scholarly journals 1147. Improving Accessibility and Antibiotic Prescribing with an Enhanced Digital Antibiogram

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S409-S409
Author(s):  
Julia K Yarahuan ◽  
Brandon Hunter ◽  
Devin Nadar ◽  
Nitin Gujral ◽  
Andrew M Fine ◽  
...  

Abstract Background Institutional antibiograms play a key role in antimicrobial stewardship and may provide a venue for clinical decision support. Our institution recently transitioned our paper antibiogram to an enhanced digital antibiogram with antibiotic recommendations for common pediatric infections. The objectives of this study were (1) to improve the accessibility of our institutional antibiogram through a digital platform and (2) to improve trainee confidence when selecting empiric antibiotics by integrating clinical decision support. Methods The digital antibiogram was developed and evaluated at a tertiary children’s hospital. The tool was developed iteratively over one year by our innovation and digital health accelerator with recommendations for empiric antibiotic selection provided by experts in pediatric infectious diseases (see Figure 1 for example). Usability pilot testing was performed with a group of ordering providers and the tool was released internally in October 2018. A paired pre- and post- implementation survey evaluated residents’ perceptions of the accessibility of the paper vs. digital antibiogram and their confidence when selecting empiric antibiotics. Data were analyzed by Fisher exact test. Results During the 3 months after release, the digital antibiogram was accessed 1014 times with similar proportions of views for susceptibility data, dosing, and empiric antibiotic recommendations. Of the 31 pediatric residents who responded to both pre- and post- implementation surveys, only 59% had access to a copy of the paper antibiogram. Following release of the digital antibiogram, residents referred to antibiotic susceptibilities more frequently (P < 0.05, Figure 2) and were more frequently more confident when selecting the correct antibiotic dose (P < 0.01, Figure 3). See Figure 4 for dosing recommendation example. Conclusion Providing antibiotic susceptibility and dosing recommendations digitally improved accessibility and resident confidence during antibiotic prescribing. Our digital tool provides a successful platform for displaying the antibiotic data and recommendations that enable appropriate antibiotic use. Disclosures All authors: No reported disclosures.

Author(s):  
Emily S. Patterson ◽  
Giavanna N. DiLoreto ◽  
Rohith Vanam ◽  
Erinn Hade ◽  
Courtney Hebert

Human factors engineering can enhance software usefulness and usability. We describe a multi-method approach to improve clinical decision support (CDS) for antibiotic stewardship. We employed a heuristic review to generate recommendations to improve the usability of a prototype CDS to support empiric antibiotic prescribing in the hospital setting. We then engaged in a design improvement cycle in collaboration with software programmers, which resulted in additional enhancements to our prototype. Finally, we used the revised prototype during three walkthrough demonstration interviews with physician and pharmacist subject matter experts. These walkthrough interviews generated recommendations to improve the interface, functionality, and tailoring for groups of users. We discuss common elements of the recommendations for models for using clinical decision support in general.


2020 ◽  
Vol 41 (S1) ◽  
pp. s368-s368
Author(s):  
Mary Acree ◽  
Kamaljit Singh ◽  
Urmila Ravichandran ◽  
Jennifer Grant ◽  
Gary Fleming ◽  
...  

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


Author(s):  
Nalika Ulapane ◽  
Nilmini Wickramasinghe

The use of mobile solutions for clinical decision support is still a rather nascent area within digital health. Shedding light on this important application of mobile technology, this chapter presents the initial findings of a scoping review. The review's primary objective is to identify the state of the art of mobile solution based clinical decision support systems and the persisting critical issues. The authors contribute by classifying identified critical issues into two matrices. Firstly, the issues are classified according to a matrix the authors developed, to be indicative of the stage (or timing) at which the issues occur along the timeline of mobile solution development. This classification includes the three classes: issues persisting at the (1) stage of developing mobile solutions, (2) stage of evaluating developed solutions, and (3) stage of adoption of developed solutions. Secondly, the authors present a classification of the same issues according to a standard socio-technical matrix containing the three classes: (1) technological, (2) process, and (3) people issues.


2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S806-S807
Author(s):  
Cindy L Hoegg ◽  
Katie L Williams ◽  
Eric Shelov ◽  
Talene A Metjian ◽  
Ana Maria Cardenas ◽  
...  

Abstract Background Clinical decision support for Clostridioides difficile infection (CDI) diagnostics reduces inappropriate testing, leading to decreased need for isolation and antibiotic use. Our institution utilized manual discontinuation by laboratory staff of CDI testing for inappropriate specimens, including formed stool and age < 1 year. We aimed to assess the financial impact of instituting a CDI best practice alert at a quaternary care children’s hospital. Methods A multidisciplinary team mapped inappropriate testing criteria identified from literature review with discrete fields in our electronic health record (EHR, EpicCare) to design an alert. The exclusion criteria identified included: (1) age < 1 year; (2) positive C. difficile test within past 14 days; (3) less than or equal to 3 unformed stools in past 24 hours; (4) current receipt of CDI-directed therapy; or (5) laxative use or barium exposure in prior 48 hours. 6 months of data prior to implementation were reviewed to estimate impact of the alert. At implementation, any exclusion criteria detected in the EHR at the time of order entry triggered an alert to deter CDI testing. Cost estimates for averted tests (Quick Check Complete Assay/Illumigene) included cost of test ($50), cost of isolation/personal protective equipment ($159/day), and cost of treatment with oral vancomycin in false-positives ($2250/treatment course). Results In a 6-month pre-implementation period, 586 tests for CDI were ordered; of which, 23% were identified by our criteria as inappropriate. During the first 3 months of alert implementation, 256 tests were ordered, of which 105 (41%) caused the alert to fire. Of those, 56 tests were not ordered, for a 22% reduction in testing. Laboratory staff continued to manually stop tests not meeting criteria, such as patient age <1 year when possible. Based on avoidance of testing, use of PPE, and 10 day antibiotic treatment for false-positives (assumed 25% by literature review), this translated to cost savings of $69,916, and an annual cost savings of $279,664. Conclusion Implementation of an alert for select patients using a bioinformatics algorithm reduced inappropriate CDI testing. Clinical decision support for CDI can lead to substantial cost savings for both antibiotic use and isolation precautions. Disclosures All authors: No reported disclosures.


2019 ◽  
Vol 41 (3) ◽  
pp. 552-581 ◽  
Author(s):  
Eduardo Carracedo-Martinez ◽  
Christian Gonzalez-Gonzalez ◽  
Antonio Teixeira-Rodrigues ◽  
Jesus Prego-Dominguez ◽  
Bahi Takkouche ◽  
...  

2020 ◽  
Author(s):  
Mengting Ji ◽  
Xiaoyun Chen ◽  
Georgi Z. Genchev ◽  
Ting Xu ◽  
Mingyue Wei ◽  
...  

Abstract BackgroundAI-enabled Clinical Decision Support Systems (AI+CDSSs) were heralded to contribute greatly to the advancement of healthcare services. At present, there is an increased availability of monetary funds and technical expertise invested in projects and proposals targeting the building and implementation of such systems. Therefore, in this context of large funds and technical devotion, understanding the actual system implementation status in clinical practice is imperative. The objective of this research was to understand: 1) the current clinical implementations of AI+CDSSs in Chinese hospitals and 2) concerns regarding AI+CDSSs current and future implementations.MethodsA survey supported by the China Digital Medicine journal was performed. We employed stratified cluster sampling and investigated tertiary hospitals from 6 provinces and province-level cities. Descriptive analysis, two-sided Fisher exact test, and Mann-Whitney U-test were utilized for analysis. ResultsResponses were collected from 160 respondents. The analyzable response rate was 86.96%. Thirty-eight of the surveyed hospitals (23.75%) had implemented AI+CDSSs. There were statistical differences on grade, scales, and medical volume between the two groups of hospitals (implemented vs. not-implemented AI+CDSSs, p<0.05). On the 5-point Likert scale, 81.58% (31/38) of respondents rated their overall satisfaction with the systems as 3 to 4. The three most-common concerns were system functions improvement and integration into the clinical process, data quality and data sharing mechanism improvement, and methodological bias.ConclusionsWhile AI+CDSSs were not yet wide-spread in Chinese clinical settings, clinical professionals recognize the potential benefits and challenges regarding in-hospital AI+CDSSs.


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