title: Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.
abstract: Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.
authors.affiliation: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK berkan.sesen@gmail.com.
authors.forename: M Berkan
authors.initials: MB
authors.lastname: Sesen
authors.affiliation: Clinical Effectiveness and Evaluation Unit, Royal College of Physicians of London, London NW1 4LE, UK Department of Respiratory Medicine, Glenfield Hospital, Leicester LE3 9QP, UK.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Peake
authors.affiliation: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK.
authors.forename: Rene
authors.initials: R
authors.lastname: Banares-Alcantara
authors.affiliation: Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Oxford OX3 7LJ, UK.
authors.forename: Donald
authors.initials: D
authors.lastname: Tse
authors.affiliation: Mirada Medical, Oxford OX1 1BY, UK.
authors.forename: Timor
authors.initials: T
authors.lastname: Kadir
authors.affiliation: Clinical Effectiveness and Evaluation Unit, Royal College of Physicians of London, London NW1 4LE, UK.
authors.forename: Roz
authors.initials: R
authors.lastname: Stanley
authors.affiliation: Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Oxford OX3 7LJ, UK.
authors.forename: Fergus
authors.initials: F
authors.lastname: Gleeson
authors.affiliation: Department of Oncology, University of Oxford, Oxford OX3 7DQ, UK.
authors.forename: Michael
authors.initials: M
authors.lastname: Brady
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016208:Databases, Factual; D003657:Decision Making; D020000:Decision Support Systems, Clinical; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000628:therapy*; D008495:Medical Oncology / Q000379:methods*; D017410:Practice Guidelines as Topic; D011336:Probability; D012984:Software
keywords: computerized decision support; lung cancer care; medical informatics