regression analysis. Results: Of 306 patients with suspected infection and positive qSOFA scores at presentation, 265 patients (86.6%) developed sepsis (including 71 patients with septic shock). The 3-hour resuscitation bundle compliance rate did not differ significantly between the patients before and after the routine implementation of the qSOFA-based sepsis CDSS (63.7% vs. 52.6%; P = 0.071). Multivariate analysis showed that age (AOR [adjusted odds ratio], 1.033; P = 0.002) and body temperature (AOR, 1.677; P < 0.001) were associated with bundle compliance. Conclusions: Among patients with a positive qSOFA score at presentation, sepsis developed in 86.6%, which means the qSOFA-based sepsis CDSS may be helpful; however, it was not associated with improved bundle compliance. Future quality improvement studies with multifactorial, hospital-wide approaches using sepsis CDSS tools are warranted.

scholarly journals 1004. Clinical Decision Support System Alerts for HIV Retention in Care – A Pilot Implementation Research Study

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S530-S531
Author(s):  
Michael Leonard ◽  
Rachel P Weber ◽  
Laurence Brunet ◽  
Bernard Davis ◽  
Christopher Polk ◽  
...  
Keyword(s):  
Decision Support ◽  
Pilot Study ◽  
Clinical Decision Support ◽  
Support System ◽  
Implementation Research ◽  
Clinical Decision Support System ◽  
Retention In Care ◽  
Clinical Decision ◽  
Before And After ◽  
Over Time

Abstract Background Clinical decision support system (CDSS) alerts may help retain people living with HIV (PLWH) in care. A system of CDSS alerts utilizing the CHORUS™ portal was developed to identify PLWH at risk of being lost to care. To evaluate feasibility for a larger scale study, a before and after implementation research pilot study was implemented in the OPERA Cohort at three clinic sites in a southeastern US city. Methods Periods without intervention (before) or with CDSS alerts (after) were followed by 3 months of follow up. The study population consisted of PLWH with ≥ 1 electronic health record entry in the 2 years prior to, or during, the before or after period (Fig 1). To support clinicians through a discrete implementation strategy, alerts warning of suboptimal patient attendance were generated daily for the eligible PLWH at each site; providers or other clinic staff could respond to the alerts (Fig 2). Alerts, responses, and visits (i.e., meeting with provider or HIV lab measurement) were characterized. The proportion of PLWH with ≥ 1 visit in the before and after periods were compared at each site by Pearson’s Chi-square. Figure 1. Pilot study timeline Figure 2. CDSS alert criteria and response options Results A total of 12,230 PLWH were eligible (sites A: 11,271; B: 733; C: 1,344 PLWH), with &gt; 75% in both the before and after periods. The ratio of alerts to responses was 11.9 at site A (2,245 alerts to 189 responses in 309 days; Fig 3A), and comparatively lower at sites B (756 alerts to 334 responses in 352 days, ratio=2.2; Fig 3B) and C (1,305 alerts to 896 responses in 246 days, ratio=1.5; Fig 3C). Responses to alerts were sporadic at sites A and B and consistent at site C. After the intervention, the proportion of PLWH with ≥ 1 visit stayed the same at site A (46% in both periods; p=0.47), decreased at site B (91% to 80%; p&lt; 0.01), and increased at site C (72% to 81%; p&lt; 0.01). Figure 3. Alerts and responses over time in (A) Site A, (B) Site B, and (C) Site C Conclusion This pilot study was ecological by design: measures of retention in care were compared over two calendar periods, without accounting for changes in study populations, clinic characteristics, and policies in place over time (which could have impacted clinic attendance). Though engagement with the CDSS was suboptimal at some sites, this implementation pilot study has demonstrated the ability to implement a CDSS aimed at identifying at-risk PLWH, while highlighting areas for improvement in future larger scale studies. Disclosures Joel Wesley Thompson, MHS, PA-C, AAHIVS, DFAAPA, MHS, PA-C, AAHIVS, DFAAPA, Gilead (Shareholder, Speaker’s Bureau)Janssen (Speaker’s Bureau)Theratechnologies (Speaker’s Bureau)ViiV (Speaker’s Bureau) Tammeka Evans, MoP, ViiV Healthcare (Employee)

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