Journal article
Can artificial intelligence increase the efficiency of referrals from primary to specialized care?
Revista española de enfermedades digestivas, Vol.115(3), pp.141-142
03/2023
DOI: 10.17235/reed.2022.9020/2022
PMID: 35770554
Abstract
The overload of the current healthcare model makes the search for strategies to improve process efficiency essential. An artificial intelligence (AI) program based on Natural Language Processing pipelines (1-5) was used. It analyzed the referrals from primary care to Gastroenterology in the health area corresponding to our hospital in order to identify the most frequent reasons for consultation and to assign them a protocol for the performance of complementary tests before being seen for the first time in specialized care. We compared all referrals received in the first half of 2018, prior to the implementation of the AI pathway July 2018, with those received in the first half of 2019. Our aim was to evaluate the efficiency of this program in terms of discharges, need for additional tests and the number of follow-up visits required (number of follow-up visits/number of first visits in a given time period, FU/F index). In 2018, 1799 referrals were received, 1309 within our health area and 490 from outside the area. In 2019, 2261 referrals were received, 1392 from our area and 869 out-of-area. The AI pathway was applied to 31.4% of the area-referred patients. Overall, in 2019, the number of blood tests and CT scans requested at the first visit decreased (55.3 vs 61.4% and 4.4 vs 7.4% respectively, p<0.05 for both comparisons). The FU/F index in 2019 was 1.9 ± 0.04 vs 2.26 ± 0.07 in 2018 (p<0.05). When analysing patients from our health area, a higher number of discharges at the first consultation was observed during 2019 The number of requested supplementary exams among patients referred using the AI pathway was reduced compared to 2018. The FU/F index in patients referred using the AI pathway was 1.72 ± 0.08 vs 2.25 ± 0.08 in 2018 (p<0.05) and 1.93 ± 0.07 in those referred through the standard pathway in 2019 (p=0.07). Among patients referred from outside our health area, the number of endoscopies requested in 2019 was higher. The FU/F index improved in 2019 (1.95 ± 0.06 vs 2.29 ± 0.13, p<0.05). The number of patients referred using the AI pathway remains low, which could explain the lack of differences observed in the number of discharges or tests requested compared to patients referred via the standard pathway. However, the number of endoscopies and follow up visits requested for these patients did decrease.
Details
- Title: Subtitle
- Can artificial intelligence increase the efficiency of referrals from primary to specialized care?
- Creators
- Marta Tejedor - Hospital Universitario Infanta LeonorAntonio Herrero - Hospital Universitario Infanta LeonorCarlos Castresana - InformaticaRaúl Mesón - Hospital Universitario Infanta LeonorJuan Carlos Taracido - Hospital Universitario Infanta LeonorMarta Sánchez - Hospital Universitario Infanta LeonorMaría Delgado - Hospital Universitario Infanta Leonor
- Resource Type
- Journal article
- Publication Details
- Revista española de enfermedades digestivas, Vol.115(3), pp.141-142
- DOI
- 10.17235/reed.2022.9020/2022
- PMID
- 35770554
- ISSN
- 1130-0108
- eISSN
- 2340-4167
- Language
- English
- Date published
- 03/2023
- Academic Unit
- Internal Medicine
- Record Identifier
- 9984695830902771
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