Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique
Abstract Background A subgroup of patients with asthma has been reported to have an increased risk for asthma-associated infectious and inflammatory multimorbidities (AIMs). To systematically investigate the association of asthma with AIMs using a large patient cohort, it is desired to leverage a br...
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2021
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oai:doaj.org-article:09ad4a321ac44fa69f80cdbfc158cb832021-11-14T12:29:21ZEstablishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique10.1186/s12911-021-01663-y1472-6947https://doaj.org/article/09ad4a321ac44fa69f80cdbfc158cb832021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01663-yhttps://doaj.org/toc/1472-6947Abstract Background A subgroup of patients with asthma has been reported to have an increased risk for asthma-associated infectious and inflammatory multimorbidities (AIMs). To systematically investigate the association of asthma with AIMs using a large patient cohort, it is desired to leverage a broad range of electronic health record (EHR) data sources to automatically identify AIMs accurately and efficiently. Methods We established an expert consensus for an operational definition for each AIM from EHR through a modified Delphi technique. A series of questions about the operational definition of 19 AIMS (11 infectious diseases and 8 inflammatory diseases) was generated by a core team of experts who considered feasibility, balance between sensitivity and specificity, and generalizability. Eight internal and 5 external expert panelists were invited to individually complete a series of online questionnaires and provide judgement and feedback throughout three sequential internal rounds and two external rounds. Panelists’ responses were collected, descriptive statistics tabulated, and results reported back to the entire group. Following each round the core team of experts made iterative edits to the operational definitions until a moderate (≥ 60%) or strong (≥ 80%) level of consensus among the panel was achieved. Results Response rates for each Delphi round were 100% in all 5 rounds with the achievement of the following consensus levels: (1) Internal panel consensus: 100% for 8 definitions, 88% for 10 definitions, and 75% for 1 definition, (2) External panel consensus: 100% for 12 definitions and 80% for 7 definitions. Conclusions The final operational definitions of AIMs established through a modified Delphi technique can serve as a foundation for developing computational algorithms to automatically identify AIMs from EHRs to enable large scale research studies on patient’s multimorbidities associated with asthma.Jungwon YoonHeather BillingsChung-Il WiElissa HallSunghwan SohnJung Hyun KwonEuijung RyuPragya ShresthaHongfang LiuYoung J. JuhnBMCarticleDelphiAsthmaMultimorbiditiesElectronic health recordsNatural language processingComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Delphi Asthma Multimorbidities Electronic health records Natural language processing Computer applications to medicine. Medical informatics R858-859.7 |
| spellingShingle |
Delphi Asthma Multimorbidities Electronic health records Natural language processing Computer applications to medicine. Medical informatics R858-859.7 Jungwon Yoon Heather Billings Chung-Il Wi Elissa Hall Sunghwan Sohn Jung Hyun Kwon Euijung Ryu Pragya Shrestha Hongfang Liu Young J. Juhn Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique |
| description |
Abstract Background A subgroup of patients with asthma has been reported to have an increased risk for asthma-associated infectious and inflammatory multimorbidities (AIMs). To systematically investigate the association of asthma with AIMs using a large patient cohort, it is desired to leverage a broad range of electronic health record (EHR) data sources to automatically identify AIMs accurately and efficiently. Methods We established an expert consensus for an operational definition for each AIM from EHR through a modified Delphi technique. A series of questions about the operational definition of 19 AIMS (11 infectious diseases and 8 inflammatory diseases) was generated by a core team of experts who considered feasibility, balance between sensitivity and specificity, and generalizability. Eight internal and 5 external expert panelists were invited to individually complete a series of online questionnaires and provide judgement and feedback throughout three sequential internal rounds and two external rounds. Panelists’ responses were collected, descriptive statistics tabulated, and results reported back to the entire group. Following each round the core team of experts made iterative edits to the operational definitions until a moderate (≥ 60%) or strong (≥ 80%) level of consensus among the panel was achieved. Results Response rates for each Delphi round were 100% in all 5 rounds with the achievement of the following consensus levels: (1) Internal panel consensus: 100% for 8 definitions, 88% for 10 definitions, and 75% for 1 definition, (2) External panel consensus: 100% for 12 definitions and 80% for 7 definitions. Conclusions The final operational definitions of AIMs established through a modified Delphi technique can serve as a foundation for developing computational algorithms to automatically identify AIMs from EHRs to enable large scale research studies on patient’s multimorbidities associated with asthma. |
| format |
article |
| author |
Jungwon Yoon Heather Billings Chung-Il Wi Elissa Hall Sunghwan Sohn Jung Hyun Kwon Euijung Ryu Pragya Shrestha Hongfang Liu Young J. Juhn |
| author_facet |
Jungwon Yoon Heather Billings Chung-Il Wi Elissa Hall Sunghwan Sohn Jung Hyun Kwon Euijung Ryu Pragya Shrestha Hongfang Liu Young J. Juhn |
| author_sort |
Jungwon Yoon |
| title |
Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique |
| title_short |
Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique |
| title_full |
Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique |
| title_fullStr |
Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique |
| title_full_unstemmed |
Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique |
| title_sort |
establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified delphi technique |
| publisher |
BMC |
| publishDate |
2021 |
| url |
https://doaj.org/article/09ad4a321ac44fa69f80cdbfc158cb83 |
| work_keys_str_mv |
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