TY - JOUR
T1 - Enhancing predictions of patient conveyance using emergency call handler free text notes for unconscious and fainting incidents reported to the London Ambulance Service
AU - Tollinton, Liam
AU - Metcalf, Alexander M
AU - Velupillai, Sumithra
PY - 2020/9
Y1 - 2020/9
N2 - Objective: Pre-hospital emergency medical services use clinical decision support systems (CDSS) to triage calls. Call handlers often supplement this by making free text notes covering key incident information. We investigate whether machine learning approaches using features from such free text notes can improve prediction of unconscious patients who require conveyance. Materials and methods: We analysed a subset of all London Ambulance Service calls that were triaged through the Medical Priority Dispatch System (MPDS) as involving an unconscious or fainting patient in 2018. We use and compare two machine learning algorithms: random forest (RF) and gradient boosting machine (GBM). For each incident, we predict whether the patient will be conveyed to a hospital emergency department or equivalent using as features 1) the MPDS code, 2) the free text notes and 3) the two together. We evaluate model performance using the area under the curve (AUC) metric. Given the imbalance of outcomes (patient conveyed 71 %, not conveyed 29 %), we also consider sensitivity and specificity. Results: Using only the MPDS code resulted in an AUC of 0.57. Using the text notes gave an improved AUC score of 0.63 and combining the two gave an AUC score of 0.64 (scores were similar for RF and GBM). GBM models scored better on sensitivity (0.93 vs 0.62 for RF in the combined model), but specificity was lower (0.17 vs. 0.56 for RF in the combined model). Conclusions: Using information contained in the free text notes made by call handlers in combination with MPDS improves prediction of unconscious and fainting patients requiring conveyance to a hospital emergency department (or equivalent) when compared with machine learning models using MPDS codes only. This suggests there is some useful information in unstructured data captured by emergency call handlers that complements MPDS codes. Quantifying this gain can help inform emergency medical service policy when evaluating the decision to expand or augment existing CDSS.
AB - Objective: Pre-hospital emergency medical services use clinical decision support systems (CDSS) to triage calls. Call handlers often supplement this by making free text notes covering key incident information. We investigate whether machine learning approaches using features from such free text notes can improve prediction of unconscious patients who require conveyance. Materials and methods: We analysed a subset of all London Ambulance Service calls that were triaged through the Medical Priority Dispatch System (MPDS) as involving an unconscious or fainting patient in 2018. We use and compare two machine learning algorithms: random forest (RF) and gradient boosting machine (GBM). For each incident, we predict whether the patient will be conveyed to a hospital emergency department or equivalent using as features 1) the MPDS code, 2) the free text notes and 3) the two together. We evaluate model performance using the area under the curve (AUC) metric. Given the imbalance of outcomes (patient conveyed 71 %, not conveyed 29 %), we also consider sensitivity and specificity. Results: Using only the MPDS code resulted in an AUC of 0.57. Using the text notes gave an improved AUC score of 0.63 and combining the two gave an AUC score of 0.64 (scores were similar for RF and GBM). GBM models scored better on sensitivity (0.93 vs 0.62 for RF in the combined model), but specificity was lower (0.17 vs. 0.56 for RF in the combined model). Conclusions: Using information contained in the free text notes made by call handlers in combination with MPDS improves prediction of unconscious and fainting patients requiring conveyance to a hospital emergency department (or equivalent) when compared with machine learning models using MPDS codes only. This suggests there is some useful information in unstructured data captured by emergency call handlers that complements MPDS codes. Quantifying this gain can help inform emergency medical service policy when evaluating the decision to expand or augment existing CDSS.
KW - Clinical decision support
KW - Emergency medical services
KW - Machine learning
KW - Medical Priority Dispatch System
KW - Natural language processing
UR - http://www.scopus.com/inward/record.url?scp=85087670999&partnerID=8YFLogxK
U2 - 10.1016/j.ijmedinf.2020.104179
DO - 10.1016/j.ijmedinf.2020.104179
M3 - Article
SN - 1386-5056
VL - 141
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
M1 - 104179
ER -