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.

Improving the adoption of thyroid cancer clinical practice guidelines

“…Moreover, recommendations provided by MATE demonstrated better concordance with CPGs than the multidisciplinary meetings (97.7% vs. 93.2%) . Lung Cancer Assistant (LCA) is a clinical decision support system based on CPGs for lung cancer . Patient records were reviewed retrospectively from the English Lung Cancer Audit Database (LUCADA) and compared to results from LCA.…”

Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention

“…In spite of the critical nature of this resource, much of rule-based CDS to date has been relatively simplistic, and few examples of complex decision algorithms with dozens of decision points have been implemented [2,3]. As increasingly complex clinical protocols are implemented through CDS, innovative approaches will be required to thoroughly and rigorously validate the accuracy of these CDS systems [4]. …”

Multidisciplinary Lung Cancer Meetings: Improving the practice of radiation oncology and facing future challenges

“…Clinical decision support systems are being designed with the aim to improve evidence‐based decision making, reduce erroneous omissions from the data collection and improve MDM efficiency. These clinical decision support systems consolidate information from different sources and allow for processing of individual patient factors, resulting in patient‐specific and evidence‐based recommendations . The concept of an electronic, clinical decision support system has been further enhanced by a multidisciplinary team in the United Kingdom, who have created a hybrid software system of rule‐based and probabilistic modelling, called the Lung Cancer Assistant .…”

“…access to treatment facilities, post‐treatment quality of life) and patient‐related factors (e.g. patient preferences) . Nevertheless, the creators of the Lung Cancer Assistant have clearly shown a potential future application of modern technology that is likely to contribute to the ongoing evolution of Lung Cancer MDM.…”