Literature DB >> 24701413

Computerized decision support in adult and pediatric critical care.

Cydni N Williams1, Susan L Bratton1, Eliotte L Hirshberg1.   

Abstract

Computerized decision support (CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization of meaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.

Entities:  

Keywords:  Clinical decision support systems; Computer-assisted decision making; Computers; Critical care

Year:  2013        PMID: 24701413      PMCID: PMC3953873          DOI: 10.5492/wjccm.v2.i4.21

Source DB:  PubMed          Journal:  World J Crit Care Med        ISSN: 2220-3141


  84 in total

Review 1.  Artificial intelligence applications in the intensive care unit.

Authors:  C W Hanson; B E Marshall
Journal:  Crit Care Med       Date:  2001-02       Impact factor: 7.598

2.  Effect of level of patient acuity on clinical decision making of critical care nurses with varying levels of knowledge and experience.

Authors:  S B Henry
Journal:  Heart Lung       Date:  1991-09       Impact factor: 2.210

3.  Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review.

Authors:  D L Hunt; R B Haynes; S E Hanna; K Smith
Journal:  JAMA       Date:  1998-10-21       Impact factor: 56.272

4.  Computer-assisted decision support for changing practice in severe sepsis and septic shock.

Authors:  S Tafelski; I Nachtigall; M Deja; A Tamarkin; T Trefzer; E Halle; K D Wernecke; C Spies
Journal:  J Int Med Res       Date:  2010 Sep-Oct       Impact factor: 1.671

5.  Computer-driven management of prolonged mechanical ventilation and weaning: a pilot study.

Authors:  Lila Bouadma; François Lellouche; Belen Cabello; Solenne Taillé; Jordi Mancebo; Michel Dojat; Laurent Brochard
Journal:  Intensive Care Med       Date:  2005-08-23       Impact factor: 17.440

6.  Comparison of the effectiveness and safety of two insulin infusion protocols in the management of hyperglycemia in critically ill children.

Authors:  Claudiu Faraon-Pogaceanu; Kenneth J Banasiak; Eliotte L Hirshberg; Edward Vincent S Faustino
Journal:  Pediatr Crit Care Med       Date:  2010-11       Impact factor: 3.624

7.  Electronic health record-based decision support to improve asthma care: a cluster-randomized trial.

Authors:  Louis M Bell; Robert Grundmeier; Russell Localio; Joseph Zorc; Alexander G Fiks; Xuemei Zhang; Tyra Bryant Stephens; Marguerite Swietlik; James P Guevara
Journal:  Pediatrics       Date:  2010-03-15       Impact factor: 7.124

8.  Efficacy of computerized decision support for mechanical ventilation: results of a prospective multi-center randomized trial.

Authors:  T D East; L K Heermann; R L Bradshaw; A Lugo; R M Sailors; L Ershler; C J Wallace; A H Morris; B McKinley; A Marquez; A Tonnesen; L Parmley; W Shoemaker; P Meade; P Thaut; T Hill; M Young; J Baughman; M Olterman; V Gooder; B Quinn; W Summer; V Valentine; J Carlson; K Steinberg
Journal:  Proc AMIA Symp       Date:  1999

Review 9.  Clinical decision support for imaging in the era of the Patient Protection and Affordable Care Act.

Authors:  Hanna M Zafar; Angela M Mills; Ramin Khorasani; Curtis P Langlotz
Journal:  J Am Coll Radiol       Date:  2012-12       Impact factor: 5.532

Review 10.  Clinical decision support systems to improve utilization of thromboprophylaxis: a review of the literature and experience with implementation of a computerized physician order entry program.

Authors:  Paul Adams; Jeff M Riggio; Lynda Thomson; Renee Brandell-Marino; Geno Merli
Journal:  Hosp Pract (1995)       Date:  2012-08
View more
  6 in total

1.  A Pediatric Intensive Care Unit Bedside Computer Clinical Decision Support Protocol for Hyperglycemia Is Feasible, Safe and Offers Advantages.

Authors:  Eliotte L Hirshberg; Michael J Lanspa; Emily L Wilson; Katherine A Sward; Al Jephson; Gitte Y Larsen; Alan H Morris
Journal:  Diabetes Technol Ther       Date:  2017-03-01       Impact factor: 6.118

2.  Health care professionals' knowledge of commonly used sedative, analgesic and neuromuscular drugs: A single center (Rambam Health Care Campus), prospective, observational survey.

Authors:  Danny Epstein; Yaniv Steinfeld; Erez Marcusohn; Hanna Ammouri; Asaf Miller
Journal:  PLoS One       Date:  2020-01-10       Impact factor: 3.240

Review 3.  Decision Support Capabilities of Telemedicine in Emergency Prehospital Care: Systematic Review.

Authors:  Yesul Kim; Christopher Groombridge; Lorena Romero; Steven Clare; Mark Christopher Fitzgerald
Journal:  J Med Internet Res       Date:  2020-12-08       Impact factor: 5.428

4.  Barriers to and Facilitators for Acceptance of Comprehensive Clinical Decision Support System-Driven Care Maps for Patients With Thoracic Trauma: Interview Study Among Health Care Providers and Nurses.

Authors:  Emma K Jones; Alyssa Banks; Genevieve B Melton; Carolyn M Porta; Christopher J Tignanelli
Journal:  JMIR Hum Factors       Date:  2022-03-16

5.  Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing.

Authors:  Antje Wulff; Marcel Mast; Marcus Hassler; Sara Montag; Michael Marschollek; Thomas Jack
Journal:  Methods Inf Med       Date:  2020-10-14       Impact factor: 2.176

6.  Performance of an Electronic Decision Support System as a Therapeutic Intervention During a Multicenter PICU Clinical Trial: Heart and Lung Failure-Pediatric Insulin Titration Trial (HALF-PINT).

Authors:  Eliotte L Hirshberg; Jamin L Alexander; Lisa A Asaro; Kerry Coughlin-Wells; Garry M Steil; Debbie Spear; Cheryl Stone; Vinay M Nadkarni; Michael S D Agus
Journal:  Chest       Date:  2021-04-29       Impact factor: 9.410

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.