REFINING PROVIDER PAYMENT SYSTEM OF INDIA’S GOVERNMENT FUNDED HEALTH INSURANCE: A FRAMEWORK FOR DEVELOPING NOVEL COUNTRY-SPECIFIC DIAGNOSTIC RELATED GROUP SYSTEM
Author(s)
Jyoti Dixit, Sr., PhD, MPH1, Bhawna Sirohi, MD2, Rajkumar Kottayasamy Seenivasagam, MD3, Nidhi Gupta, MD, DNB4, Rupinder Khurana, MPH1, Ravita Laura, PhD1, Ritin Mohindra, MD5, Shankar Prinja, MD, MSc6.
1Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India, 2Institute of MedicDepartment of Medical Oncology, Balco Medical Centre, Raipur, Chhattisgarh., Chattisgarh, India, 3PSG Institute of Medical Sciences and Research, Coimbatore., Coimbatore, India, 4Department of Radiation Oncology, Government Medical College and Hospital, Chandigarh, Chandigarh, India, 5Department of Internal Medicine, Post Graduate Institute of Medical Education and Research (PGIME, Postgraduate Institute of Medical Education and Research, Chandigarh, India, 6Department of Radiation Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
1Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India, 2Institute of MedicDepartment of Medical Oncology, Balco Medical Centre, Raipur, Chhattisgarh., Chattisgarh, India, 3PSG Institute of Medical Sciences and Research, Coimbatore., Coimbatore, India, 4Department of Radiation Oncology, Government Medical College and Hospital, Chandigarh, Chandigarh, India, 5Department of Internal Medicine, Post Graduate Institute of Medical Education and Research (PGIME, Postgraduate Institute of Medical Education and Research, Chandigarh, India, 6Department of Radiation Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
OBJECTIVES: Provider payment methods are a key policy instrument for improving efficiency and quality in health systems. India’s Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PM-JAY) reimburses inpatient services through fixed-price packages adjusted for hospital-level characteristics but not for patient-level heterogeneity. This limitation is particularly salient for oncology care, which constitutes a substantial share of PM-JAY packages and is characterised by marked clinical and cost variability.
METHODS: We adopted a structured, multi-step normative approach to estimate costs of medical and surgical oncology health benefit packages. Fixed costs were derived from empirical data, while variable costs were estimated using standard treatment guidelines and expert consultation. Fixed costs were adjusted for length of stay, and variable costs for clinical factors affecting dosage and treatment frequency. Relative weights were derived to capture cost variation relative to a base case, and decision rules were developed to guide reimbursement adjustments when multiple clinical factors were present.
RESULTS: Across medical and surgical oncology packages, comorbidities were the primary driver of cost variation. In medical oncology, costs for patients with comorbidities ranged from 35% below to 52% above base prices. Older age, compromised nutritional status, and poorer performance status were associated with 5-10% lower costs. In surgical oncology, comorbidities increased costs by 30-35%, and poor nutritional status or performance status ≥2 was associated with 20-25% higher costs.
CONCLUSIONS: The proposed decision rules provide a pragmatic approach to adjust bundled payment rates for overlapping patient-level characteristics while avoiding double counting of shared costs.
METHODS: We adopted a structured, multi-step normative approach to estimate costs of medical and surgical oncology health benefit packages. Fixed costs were derived from empirical data, while variable costs were estimated using standard treatment guidelines and expert consultation. Fixed costs were adjusted for length of stay, and variable costs for clinical factors affecting dosage and treatment frequency. Relative weights were derived to capture cost variation relative to a base case, and decision rules were developed to guide reimbursement adjustments when multiple clinical factors were present.
RESULTS: Across medical and surgical oncology packages, comorbidities were the primary driver of cost variation. In medical oncology, costs for patients with comorbidities ranged from 35% below to 52% above base prices. Older age, compromised nutritional status, and poorer performance status were associated with 5-10% lower costs. In surgical oncology, comorbidities increased costs by 30-35%, and poor nutritional status or performance status ≥2 was associated with 20-25% higher costs.
CONCLUSIONS: The proposed decision rules provide a pragmatic approach to adjust bundled payment rates for overlapping patient-level characteristics while avoiding double counting of shared costs.
Conference/Value in Health Info
2026-09, ISPOR Asia Pacific 2026, Bangkok, Thailand
Value in Health, Volume 55, Issue S1
Code
HSD3
Topic
Health Service Delivery & Process of Care
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Oncology, STA: Generics