Translating Research to Policy: Setting Provider Payment Rates for Strategic Purchasing under India's National Publicly Financed Health Insurance Scheme.
Journal
Applied health economics and health policy
ISSN: 1179-1896
Titre abrégé: Appl Health Econ Health Policy
Pays: New Zealand
ID NLM: 101150314
Informations de publication
Date de publication:
05 2021
05 2021
Historique:
accepted:
20
12
2020
pubmed:
20
1
2021
medline:
23
11
2021
entrez:
19
1
2021
Statut:
ppublish
Résumé
In 2018, the Government of India launched Ayushman Bharat Pradhan Mantri-Jan Aarogya Yojana (AB PM-JAY), a large tax-funded health insurance scheme. In this paper, we present findings of the Costing of Health Services in India (CHSI) study, describe the process of use of cost evidence for price-setting under AB PM-JAY, and estimate its fiscal impact. Reference costs were generated from the first phase of CHSI study, which sampled 11 tertiary public hospitals from 11 Indian states. Cost for Health Benefit Packages (HBPs) was estimated using mixed (top-down and bottom-up) micro-costing methods. The process adopted for price-setting under AB PM-JAY was observed. The cost of each HBP was compared with AB PM-JAY prices before and after the revision, and the budgetary impact of this revision in prices was estimated. Following the CHSI study evidence and price consultations, 61% of AB PM-JAY HBP prices were increased while 18% saw a decline in the prices. In absolute terms, the mean increase in HBP price was ₹14,000 (₹450-₹1,65,000) and a mean decline of ₹6,356 (₹200-₹74,500) was observed. Nearly 42% of the total HBPs, in 2018, had a price that was less than 50% of the true cost, which declined to 20% in 2019. The evidence-informed revision of HBP prices is estimated to have a minimal fiscal impact (0.7%) on the AB PM-JAY claims pay-out. Evidence-informed price-setting helped to reduce wide disparities in cost and price, as well as aligning incentives towards broader health system goals. Such strategic purchasing and price-setting requires the creation of systems of generating evidence on the cost of health services. Further research is recommended to develop a cost-function to study changes in cost with variations in time, region, prices, skill-mix and other factors.
Sections du résumé
BACKGROUND
In 2018, the Government of India launched Ayushman Bharat Pradhan Mantri-Jan Aarogya Yojana (AB PM-JAY), a large tax-funded health insurance scheme. In this paper, we present findings of the Costing of Health Services in India (CHSI) study, describe the process of use of cost evidence for price-setting under AB PM-JAY, and estimate its fiscal impact.
METHODS
Reference costs were generated from the first phase of CHSI study, which sampled 11 tertiary public hospitals from 11 Indian states. Cost for Health Benefit Packages (HBPs) was estimated using mixed (top-down and bottom-up) micro-costing methods. The process adopted for price-setting under AB PM-JAY was observed. The cost of each HBP was compared with AB PM-JAY prices before and after the revision, and the budgetary impact of this revision in prices was estimated.
FINDINGS
Following the CHSI study evidence and price consultations, 61% of AB PM-JAY HBP prices were increased while 18% saw a decline in the prices. In absolute terms, the mean increase in HBP price was ₹14,000 (₹450-₹1,65,000) and a mean decline of ₹6,356 (₹200-₹74,500) was observed. Nearly 42% of the total HBPs, in 2018, had a price that was less than 50% of the true cost, which declined to 20% in 2019. The evidence-informed revision of HBP prices is estimated to have a minimal fiscal impact (0.7%) on the AB PM-JAY claims pay-out.
INTERPRETATION
Evidence-informed price-setting helped to reduce wide disparities in cost and price, as well as aligning incentives towards broader health system goals. Such strategic purchasing and price-setting requires the creation of systems of generating evidence on the cost of health services. Further research is recommended to develop a cost-function to study changes in cost with variations in time, region, prices, skill-mix and other factors.
Identifiants
pubmed: 33462775
doi: 10.1007/s40258-020-00631-3
pii: 10.1007/s40258-020-00631-3
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
353-370Investigateurs
Jyotsna Naik
(J)
Malkeet Singh
(M)
Himanshi Tomar
(H)
Rakesh Bahl
(R)
Amit Sachdeva
(A)
Sharminder Kaur
(S)
Sanjay Kumar
(S)
Setu Sinha
(S)
Varsha Singh
(V)
Avijit Hazra
(A)
Raghunath Misra
(R)
Divya Mehrotra
(D)
K Narayanasamy
(K)
A Chitra
(A)
Arun Gupta
(A)
Pankaj Bahuguna
(P)
Kusum Lata Gaur
(KL)
Jai Prakash Pankaj
(JP)
Dharmesh Kumar Sharma
(DK)
Gajanan D Velhal
(GD)
Amit S Bhondve
(AS)
Prakash Patel
(P)
Amit C Patel
(AC)
Rajendra Joshi
(R)
Kondeti Madhavi
(K)
Pulaganti Madhusudana
(P)
Bhabagrahi Rath
(B)
Sashi Bhusan Biswal
(SB)
Ankita Panigrahy
(A)
Commentaires et corrections
Type : ErratumIn
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