Literature DB >> 23239213

Future of electronic health records: implications for decision support.

Brian Rothman1, Joan C Leonard, Michael M Vigoda.   

Abstract

The potential benefits of the electronic health record over traditional paper are many, including cost containment, reductions in errors, and improved compliance by utilizing real-time data. The highest functional level of the electronic health record (EHR) is clinical decision support (CDS) and process automation, which are expected to enhance patient health and healthcare. The authors provide an overview of the progress in using patient data more efficiently and effectively through clinical decision support to improve health care delivery, how decision support impacts anesthesia practice, and how some are leading the way using these systems to solve need-specific issues. Clinical decision support uses passive or active decision support to modify clinician behavior through recommendations of specific actions. Recommendations may reduce medication errors, which would result in considerable savings by avoiding adverse drug events. In selected studies, clinical decision support has been shown to decrease the time to follow-up actions, and prediction has proved useful in forecasting patient outcomes, avoiding costs, and correctly prompting treatment plan modifications by clinicians before engaging in decision-making. Clinical documentation accuracy and completeness is improved by an electronic health record and greater relevance of care data is delivered. Clinical decision support may increase clinician adherence to clinical guidelines, but educational workshops may be equally effective. Unintentional consequences of clinical decision support, such as alert desensitization, can decrease the effectiveness of a system. Current anesthesia clinical decision support use includes antibiotic administration timing, improved documentation, more timely billing, and postoperative nausea and vomiting prophylaxis. Electronic health record implementation offers data-mining opportunities to improve operational, financial, and clinical processes. Using electronic health record data in real-time for decision support and process automation has the potential to both reduce costs and improve the quality of patient care.
© 2012 Mount Sinai School of Medicine.

Entities:  

Mesh:

Year:  2012        PMID: 23239213     DOI: 10.1002/msj.21351

Source DB:  PubMed          Journal:  Mt Sinai J Med        ISSN: 0027-2507


  26 in total

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Authors:  James F Holmes; Joshua Freilich; Sandra L Taylor; David Buettner
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Review 2.  Future Research in Health Information Technology: A Review.

Authors:  Morteza Hemmat; Haleh Ayatollahi; Mohammad Reza Maleki; Fatemeh Saghafi
Journal:  Perspect Health Inf Manag       Date:  2017-01-01

Review 3.  "Big data" and the electronic health record.

Authors:  M K Ross; W Wei; L Ohno-Machado
Journal:  Yearb Med Inform       Date:  2014-08-15

Review 4.  Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.

Authors:  Benjamin A Goldstein; Ann Marie Navar; Michael J Pencina; John P A Ioannidis
Journal:  J Am Med Inform Assoc       Date:  2016-05-17       Impact factor: 4.497

5.  Reporting of demographic data and representativeness in machine learning models using electronic health records.

Authors:  Selen Bozkurt; Eli M Cahan; Martin G Seneviratne; Ran Sun; Juan A Lossio-Ventura; John P A Ioannidis; Tina Hernandez-Boussard
Journal:  J Am Med Inform Assoc       Date:  2020-12-09       Impact factor: 4.497

6.  Critical Care Organizations: Building and Integrating Academic Programs.

Authors:  Jason E Moore; John M Oropello; Daniel Stoltzfus; Henry Masur; Craig M Coopersmith; Joseph Nates; Christopher Doig; John Christman; R Duncan Hite; Derek C Angus; Stephen M Pastores; Vladimir Kvetan
Journal:  Crit Care Med       Date:  2018-04       Impact factor: 7.598

7.  Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.

Authors:  Wei-Chun Lin; Jimmy S Chen; Joel Kaluzny; Aiyin Chen; Michael F Chiang; Michelle R Hribar
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

8.  CRcoder: An Interactive Web Application and SAS Macro to Support Personalized Clinical Decisions.

Authors:  Gail J McAvay; Terrence E Murphy; George O Agogo; Heather Allore
Journal:  Perm J       Date:  2019-12-18

9.  Patient-Specific Explanations for Predictions of Clinical Outcomes.

Authors:  Mohammadamin Tajgardoon; Malarkodi J Samayamuthu; Luca Calzoni; Shyam Visweswaran
Journal:  ACI open       Date:  2019-11-10

10.  Strategies to increase uptake of maternal pertussis vaccination.

Authors:  Kavin M Patel; Laia Vazquez Guillamet; Lauren Pischel; Mallory K Ellingson; Azucena Bardají; Saad B Omer
Journal:  Expert Rev Vaccines       Date:  2021-07-21       Impact factor: 5.683

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