Abstract
In recent years we have witnessed the increasing adoption of clinical practice guidelines (CPGs) as decision support tools that guide medical treatment. As CPGs gain popularity, it has become evident that physicians frequently deviate from CPG recommendations, both erroneously and due to sound medical rationale. In this study we developed a methodology to computationally identify these deviation cases and understand their movitation. This was achieved using an integrated approach consisting of natural language processing, data modeling, and comparison methods to characterize deviations from CPG recommendations for 1431 adult soft tissue sarcoma patients. The results show that 48.9% of patient treatment programs deviate from CPG recommendations, with the largest deviation type being overtreatment, followed by differences in drug treatments. Interestingly, we identified over a dozen potential reasons for these deviations, with those directly related to the patients' cancer status being most abundant. These findings can be used to modify CPGs, increase adherence to CPG recommendations, reduce treatment cost, and potentially impact sarcoma care. Our approach can be applied to additional diseases that are subject to high deviation levels from CPGs.
| Original language | English |
|---|---|
| Title of host publication | Studies in Health Technology and Informatics |
| Publisher | IOS Press |
| Pages | 280-284 |
| Number of pages | 5 |
| Volume | 216 |
| ISBN (Print) | 9781614995630 |
| DOIs | |
| Publication status | Published - 2015 |
| Event | 15th World Congress on Health and Biomedical Informatics, MEDINFO 2015 - Sao Paulo, Brazil Duration: Aug 19 2015 → Aug 23 2015 |
Publication series
| Name | Studies in Health Technology and Informatics |
|---|---|
| Volume | 216 |
| ISSN (Print) | 09269630 |
| ISSN (Electronic) | 18798365 |
Other
| Other | 15th World Congress on Health and Biomedical Informatics, MEDINFO 2015 |
|---|---|
| Country | Brazil |
| City | Sao Paulo |
| Period | 8/19/15 → 8/23/15 |
Fingerprint
Keywords
- Decision Support Techniques [L01.700.508.190]
- Natural Language Processing [L01.224.065.580]
- Physician's Practice Patterns [N04.590.748]
- Practice Guideline [V02.515.500]
- Sarcoma [C04.557.450.795]
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
- Health Information Management
Cite this
Understanding Deviations from Clinical Practice Guidelines in Adult Soft Tissue Sarcoma. / Goldbraich, Esther; Waks, Zeev; Farkash, Ariel; Monti, Marco; Torresani, Michele; Bertulli, Rossella; Casali, Paolo Giovanni; Carmeli, Boaz.
Studies in Health Technology and Informatics. Vol. 216 IOS Press, 2015. p. 280-284 (Studies in Health Technology and Informatics; Vol. 216).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Understanding Deviations from Clinical Practice Guidelines in Adult Soft Tissue Sarcoma
AU - Goldbraich, Esther
AU - Waks, Zeev
AU - Farkash, Ariel
AU - Monti, Marco
AU - Torresani, Michele
AU - Bertulli, Rossella
AU - Casali, Paolo Giovanni
AU - Carmeli, Boaz
PY - 2015
Y1 - 2015
N2 - In recent years we have witnessed the increasing adoption of clinical practice guidelines (CPGs) as decision support tools that guide medical treatment. As CPGs gain popularity, it has become evident that physicians frequently deviate from CPG recommendations, both erroneously and due to sound medical rationale. In this study we developed a methodology to computationally identify these deviation cases and understand their movitation. This was achieved using an integrated approach consisting of natural language processing, data modeling, and comparison methods to characterize deviations from CPG recommendations for 1431 adult soft tissue sarcoma patients. The results show that 48.9% of patient treatment programs deviate from CPG recommendations, with the largest deviation type being overtreatment, followed by differences in drug treatments. Interestingly, we identified over a dozen potential reasons for these deviations, with those directly related to the patients' cancer status being most abundant. These findings can be used to modify CPGs, increase adherence to CPG recommendations, reduce treatment cost, and potentially impact sarcoma care. Our approach can be applied to additional diseases that are subject to high deviation levels from CPGs.
AB - In recent years we have witnessed the increasing adoption of clinical practice guidelines (CPGs) as decision support tools that guide medical treatment. As CPGs gain popularity, it has become evident that physicians frequently deviate from CPG recommendations, both erroneously and due to sound medical rationale. In this study we developed a methodology to computationally identify these deviation cases and understand their movitation. This was achieved using an integrated approach consisting of natural language processing, data modeling, and comparison methods to characterize deviations from CPG recommendations for 1431 adult soft tissue sarcoma patients. The results show that 48.9% of patient treatment programs deviate from CPG recommendations, with the largest deviation type being overtreatment, followed by differences in drug treatments. Interestingly, we identified over a dozen potential reasons for these deviations, with those directly related to the patients' cancer status being most abundant. These findings can be used to modify CPGs, increase adherence to CPG recommendations, reduce treatment cost, and potentially impact sarcoma care. Our approach can be applied to additional diseases that are subject to high deviation levels from CPGs.
KW - Decision Support Techniques [L01.700.508.190]
KW - Natural Language Processing [L01.224.065.580]
KW - Physician's Practice Patterns [N04.590.748]
KW - Practice Guideline [V02.515.500]
KW - Sarcoma [C04.557.450.795]
UR - http://www.scopus.com/inward/record.url?scp=84951957453&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951957453&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-564-7-280
DO - 10.3233/978-1-61499-564-7-280
M3 - Conference contribution
AN - SCOPUS:84951957453
SN - 9781614995630
VL - 216
T3 - Studies in Health Technology and Informatics
SP - 280
EP - 284
BT - Studies in Health Technology and Informatics
PB - IOS Press
ER -