1. Introduction

Human capital, as represented by human health, is a fundamental component of the nation’s economic progress. It occupies position in Maslow’s hierarchy of human needs. Health may be defined as mental, bodily, and social well-being, which are the resources needed to live a full and happy life. A healthy individual contributes significantly to a country’s overall development and progress. Health receives significant attention in the budgetary policies of a nation, across the developed and developing countries [1]. The effects of healthcare spending on economic performance of an economy are crucial to take into account. The gross domestic product (GDP) of a country can rise immensely due to improvements in healthcare and health outcomes, and vice versa. The stock of human capital is substantially influenced by the healthcare industry. Economic growth, as represented by the sustained increase in productive capacity of a country over time, is impacted positively by higher levels of healthcare spending because it enhances overall productivity of human capital. The effect of health on savings is a crucial component of the link between healthcare spending and economic development [2]. A person’s life expectancy may be extended by good health, which also boosts the incentive to save (marginal propensity to save) and investment (marginal propensity to invest) in businesses, both of which are positive factors that improve economic performance. The Human Development Index (HDI) indicates a substantial and positive relationship between an individual’s position in the income distribution and their health outcomes within a nation.

The government’s involvement in fostering social welfare is vital. By investing in measures that promote social welfare, the government facilitates its citizens to live better lives. In health sector, India has made enormous strides over the past decades. According to the WHO, the life expectancy in India has crossed 70 years in 2023 from a mere 35 years in 1950 [3]. The infant mortality rate per thousand of population has significantly decreased from 164 in 1950 to just 27 in 2023. In 2023, India’s Maternal Mortality Ratio (MMR) has drastically decreased compared to 1950 levels, dropping from an estimated 1,000-2,000 deaths per 100,000 live births to around 97 in recent years, reflecting advancements in healthcare access, maternal care, and public health initiatives [4]. Many diseases, such as polio, guinea worm disease, yaws, and tetanus, have been successfully eradicated over the time. In an attempt to achieve the goal of universal healthcare access for all of its citizens, the government of India over the years came up with various public health programs like Universal Immunization Programme, National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Disease and Stroke (NPCDCS), National Nutrition Programme (NNP), Women and Child Development Programme, Integrated Child Development Services (ICDS) etc. With AB-PMJAY being the most recent one, which incorporated all the previous flagship insurance schemes, the following section will provide a brief introduction of the AB-PMJAY scheme.

Introduction to AB-PMJAY

The Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) is a flagship health insurance scheme launched by the Government of India on 23 September 2018. The scheme provides health coverage of up to ₹5 lakh per family per year for secondary and tertiary hospitalization to economically vulnerable households. It targets nearly 12 crore families (about 55 crore beneficiaries) representing the bottom 40% of India’s population. Beneficiaries are identified using the Socio-Economic Caste Census (SECC) 2011 database [5]. The main objective of AB-PMJAY is to reduce financial burden caused by medical expenses and improve access to quality healthcare services, contributing to the achievement of Sustainable Development Goal 3 (Good Health and Well-being).

AB-PMJAY-SEHAT

The AB-PMJAY SEHAT (Social Endeavour for Health and Telemedicine) scheme was launched on 26 December 2020 to extend health insurance coverage to all residents of Jammu and Kashmir. The scheme provides ₹5 lakh annual health coverage per household for medical and surgical treatments in empaneled public and private hospitals. It includes coverage for pre-existing diseases, pre- and post-hospitalization expenses, and has no limit on family size or age. The scheme also offers national portability, allowing beneficiaries to receive treatment anywhere in India [6-8]. The program aims to strengthen healthcare access and provide financial protection to the population of Jammu and Kashmir. AB-PMJAY-SEHAT aids in bridging the gap in rural and remote residents’ access to healthcare services. This lessens the financial strain of medical expenses, which frequently causes low-income families to become bankrupt or incur debt. The implementation of this decentralized approach promotes innovation and accountability at the state level, resulting in improved healthcare service delivery. The remainder of the paper is organized as follows. Section two provides a review of the past literature related to the AB-PMJAY scheme. Data and Methodology followed in the paper is discussed in Section three. While, Section four provides a detailed analysis on the functioning of AB-PMJAY-SEHAT in District Baramulla of Jammu and Kashmir. The last section concludes the study and provides some policy recommendations.

2. Literature Review

Several studies have examined the accessibility, utilization, and impact of the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) on healthcare access and financial protection in India.

[9-11] analyzed the impact of PMJAY enrollment on hospital service utilization and financial protection in the state of Chhattisgarh. The study used three repeated cross-sectional datasets, including two from the National Sample Survey (NSS) health rounds of 2004 and 2014 and primary data collected in 2019 during the first year of PMJAY implementation. Advanced statistical methods such as Propensity Score Matching and instrumental variable analysis were applied. The findings indicated that enrollment in PMJAY did not significantly increase hospital utilization or reduce catastrophic health expenditures and out-of-pocket expenses. The study also reported that out-of-pocket expenditures were higher when services were obtained from private healthcare facilities. [12] reviewed existing literature on Ayushman Bharat and related health insurance programs using databases such as Google Scholar and Scopus. The review highlighted the importance of strengthening India’s healthcare system, especially after the challenges exposed by the COVID-19 pandemic. The authors emphasized that Ayushman Bharat is a comprehensive health policy initiative that aims to improve access to affordable healthcare and strengthen health insurance coverage for economically disadvantaged populations. [6] examined beneficiary awareness, program features, and the identification process under PMJAY. The study was conducted using a two-stage sampling design and included a survey of 2700 households, 30 key informant interviews, and field observations from Tamil Nadu, Haryana, and Bihar. The results showed that awareness of the PMJAY scheme was relatively low in Bihar and Haryana during the early stages of implementation but was higher in Tamil Nadu, where the program was integrated with an existing state health insurance scheme. The study recommended increasing public awareness, improving beneficiary identification processes, and addressing supply-side challenges to enhance the overall effectiveness of the scheme. [13-14] evaluated the effectiveness of AB-PMJAY by comparing health outcomes of women and children in program-implementing states (Meghalaya and Karnataka) with non-implementing states (Telangana and West Bengal). The study used secondary data from the National Family Health Survey (NFHS) 2015–16 and 2019–20 and applied a difference-in-difference analysis. The results showed no statistically significant differences in health outcomes between implementing and non-implementing states. However, the authors noted that since the program is still relatively new, its long-term impact may become more evident over time. [15] assessed the impact of PM-JAY on health insurance coverage among the poorest sections of the Indian population using nationally representative NFHS data from 2015–16 and 2019–21 covering over 633,000 households. The study found that public health insurance coverage increased significantly in both rural and urban areas after the introduction of PM-JAY. The results also indicated a reduction in pro-rich inequality in insurance coverage, particularly in rural regions. However, the authors concluded that although coverage has increased, it is still insufficient to achieve universal health coverage for the poorest populations. [16;4] examined the impact of the AB-PMJAY-SEHAT scheme on cardiac healthcare delivery in the Kashmir Valley. Using an observational study design, the researchers compared data on cardiac interventions performed before and after the implementation of the scheme. The study revealed a substantial increase in both elective and emergency cardiac procedures following the introduction of the program. Additionally, there was an increase in the use of advanced cardiac devices such as CRT-P, CRT-D, and AICD, indicating improved access to specialized cardiac care.

[23] investigated the implementation experiences of PM-JAY across three different models: insurance-based implementation in Chhattisgarh, a hybrid model in Tamil Nadu, and a third-party administrator model in Uttar Pradesh. Using a qualitative approach, the study conducted 92 interviews with stakeholders involved in program implementation. The findings suggested that the flexibility of PM-JAY guidelines allowed states to adapt implementation strategies according to their local contexts. However, challenges remained in areas such as capacity building, program management, and coordination among stakeholders. [9-11] analyzed the effectiveness of publicly funded health insurance programs, including PM-JAY, in reducing out-of-pocket expenditure (OOPE) and distress financing for institutional deliveries. Using data from NFHS-5 (2019–21) and applying OLS regression analysis, the study found no significant association between PM-JAY enrollment and reduction in OOPE. The study also highlighted that expenses in private hospitals were substantially higher than in public hospitals, and cesarean deliveries were more common in private healthcare facilities. [17-18] examined awareness and utilization of AB-PMJAY in rural areas of Bihar. The community-based cross-sectional study included 802 households selected through multistage sampling. Data were collected using pre-tested questionnaires and analyzed using Pearson’s chi-square test. The results showed that 68.6% of participants were aware of the scheme, while 78.9% of eligible participants had knowledge about AB-PMJAY. However, actual utilization of the scheme was very low, with only 1.3% of eligible respondents reporting usage. The study also found that awareness was significantly associated with factors such as employment status, ration card possession, and social category. [3] evaluated the Ayushman Bharat Program and discussed key recommendations for its effective implementation. The study emphasized that proper planning, strong governance, and efficient implementation mechanisms are essential for achieving the program’s objectives. It concluded that if implemented effectively and supported by additional healthcare reforms, Ayushman Bharat has the potential to transform the Indian healthcare system and move the country closer to achieving universal health coverage. [12] examined the effectiveness of AB-PMJAY in reducing catastrophic health expenditure (CHE) among patients admitted to a tertiary hospital in Himachal Pradesh. The hospital-based cross-sectional study was conducted between August 2020 and October 2021, collecting data from medical and surgical departments. Information on total monthly family expenditure (TMFE), out-of-pocket expenditure (OOPE), and indirect illness-related expenditure was collected before and after hospitalization. The study found that the proportion of OOPE relative to family expenditure decreased significantly from 76.1% before admission to 30.0% after hospitalization, indicating that the AB-PMJAY scheme helped reduce financial burden and catastrophic health expenditure for patients, the reviewed studies highlight the importance of continuous monitoring and evaluation of the PM-JAY program. Strengthening healthcare infrastructure, improving service quality, and addressing regional disparities have been suggested to enhance the effectiveness of the scheme [19-23]. Despite the growing body of research on PM-JAY, there is limited evidence regarding its performance in the Union Territory of Jammu and Kashmir. Therefore, the present study aims to examine the awareness, accessibility, quality of care, and impact of PM-JAY SEHAT on healthcare utilization and health-related behavior in Baramulla District.

• Data and Methodology

Data and variable description

This section describes the data sources, variables, and analytical framework used to investigate the awareness and accessibility of the AB-PMJAY-SEHAT scheme, It focuses on different aspects of healthcare utilization under the scheme, such as type of treatment availed, demographic characteristics of patients (urban-rural and gender disparities), and expenditure incurred.

The study utilizes secondary data obtained from two hospitals (such as GMC Baramulla and SDH Kreeri) empaneled under the AB-PMJAY-SEHAT scheme. The data spans 2020 to 2024 and contains anonymized information of beneficiaries who availed healthcare services under the scheme.

The dataset includes information on the annual count of patients (utilization trends) availing treatments across different medical specialties, including:

1. General Medicine
2. General Surgery
3. Ophthalmology
4. Ear, Nose, and Throat (ENT)
5. Orthopedics
6. Other medical services

Data on the total expenditure (in INR) incurred on select treatments, particularly general surgery, ophthalmology, ENT, and orthopedics, were also collected over time to observe financial trends in healthcare spending under the scheme. Additionally, the data were stratified based on key demographic variables like (1) urban-rural residency in order to examine potential disparities in healthcare accessibility between urban and rural populations, (2) gender in order to assess whether there exists any gender-based disparity in the utilization of healthcare services.

Methodology

The present study employs a mixed-methods analytical approach, combining descriptive statistics, trend analysis, and disparity assessments to address the research objectives.

• Descriptive and Trend Analysis:

Annual utilization rates with respective to each treatment type are plotted using bar graph to identify increasing or decreasing trend over the study period.

Further, growth rates are calculated using the formula:

Growth Rate =  x 100

• Disparity Analysis

Urban-rural disparities: Utilization rates are stratified by residency status. The disproportionality index is calculated as:

Index=

Here a value greater than one indicates rural overutilization, while a value less than one indicates underutilization.

Gender disparities: The following index is used to calculate gender disproportionality index:

Index=

Here a value of less than one indicates male dominance, while a value of greater than one indicates female dominance. Male-to-female ratios are computed for each treatment category.

In order to compare the mean difference between (1) rural and urban area patients (2) male and female patients (3) different age group patients availing healthcare services under the PM-JAY scheme, we can use an independent (unpaired) t-test. It is important to note that we have used 40 years as the threshold to categories patients into two age groups. We can proceed as follows:

Null Hypothesis (H₀): There is no significant difference in the mean number of patients availing healthcare services across different groups.

Alternative Hypothesis (H₁): There is a significant difference in the mean number of patients availing healthcare services across different groups.

However, before conducting the formal t-test, we need to ensure that the following assumptions are met:

(1) Independence: The two patient groups are independent.

(2) Normality: The distribution of the number of patients in each patient group should be approximately normal. Here, we can use a Shapiro-Wilk test as the sample size in the present study is less than 50.

(3) Equal Variance: We can use the Levene’s test for this purpose.

• Analysis

This section provides a comprehensive analysis of the trends in treatment type under PMJAY-SEHAT at Government Medical College (GMC) Baramulla and Sub-District Hospital (SDH) Keeri Baramulla between 2020 and 2024. By evaluating longitudinal data, this analysis highlights shifts in healthcare utilization patterns across specialties, including general medicine, general surgery, ophthalmology, ENT, and orthopedics category. The findings aim to contextualize the evolving healthcare demands, and institutional capacities during the study period. This section synthesizes visual data from bar charts (Graphs 1 and 2) to draw meaningful conclusions about treatment trends in the region.

Graph 1 reveals a consistent upward trajectory in patient numbers across general medicine, general surgery, ophthalmology, ENT, and orthopedics from 2020 to 2024. For instance, orthopedics saw a % increase in patients by 2024, likely reflecting rising trauma cases or improved surgical infrastructure. Similarly, ophthalmology and ENT treatment types experienced growth, aligning with expanded outreach programs for cataract surgeries and hearing-related interventions.

In contrast, the “other” treatment category (encompassing niche or less common specialties) displayed a unique trend: patient numbers surged in 2022 but declined sharply thereafter. This increase could correlate with temporary referrals during the COVID-19 pandemic’s aftermath, while the subsequent decline may indicate shifting priorities or resource reallocation.

A nearly identical pattern was observed at SDH Keeri, reinforcing the reliability of these findings, as shown in Graph 2. The sustained growth in core specialties underscores their critical role in addressing the region’s primary healthcare needs. The rise in mainstream specialties highlights improved access and trust in institutional care.

Rural-Urban disparity

This sub-section examines urban-rural disparities in healthcare access by evaluating the proportion of beneficiaries from urban and rural areas availing treatment across five categories: general surgery, ophthalmology (optho), ENT, orthopedics (ortho), and others. Graphical representations (Graphs 3 to 7) illustrate trends in patient numbers stratified by residency and total utilization. While both urban and rural populations demonstrate an upward trajectory in treatment uptake across all categories, significant disparities persist in the proportion of urban beneficiaries relative to their rural counterparts.

Graphs 3 to 7 collectively reveal a consistent increase in the total number of patients accessing treatments over the study period, reflecting broader healthcare program expansion. However, rural beneficiaries consistently outnumber urban beneficiaries in absolute terms and as a proportion of total patients. This disparity underscores systemic inequities in resource distribution, awareness, or accessibility between urban and rural regions.

Graph 3 shows rural patients dominate general surgery services, constituting approximately 90% of total beneficiaries compared to urban patients (10%). While both groups show incremental growth, the rural curve rises more steeply, suggesting better infrastructure (e.g., specialized hospitals) and referral systems in rural areas. Rural populations likely face barriers such as distance to tertiary care centers and financial constraints.

Ophthalmology services exhibit a larger and persistent gap. Rural beneficiaries account for 95% of total patients, with urban participation at 5%. The upward trend in rural utilization may reflect targeted outreach programs for cataract surgeries or vision camps. However, urban centers retain advantages in advanced diagnostic technologies and specialist availability.

The disparity again widens in ENT services, with rural beneficiaries representing 79% of total patients, likely due to access to ENT specialists and diagnostic tools (e.g., audiometry). While as, urban areas lag significantly (21%). Cultural or awareness gaps in seeking care for non-life-threatening ENT conditions may further exacerbate this divide.

Orthopedic care shows constant utilization over the time with the exception of 2021 year. However, the rural-urban disparity is significant, with rural beneficiaries accounting 96% compared to urban 4%. Urban dominance aligns with the concentration of trauma centers and rehabilitation facilities in cities.

The “Others” category (e.g., dermatology, dental) mirrors broader trends, with rural beneficiaries (90%) surpassing urban beneficiaries (10%). This reflects rural-centric availability of niche healthcare services and socioeconomic factors influencing health-seeking behavior.

We now employ an unpaired t-test to statistically compare the mean differences between rural and urban beneficiaries. This parametric test is appropriate for evaluating whether the two independent groups—rural and urban populations—differ significantly in their utilisation of scheme benefits. By applying this method, we test the null hypothesis that no true difference exists between the population means. The analysis will quantify the magnitude and statistical significance of geographic disparities, providing empirical evidence to inform targeted policy interventions.

Here we will use the Shapiro-Wilk test to evaluate whether the dataset is drawn from a normally distributed population. The following hypotheses are formulated:

Null hypothesis (H₀): The data are normally distributed.

Alternative hypothesis (H₁): The data are not normally distributed.

The decision rule is: A p-value < 0.05 leads to rejection of H₀, indicating significant deviation from normality, while failure to reject H₀ suggests the data may approximate a normal distribution.

Gender disparity

To empirically assess gender-based disparities in access to healthcare services under the PM-JAY SEHAT scheme, we will employ a formal two-sample independent t-test. The t-test is selected for its robustness in comparing the means of two independent groups—in this case, male and female beneficiaries—to determine whether statistically significant differences exist in their utilisation rates of PM-JAY services. However, before applying t-test, we need to confirm the normality of the data. Accordingly, we will use the Shapiro-Wilk test to evaluate whether the dataset is drawn from a normally distributed population.

• Conclusion and Policy Implications

The present research aimed to empirically examine the awareness and accessibility of PMJAY-SEHAT scheme in district Baramulla of J&K using secondary data on such variables as number of PMJAY cards issued, expenditure incurred on card, and type of treatment surgeries during the time period of 2020 to 2024. It also used the conditional variables such as the gender of the patient and rural—urban status of the target population. Our analysis reveals a sustained and significant increase in utilization of healthcare services across different medical disciplines, including general medicine, general surgery, ophthalmology, ENT, and orthopaedics during the sample period. This finding suggests a rising adoption and efficient delivery of health services under the PM-JAY SEHAT scheme, importantly in categories like orthopedics and ophthalmology, where specialized interventions appear to be driving higher patient numbers, the study also highlights critical disparities in access to the SEHAT scheme between rural and urban populations, with rural beneficiaries excessively dominating services such as general surgery and ophthalmology. This dominance is the outcome of targeted outreach and a lack of alternative healthcare options to the rural communities [24], the study observed a significant gender-based disparity, with female beneficiaries exhibiting greater utilization in general medicine and general surgery. On the other hand, male beneficiaries exhibited greater utilization of services in ophthalmology, ENT, and orthopaedics. This finding calls for gender-sensitive policy strategies to ensure that the SEHAT scheme successfully addresses the unique healthcare needs of male and female patients respectively. Finally, the study revealed that the SEHAT scheme continued its ability to provide equitable healthcare access to the beneficiaries.

Based on findings of the present study, the policy recommendations to improve the effectiveness of the PM-JAY SEHAT scheme include: In order to mitigate the identified gender and rural—urban disparity, policy should focus on reducing the information gaps and barriers to accessibility and utilization of high-demand services like general surgery and ophthalmology. Moreover, policy should also focus on diversification and expansion of healthcare services across rural areas. Finally, the government should focus on tracking patient demographic details, utilization patterns, and gender-based usage to make evidence-based decisions for the efficient delivery of PM-JAY SEHAT scheme in order to better serve the diverse and evolving healthcare needs of the beneficiaries.

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