SEGMENTASI DOKTER BERDASARKAN VOLUME KASUS KANKER PROSTAT DAN ADOPSI TERAPI NHT DI INDONESIA MENGGUNAKAN KLUSTERING K-MEANS
Abstract
Transformasi pendekatan terapi kanker prostat memerlukan pemetaan yang akurat terhadap profil dokter, baik dari segi volume penanganan kasus maupun tingkat adopsi terapi hormonal seperti New Hormonal Therapy (NHT). Penelitian ini bertujuan untuk melakukan segmentasi dokter berdasarkan karakteristik kasus kanker prostat yang mereka tangani serta tingkat pemanfaatan NHT, guna memberikan rekomendasi yang lebih terarah untuk strategi edukasi medis dan pemasaran farmasi. Metode K-Means Clustering digunakan untuk menganalisis data kategorikal dari sepuluh variabel utama, termasuk kategori volume kasus kanker prostat (total, mHSPC, nmCRPC, mCRPC), tingkat penggunaan NHT, usia dokter, spesialisasi, lokasi praktik, provinsi, dan saluran komunikasi yang disukai. Dataset mencakup 238 dokter spesialis urologi dan onkologi. Data diproses terlebih dahulu dengan metode one-hot encoding dan distandarisasi sebelum dilakukan klasterisasi. Analisis menghasilkan lima klaster utama dengan karakteristik yang berbeda-beda. Salah satu klaster mengidentifikasi dokter dengan volume kasus tinggi dan tingkat adopsi NHT yang optimal, menjadikannya kandidat ideal untuk dilibatkan sebagai Key Opinion Leader (KOL). Sebaliknya, klaster lain dengan volume kasus rendah dan pengalaman terbatas terhadap NHT memerlukan intervensi edukasi yang bersifat mendasar. Temuan ini memberikan wawasan praktis bagi perusahaan farmasi dan penyedia edukasi medis dalam merancang strategi komunikasi berbasis data. Penelitian ini menunjukkan bahwa K-Means Clustering merupakan pendekatan berbasis data yang efektif dalam mendukung pengambilan keputusan strategis dalam pemasaran farmasi, berdasarkan perilaku klinis dan kesiapan adopsi dari tenaga medis. Rekomendasi ke depan mencakup pemanfaatan hasil segmentasi ini untuk personalisasi konten edukatif dan penentuan prioritas intervensi berbasis wilayah.
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