scholarly journals Reduction of inappropriate medication in older populations by electronic decision support (the PRIMA-eDS project): a survey of general practitioners’ experiences

2019 ◽  
Vol 26 (11) ◽  
pp. 1323-1332 ◽  
Author(s):  
Anja Rieckert ◽  
Anne-Lisa Teichmann ◽  
Eva Drewelow ◽  
Celine Kriechmayr ◽  
Giuliano Piccoliori ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
General Practitioners ◽  
Inappropriate Medication ◽  
Decision Support Tool ◽  
Health Record ◽  
Electronic Decision Support ◽  
Support Tool ◽  
Electronic Health ◽  
Older Populations

Abstract Objective We sought to investigate the experiences of general practitioners (GPs) with an electronic decision support tool to reduce inappropriate polypharmacy in older patients (the PRIMA-eDS [Polypharmacy in chronic diseases: Reduction of Inappropriate Medication and Adverse drug events in older populations by electronic Decision Support] tool) in a multinational sample of GPs and to quantify the findings from a prior qualitative study on the PRIMA-eDS-tool. Materials and Methods Alongside the cluster randomized controlled PRIMA-eDS trial, a survey was conducted in all 5 participating study centers (Bolzano, Italy; Manchester, United Kingdom; Salzburg, Austria; Rostock, Germany; and Witten, Germany) between October 2016 and July 2017. Data were analyzed using descriptive statistics and chi-square tests. Results Ninety-one (n = 160) percent of the 176 questionnaires were returned. Thirty-two percent of the respondents reported that they did not cease drugs because of the medication check. The 68% who had discontinued drugs comprise 57% who had stopped on average 1 drug and 11% who had stopped 2 drugs or more per patient. The PRIMA-eDS tool was found to be useful (69%) and the recommendations were found to help to increase awareness (86%). The greatest barrier to implementing deprescribing recommendations was the perceived necessity of the medication (69%). The majority of respondents (65%) would use the electronic medication check in routine practice if it was part of the electronic health record. Conclusions GPs generally viewed the PRIMA-eDS medication check as useful and as informative. Recommendations were not always followed due to various reasons. Many GPs would use the medication check if integrated into the electronic health record.

scholarly journals Reduced Length of Stay Using Clinical Decision Support Tool (ASAP) for Empiric Antibiotic Selection

2020 ◽  
Vol 41 (S1) ◽  
pp. s368-s368
Author(s):  
Mary Acree ◽  
Kamaljit Singh ◽  
Urmila Ravichandran ◽  
Jennifer Grant ◽  
Gary Fleming ◽  
...  
Keyword(s):  
Decision Support ◽  
Length Of Stay ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Record ◽  
Antibiotic Selection ◽  
Support Tool ◽  
Empiric Antibiotic

Background: Empiric antibiotic selection is challenging and requires knowledge of the local antibiogram, national guidelines and patient-specific factors, such as drug allergy and recent antibiotic exposure. Clinical decision support for empiric antibiotic selection has the potential to improve adherence to guidelines and improve patient outcomes. Methods: At NorthShore University HealthSystem, a 4-hospital, 789 bed system, an automated point-of-care decision support tool referred to as Antimicrobial Stewardship Assistance Program (ASAP) was created for empiric antibiotic selection for 4 infectious syndromes: pneumonia, skin and soft-tissue infections, urinary tract infection, and intra-abdominal infection. The tool input data from the electronic health record, which can be modified by any user. Using an algorithm created with electronic health record data, antibiogram data, and national guidelines, the tool produces an antibiotic recommendation that can be ordered via a link to order entry. If the tool identifies a patient with a high likelihood for a multidrug-resistant infection, a consultation by an infectious diseases specialist is recommended. Utilization of the tool and associated outcomes were evaluated from July 2018 to May 2019. Results: The ASAP tool was executed by 140 unique, noninfectious diseases providers 790 times. The tool was utilized most often for pneumonia (194 tool uses), followed by urinary tract infection (166 tool uses). The most common provider type to use the tool was an internal medicine hospitalist. The tool increased adherence to the recommended antibiotic regimen for each condition. Antibiotic appropriateness was assessed by an infectious diseases physician. Antibiotics were considered appropriate when they were similar to the antibiotic regimen recommended by the ASAP. Inappropriate antibiotics were classified as broad or narrow. When antibiotic coverage was appropriate, hospital length of stay was statistically significantly shorter (4.8 days vs 6.8 days for broad antibiotics vs 7.4 days for narrow antibiotics; P < .01). No significant differences were identified in mortality or readmission. Conclusions: A clinical decision support tool in the electronic health record can improve adherence to recommended empiric antibiotic therapy. Use of appropriate antibiotics recommended by such a tool can reduce hospital length of stay.Funding: NoneDisclosures: None

scholarly journals Development of an antimicrobial stewardship module in an electronic health record: Options to enhance daily antimicrobial stewardship activities

2021 ◽  
Author(s):  
Kathryn Dzintars ◽  
Valeria M Fabre ◽  
Edina Avdic ◽  
Janessa Smith ◽  
Victoria Adams-Sommer ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Antimicrobial Stewardship ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Johns Hopkins Hospital ◽  
Health Record ◽  
Support Tool ◽  
Electronic Health

Abstract Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose The purpose of this manuscript is to describe our experience developing an antimicrobial stewardship (AS) module as a clinical decision support tool in the Epic electronic health record (EHR). Summary Clinical decision support systems within the EHR can be used to decrease use of broad-spectrum antibiotics, improve antibiotic selection and dosing, decrease adverse effects, reduce antibiotic costs, and reduce the development of antibiotic resistance. The Johns Hopkins Hospital constructed an AS module within Epic. Customized stewardship alerts and scoring systems were developed to triage patients requiring stewardship intervention. This required a multidisciplinary approach with a team comprising AS physicians and pharmacists and Epic information technology personnel, with assistance from clinical microbiology and infection control when necessary. In addition, an intervention database was enhanced with stewardship-specific interventions, and workbench reports were developed specific to AS needs. We herein review the process, advantages, and challenges associated with the development of the Epic AS module. Conclusion Customizing an AS module in an EHR requires significant time and expertise in antimicrobials; however, AS modules have the potential to improve the efficiency of AS personnel in performing daily stewardship activities and reporting through a single system.

Abstract WP290: Impact of TIA Training for General Practitioners on 90-Day Stroke Outcomes Following TIA

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Annemarei Ranta ◽  
Susan Dovey ◽  
John Gommans ◽  
Mark Weatherall
Keyword(s):  
Decision Support ◽  
General Practitioners ◽  
Controlled Trial ◽  
Decision Support Tool ◽  
Minor Stroke ◽  
Education Session ◽  
Vascular Events ◽  
Electronic Decision Support ◽  
Support Tool ◽  
Significant Difference

Introduction: The FASTEST trial demonstrated benefit of a TIA/stroke electronic decision support tool for the management of patients with TIA or minor stroke in primary. As part of the trial general practitioners (GPs) were offered an educational session on TIA and stroke prior to trial begin. Hypothesis: GP TIA/stroke education has a beneficial effect on patient outcomes especially if combined with the use of electronic decision support. Methods: The FASTEST trial was a multi-centre, single blind, parallel group, cluster randomised controlled trial comparing TIA/stroke electronic decisions support guided primary care management with usual care. A one-hour pre-trial TIA education session was offered to all participating GPs. Results: Of 181 participating GPs 79 (43.7%) attended a pre-trial education session and 140/291 (48.1%) trial patients were managed by GPs who attended education. Overall, there was no significant difference in 90-day stroke events in patients treated by GPs who attended (2/140 (1.4%)) versus those who did not attend the education session (5/151 (3.3%)); cluster adjusted OR 0.42, 95% CI 0.08-2.21; p=0.30). GP education that was reinforced by subsequent access to the electronic decision support tool during the trial did result in fewer 90-day strokes (0/71; 0%) when compared with patients treated by GPs who neither accessed education nor the TIA/stroke tool (3/50; 6.0%); p=0.033. Similarly there were fewer 90-day vascular events or deaths when education was combined with access to the tool (1/71 (1.4%) versus 8/50 (16%); cluster adjusted OR 0.075, 95%CI 0.02-0.62; p=0.016). When either only the tool or only training were accessed results fell between the extremes with the tool alone performing better than training alone (90-day vascular event or death rate 6/101; 5.9% versus 9/69 13%). Conclusion: GP training alone does not significantly reduce 90-day stroke events following TIA, however, education in combination with access to TIA/stroke electronic decision can enhance the reduction of 90-day stroke and vascular events.

scholarly journals Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool

2020 ◽  
Vol 10 (3) ◽  
pp. 103
Author(s):  
David Gallagher ◽  
Congwen Zhao ◽  
Amanda Brucker ◽  
Jennifer Massengill ◽  
Patricia Kramer ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Hospital Readmissions ◽  
General Medicine ◽  
Clinical Decision ◽  
Unplanned Readmission ◽  
Health Record ◽  
Support Tool ◽  
Electronic Health

Unplanned hospital readmissions represent a significant health care value problem with high costs and poor quality of care. A significant percentage of readmissions could be prevented if clinical inpatient teams were better able to predict which patients were at higher risk for readmission. Many of the current clinical decision support models that predict readmissions are not configured to integrate closely with the electronic health record or alert providers in real-time prior to discharge about a patient’s risk for readmission. We report on the implementation and monitoring of the Epic electronic health record—“Unplanned readmission model version 1”—over 2 years from 1/1/2018–12/31/2019. For patients discharged during this time, the predictive capability to discern high risk discharges was reflected in an AUC/C-statistic at our three hospitals of 0.716–0.760 for all patients and 0.676–0.695 for general medicine patients. The model had a positive predictive value ranging from 0.217–0.248 for all patients. We also present our methods in monitoring the model over time for trend changes, as well as common readmissions reduction strategies triggered by the score.

Using a Clinical Decision Support Tool to Increase Chlamydia Screening Across a Large Primary Care Pediatric Network

2018 ◽  
Vol 57 (14) ◽  
pp. 1638-1641
Author(s):  
David Karas ◽  
Stephen Sondike ◽  
James Fitzgibbon ◽  
Mark Redding ◽  
Miraides Brown
Keyword(s):  
Electronic Health Record ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Chlamydia Screening ◽  
Health Record ◽  
Screening Rate ◽  
Support Tool ◽  
Before And After ◽  
Electronic Health ◽  
Large Primary

We aim to demonstrate increased chlamydia screening across a large pediatric network using an electronic health record–based intervention. We developed a pop-up notification that alerted providers that chlamydia screening was recommended during a well adolescent visit, when appropriate. We compared chlamydia screening rates before and after the implementation of the alert. The screening rate for chlamydia improved from 2.40% in the year before intervention to 5.01% in the year after intervention ( P < .01). In conclusion, an electronic health record intervention was successfully able to significantly increase rates of chlamydia screening across a large pediatric network.

scholarly journals Dissemination of child abuse clinical decision support: Moving beyond a single electronic health record

2021 ◽  
Vol 147 ◽  
pp. 104349
Author(s):  
Thomas McGinn ◽  
David A. Feldstein ◽  
Isabel Barata ◽  
Emily Heineman ◽  
Joshua Ross ◽  
...  
Keyword(s):  
Child Abuse ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health

scholarly journals Drivers of expectations: Why are Norwegian general practitioners skeptical of a prospective electronic health record?

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822098729
Author(s):  
Morten Hertzum ◽  
Gunnar Ellingsen ◽  
Line Melby
Keyword(s):  
Electronic Health Record ◽  
General Practitioners ◽  
Current System ◽  
Implementation Process ◽  
Health Record ◽  
Early Implementation ◽  
Ehr Adoption ◽  
Electronic Health

While expectations are well-known drivers of electronic health record (EHR) adoption, the drivers of expectations are more elusive. On the basis of interviews with general practitioners (GPs), we investigate how the early implementation process drives their expectations of an EHR that is being implemented in Norway. The GPs’ expectations of the prospective EHR are driven by (a) satisfying experiences with their current system, (b) the transfer of others’ experiences with the prospective EHR, (c) a sense of alignment, or lack thereof, with those in charge of the implementation process, (d) uncertainty about the inclusion of GP needs, and (e) competing technological futures. To manage expectations, starting early is important. Mismanaged expectations produce a need for convincing people to reverse their expectations. This appears to be the situation in Norway, where the GPs are currently skeptical of the prospective EHR.

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