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

OBJECTIVES: Reimbursement 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. DESIGN: Exploratory analysis using regression-based cost function on data from a multisite health facility costing study-the Cost of Health Services in India (CHSI) Study. SETTING: The 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. PARTICIPANTS: 521 sites from 57 healthcare facilities in 11 states of India. INTERVENTIONS: Medical and surgical packages of PM-JAY. PRIMARY AND SECONDARY OUTCOME MEASURES: The 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. RESULTS: The 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. CONCLUSION: Our 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.

Impact of India's publicly financed health insurance scheme on public sector district hospitals: a health financing perspective.

Background: Districts hospitals in India play a pivotal role in delivering health care services in the public sector and are empanelled under India's national health insurance scheme i.e. Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (PMJAY). In this paper, we evaluate the extent to which the PMJAY impacts the district hospitals from a financing perspective. Methods: We used cost data from India's nationally representative costing study-'Costing of Health Services in India' (CHSI) to determine the incremental cost of treating PMJAY patients, after adjusting for resources that are paid through supply-side government financing route. Second, we used data on number and claim value paid to public district and sub-district hospitals during 2019, to determine the additional revenue generated through PMJAY. The annual net financial gain per district hospital was estimated as the difference between payments under PMJAY, and the incremental cost of delivering the services. Findings: At current levels of utilisation, the district hospitals in India gain a net annual financial benefit of $ 26.1 (₹ 1839.3) million, which can potentially increase up to $ 41.8 (₹ 2942.9) million with an increase in the share of patient volume. For an average district hospital, we estimate net annual financial gain of $ 169,607 (₹ 11.9 million), increasing up to $ 271,372 (₹ 19.1 million) per hospital with increased utilisation. Interpretation: Demand-side financing mechanisms can be used to strengthen the public sector. Increasing utilisation of district hospitals, by either gatekeeping or improving availability of services will enhance financial gains for district hospitals and strengthen public sector. Funding: Department of Health Research, Ministry of Health & Family Welfare, Government of India.

CHSI costing study-Challenges and solutions for cost data collection in private hospitals in India.

INTRODUCTION: Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals. METHODS: The process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches-an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions. RESULTS: The mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5). DISCUSSION: Cost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.

Cost of hospital services in India: a multi-site study to inform provider payment rates and Health Technology Assessment.

The 'Cost of Health Services in India (CHSI)' is the first large scale multi-site facility costing study to incorporate evidence from a national sample of both private and public sectors at different levels of the health system in India. This paper provides an overview of the extent of heterogeneity in costs caused by various supply-side factors.A total of 38 public (11 tertiary care and 27 secondary care) and 16 private hospitals were sampled from 11 states of India. From the sampled facilities, a total of 327 specialties were included, with 48, 79 and 200 specialties covered in tertiary, private and district hospitals respectively. A mixed methodology consisting of both bottom-up and top-down costing was used for data collection. Unit costs per service output were calculated at the cost centre level (outpatient, inpatient, operating theatre, and ICU) and compared across provider type and geographical location.The unadjusted cost per admission was highest for tertiary facilities (₹ 5690, 75 USD) followed by private facilities (₹ 4839, 64 USD) and district hospitals (₹ 3447, 45 USD). Differences in unit costs were found across types of providers, resulting from both variations in capacity utilisation, length of stay and the scale of activity. In addition, significant differences in costs were found associated with geographical location (city classification).The reliance on cost information from single sites or small samples ignores the issue of heterogeneity driven by both demand and supply-side factors. The CHSI cost data set provides a unique insight into cost variability across different types of providers in India. The present analysis shows that both geographical location and the scale of activity are important determinants for deriving the cost of a health service and should be accounted for in healthcare decision making from budgeting to economic evaluation and price-setting.

Cost of Surgical Care at Public Sector District Hospitals in India: Implications for Universal Health Coverage and Publicly Financed Health Insurance Schemes.

BACKGROUND: In low- and middle-income countries (LMICs), provisioning for surgical care is a public health priority. Ayushman Bharat Pradhan Mantri-Jan Aarogya Yojana (AB PM-JAY) is India's largest national insurance scheme providing free surgical and medical care. In this paper, we present the costs of surgical health benefit packages (HBPs) for secondary care in public district hospitals. METHODS: The costs were estimated using mixed (top-down and bottom-up) micro-costing methods. In phase II of the Costing of Health Services in India (CHSI) study, data were collected from a sample of 27 district hospitals from nine states of India. The district hospitals were selected using stratified random sampling based on the district's composite development score. We estimated unit costs for individual services-outpatient (OP) visit, per bed-day in inpatient (IP) and intensive care unit (ICU) stays, and surgical procedures. Together, this was used to estimate the cost of 250 AB PM-JAY HBPs. RESULTS: At the current level of utilization, the mean cost per OP consultation varied from US$4.10 to US$2.60 among different surgical specialities. The mean unit cost per IP bed-day ranged from US$13.40 to US$35.60. For the ICU, the mean unit cost per bed-day was US$74. Further, the unit cost of HBPs varied from US$564 for bone tumour excision to US$49 for lid tear repair. CONCLUSIONS: Data on the cost of delivering surgical care at the level of district hospitals is of critical value for evidence-based policymaking, price-setting for surgical care and planning to strengthen the availability of high quality and cost-effective surgical care in district hospitals.

Translating Research to Policy: Setting Provider Payment Rates for Strategic Purchasing under India's National Publicly Financed Health Insurance Scheme.

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.

Cost of Treatment for Cervical Cancer in India.

INTRODUCTION: Cervical cancer is a major public health problem in India leading to high economic burden, which is disproportionately borne by the patients as out-of-pocket expenditure (OOPE). Several publicly financed health insurance schemes (PFHIs) in India cover the treatment for cervical cancer. However, the provider payment rates for health benefit packages (HBP) under these PFHIs are not based on scientific evidence. We undertook this study to estimate the cost of services provided for treatment of cervical cancer and  cost of the package of care for cervical cancer in India. METHODS: The study was undertaken at a large public tertiary hospital in North India. The health system cost was assessed using a mixed micro-costing approach. The data were collected for all the resources utilized during service delivery for cervical cancer patients. To evaluate the OOPE, randomly selected 248 patients were interviewed following the cost of illness approach. Logistic regression was used to assess the factors associated with catastrophic health expenditure (CHE). RESULTS: Health system cost for different cervical cancer treatment modalities i.e. radiotherapy, brachytherapy, chemotherapy and surgery, ranges from INR 19,494 to 41,388 (USD 291 - 617). Furthermore, patients spent INR 4,042 to 23,453 ( USD 60 - 350) as OOPE. Nearly 62% patients incurred CHE, and 30% reported distress financing. The odds of CHE (OR: 25.39, p-value: <0.001) and distress financing (OR: 15.37, p-value: 0.001) were significantly higher in poorest-income quintile. The HBP cost varies from INR 45,364 to 64,422 (USD 676 - 960) for brachytherapy and radiotherapy respectively. CONCLUSION: Cervical cancer treatment leads to high OOPE in India, which imposes financial hardship, especially for the poorest. The coverage of risk pooling mechanisms like PHFIs should be enhanced. The findings of our study should be used to set the reimbursement rates of providing cervical cancer treatment under PFHI schemes.

Establishing reference costs for the health benefit packages under universal health coverage in India: cost of health services in India (CHSI) protocol.

INTRODUCTION: To achieve universal health coverage, the Government of India has introduced Ayushman Bharat - Pradhan Mantri Jan Arogya Yojana (AB - PMJAY), a large tax-funded national health insurance scheme for the provision of secondary and tertiary care services in public and private hospitals. AB - PMJAY reimburses care for 1573 health benefit packages (HBPs). HBPs are designed to cover the treatment of diseases/conditions with high incidence/prevalence or which contribute to high out-of-pocket expenditure. However, there is a dearth of reference cost data against which provider payment rates can be assessed. METHODS AND ANALYSIS: The CHSI (Cost of Health Services in India) study will collect cost data from 13 Indian states covering 52 public and 40 private hospitals, using a mixed economic costing methodology (top-down and bottom-up), to generate unit costs for the HBPs. States will be sampled to capture economic status, development indicators and health service utilisation heterogeneity. The public sector hospitals will be chosen at secondary and tertiary care level. One tertiary facility will be selected from each state. At secondary level, three districts per state will be selected randomly from the district composite development score ranking. The private sector hospital sample will be stratified by nature of ownership (for-profit and not-for-profit), type of city (tier 1, 2 or 3) and size of the hospital (number of beds). Average costs for each HBP will be calculated across the different facility types. Multiple scenarios will be used to suggest rates which could be negotiated with the providers. Overall, the study will provide economic cost data for price setting, strategic purchasing, health technology assessment and a national cost database of India. ETHICS AND DISSEMINATION: The approval has been obtained from the Institutional Ethics Committee and Institutional Collaborative Committee of the Post Graduate Institute of Medical Education and Research, Chandigarh, India. The results shall be disseminated in conferences and peer-reviewed articles.

Process evaluation of health system costing - Experience from CHSI study in India.

BACKGROUND: A national study, 'Costing of healthcare services in India' (CHSI) aimed at generating reliable healthcare cost estimates for health technology assessment and price-setting is being undertaken in India. CHSI sampled 52 public and 40 private hospitals in 13 states and used a mixed micro-costing approach. This paper aims to outline the process, challenges and critical lessons of cost data collection to feed methodological and quality improvement of data collection. METHODS: An exploratory survey with 3 components-an online semi-structured questionnaire, group discussion and review of monitoring data, was conducted amongst CHSI data collection teams. There were qualitative and quantitative components. Difficulty in obtaining individual data was rated on a Likert scale. RESULTS: Mean time taken to complete cost data collection in one department/speciality was 7.86(±0.51) months, majority of which was spent on data entry and data issues resolution. Data collection was most difficult for determination of equipment usage (mean difficulty score 6.59±0.52), consumables prices (6.09±0.58), equipment price(6.05±0.72), and furniture price(5.64±0.68). Human resources, drugs & consumables contributed to 78% of total cost and 31% of data collection time. However, furniture, overheads and equipment consumed 51% of time contributing only 9% of total cost. Seeking multiple permissions, absence of electronic records, multiple sources of data were key challenges causing delays. CONCLUSIONS: Micro-costing is time and resource intensive. Addressing key issues prior to data collection would ease the process of data collection, improve quality of estimates and aid priority setting. Electronic health records and availability of national cost data base would facilitate conducting costing studies.