scholarly journals A Clinical Decision Support Intervention to Improve Inpatient Pediatric Influenza Vaccination

2020 ◽  
Vol 41 (S1) ◽  
pp. s92-s93
Author(s):  
Omar Elsayed-Ali ◽  
Swaminathan Kandaswamy ◽  
Andi Shane ◽  
Stephanie Jernigan ◽  
Patricia Lantis ◽  
...  
Keyword(s):  
Decision Support ◽  
Influenza Vaccine ◽  
Influenza Vaccination ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Hospitalized Patients ◽  
Intervention Period ◽  
Vaccination Rates ◽  
Order Sets ◽  
Historical Controls

Background: Pediatric influenza vaccination rates remain <50% in the United States. Children with chronic medical conditions are at higher risk of morbidity and mortality from influenza, yet most experience missed opportunities for immunization in outpatient settings. In an adult cohort study, 74% of patients who had not received the influenza vaccine before or during hospitalization remained unvaccinated through the rest of the season. Thus, inpatient settings represent another important opportunity for vaccinating an especially susceptible population. In addition, 4 published studies have shown promise in improving inpatient pediatric influenza vaccination. However, these studies had limited effect sizes and included interventions requiring ongoing maintenance with dedicated staff. In this study, we hypothesized that a clinical decision support (CDS) intervention designed with user-centered design principles would increase inpatient influenza vaccine administration rates in the 2019–2020 influenza season. Methods: We performed a workflow analysis of different care settings to determine optimal timing of influenza vaccine decision support. Through formative usability testing with frontline clinicians, we developed electronic health record (EHR) prototypes of an order set module containing a default influenza vaccine order. This module was dynamically incorporated into order sets for patients meeting the following criteria: ≥6 months old, no prior influenza vaccine in the current season in our medical system or the state immunization registry, and no prior anaphylaxis to the vaccine. We implemented the CDS into select order sets based on operational leader support. We compared the proportion of eligible hospitalized patients in which the influenza vaccine was administered between our intervention period and the 2018–2019 season (historical controls). To account for secular trends, we also compared the vaccination rates for hospitalized patients exposed to our CDS to those that were not exposed to the CDS during the intervention period (concurrent controls). Results: During the intervention period (September 5, 2019–November 1, 2019), influenza vaccine was administered to 762 of 3,242 (24%) of eligible patients, compared to 360 of 2,875 (13%) among historical controls (P < .0001). Among the 42% of patients exposed to the CDS, vaccination rates were 33% compared to 9% for concurrent controls (p < .0001). Our intervention was limited by end-user uptake, with some physicians or nurses discontinuing the default vaccine order. In addition, early in the intervention, some vaccines were ordered but not administered, leading to vaccine waste. Conclusions: CDS targeting eligible hospitalized patients for influenza vaccination incorporated early into the workflow of nurses and ordering clinicians can substantially improve influenza vaccination rates among this susceptible and hard-to-reach population.Funding: NoneDisclosures: None

scholarly journals Modified early warning score-based clinical decision support: cost impact and clinical outcomes in sepsis

JAMIA Open ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 261-268
Author(s):  
Devin J Horton ◽  
Kencee K Graves ◽  
Polina V Kukhareva ◽  
Stacy A Johnson ◽  
Maribel Cedillo ◽  
...  
Keyword(s):  
Decision Support ◽  
Acute Care ◽  
Direct Cost ◽  
Clinical Decision Support ◽  
Early Warning ◽  
Clinical Decision ◽  
Early Warning Score ◽  
Total Direct Cost ◽  
Intervention Period ◽  
Support Cost

Abstract Objective The objective of this study was to assess the clinical and financial impact of a quality improvement project that utilized a modified Early Warning Score (mEWS)-based clinical decision support intervention targeting early recognition of sepsis decompensation. Materials and Methods We conducted a retrospective, interrupted time series study on all adult patients who received a diagnosis of sepsis and were exposed to an acute care floor with the intervention. Primary outcomes (total direct cost, length of stay [LOS], and mortality) were aggregated for each study month for the post-intervention period (March 1, 2016–February 28, 2017, n = 2118 visits) and compared to the pre-intervention period (November 1, 2014–October 31, 2015, n = 1546 visits). Results The intervention was associated with a decrease in median total direct cost and hospital LOS by 23% (P = .047) and .63 days (P = .059), respectively. There was no significant change in mortality. Discussion The implementation of an mEWS-based clinical decision support system in eight acute care floors at an academic medical center was associated with reduced total direct cost and LOS for patients hospitalized with sepsis. This was seen without an associated increase in intensive care unit utilization or broad-spectrum antibiotic use. Conclusion An automated sepsis decompensation detection system has the potential to improve clinical and financial outcomes such as LOS and total direct cost. Further evaluation is needed to validate generalizability and to understand the relative importance of individual elements of the intervention.

scholarly journals Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System

2016 ◽  
Vol 8s2 ◽  
pp. BII.S40208
Author(s):  
Sripriya Rajamani ◽  
Aaron Bieringer ◽  
Stephanie Wallerius ◽  
Daniel Jensen ◽  
Tamara Winden ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision ◽  
Population Based ◽  
Direct Access ◽  
Care Providers ◽  
Study Objective ◽  
Vaccination Rates ◽  
Electronic Health

Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.

scholarly journals Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial

2020 ◽  
Author(s):  
Nicolas Delvaux ◽  
Veerle Piessens ◽  
Tine De Burghgraeve ◽  
Pavlos Mamouris ◽  
Bert Vaes ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Laboratory Test ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Diagnostic Error ◽  
Order Sets ◽  
Cluster Randomized ◽  
Physician Order Entry ◽  
Test Ordering

Abstract Background Inappropriate laboratory test ordering poses an important burden for healthcare. Clinical decision support systems (CDSS) have been cited as promising tools to improve laboratory test ordering behavior. The objectives of this study were to evaluate the effects of an intervention that integrated a clinical decision support service into a computerized physician order entry (CPOE) on the appropriateness and volume of laboratory test ordering, and on diagnostic error in primary care.Methods This study was a pragmatic, cluster randomized, open label, controlled clinical trial. Setting 280 general practitioners (GPs) from 72 primary care practices in Belgium. Patients Patients aged ≥18 years with a laboratory test order for at least one of 17 indications; cardiovascular disease management, hypertension, check-up, chronic kidney disease (CKD), thyroid disease, type 2 diabetes mellitus, fatigue, anemia, liver disease, gout, suspicion of acute coronary syndrome (ACS), suspicion of lung embolism, rheumatoid arthritis, sexually transmitted infections (STI), acute diarrhea, chronic diarrhea, and follow-up of medication. Interventions The CDSS was integrated into a computerized physician order entry (CPOE) in the form of evidence-based order sets that suggested appropriate tests based on the indication provided by the general physician. Measurements The primary outcome of the ELMO study was the proportion of appropriate tests over the total number of ordered tests and inappropriately not-requested tests. Secondary outcomes of the ELMO study included diagnostic error, test volume and cascade activities.Results CDSS increased the proportion of appropriate tests by 0.21 (95% CI 0.16 - 0.26, p<.0001) for all tests included in the study. GPs in the CDSS arm ordered 7 (7.15 (95% CI 3.37 - 10.93, p=.0002)) tests fewer per panel. CDSS did not increase diagnostic error. The absolute difference in proportions was a decrease of 0.66% (95% CI 1.4% decrease - 0.05% increase) in possible diagnostic error.Conclusions A CDSS in the form of order sets, integrated within the CPOE improved appropriateness and decreased volume of laboratory test ordering without increasing diagnostic error. Trial Registration Clinicaltrials.gov Identifier: NCT02950142, registered on October 25, 2016Funding source This study was funded through the Belgian Health Care Knowledge Centre (KCE) Trials Programme agreement KCE16011.

scholarly journals Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Nicolas Delvaux ◽  
Veerle Piessens ◽  
Tine De Burghgraeve ◽  
Pavlos Mamouris ◽  
Bert Vaes ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Laboratory Test ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Diagnostic Error ◽  
Order Sets ◽  
Cluster Randomized ◽  
Physician Order Entry ◽  
Test Ordering

Abstract Background Inappropriate laboratory test ordering poses an important burden for healthcare. Clinical decision support systems (CDSS) have been cited as promising tools to improve laboratory test ordering behavior. The objectives of this study were to evaluate the effects of an intervention that integrated a clinical decision support service into a computerized physician order entry (CPOE) on the appropriateness and volume of laboratory test ordering, and on diagnostic error in primary care. Methods This study was a pragmatic, cluster randomized, open-label, controlled clinical trial. Setting Two hundred eighty general practitioners (GPs) from 72 primary care practices in Belgium. Patients Patients aged ≥ 18 years with a laboratory test order for at least one of 17 indications: cardiovascular disease management, hypertension, check-up, chronic kidney disease (CKD), thyroid disease, type 2 diabetes mellitus, fatigue, anemia, liver disease, gout, suspicion of acute coronary syndrome (ACS), suspicion of lung embolism, rheumatoid arthritis, sexually transmitted infections (STI), acute diarrhea, chronic diarrhea, and follow-up of medication. Interventions The CDSS was integrated into a computerized physician order entry (CPOE) in the form of evidence-based order sets that suggested appropriate tests based on the indication provided by the general physician. Measurements The primary outcome of the ELMO study was the proportion of appropriate tests over the total number of ordered tests and inappropriately not-requested tests. Secondary outcomes of the ELMO study included diagnostic error, test volume, and cascade activities. Results CDSS increased the proportion of appropriate tests by 0.21 (95% CI 0.16–0.26, p < 0.0001) for all tests included in the study. GPs in the CDSS arm ordered 7 (7.15 (95% CI 3.37–10.93, p = 0.0002)) tests fewer per panel. CDSS did not increase diagnostic error. The absolute difference in proportions was a decrease of 0.66% (95% CI 1.4% decrease–0.05% increase) in possible diagnostic error. Conclusions A CDSS in the form of order sets, integrated within the CPOE improved appropriateness and decreased volume of laboratory test ordering without increasing diagnostic error. Trial registration ClinicalTrials.gov Identifier: NCT02950142, registered on October 25, 2016

Reducing inappropriate PSA-based prostate cancer screening in men age 75 or older with a highly specific computerized clinical decision support tool.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 233-233
Author(s):  
Jeremy B. Shelton ◽  
Lee Ochotorena ◽  
Carol J. Bennett ◽  
Paul Shekelle ◽  
Caroline Goldzweig
Keyword(s):  
Prostate Cancer ◽  
Decision Support ◽  
Cancer Screening ◽  
Clinical Decision Support ◽  
Prostate Cancer Screening ◽  
Specific Antigen ◽  
Clinical Decision ◽  
Rate Of Change ◽  
Screening Rate ◽  
Intervention Period

233 Background: The value of PSA-based screening for prostate cancer is a topic of intense debate, however the Veterans Health Administration's (VHA) national clinical policy is to use age as a proxy for life expectancy and avoid screening in men ≥ age 75. To facilitate this we developed and implemented a highly specific computerized clinical decision support (CCDS) reminder to remind providers of current guidelines, at the moment of entering an inappropriate PSA order. Methods: We defined screening PSA as: any PSA ordered on men excluding those a) with a diagnosis of existing malignant prostate disease or “elevated prostate specific antigen”, b) who are using either enhancers or suppressors of testosterone, or d) who had a PSA of 2.5ng/ml or greater on either of the two most recent PSA tests. We measured PSA-based prostate cancer screening rates using this definition and on a monthly basis from 07/2011 to 07/2013. Using an interrupted time-series design, we turned the reminder on from 6/2012-8/2012 and then again from 1/2013-4/2013. Results: There were a total of 24,705 men eligible for screening during the two year period of analysis and 1,524 men who were screened. The mean screening rate during the 12 months prior to the study period was 7.8% among men, and during the 12 months of the intervention period it was 4.3%. During the 12 month baseline period the screening rate declined by 29.3%. During the two periods when the CCDS tool was turned on the screening rate feel by 59.7% and 29.8%, whereas during the two periods when it was off, it rose by 84.3% and 18.4%. Conclusions: The overall reduction in screening rate before and after the intervention period is likely substantially confounded by the secular event of the May, 2012 release of the USPSTF grade D recommendation against all PSA-based screening and its substantial media coverage. Despite this, the striking correlation between rate of change in screening rate and the turning on and off of the CCDS tool, suggests that this highly specific CCDS tool was able to reduce inappropriate PSA-based screening, even in an era of significant public discussion of the merits of PSA-based prostate cancer screening.

scholarly journals Effect of Interventions With a Clinical Decision Support System for Hospitalized Older Patients: Systematic Review Mapping Implementation and Design Factors

10.2196/28023 ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. e28023
Author(s):  
Birgit A Damoiseaux-Volman ◽  
Nathalie van der Velde ◽  
Sil G Ruige ◽  
Johannes A Romijn ◽  
Ameen Abu-Hanna ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Older Patients ◽  
Clinical Decision ◽  
Risk Of Bias ◽  
Hospitalized Patients ◽  
Effective Interventions ◽  
Design Factors ◽  
Implementation Factors

Background Clinical decision support systems (CDSSs) form an implementation strategy that can facilitate and support health care professionals in the care of older hospitalized patients. Objective Our study aims to systematically review the effects of CDSS interventions in older hospitalized patients. As a secondary aim, we aim to summarize the implementation and design factors described in effective and ineffective interventions and identify gaps in the current literature. Methods We conducted a systematic review with a search strategy combining the categories older patients, geriatric topic, hospital, CDSS, and intervention in the databases MEDLINE, Embase, and SCOPUS. We included controlled studies, extracted data of all reported outcomes, and potentially beneficial design and implementation factors. We structured these factors using the Grol and Wensing Implementation of Change model, the GUIDES (Guideline Implementation with Decision Support) checklist, and the two-stream model. The risk of bias of the included studies was assessed using the Cochrane Collaboration’s Effective Practice and Organisation of Care risk of bias approach. Results Our systematic review included 18 interventions, of which 13 (72%) were effective in improving care. Among these interventions, 8 (6 effective) focused on medication review, 8 (6 effective) on delirium, 7 (4 effective) on falls, 5 (4 effective) on functional decline, 4 (3 effective) on discharge or aftercare, and 2 (0 effective) on pressure ulcers. In 77% (10/13) effective interventions, the effect was based on process-related outcomes, in 15% (2/13) interventions on both process- and patient-related outcomes, and in 8% (1/13) interventions on patient-related outcomes. The following implementation and design factors were potentially associated with effectiveness: a priori problem or performance analyses (described in 9/13, 69% effective vs 0/5, 0% ineffective interventions), multifaceted interventions (8/13, 62% vs 1/5, 20%), and consideration of the workflow (9/13, 69% vs 1/5, 20%). Conclusions CDSS interventions can improve the hospital care of older patients, mostly on process-related outcomes. We identified 2 implementation factors and 1 design factor that were reported more frequently in articles on effective interventions. More studies with strong designs are needed to measure the effect of CDSS on relevant patient-related outcomes, investigate personalized (data-driven) interventions, and quantify the impact of implementation and design factors on CDSS effectiveness. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews): CRD42019124470; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=124470.

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