Literature DB >> 30959468

A Systematic Review of Clinical Decision Support Systems for Clinical Oncology Practice.

Pamala A Pawloski1,2, Gabriel A Brooks3, Matthew E Nielsen4, Barbara A Olson-Bullis1.   

Abstract

BACKGROUND: Electronic health records are central to cancer care delivery. Electronic clinical decision support (CDS) systems can potentially improve cancer care quality and safety. However, little is known regarding the use of CDS systems in clinical oncology and their impact on patient outcomes.
METHODS: A systematic review of peer-reviewed studies was performed to evaluate clinically relevant outcomes related to the use of CDS tools for the diagnosis, treatment, and supportive care of patients with cancer. Peer-reviewed studies published from 1995 through 2016 were included if they assessed clinical outcomes, patient-reported outcomes (PROs), costs, or care delivery process measures.
RESULTS: Electronic database searches yielded 2,439 potentially eligible papers, with 24 studies included after final review. Most studies used an uncontrolled, pre-post intervention design. A total of 23 studies reported improvement in key study outcomes with use of oncology CDS systems, and 12 studies assessing the systems for computerized chemotherapy order entry demonstrated reductions in prescribing error rates, medication-related safety events, and workflow interruptions. The remaining studies examined oncology clinical pathways, guideline adherence, systems for collection and communication of PROs, and prescriber alerts.
CONCLUSIONS: There is a paucity of data evaluating clinically relevant outcomes of CDS system implementation in oncology care. Currently available data suggest that these systems can have a positive impact on the quality of cancer care delivery. However, there is a critical need to rigorously evaluate CDS systems in oncology to better understand how they can be implemented to improve patient outcomes.

Entities:  

Mesh:

Year:  2019        PMID: 30959468      PMCID: PMC6563614          DOI: 10.6004/jnccn.2018.7104

Source DB:  PubMed          Journal:  J Natl Compr Canc Netw        ISSN: 1540-1405            Impact factor:   11.908


  44 in total

Review 1.  The use and interpretation of quasi-experimental studies in medical informatics.

Authors:  Anthony D Harris; Jessina C McGregor; Eli N Perencevich; Jon P Furuno; Jingkun Zhu; Dan E Peterson; Joseph Finkelstein
Journal:  J Am Med Inform Assoc       Date:  2005-10-12       Impact factor: 4.497

2.  Reducing Overuse of Colony-Stimulating Factors in Patients With Lung Cancer Receiving Chemotherapy: Evidence From a Decision Support-Enabled Program.

Authors:  Gboyega Adeboyeje; Abiy Agiro; Jennifer Malin; Michael J Fisch; Andrea DeVries
Journal:  J Oncol Pract       Date:  2017-03-04       Impact factor: 3.840

3.  Impact of electronic chemotherapy order forms on prescribing errors at an urban medical center: results from an interrupted time-series analysis.

Authors:  K Elsaid; T Truong; M Monckeberg; H McCarthy; J Butera; C Collins
Journal:  Int J Qual Health Care       Date:  2013-10-16       Impact factor: 2.038

4.  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

5.  Assessment of efficiency and safety of the comprehensive Chemotherapy Assistance Program for ordering oncology medications.

Authors:  Eun Cho; Hyo-Jung Kim; Gun Min Kim; Jaeyong Kum; Hye-Kyung Chung; Chuhl Joo Lyu; Joong Bae Ahn; Sang Joon Shin
Journal:  Int J Med Inform       Date:  2013-03-06       Impact factor: 4.046

6.  Using an enhanced oral chemotherapy computerized provider order entry system to reduce prescribing errors and improve safety.

Authors:  Christine M Collins; Khaled A Elsaid
Journal:  Int J Qual Health Care       Date:  2010-11-16       Impact factor: 2.038

7.  The differences in health outcomes between Web-based and paper-based implementation of a clinical pathway for radical nephrectomy.

Authors:  P L Chang; Y C Li; S H Lee
Journal:  BJU Int       Date:  2002-10       Impact factor: 5.588

8.  A before-after study using OncoDoc, a guideline-based decision support-system on breast cancer management: impact upon physician prescribing behaviour.

Authors:  J Bouaud; B Séroussi; E C Antoine; L Zelek; M Spielmann
Journal:  Stud Health Technol Inform       Date:  2001

9.  Physicians' Attitudes Towards the Advice of a Guideline-Based Decision Support System: A Case Study With OncoDoc2 in the Management of Breast Cancer Patients.

Authors:  Jacques Bouaud; Jean-Philippe Spano; Jean-Pierre Lefranc; Isabelle Cojean-Zelek; Brigitte Blaszka-Jaulerry; Laurent Zelek; Axel Durieux; Christophe Tournigand; Alexandra Rousseau; Pierre-Yves Vandenbussche; Brigitte Séroussi
Journal:  Stud Health Technol Inform       Date:  2015

10.  Web-based clinical pathway for reducing practice variations in radical prostatectomy.

Authors:  Yu-Chao Hsu; Ke-Hung Tsui; Chien-Lun Chen; Sheng-Hui Lee; Ya-Shen Wu; Phei-Lang Chang
Journal:  Chang Gung Med J       Date:  2008 Nov-Dec
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  15 in total

1.  Clinical decision support for therapeutic decision-making in cancer: A systematic review.

Authors:  Melissa Beauchemin; Meghan T Murray; Lillian Sung; Dawn L Hershman; Chunhua Weng; Rebecca Schnall
Journal:  Int J Med Inform       Date:  2019-08-12       Impact factor: 4.046

Review 2.  Clinical Decision Support Systems.

Authors:  Andreas Teufel; Harald Binder
Journal:  Visc Med       Date:  2021-09-28

3.  From Race-Based to Precision Oncology: Leveraging Behavioral Economics and the Electronic Health Record to Advance Health Equity in Cancer Care.

Authors:  Kelsey S Lau-Min; Carmen E Guerra; Katherine L Nathanson; Justin E Bekelman
Journal:  JCO Precis Oncol       Date:  2021-02-17

Review 4.  Decision Support Systems in Prostate Cancer Treatment: An Overview.

Authors:  Y van Wijk; I Halilaj; E van Limbergen; S Walsh; L Lutgens; P Lambin; B G L Vanneste
Journal:  Biomed Res Int       Date:  2019-06-06       Impact factor: 3.411

5.  A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases.

Authors:  Alberto Comesaña-Campos; Manuel Casal-Guisande; Jorge Cerqueiro-Pequeño; José-Benito Bouza-Rodríguez
Journal:  Int J Environ Res Public Health       Date:  2020-11-20       Impact factor: 3.390

6.  Barriers and Facilitators for Implementation of a Computerized Clinical Decision Support System in Lung Cancer Multidisciplinary Team Meetings-A Qualitative Assessment.

Authors:  Sosse E Klarenbeek; Olga C J Schuurbiers-Siebers; Michel M van den Heuvel; Mathias Prokop; Marcia Tummers
Journal:  Biology (Basel)       Date:  2020-12-25

7.  12th Korea Healthcare Congress 2021; 김치국부터 마시지 말라; The Time for Digital Health is Almost Here.

Authors:  Harlan M Krumholz
Journal:  Yonsei Med J       Date:  2022-05       Impact factor: 2.759

Review 8.  Creation of clinical algorithms for decision-making in oncology: an example with dose prescription in radiation oncology.

Authors:  Fabio Dennstädt; Theresa Treffers; Thomas Iseli; Cédric Panje; Paul Martin Putora
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-12       Impact factor: 2.796

Review 9.  The Effect of Higher Level Computerized Clinical Decision Support Systems on Oncology Care: A Systematic Review.

Authors:  Sosse E Klarenbeek; Harm H A Weekenstroo; J P Michiel Sedelaar; Jurgen J Fütterer; Mathias Prokop; Marcia Tummers
Journal:  Cancers (Basel)       Date:  2020-04-22       Impact factor: 6.639

Review 10.  Drug Dosing Recommendations for All Patients: A Roadmap for Change.

Authors:  J Robert Powell; Jack Cook; Yaning Wang; Richard Peck; Dan Weiner
Journal:  Clin Pharmacol Ther       Date:  2020-07-12       Impact factor: 6.903

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