Intelligent Palliative Care Based on Patient-Reported Outcome Measures

Context. The growth of patient reported outcome measures data in palliative care provides an opportunity for machine learning to identify patterns in patient responses signifying different phases of illness. Objectives. The study will explore if machine learning and network analysis can identify phases in patient palliative status through symptoms reported on the Integrated Palliative Care Outcome Scale (IPOS). Methods. A partly cross-sectional and partially longitudinal observational study was undertaken using the Australasian Karnofsky Performance Scale (AKPS); Integrated Palliative Care Outcome Scale (IPOS); Phase of Illness (POI). Patient palliative records (n = 1507, 65% stable, 20% unstable, 9% deteriorating, 2% terminal) from 804 adult patients enrolled in a New Zealand palliative care service were analysed using a combination of statistical, machine learning and network analysis techniques. Results. Data from IPOS showed considerable variation with phase. Also, network analysis showed clear associations between items by phase. Six machine learning techniques identified the most important variables for predicting possible transition between phases of illness. Network analysis for all patients showed that Poor Appetite and Loss of Energy were central IPOS items, with Loss of Energy linked to Drowsiness, Shortness of Breath and Lack of Mobility on the one hand, and Poor Appetite linked to Nausea, Vomiting, Constipation and Sore and Dry Mouth on the other. Conclusion. These preliminary results, when coupled with the latest technological developments in mobile apps and wearable technology, could point the way to increased use of digital therapeutics in continuous palliative care monitoring. (C) 2021 The Authors. Published by Elsevier Inc. on behalf of American Academy of Hospice and Palliative Medicine.

Automated summary

This study investigates whether machine learning and network analysis can identify different phases of patient palliative status based on symptoms reported on the Integrated Palliative Care Outcome Scale (IPOS). The results show significant variation in symptoms among different phases and clear associations between specific symptoms. Machine learning techniques are also utilized to predict the possible transition between phases. These findings, coupled with advancements in mobile apps and wearable technology, suggest the potential for increased use of digital therapeutics in continuous palliative care monitoring.