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Background: A universal consensus regarding standardized pain outcomes does not exist. The personalized pain goal has been suggested as a clinically relevant outcome measure. Aim: To assess the feasibility of obtaining a personalized pain goal and to compare a clinically based personalized pain goal definition versus a research-based study definition for stable pain. Design: Prospective longitudinal descriptive study. Measures: The attending physician completed routine assessments, including a personalized pain goal and the Edmonton Classification System for Cancer Pain, and followed patients daily until stable pain control, death, or discharge. Stable pain for cognitively intact patients was defined as pain intensity less than or equal to desired pain intensity goal (personalized pain goal definition) or pain intensity ⩽3 (Edmonton Classification System for Cancer Pain study definition) for three consecutive days with Setting/participants: A total of 300 consecutive advanced cancer patients were recruited from two acute care hospitals and a tertiary palliative care unit. Results: In all, 231/300 patients (77%) had a pain syndrome; 169/231 (73%) provided a personalized pain goal, with 113/169 (67%) reporting a personalized pain goal ⩽3 (median = 3, range = 0–10). Using the personalized pain goal definition as the gold standard, sensitivity and specificity of the Edmonton Classification System for Cancer Pain definition were 71.3% and 98.5%, respectively. For mild (0–3), moderate (4–6), and severe (7–10) pain, the highest sensitivity was for moderate pain (90.5%), with high specificity across all three categories (95%–100%). Conclusion: The personalized pain goal is a feasible outcome measure for cognitively intact patients. The Edmonton Classification System for Cancer Pain definition closely resembles patient-reported personalized pain goals for stable pain and would be appropriate for research purposes. For clinical pain management, it would be important to include the personalized pain goal as standard practice.

Objectives: Single, program wide multi-site prospective study examining the (1) accuracy of non-cancer clinician prediction survival (CPS) by palliative practitioners on 1st assessment. (2) Factors affecting CPS; (3) potential impact on clinical care.Design and methods: Single, program wide multi-site prospective study (n = 282). CPS was divided into four clinically relevant time periods (≤2 weeks, >2 to ≤6 weeks, >6 to ≤12 weeks, and >12 weeks). Multivariate analysis was done on six predictor variables.Results: More than ½ of patients died before their clinically relevant predicted time periods (of more than 2 weeks). Only one out of the six predictor variables impacted on clinician accuracy on logistic regression and that being the survival prediction time periods.Conclusion: Non-cancer CPS, even by palliative practitioners, remains overly optimistic. This raises the question in that these individuals may have been potentially over-treated.

Survival prediction of advanced cancer patients remains an important task for palliative clinicians. It has transformed from an art form into a more scientific branch of the discipline with the evolution of palliative medicine and use of statistical estimates of survival. Both clinician predicted survival and actuarial estimation of survival have their uses and drawbacks. This article gives a practical and quick summary of the pros and cons of clinician survival prediction and actuarial-based prognostic tools used at the bedside.

Despite a trend to formalize and codify medical information, natural language communications still play a prominent role in health care workflows, in particular when it comes to hand-overs between providers. Natural language processing (NLP) attempts to bridge the gap between informal, natural language information and coded, machine-interpretable data. This paper reports on a study that applies an advanced NLP method for the extraction of sentinel events in palliative care consult letters. Sentinel events are of interest to predict survival and trajectory for patients with acute palliative conditions. Our NLP method combines several novel characteristics, e.g., the consideration of topological knowledge structures sourced from an ontological terminology system (SNOMED CT). The method has been applied to the extraction of different types of sentinel events, including simple facts, temporal conditions, quantities, and degrees. A random selection of 215 anonymized consult letters was used for the study. The results of the NLP extraction were evaluated by comparison with coded sentinel event data captured independently by clinicians. The average accuracy of the automated extraction was 73.6%.