TY - JOUR
T1 - The effect of higher level computerized clinical decision support systems on oncology care
T2 - A systematic review
AU - Klarenbeek, Sosse E.
AU - Weekenstroo, Harm H.A.
AU - Sedelaar, J. P.Michiel
AU - Fütterer, Jurgen J.
AU - Prokop, Mathias
AU - Tummers, Marcia
N1 - Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/4/22
Y1 - 2020/4/22
N2 - Background: To deal with complexity in cancer care, computerized clinical decision support systems (CDSSs) are developed to support quality of care and improve decision-making. We performed a systematic review to explore the value of CDSSs using automated clinical guidelines, Artificial Intelligence, datamining or statistical methods (higher level CDSSs) on the quality of care in oncology. Materials and Methods: The search strategy combined synonyms for ‘CDSS’ and ‘cancer.’ Pubmed, Embase, The Cochrane Library, Institute of Electrical and Electronics Engineers, Association of Computing Machinery digital library and Web of Science were systematically searched from January 2000 to December 2019. Included studies evaluated the impact of higher level CDSSs on process outcomes, guideline adherence and clinical outcomes. Results: 11,397 studies were selected for screening, after which 61 full-text articles were assessed for eligibility. Finally, nine studies were included in the final analysis with a total population size of 7985 patients. Types of cancer included breast cancer (63.1%), lung cancer (27.8%), prostate cancer (4.1%), colorectal cancer (3.1%) and other cancer types (1.9%). The included studies demonstrated significant improvements of higher level CDSSs on process outcomes and guideline adherence across diverse settings in oncology. No significant differences were reported for clinical outcomes. Conclusion: Higher level CDSSs seem to improve process outcomes and guidelines adherence but not clinical outcomes. It should be noticed that the included studies primarily focused on breast and lung cancer. To further explore the impact of higher level CDSSs on quality of care, high-quality research is required.
AB - Background: To deal with complexity in cancer care, computerized clinical decision support systems (CDSSs) are developed to support quality of care and improve decision-making. We performed a systematic review to explore the value of CDSSs using automated clinical guidelines, Artificial Intelligence, datamining or statistical methods (higher level CDSSs) on the quality of care in oncology. Materials and Methods: The search strategy combined synonyms for ‘CDSS’ and ‘cancer.’ Pubmed, Embase, The Cochrane Library, Institute of Electrical and Electronics Engineers, Association of Computing Machinery digital library and Web of Science were systematically searched from January 2000 to December 2019. Included studies evaluated the impact of higher level CDSSs on process outcomes, guideline adherence and clinical outcomes. Results: 11,397 studies were selected for screening, after which 61 full-text articles were assessed for eligibility. Finally, nine studies were included in the final analysis with a total population size of 7985 patients. Types of cancer included breast cancer (63.1%), lung cancer (27.8%), prostate cancer (4.1%), colorectal cancer (3.1%) and other cancer types (1.9%). The included studies demonstrated significant improvements of higher level CDSSs on process outcomes and guideline adherence across diverse settings in oncology. No significant differences were reported for clinical outcomes. Conclusion: Higher level CDSSs seem to improve process outcomes and guidelines adherence but not clinical outcomes. It should be noticed that the included studies primarily focused on breast and lung cancer. To further explore the impact of higher level CDSSs on quality of care, high-quality research is required.
KW - Clinical decision support system
KW - Evidence-based medicine
KW - Implementation
KW - Neoplasm
KW - Systematic review
UR - http://www.scopus.com/inward/record.url?scp=85083789256&partnerID=8YFLogxK
U2 - 10.3390/cancers12041032
DO - 10.3390/cancers12041032
M3 - Review article
AN - SCOPUS:85083789256
SN - 2072-6694
VL - 12
JO - Cancers
JF - Cancers
IS - 4
M1 - 1032
ER -