Refining the provider payment system of India’s government-funded health insurance programme: an econometric analysis

Abstract

ObjectivesReimbursement rates in national health insurance schemes are frequently weighted to account for differences in the costs of service provision. To determine weights for a differential case-based payment system under India’s publicly financed national health insurance scheme, the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PM-JAY), by exploring and quantifying the influence of supply-side factors on the costs of inpatient admissions and surgical procedures.DesignExploratory analysis using regression-based cost function on data from a multisite health facility costing study—the Cost of Health Services in India (CHSI) Study.SettingThe CHSI Study sample included 11 public sector tertiary care hospitals, 27 public sector district hospitals providing secondary care and 16 private hospitals, from 11 Indian states.Participants521 sites from 57 healthcare facilities in 11 states of India.InterventionsMedical and surgical packages of PM-JAY.Primary and secondary outcome measuresThe cost per bed-day and cost per surgical procedure were regressed against a range of factors to be considered as weights including hospital location, presence of a teaching function and ownership. In addition, capacity utilisation, number of beds, specialist mix, state gross domestic product, State Health Index ranking and volume of patients across the sample were included as variables in the models. Given the skewed data, cost variables were log-transformed for some models.ResultsThe estimated mean costs per inpatient bed-day and per procedure were 2307 and 10 686 Indian rupees, respectively. Teaching status, annual hospitalisation, bed size, location of hospital and average length of hospitalisation significantly determine the inpatient bed-day cost, while location of hospital and teaching status determine the procedure costs. Cost per bed-day of teaching hospitals was 38–143.4% higher than in non-teaching hospitals. Similarly, cost per bed-day was 1.3–89.7% higher in tier 1 cities, and 19.5–77.3% higher in tier 2 cities relative to tier 3 cities, respectively. Finally, cost per surgical procedure was higher by 10.6–144.6% in teaching hospitals than non-teaching hospitals; 12.9–171.7% higher in tier 1 cities; and 33.4–140.9% higher in tier 2 cities compared with tier 3 cities, respectively.ConclusionOur study findings support and validate the recently introduced differential provider payment system under the PM-JAY. While our results are indicative of heterogeneity in hospital costs, other considerations of how these weights will affect coverage, quality, cost containment, as well as create incentives and disincentives for provider and consumer behaviour, and integrate with existing price mark-ups for other factors, should be considered to determine the future revisions in the differential pricing scheme.

Understanding the extent of economic evidence usage for informing policy decisions in the context of India’s national health insurance scheme: Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (PM-JAY)

“…In parallel, an analysis of the CHSI study data was undertaken to inform potential price weights for refining differential provider payment rates for different categories of hospitals. 28 In addition, evidence from cost-effectiveness studies/HTA was also taken into consideration; however, this was limited to the packages within the oncology specialty. The refined HBP list, finalised by the speciality-wise committees, was then presented to SHAs, clinical experts, healthcare associations, and industry partners.…”

“…Furthermore, price weights were constructed to inform differential pricing for the empanelled hospitals. 28 It was hypothesised that various supply-side factors such as provider type (public tertiary, district and private hospitals), geographical location (rural, urban, metro, non-metro), and level of care (secondary, tertiary) could influence the cost of service provision. Based on the findings of the analysis from the CHSI data, a price weight of 15–17% for tier 2 cities and 15–25% for tier 3 cities, respectively, in addition to the base rate, was recommended.…”

“…Similarly, a price weight of 10% and 25% for medical and surgical tertiary care packages was also recommended. 28 29 …”

National hospital costing systems matter for universal healthcare: the India PM-JAY experience

“…In the long term, the goal was to develop systems to enable the transformation of the provider payment system and the adoption of a diagnosis-related group-based system. 29 The pilot was initiated in 61 hospitals empanelled under PM-JAY across five Indian states. The hospitals chosen for the pilot programme were carefully selected to represent the diverse nature of healthcare in India.…”