Analisis Deskriptif Karakteristik Pasien Osteoartritis Lanjut Usia pada Pola Keluhan Nyeri, Komorbiditas, dan Faktor Demografis dengan Kombinasi AI (Artificial Intelligence) di Wilayah Kerja Puskesmas Tirta Jaya, Kabupaten Tanah Laut

Muhammad Husein Syawaludin; Melly Anisa Putri; Wendelinus Imus; Eva Siska; M. Rizali

doi:10.53863/kst.v8i02.2460

Authors

Muhammad Husein Syawaludin Politeknik Kesehatan Borneo Citra Medika, Tanah Laut, Indonesia
Melly Anisa Putri Politeknik Kesehatan Borneo Citra Medika, Tanah Laut, Indonesia
Wendelinus Imus Politeknik Kesehatan Borneo Citra Medika, Tanah Laut, Indonesia
Eva Siska Politeknik Kesehatan Borneo Citra Medika, Tanah Laut, Indonesia
M. Rizali Politeknik Kesehatan Borneo Citra Medika, Tanah Laut, Indonesia

DOI:

https://doi.org/10.53863/kst.v8i02.2460

Keywords:

osteoarthritis; elderly; pain scale; comorbidity; artificial intelligence

Abstract

Osteoarthritis (OA) is the most common degenerative joint disease among older adults and frequently co-occurs with various comorbid conditions. This study aimed to descriptively analyze the characteristics of elderly OA patients (aged 60 years and above) based on pain complaint patterns, comorbidity, and demographic factors in the working area of Tirta Jaya Public Health Center, Tanah Laut Regency, supported by a combination of artificial intelligence (AI) in the data processing and synthesis process. This was a descriptive observational study using retrospective secondary data from elderly patient visit records from 1 January to 31 December 2025. Of 325 elderly patients recorded with various diagnoses, 52 patients (16.0%) were diagnosed with osteoarthritis and comprised the study sample. Female patients were more dominant (55.8%) than male (44.2%), with a mean age of 69.0 years (SD 6.4; range 61.2-87.3 years) and the majority in the 60-69 year age group (71.2%). Pain scale (VAS) was recorded in 26 of 52 patients (50.0%) with a mean of 4.1 (moderate pain being the most common category, 57.7%). All patients (100%) had at least one comorbid diagnosis recorded throughout 2025, with a mean of 6.65 distinct comorbid diagnoses per patient; the most common comorbidities were secondary hypertension (67.3%), dorsalgia (48.1%), and abnormal blood chemistry findings (32.7%). More than half of the patients (51.9%) had blood pressure ≥140/90 mmHg at the time of examination. This study concludes that osteoarthritis among older adults in the Tirta Jaya Public Health Center working area is closely associated with cardiometabolic and musculoskeletal multimorbidity, while completeness of pain scale and functional data recording still needs improvement. The AI combination used as a descriptive analysis tool was shown to accelerate the organization of medical record data without replacing the researcher's clinical interpretation, provided that its benefits are safeguarded by a dual-verification protocol mitigating source-data representation bias, automation bias, confirmation bias, and cross-lingual bias

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