Machine learning can improve palliative care

eHealthNews.nz: Clinical Software

Tuesday, 15 February 2022

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NEWS - eHealthNews.nz editor Rebecca McBeth

Critical changes in the clinical status of palliative patients can be predicted using machine learning, a new study shows.

Lead author of the study, Margaret Sandham at Auckland University of Technology (AUT), says that by monitoring key symptom changes in real time using machine learning, clinicians can enable early interventions to pre-empt and prevent patient deterioration.

The potential applications, including mobile apps and wearable technology, could be used by clinicians and family caregivers to improve quality of life for terminally ill patients.

AUT researchers, together with co-authors from Waitematā DHB and Kings College London, collaborated on the study which aimed to identify whether machine learning could predict changes in patients’ clinical status using the Integrated Palliative Care Outcomes Scale (IPOS).

Anonymised self-reported symptoms from 800 adults, who were enrolled in palliative care services in New Zealand, was analysed through a combination of statistical tools, machine learning, and network visualisation.

The study identified the variables for predicting transitions between phases of illness using six machine learning techniques. Network analysis of these variables revealed that poor appetite and loss of energy are the most critical symptoms.

“This is the first time palliative-specific data has been used in machine learning, as far as we are aware,” says Sandham.

The findings of the study, Intelligent palliative care based on patient-reported outcome measures, were published in the Journal of Pain and Symptom Management.

These preliminary results indicate that future digital therapeutics in palliative care, based on mobile apps and wearable devices, could focus on sensors dealing with pain, nausea, mobility, weakness, and shortness of breath – generating a data feed linked to a patient profile, to be used by specialists and non-specialists alike.

“Automatic hourly readings from different sensors can be a non-intrusive method for detecting changes in clinical status, often in advance of periodic clinical assessments or patient-reported outcomes,” says Sandham.

“Such is the progress in these systems and technologies that the question for palliative care is not whether they will be used, but when and how.”

In New Zealand, palliative care is provided across different environments by any number of people, including hospital and hospice staff, district nurses, general practitioners, and whānau.

“Integrating patient-reported outcome measures into routine care, in an ethically responsible way, would contribute to common knowledge of patient symptoms and experiences, and a shared language between clinicians and family caregivers,” says Sandham.