Improving palliative care with machine learning and routine data: a rapid review

Abstract

Introduction: Improving palliative care is a priority worldwide as this population experiences poor outcomes and accounts disproportionately for costs. In clinical practice, physician judgement is the core method of identifying palliative care needs but has important limitations. Machine learning (ML) is a subset of artificial intelligence advancing capacity to identify patterns and make predictions using large datasets. ML has the potential to improve clinical decision-making and policy design, but there has been no systematic assembly of current evidence. Methods: We conducted a rapid review, searching systematically seven databases from inception to December 31st, 2018: EMBASE, MEDLINE, Cochrane Library, PsycINFO, WOS, SCOPUS and ECONLIT. We included peer-reviewed studies that used ML approaches on routine data to improve palliative care for adults. Our specified outcomes were survival, quality of life (QoL), place of death, costs, and receipt of high-intensity treatment near end of life. We did not search grey literature. Results: The database search identified 426 citations. We discarded 162 duplicates and screened 264 unique title/abstracts, of which 22 were forwarded for full text review. Three papers were included, 18 papers were excluded and one full text was sought but unobtainable. One paper predicted six-month mortality, one paper predicted 12-month mortality and one paper cross-referenced predicted 12-month mortality with healthcare spending. ML-informed models outperformed logistic regression in predicting mortality where data inputs were relatively strong, but those using only basic administrative data had limited benefit from ML. Identifying poor prognosis does not appear effective in tackling high costs associated with serious illness. Conclusion: While ML can in principle help to identify those at risk of adverse outcomes and inappropriate treatment, applications to policy and practice are formative. Future research must not only expand scope to other outcomes and longer timeframes, but also engage with individual preferences and ethical challenges.

Prediction model for short-term mortality after palliative radiotherapy for patients having advanced cancer: a cohort study from routine electronic medical data

“…Additionally, it could help clinicians identify patients unlikely to benefit from RT beyond 30 days and those who may instead benefit from earlier palliative care referral and end-of-life planning. Machine learning techniques have the potential to improve clinical decision-making by identifying those at increased risk of poor mortality 38 . In 3 studies summarized by a systematic review, machine learning techniques are better than routine logistic regression in building model for mortality prediction in older and/or hospitalized adults, if enough data are obtained [38][39][40][41] .…”

“…Machine learning techniques have the potential to improve clinical decision-making by identifying those at increased risk of poor mortality 38 . In 3 studies summarized by a systematic review, machine learning techniques are better than routine logistic regression in building model for mortality prediction in older and/or hospitalized adults, if enough data are obtained [38][39][40][41] . Future research is needed to incorporate machine learning techniques and to determine the generalizability and feasibility of the application of prediction tool in clinical settings.…”

Machine learning in oncology—Perspectives in patient-reported outcome research

“…These two studies show that PROs can be used both as a predictor and a criterion in machine learning models; however, there are only a few studies in the literature that combine machine learning methods with PRO data. In a systematic review of machine learning approaches in palliative care research, a field in which PROs should play an important role, no studies were found that included PROs [26].…”

Machine Learning in der Onkologie – Perspektiven in der Patient-Reported-Outcome-Forschung

“…Allerdings finden sich in der Literatur nur vereinzelt Studien, welche Machine-Learning-Verfahren mit PRO-Daten kombinieren. Auch in einem syste-matischenReviewzu Machine-Learning-Ansätzen in der palliativen Behandlung von Patienten, einem Feld, in welchem PRO eigentlich eine bedeutende Rolle spielen sollten, wurden keine Studien gefunden, die PRO einbeziehen [24].…”