BMC health services research · 2026
Understanding research priorities in home health care: a topic modeling analysis of the health services literature.
СтатьяНазвание и аннотация: Перевод в очереди
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Bae Mijin, Kim Minsung, Kim Aeri, Lee Hana, Woo Kyungmi
Аннотация
BACKGROUND: Home health care (HHC) has become vital amid global aging, rising chronic disease burdens, and shifts toward community-based care. Yet, research priorities remain fragmented. This study aimed to map the evolution of HHC scholarship and identify emerging and underexplored themes.
METHODS: We analyzed 39,548 PubMed-indexed HHC publications (1952-April 2025) using Latent Dirichlet Allocation topic modeling. A hybrid Konstanz Information Miner-Python workflow supported preprocessing, parameter optimization, and topic extraction. Model performance was assessed with perplexity, coherence, and clustering validation.
RESULTS: Ten coherent topics were identified, spanning clinical, technological, and population-focused domains. Healthcare resource utilization remained the most consistent and dominant theme. Rapidly expanding areas included telehealth adoption, technology-assisted caregiving, and end-of-life services. Temporal analysis revealed increasing attention to digitally integrated, person-centered models since the 2010s, accelerated by the COVID-19 pandemic. Pediatric care and high-risk population screening were notably underrepresented.
CONCLUSIONS: This study provides a macro-level mapping of seven decades of HHC research, highlighting evolving priorities and persistent blind spots. Topic modeling offers valuable insights for shaping a more inclusive, evidence-informed research agenda and guiding policy development in home-based care.
METHODS: We analyzed 39,548 PubMed-indexed HHC publications (1952-April 2025) using Latent Dirichlet Allocation topic modeling. A hybrid Konstanz Information Miner-Python workflow supported preprocessing, parameter optimization, and topic extraction. Model performance was assessed with perplexity, coherence, and clustering validation.
RESULTS: Ten coherent topics were identified, spanning clinical, technological, and population-focused domains. Healthcare resource utilization remained the most consistent and dominant theme. Rapidly expanding areas included telehealth adoption, technology-assisted caregiving, and end-of-life services. Temporal analysis revealed increasing attention to digitally integrated, person-centered models since the 2010s, accelerated by the COVID-19 pandemic. Pediatric care and high-risk population screening were notably underrepresented.
CONCLUSIONS: This study provides a macro-level mapping of seven decades of HHC research, highlighting evolving priorities and persistent blind spots. Topic modeling offers valuable insights for shaping a more inclusive, evidence-informed research agenda and guiding policy development in home-based care.