scholarly journals How do general practitioners access guidelines and utilise electronic medical records to make clinical decisions on antibiotic use? Results from an Australian qualitative study

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e028329 ◽  
Author(s):  
Ruby Biezen ◽  
Cassandra Roberts ◽  
Kirsty Buising ◽  
Karin Thursky ◽  
Douglas Boyle ◽  
...  
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Electronic Medical Records ◽  
General Practitioners ◽  
Clinical Decision Support ◽  
Medical Records ◽  
Clinical Decision ◽  
Antibiotic Prescribing ◽  
Evidence Based ◽  
Practice Culture

ObjectiveThis study aimed to explore how general practitioners (GPs) access and use both guidelines and electronic medical records (EMRs) to assist in clinical decision-making when prescribing antibiotics in Australia.DesignThis is an exploratory qualitative study with thematic analysis interpreted using the Theory of Planned Behaviour (TPB) framework.SettingThis study was conducted in general practice in Victoria, Australia.ParticipantsTwenty-six GPs from five general practices were recruited to participate in five focus groups between February and April 2018.ResultsGPs expressed that current EMR systems do not provide clinical decision support to assist with antibiotic prescribing. Access and use of guidelines were variable. GPs who had more clinical experience were less likely to access guidelines than younger and less experienced GPs. Guideline use and guideline-concordant prescribing was facilitated if there was a practice culture encouraging evidence-based practice. However, a lack of access to guidelines and perceived patients’ expectation and demand for antibiotics were barriers to guideline-concordant prescribing. Furthermore, guidelines that were easy to access and navigate, free, embedded within EMRs and fit into the clinical workflow were seen as likely to enhance guideline use.ConclusionsCurrent barriers to the use of antibiotic guidelines include GPs’ experience, patient factors, practice culture, and ease of access and cost of guidelines. To reduce inappropriate antibiotic prescribing and to promote more rational use of antibiotic in the community, guidelines should be made available, accessible and easy to use, with minimal cost to practicing GPs. Integration of evidence-based antibiotic guidelines within the EMR in the form of a clinical decision support tool could optimise guideline use and increase guideline-concordant prescribing.

scholarly journals Facilitating Genetics Aware Clinical Decision Support: Putting the eMERGE Infrastructure into Practice

ACI Open ◽  
2021 ◽  
Vol 05 (02) ◽  
pp. e54-e58
Author(s):  
Casey Overby Taylor ◽  
Luke V. Rasmussen ◽  
Laura J. Rasmussen-Torvik ◽  
Cynthia A. Prows ◽  
David A. Dorr ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Medical Records ◽  
Clinical Decision Support ◽  
Medical Records ◽  
Case Reports ◽  
Clinical Decision ◽  
Phase Iii ◽  
Health Record ◽  
It Infrastructure ◽  
Emerge Network

AbstractThis editorial provides context for a series of published case reports in ACI Open by summarizing activities and outputs of joint electronic health record integration and pharmacogenomics workgroups in the NIH-funded electronic Medical Records and Genomics (eMERGE) Network. A case report is a useful tool to describe the range of capabilities that an IT infrastructure or a particular technology must support. The activities we describe have informed infrastructure requirements used during eMERGE phase III, provided a venue to share experiences and ask questions among other eMERGE sites, summarized potential hazards that might be encountered for specific clinical decision support (CDS) implementation scenarios, and provided a simple framework that captured progress toward implementing CDS at eMERGE sites in a consistent format.

scholarly journals Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals

2020 ◽  
Author(s):  
Bethany A Van Dort ◽  
Wu Yi Zheng ◽  
Vivek Sundar ◽  
Melissa T Baysari
Keyword(s):  
Decision Support ◽  
Electronic Medical Records ◽  
Clinical Decision Support ◽  
Medical Records ◽  
Web Of Science ◽  
Clinical Decision ◽  
Internal Governance ◽  
Effective Strategies ◽  
Multiple Strategies ◽  
Governance Processes

Abstract Objective To identify and summarize the current internal governance processes adopted by hospitals, as reported in the literature, for selecting, optimizing, and evaluating clinical decision support (CDS) alerts in order to identify effective approaches. Materials and methods Databases (Medline, Embase, CINAHL, Scopus, Web of Science, IEEE Xplore Digital Library, CADTH, and WorldCat) were searched to identify relevant papers published from January 2010 to April 2020. All paper types published in English that reported governance processes for selecting and/or optimizing CDS alerts in hospitals were included. Results Eight papers were included in the review. Seven papers focused specifically on medication-related CDS alerts. All papers described the use of a multidisciplinary committee to optimize alerts. Other strategies included the use of clinician feedback, alert data, literature and drug references, and a visual dashboard. Six of the 8 papers reported evaluations of their CDS alert modifications following the adoption of optimization strategies, and of these, 5 reported a reduction in alert rate. Conclusions A multidisciplinary committee, often in combination with other approaches, was the most frequent strategy reported by hospitals to optimize their CDS alerts. Due to the limited number of published processes, variation in system changes, and evaluation results, we were unable to compare the effectiveness of different strategies, although employing multiple strategies appears to be an effective approach for reducing CDS alert numbers. We recommend hospitals report on descriptions and evaluations of governance processes to enable identification of effective strategies for optimization of CDS alerts in hospitals.

scholarly journals OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records

2015 ◽  
Vol 23 (2) ◽  
pp. 339-348 ◽  
Author(s):  
Jonathan H Chen ◽  
Tanya Podchiyska ◽  
Russ B Altman
Keyword(s):  
Decision Support ◽  
Hospital Admission ◽  
Electronic Medical Records ◽  
Clinical Decision Support ◽  
Medical Records ◽  
Life Support ◽  
Clinical Decision ◽  
Grand Challenge ◽  
Temporal Relationships ◽  
Common Items

Abstract Objective: To answer a “grand challenge” in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or Amazon.com’s product recommender. Materials and Methods: EMR data were extracted from 1 year of hospitalizations (>18K patients with >5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the ∼1.5K most common items to drive an order recommender. The authors assessed the recommender’s ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Results: Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% ( P  < 10 −10 ) for hospital admission orders. Relative risk-based association methods improve inverse frequency weighted recall from 4% to 16% ( P  < 10 −16 ). The framework yields a prediction receiver operating characteristic area under curve (c-statistic) of 0.84 for 30 day mortality, 0.84 for 1 week need for ICU life support, 0.80 for 1 week hospital discharge, and 0.68 for 30-day readmission. Discussion: Recommender results quantitatively improve on reference benchmarks and qualitatively appear clinically reasonable. The method assumes that aggregate decision making converges appropriately, but ongoing evaluation is necessary to discern common behaviors from “correct” ones. Conclusions: Collaborative filtering recommender algorithms generate clinical decision support that is predictive of real practice patterns and clinical outcomes. Incorporating temporal relationships improves accuracy. Different evaluation metrics satisfy different goals (predicting likely events vs. “interesting” suggestions).

scholarly journals Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Elizabeth Ford ◽  
Natalie Edelman ◽  
Laura Somers ◽  
Duncan Shrewsbury ◽  
Marcela Lopez Levy ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
General Practitioners ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Structured Interview ◽  
Qualitative Interview Study ◽  
High Level

Abstract Background Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. Methods We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. Results We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. Conclusions Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.

Sign in / Sign up

Export Citation Format

Share Document