Locating the precise window when patients suffering from chronic obstructive pulmonary disease (COPD) or heart failure (HF) enter the palliative stage remains a persistent clinical obstacle. Detecting this transition early is vital for mapping patient requirements and launching holistic supportive care, yet such recognition often comes too late. Because the majority of individuals diagnosed with HF and COPD remain at home for most of their illness, identifying these shifts within community care networks is vital for predicting needs and preserving patient well-being. On a global scale, only 14% of those who require end-of-life care actually receive it. Primary care settings infrequently deploy formal screening instruments, leaving identification almost entirely to individual clinical judgment. Machine learning (ML) offers a viable approach to uncovering hidden patterns in routine medical records, enabling earlier, more standardized detection of supportive care needs. Consequently, this investigation sought to establish and rank the clinical indicators and symptoms that reflect a shift into the palliative phase for individuals with COPD or HF. These insights are intended to guide the engineering of an ML infrastructure that helps community-based nurses promptly identify palliative care needs. Operating within a pair of community care agencies, this mixed-methods project was executed across four distinct stages: (1) qualitative interviews utilizing a semi-structured format with nine community nursing practitioners; (2) an accelerated literature evaluation of 13 publications addressing palliative indicators in HF and COPD populations; (3) two multidisciplinary focus group sessions featuring 18 experts; and (4) an assessment survey distributed to 115 community nursing staff members to validate and rank the identified indicators. The qualitative findings were analyzed using thematic analysis, whereas descriptive statistics were used to evaluate survey outcomes. Clinical decline emerged as a multi-faceted phenomenon spanning physical, psychological, social, and spiritual realms, unfolding alongside escalating healthcare demands. Physical decline was characterized by aggravating symptoms and diminishing functional independence. In contrast, the psychological and social spheres were marked by anxiety, isolation, and an increasing reliance on others, which was frequently brought to light by family caregivers. The spiritual realm was marked by existential suffering and a fading sense of purpose. Heightened care requirements were evidenced by greater healthcare utilization, unscheduled medical interventions, and emergency hospitalizations. The data from all research steps were synthesized to rank the core indicators most relevant to daily community nursing practice. This research mapped and prioritized the specific clinical indicators used by community-based nursing staff to detect the palliative stage in individuals living with HF and COPD. These data establish a practice-grounded framework for engineering an operational ML tool designed to optimize the early detection of palliative needs and facilitate holistic community-based care.
