Enhancing Palliative Care Registration Using an Automated Robotic Process with Clinician Validation: A PRADA Prospective Cohort Study

摘要

Abstract Background Ineffective care planning at the end of life carries high rates of unscheduled hospitalisation and avoidable deaths in hospital. Palliative care registration (PCR) and crucially, care planning, helps improve adverse outcomes. However, identification for registration often occurs late or not at all. Aim To improve PCR accuracy and uptake by developing an automated digital tool, utilising palliative care process markers, combined with clinical judgement, to identify those with palliative care needs. Design and Setting A primary care based prospective cohort study in Wolverhampton. Method All adults (n=39,079) were included. A robotic process tool (BOT) identified those not on the PCR using any of five palliative care process markers. BOT positive patients were assessed by GPs for the PCR. Performance metrics for prediction of subsequent 1 year mortality were determined. Results 546 patients were BOT positive. GPs added 131 patients (24%) to the PCR. This subset had the highest mortality rate (48.1%) compared to; those already on the PCR (37.2%), BOT positive patient who were not added to the PCR (19.5%) and those who were non-registered and BOT negative (0.8%) (p<0.001). The new combined PCR captured 220 (35.5%) of deaths, compared to 25.4% in the original PCR. A ‘digital safety net’ group, comprising those initially on the PCR and all BOT positive patients, accounted for 48.6% of deaths. Conclusion We developed a robotic process technique combined with GP assessment that yielded higher PCR uptake and accuracy, identifying individuals at-risk for ongoing surveillance. How this fits in Palliative care registers support the delivery of good palliative care by ensuring those who may be nearing the end of life have an assessment of their needs and supported care planning. Previous tools to support identification for palliative care registration have relied on opportunistic identification. Many people who die expectedly are not included on registers. Our new method demonstrates an automated, electronic, data driven system incorporating clinician validation. This system improved the uptake and accuracy of palliative care registration.