Literature DB >> 32810611

Special Commentary: Using Clinical Decision Support Systems to Bring Predictive Models to the Glaucoma Clinic.

Brian C Stagg1, Joshua D Stein2, Felipe A Medeiros3, Barbara Wirostko4, Alan Crandall4, M Elizabeth Hartnett4, Mollie Cummins5, Alan Morris6, Rachel Hess7, Kensaku Kawamoto8.   

Abstract

Advances in the field of predictive modeling using artificial intelligence and machine learning have the potential to improve clinical care and outcomes, but only if the results of these models are presented appropriately to clinicians at the time they make decisions for individual patients. Clinical decision support (CDS) systems could be used to accomplish this. Modern CDS systems are computer-based tools designed to improve clinician decision making for individual patients. However, not all CDS systems are effective. Four principles that have been shown in other medical fields to be important for successful CDS system implementation are (1) integration into clinician workflow, (2) user-centered interface design, (3) evaluation of CDS systems and rules, and (4) standards-based development so the tools can be deployed across health systems.
Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  artificial intelligence; clinical decision support; glaucoma; machine learning; predictive modeling

Mesh:

Year:  2020        PMID: 32810611      PMCID: PMC7854795          DOI: 10.1016/j.ogla.2020.08.006

Source DB:  PubMed          Journal:  Ophthalmol Glaucoma        ISSN: 2589-4196


  57 in total

Review 1.  Factors influencing implementation success of guideline-based clinical decision support systems: A systematic review and gaps analysis.

Authors:  E Kilsdonk; L W Peute; M W M Jaspers
Journal:  Int J Med Inform       Date:  2016-12-05       Impact factor: 4.046

Review 2.  Interface design principles for usable decision support: a targeted review of best practices for clinical prescribing interventions.

Authors:  Jan Horsky; Gordon D Schiff; Douglas Johnston; Lauren Mercincavage; Douglas Bell; Blackford Middleton
Journal:  J Biomed Inform       Date:  2012-09-17       Impact factor: 6.317

3.  Reducing Diabetic Ketoacidosis Intensive Care Unit Admissions Through an Electronic Health Record-Driven, Standardized Care Pathway.

Authors:  Karli Edholm; Katie Lappé; Polina Kukhareva; Christy Hopkins; Nathan D Hatton; Benjamin Gebhart; Heather Nyman; Emily Signor; Mikyla Davis; Kensaku Kawamoto; Stacy A Johnson
Journal:  J Healthc Qual       Date:  2020 Sep/Oct       Impact factor: 1.095

4.  Incorporating Guideline Adherence and Practice Implementation Issues into the Design of Decision Support for Beta-Blocker Titration for Heart Failure.

Authors:  Michael W Smith; Charnetta Brown; Salim S Virani; Charlene R Weir; Laura A Petersen; Natalie Kelly; Julia Akeroyd; Jennifer H Garvin
Journal:  Appl Clin Inform       Date:  2018-06-27       Impact factor: 2.342

5.  Evaluation of various machine learning methods to predict vision-related quality of life from visual field data and visual acuity in patients with glaucoma.

Authors:  Hiroyo Hirasawa; Hiroshi Murata; Chihiro Mayama; Makoto Araie; Ryo Asaoka
Journal:  Br J Ophthalmol       Date:  2014-05-02       Impact factor: 4.638

6.  Making cognitive decision support work: Facilitating adoption, knowledge and behavior change through QI.

Authors:  Charlene Weir; Cherie Brunker; Jorie Butler; Mark A Supiano
Journal:  J Biomed Inform       Date:  2016-08-28       Impact factor: 6.317

7.  Primary Open-Angle Glaucoma Preferred Practice Pattern(®) Guidelines.

Authors:  Bruce E Prum; Lisa F Rosenberg; Steven J Gedde; Steven L Mansberger; Joshua D Stein; Sayoko E Moroi; Leon W Herndon; Michele C Lim; Ruth D Williams
Journal:  Ophthalmology       Date:  2015-11-12       Impact factor: 12.079

8.  Accuracy of Kalman Filtering in Forecasting Visual Field and Intraocular Pressure Trajectory in Patients With Ocular Hypertension.

Authors:  Gian-Gabriel P Garcia; Mariel S Lavieri; Chris Andrews; Xiang Liu; Mark P Van Oyen; Michael A Kass; Mae O Gordon; Joshua D Stein
Journal:  JAMA Ophthalmol       Date:  2019-12-01       Impact factor: 8.253

9.  A systematic review of trials evaluating success factors of interventions with computerised clinical decision support.

Authors:  Stijn Van de Velde; Annemie Heselmans; Nicolas Delvaux; Linn Brandt; Luis Marco-Ruiz; David Spitaels; Hanne Cloetens; Tiina Kortteisto; Pavel Roshanov; Ilkka Kunnamo; Bert Aertgeerts; Per Olav Vandvik; Signe Flottorp
Journal:  Implement Sci       Date:  2018-08-20       Impact factor: 7.327

Review 10.  "Many miles to go …": a systematic review of the implementation of patient decision support interventions into routine clinical practice.

Authors:  Glyn Elwyn; Isabelle Scholl; Caroline Tietbohl; Mala Mann; Adrian G K Edwards; Catharine Clay; France Légaré; Trudy van der Weijden; Carmen L Lewis; Richard M Wexler; Dominick L Frosch
Journal:  BMC Med Inform Decis Mak       Date:  2013-11-29       Impact factor: 2.796

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  2 in total

1.  Interests and needs of eye care providers in clinical decision support for glaucoma.

Authors:  Brian Stagg; Joshua D Stein; Felipe A Medeiros; Mollie Cummins; Kensaku Kawamoto; Rachel Hess
Journal:  BMJ Open Ophthalmol       Date:  2021-01-15

Review 2.  Molecular Genetics of Glaucoma: Subtype and Ethnicity Considerations.

Authors:  Ryan Zukerman; Alon Harris; Alice Verticchio Vercellin; Brent Siesky; Louis R Pasquale; Thomas A Ciulla
Journal:  Genes (Basel)       Date:  2020-12-31       Impact factor: 4.096

  2 in total

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