Background

There is a global consensus to aim for universal health coverage (UHC) [1]. The main objectives of UHC agenda are to improve the access to quality healthcare while ensuring financial protection [2]. Publicly funded health insurance (PFHI) has been promoted as a model to achieve UHC in many low- and middle-income countries (LMICs), including India [3, 4]. In 2018, the central government of India launched a national PFHI scheme known as the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) [5, 6]. It replaced the earlier national PFHI scheme known as the Rashtriya Swasthya Bima Yojana (RSBY), which was in operation for a decade [7].

The AB-PMJAY covers 100 million poor households with an assured annual sum of half a million Indian rupees (around 6,000 US dollars) for hospitalisation care [8]. It provides seventeen times larger financial cover than RSBY did [7]. AB-PMJAY covers a wide range of services for secondary and tertiary inpatient care [7]. AB-PMJAY covers around 1400 services, comprehensively covering treatment, surgeries and other procedures, medicines and diagnostics, pre-operative and post-operative care, food, and accommodation [5, 7,8,9]. Like RSBY, the services under AB-PMJAY are expected to be completely free for the enrolled persons and cashless at the point of care [5, 7,8,9]. Under AB-PMJAY, state governments empanel a mix of private and public hospitals to provide a package of inpatient services at pre-defined prices. The contracting of private hospitals is a key measure to expand access for the poor households to a wide-range of inpatient care services, as the scheme aims to remove the financial barrier by making the services free. The coverage under AB-PMJAY is funded completely by the government.

Ensuring access to quality services is an important objective under the UHC agenda. The AB-PMJAY also includes it as a key objective [7]. However, there is no information available on the effects of AB-PMJAY on the quality dimension of services. The existing evidence on PFHI in India has shown its limited success in financial protection, though the evidence has been mixed on its role in increasing utilisation [10,11,12,13,14,15,16,17]. A few quantitative studies are available on AB-PMJAY, but all of those have covered its early days [7, 16, 17]. A cross-sectional study covering 200 beneficiaries of AB-PMJAY in two states in April 2019 showed that patients incurred out-of-pocket expenditure (OOPE) in private hospitals [16]. Another cross-sectional study involving data collection from six states in 2019–2020, almost a year after the launch of AB-PMJAY, found that the patients able to utilise the scheme were 21% less likely to incur catastrophic expenditure (CHE) than others [17]. The above mentioned study reported that AB-PMJAY did not result in increased utilisation of inpatient care [17]. An evaluation of AB-PMJAY was conducted in Chhattisgarh state in 2019, after the scheme had completed its first year of implementation [7]. That study was based on repeated cross-sections of household surveys before and after the launch of AB-PMJAY. It showed that AB-PMJAY was not able to improve access or financial protection for hospitalisations [7].

Since then, the policymakers at the national level have made changes in an important aspect of design of the benefit package under AB-PMJAY. The prices at which hospitals get reimbursed for services were revised upwards using evidence from a large costing study conducted by national level government research institutions [18,19,20]. The increased prices became applicable in January, 2020 [21, 22]. The current study was aimed at examining the effectiveness of AB-PMJAY in improving access, quality, and financial protection after the above measures. The timing of this evaluation was significant, as it happened after a substantive period of full-fledged implementation of the scheme. The study was expected to add to the evidence base for improving the policy and practice of AB-PMJAY and such publicly funded health insurance schemes aimed at universal health coverage.

Materials and methods

Study setting

Chhattisgarh is one of the leading states in implementing AB-PMJAY in terms of population-enrollment and the number of patients utilising the scheme [8, 22]. The statewide implementation of AB-PMJAY started in September 2018 [8]. In Chhattisgarh, the funding available for AB-PMJAY has been supplemented by the state government to allow universal population coverage under the scheme [23]. The state and central governments co-own the scheme. The number of hospitals empaneled in the state under AB-PMJAY was 1499 in 2021 [24].

Chhattisgarh had a population of around 30 million in 2021, and around three-fourths of it was rural [25]. The per capita income of Chhattisgarh was Indian rupees (INR) 83,511 which was lower than the national average of INR 98,374 in 2022-23 (at 2010-11 constant prices) [26]. The per capita total health expenditure was estimated to be INR 3416, compared to the national average of INR 4864 in 2019-20 [27]. The per capita government health expenditure in Chhattisgarh was INR 1790, close to the national average of INR 2014 in 2019-20 [28]. The density of medical doctors in Chhattisgarh was 2.9 per 10,000 population in 2018, which was lower than the national average of 7.6 per 10,000 [29].

Study design and key concepts

Sampling

For evaluating PFHI schemes, using observations of more than one time is considered appropriate [30, 31]. This study used panel data from two waves of household surveys. The two waves of the survey were carried out at annual intervals in November 2021 and November 2022, with a design to cover the same set of 3000 sample households in each wave. From the sampled households, data was collected on each individual. We expected to cover around 15,000 individuals in each annual wave. This sample size was decided based on earlier surveys conducted in the state to evaluate government insurance schemes [7]. The National Sample Survey on healthcare expenditure that is conducted by India’s Ministry of Statistics also uses a similar sample size for Chhattisgarh [32]. The 2021 and 2022 rounds covered 14,827 and 15,283 individuals, respectively. The surveys had a representative sample of Chhattisgarh, covering each of the five geographical divisions of the state.

An adequate number of hospitalisation episodes was required in the sample. This was necessary to measure the financial protection for those who had utilised inpatient care. To detect a 10% reduction in CHE incidence with enrollment as compared to the non-insured patients, we calculated the sample size required at 5% type-1 error and 10% type-2 error. We assumed around 90% of the patients would be enrolled under the scheme. According to this calculation, the total sample size required was 840 patients. The actual survey covered 1627 patients, which was sufficient for the required analysis.

Data collection

The surveys were conducted by the State Health Resource Centre, a technical agency providing support to the state department of health. The survey collected data on the socio-economic and demographic characteristics of individuals, including the consumption expenditure of their households on food and non-food purposes and the enrollment status of individuals under AB-PMJAY. The tool developed for the household survey is given in Additional File S0. Data was collected on any hospitalisation episodes within one year preceding the survey, type of hospital utilised (public/private ownership), the disease and its perceived severity, length of stay in the hospital, out-of-pocket expenditure incurred, and the perceived level of satisfaction with the quality of care received. Written informed consent was obtained from all respondents and legal representatives. The dataset was fully anonymized before starting the analysis. The ethics approval was obtained from the Institutional Ethics Committee of SHRC.

Quality

The concept of quality in healthcare is multi-dimensional and has been defined and measured in multiple ways [33,34,35,36]. We used two relatively simple indicators of quality that could be measured through a survey of patients: (a) patient satisfaction with the quality of the medical treatment; and (b) length of stay in the hospital. The patients were asked to express whether they were satisfied with the medical treatment they received (yes or no). While the relevance of measuring patient satisfaction is obvious, length of stay has also been recognized as a key measure of quality for hospital care, and longer stay is associated with lower quality and poorer outcomes [34,35,36].

Out-of-pocket expenditure (OOPE)

OOPE was calculated for each episode by adding the medical and transportation expenses and deducting any cash-reimbursements received by the patient. The OOPE amounts for 2022 were adjusted at 2021 prices for a valid comparison [7, 11].

Catastrophic health expenditure

Financial protection was measured in terms of catastrophic health expenditure (CHE) [37, 38]. Any hospitalisation episode in which OOPE crossed a defined threshold was counted as a CHE occurrence. This study used two types of definitions for the thresholds of CHE:

  1. a)

    CHE threshold as a proportion of annual consumption expenditure: This is called the budgetary method [31]. Thresholds of 10%, 25%, and 40% of a household’s total annual consumption expenditure were taken for CHE and termed CHE10, CHE25, and CHE40.

  2. b)

    CHE threshold as a proportion of annual non-food consumption expenditure: This is called the non-essential expenditure method [37, 38]. A threshold of 40% of a household’s annual non-food consumption expenditure was taken for CHE and termed CHE40-Non-Food.

Data analysis

The indicators of utilisation, OOPE, and CHE were compared for those enrolled under AB-PMJAY and the rest. Confidence intervals at 95% were reported for key indicators. Multivariate regression analysis was carried out to find the effect of AB-PMJAY on utilisation, OOPE, and CHE. The list of variables in the study is given in Additional File S1. The variables were selected based on existing studies on PFHI in the country [7, 15].

A logistic model was applied for determinants of utilisation. The independent variables included in the model were related to the socio-economic characteristics of the individuals (sex, age, place of residence (rural or urban), caste, income-quintile), insurance status (PFHI-enrolled or non-insured), perceived severity of illness, and year of the survey.

An ‘ordinary least squares’ (OLS) regression model was applied for OOPE. The OLS for logarithmic (log) transformation of OOPE has been reported for comparison. A logistic regression model was applied for the binary outcome variables, i.e., the indicators of CHE. The independent variables included in the above mentioned models on OOPE and CHE were related to: (a) socio-economic characteristics of the individuals (sex, age, place of residence (rural or urban), caste, household-size, income-quintile); (b) insurance status of the patient (PFHI-enrolled or non-insured); (c) provider characteristics (public or private); (d) characteristics of hospitalisation episode (type of disease, perceived severity of illness, length of stay); and (e) year of the survey.

Of the individuals covered in the survey, 1.2% had insurance other than AB-PMJAY, and they were excluded from the analysis since the purpose was to compare those enrolled under AB-PMJAY against patients without any insurance. In years 2021 and 2022, 32 and 3 hospitalisations, respectively were reported for the COVID-19 infection, and those were excluded from all analyses conducted in this study. The pattern of hospitalisations in Chhattisgarh in 2021 was affected by the COVID-19 pandemic, but the impact was minimal in 2022. The year of the survey (2021 and 2022) was also used as an independent variable in the regression model to control for the effect of the pandemic.

For robustness, the results were compared with the average treatment effect on the treated (ATET) under a propensity score matching (PSM) model with AB-PMJAY-enrollment as the treatment variable [7, 14]. PSM is considered a suitable method for evaluating the effect of an intervention as it creates a matching sample of the intervention (PFHI-enrolled) and the comparison (non-insured) groups [30, 31]. It is a useful method when the sampling is not based on an experimental design, and the size and characteristics of the intervention and comparison groups in the survey data may not match. The independent variables included in the PSM models were the same as those used in the regression models mentioned earlier.

The multivariate models for OOPE and CHE were repeated using the instrumental variable (IV) method. This was meant to address the potential endogeneity and selection problem in insurance enrollment [7, 11, 30, 31, 39,40,41,42]. When selection into the insurance scheme is non-random, it can lead to biased estimates of its impact on OOPE. While PSM helps in matching the two groups, it is not sufficient to address the unobserved variables. The IV method has been recognised as an effective solution to the problem of endogeneity [39,40,41,42].

A two-stage least squares (2sls) IV model was applied for OOPE, and a two-step IV-probit model was applied for CHE indicators [7, 11, 39,40,41,42]. The Wu-Hausman test for 2sls and the Wald test for IV-probit were conducted to test for endogeneity [7, 11, 39,40,41,42,43,44]. The ‘household size category’ was used as an instrumental variable because it satisfied both the criteria for a suitable instrumental variable – it was associated with scheme enrollment and was not expected to have a direct impact on the outcomes of interest, i.e., OOPE or patient satisfaction [43]. Over-identification restriction tests were applied to check the validity of the IV model [43, 44]. Significance was taken at 95% (p < 0.05). The survey data was analysed using STATA-15.

Results

The sample profile is given in Additional File S2. Out of the total surveyed population, the proportion of individuals enrolled under AB-PMJAY in 2021 and 2022 was 92.4% and 87.9%, respectively.

Utilisation

In 2021, 5.25% of the surveyed individuals had utilised inpatient care, and the proportion increased to 5.56% in 2022. The above pattern was similar for the AB-PMJAY enrolled individuals and the rest. The logistic regression model showed that utilisation of inpatient care and AB-PMJAY enrollment was not associated (Additional File S3). The PSM model also confirmed that AB-PMJAY-enrollment had no effect on utilisation (coefficient = 0.002, p = 0.652).

Hospitalisation characteristics

In 2021, 55.3% of the hospitalisation episodes were in public hospitals, and the share increased marginally to 57.6% in 2022. The types of diseases/conditions for which the hospitalisation took place and their perceived severity are reported in Additional File S2. Overall, 43.5% of the patients perceived the condition of their illness to be severe in 2021, and the proportion was 41.6% in 2022.

Quality of inpatient care

Patient satisfaction

The proportion of patients expressing satisfaction with the quality of care they received is reported in Table 1.

Table 1 Proportion of patients who were satisfied with the quality of care received for hospitalisation in public and private hospitals according to PFHI-enrollment status in Chhattisgarh − 2021 and 2022
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Length of stay

Table 2 reports the mean length of stay. The length of stay did not vary significantly for the AB-PMJAY-enrolled and the non-insured patients (p = 0.24). The average length of stay was longer in private hospitals (p = 0.01).

Table 2 Mean length of stay for hospitalisation in public and private hospitals according to PFHI-enrollment status in Chhattisgarh − 2021 and 2022
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The logistic model for determinants of patient satisfaction is given in Additional File S4. It shows that the perceived quality was not associated with AB-PMJAY enrollment. Patient satisfaction declined with increase in the length of stay. The PSM and IV models for patient satisfaction do not show any effect of AB-PMJAY enrollment on patient satisfaction (Table 3 and Additional File S5).

Table 3 Effect of enrollment under AB-PMJAY on patient satisfaction and length of stay in Chhattisgarh – Summary of results of OLS, Logistic, PSM, and IV models
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The OLS model for determinants of the length of stay is given in Additional File S6. It shows that the length of stay was not associated with AB-PMJAY enrollment. The main determinant of a longer length of stay was the utilisation of private hospitals. The PSM and IV models do not show any effect of AB-PMJAY enrollment on the length of stay (Table 3 and Additional File S7).

Financial protection

OOPE

Hospitalisation of individuals enrolled under AB-PMJAY involved significant OOPE when the private hospitals were utilised. The overall mean OOPE in private hospitals in 2021 or 2022 was around ten times larger than that in public hospitals (Table 4).

Table 4 Mean OOPE for hospitalisation in public and private hospitals according to PFHI-enrollment status in Chhattisgarh − 2021 and 2022
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The findings on the median OOPE showed that there was little difference in OOPE for the patients enrolled in the scheme and the non-insured (Table 5). The private hospitals were more expensive, irrespective of the scheme.

Table 5 Median OOPE for hospitalisation in public and private hospitals according to PFHI-enrollment status in Chhattisgarh − 2021 and 2022
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The incidence of CHE10 was similar among the AB-PMJAY-enrolled and the non-insured individuals (Table 6). Around three-fourths of the patients utilising private hospitals incurred CHE10, even when they were enrolled under AB-PMJAY.

Table 6 CHE10 for hospitalisation in public and private hospitals according to PFHI-enrollment status in Chhattisgarh − 2021 and 2022
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The incidence of other indicators of CHE, i.e., CHE25, CHE40, and CHE40-Non-Food are reported in Additional File S8 and they show a similar pattern. The Additional File S8 also reports the year-wise estimates of the indicators on patient satisfaction, length of stay, mean OOPE, median OOPE, CHE10, CHE25, CHE40 and CHE40-Non-food.

Adjusted models for OOPE

The results of OLS model applied for OOPE and the log of OOPE are available in Additional File S9 and they show that AB-PMJAY enrollment had no significant association with the size of OOPE. The above mentioned models showed that the main determinant of the amount of OOPE was utilisation of the private hospitals. Longer hospitalisations and perceived severity of illness were also associated with greater OOPE.

The PSM models for OOPE as well as the log of OOPE do not show any effect of AB-PMJAY enrollment on the size of OOPE (Table 7).

Table 7 Effect of enrollment under AB-PMJAY on OOPE and CHE for Hospital Care – Summary of results of OLS, Logistic, PSM, and IV models
Full size table

The IV model for OOPE and the log of OOPE are given in Additional File S10, and they show no relationship between OOPE and AB-PMJAY.

Adjusted models for indicators of CHE

The logistic, PSM, and IV models showed that AB-PMJAY-enrollment had no effect on CHE10 or the other three indicators of CHE (Table 5). The full results of the logistic and IV models for the CHE indicators are given in Additional Files S11 and S12 respectively. The logistic models showed that the type of hospital utilised was the main predictor of CHE occurrence. Longer hospitalisations and severe illnesses were also associated with greater chances of incurring CHE. Hospitalisations in 2022 involved a lower likelihood of CHE than in 2021.

Discussion

The current study is the first to evaluate the performance of AB-PMJAY in improving the quality of inpatient care. Patient satisfaction was found to be unrelated to whether the patient was enrolled under AB-PMJAY or not. An earlier survey that covered the care of older adults reported that 23.6% of those utilising inpatient care in Chhattisgarh were dissatisfied with the quality, and the proportion remained similar in the current study [45]. The length of stay was not associated with AB-PMJAY but mainly with the type of hospital utilised. An earlier study had also shown that hospitalisations in private hospitals in India tend to be longer [46]. It seems that private hospitals have the incentive to prolong hospitalisations so as to charge more from the patients. The AB-PMJAY has not been able to effect a change in this pattern.

The current study found that enrollment under AB-PMJAY had reached around 90% of the population in the state. This represents an improvement from the days preceding the scheme, when the enrollment under PFHI was around 60% [47]. However, the study found that enrollment under AB-PMJAY did not result in increased utilisation of inpatient care. An earlier study done in the same state after one year of AB-PMJAY’s implementation had reported the same conclusion [7]. A study covering six states also reported a similar finding regarding the effect of AB-PMJAY on utilisation [17].

A fundamental purpose of AB-PMJAY was to provide financial protection for inpatient care. The current study found that enrollment under AB-PMJAY did not reduce OOPE or protect the patients from catastrophic expenditure for hospitalisation. In our study, the mean OOPE incurred by patients enrolled under this scheme was INR 23,691, which was quite high when compared to the mean annual non-health consumption expenditure of households (INR 97365). Among those using private hospitals, 45.4% incurred CHE25 (at the 25% threshold) in 2022, and an earlier study in the same state reported that the proportion was 39.4% in 2019 after one year of AB-PMJAY implementation [7]. The proportion of inpatients in the private sector incurring CHE25 was 32.1% in 2014 when the RSBY scheme was in operation and 27.6% in 2004 when no PFHI scheme was in operation [7]. This shows that despite the introduction of PFHI schemes and the expansion of their coverage through AB-PMJAY, there has been a rising trend in catastrophic health expenditure in private hospitals. The current study shows that AB-PMJAY could not make the private hospitals affordable for the patients enrolled under its cover. Utilising public hospitals offered better protection from OOPE, irrespective of enrollment under health insurance. A study in 2019 showed that enrollment under AB-PMJAY was not able to make an impact on financial protection [7]. At that time, the empaneled private hospitals had contended that the prices at which they got reimbursed under AB-PMJAY were inadequate [18]. A large costing study was carried out nationally in 2019 to decide the reimbursement rates according to rigorous evidence [18,19,20]. It resulted in an upward revision of reimbursement rates for 61% of the services covered in the AB-PMJAY benefit-package [19]. In addition, AB-PMJAY had several advantages over its predecessor national PFHI scheme, called RSBY. The annual sum assured per family enrolled under AB-PMJAY was seventeen times larger than the RSBY [7]. The population coverage, i.e., the enrollment was also larger in AB-PMJAY than earlier schemes [7]. The number of empaneled hospitals had also increased, at least in urban areas [24]. The above changes were not successful in making AB-PMJAY effective in financial protection. The inability of PFHI in ensuring financial protection for hospital-care is consistent with other studies in India [7, 10,11,12,13,14,15]. A study of AB-PMJAY in the first year of its implementation had reported a minor effect of AB-PMJAY in reducing OOPE with 21% lower chance of CHE for those who were able to utilise the scheme [17]. Looking at the findings of the above study alongside the current evaluation, it seems that many of those enrolled under AB-PMJAY may be unable to receive the benefit of AB-PMJAY when they get hospitalised. And those who are able to access the benefit of AB-PMJAY may be getting a minor discount in payment.

Why does PFHI remain ineffective in providing financial protection in the Indian context? The current study found that the mean OOPE for utilising private hospitals remained around ten times larger than that of public hospitals. As found by other studies, utilising private hospitals was the main determinant for incurring high OOPE or catastrophic expenditure [7, 11,12,13,14,15]. The current study showed that the same pattern persisted four years after the full roll-out of AB-PMJAY. This failure seems to be related to the existing problem of ‘double-billing’ and overcharging by private providers under PFHI schemes in India [7, 11,12,13,14,15,16, 48, 49]. ‘Double billing’ refers to a fraudulent practice whereby a hospital takes cash payments from a patient while also claiming reimbursement for the same service from the government’s PFHI scheme [11]. The private hospitals were taking copayments from the patients even though their empanelment contracts specifically prohibited such a practice. The present study showed that even after implementing the increased prices, contracting was ineffective in ensuring that the private hospitals adhered to the agreed prices. The persistent failure of AB-PMJAY and other PFHI schemes in the Indian context suggests that further research is needed to develop alternative policies for UHC.

The study was conducted during a period affected by the COVID-19 pandemic. The hospital services in 2021 were badly affected. The severity of COVID-19 infections and mortality in Chhattisgarh had reduced significantly in 2022. This situation is reflected in the descriptive comparison of OOPE figures for the two years. In private hospitals, the average OOPE was greater in 2021 than in 2022.

Our study has several strengths, and it covers a lot more ground in comparison to the existing evaluations of AB-PMJAY. The study is not based on a single cross-section but involves two annual waves of data collection. It has a large sample of around 15,000 individuals in each wave, representative of a state with a population of 30 million. The state chosen has been a leading implementor of AB-PMJAY and has around 90% of its population enrolled under the PFHI scheme. The study was conducted after four years of implementation of the AB-PMJAY and thereby provides the first evaluation beyond its early days. The study is the first to evaluate AB-PMJAY on the quality of inpatient care and used two different measures for that. The methodology is robust as it confirms the results using multiple analytical methods, including those addressing potential endogeneity. Earlier studies on PFHI in India had another limitation: they could not take into account the severity of illness while analysing the variations in OOPE [7, 11]. The current study is able to overcome that limitation by including the perceived severity of the illness.

Another strength of the study is the robustness of the analytical methods used. The multi-variate regression analysis offered the advantage that its results were easy to interpret intuitively. It also shed light on the determinants contributing to OOPE, such as provider ownership. Repeating the regressions using the IV approach was useful in addressing any potential endogeneity. The PSM was useful in confirming the main findings on the effect of PFHI on matched groups of enrolled and non-insured individuals.

Several policy lessons emerge from our findings. Our study shows that coverage under a health insurance scheme may not guarantee financial protection. One set of policy measures can be focused on improving the design and implementation of AB-PMJAY. The share of private hospitals in service provision under the scheme needs to be reduced. The contracting of private providers can be limited to services that are difficult to provide through the public sector. The renewal of contracts with providers should be based on their track record of adhering to the contracts. Contracting a smaller number of private providers can perhaps make it easier for government regulators to monitor provider behaviour and enforce the contractual conditions. If the public sector starts providing the necessary range of services, it can reduce its dependence on private providers. Introducing gatekeeping through public sector hospitals may also help in reducing unnecessary medical procedures in the private sector. There is a need to learn from the experiences of other LMICs in implementing PFHI schemes. Another set of policy changes should be focused on measures beyond AB-PMJAY. It has to be realized that health insurance schemes cannot be sufficient to ensure financial protection, and additional strategies are needed for achieving the goals of universal health coverage. Improving affordable access to essential medicines and diagnostics and strengthening primary health care are examples of such measures.

Limitations

The study covers a single state of India, and similar studies in more states may be needed to capture the diversity in the large country. The study is not based on an experimental design. It does not include observations of a time before the scheme was launched and, therefore is unable to directly compare the situation before and after the scheme’s implementation. The impact of the COVID-19 pandemic cannot be ruled out, though efforts have been made in the analyses to control for it. We believe that the overall conclusion of the study still holds, in terms of persistent CHE in private hospitals, irrespective of enrollment under AB-PMJAY. This was evidenced by the pattern of OOPE in 2022, when the severe effects of the pandemic had largely abated in India. Quality in healthcare is a multi-dimensional and complex concept that is not easy to measure. Our study relied on two simple indicators of quality, and it did not attempt to capture quality in its complexity. Measurement of quality should ideally include examination of facilities and details of treatment given to patients [33]. The patient feedback collected in our study did not cover multiple aspects of experience during hospitalisation and was limited to their satisfaction with the medical treatment received.

Conclusion

Based on the analysis presented here, the study concludes that India’s AB-PMJAY scheme was not associated with improved utilisation, financial protection, or quality for inpatient care. The current study adds to the literature on the effectiveness of PFHI-based policies in the LMICs for UHC. Further research is recommended to assess the impact of PFHI schemes on financial protection in other LMICs where a major share of service delivery is through for-profit private hospitals and to draw lessons from their successes or failures.