Adoption, digital health literacy, and patient satisfaction of Ayushman Bharat Digital Mission: an analytical cross-sectional study among outpatient department attendees

Abstract

Background

The Ayushman Bharat Digital Mission (ABDM) is India’s ambitious effort to establish a complete digital health ecosystem. However, evidence on real-world adoption, user experiences, and barriers among target populations remains limited.

Objectives

To estimate the adoption of ABHA ID registration services among outpatient department (OPD) attendees in tertiary care hospitals, and to assess patient satisfaction, digital health literacy levels, and identify barriers, gaps, and limitations in accessing healthcare through ABDM.

Methods

A hospital-based cross-sectional study was conducted among 425 outpatient department (OPD) attendees across three tertiary care hospitals in Agra using multistage random sampling from September 2024 to April 2025. ABHA ID registrations adoption rates were calculated from hospital records over eight months in comparison with conventional OPD registration (non-digital paper based). Digital health literacy was assessed using the validated 21-item Digital Health Literacy Instrument (DHLI). Patient satisfaction was measured using a structured 10-item Likert scale questionnaire. Barriers, gaps, and limitations were evaluated through structured interviews. Data were analysed using descriptive and inferential statistics.

Results

The study was conducted among 425 outpatient department (OPD) attendees. In which 537,278 total OPD visits, 129,007 completed ABHA ID registrations with a below average monthly adoption rate of 23.96%. Overall digital health literacy was moderate (mean DHLI score: 2.94 ± 0.72) among 425 OPD attendees, with males scoring significantly higher than females (U = 15,922, p < .001). Education showed the strongest positive correlation with DHLI scores (ρ = 0.480, p < .001) while age showed a weak negative correlation (ρ=-0.230, p < .001). Participants demonstrated better operational skills (2.38 ± 0.64) but weaker abilities in evaluating reliability (2.18 ± 0.66) and protecting privacy (2.22 ± 0.65). Patient satisfaction was high with over 80% agreeing that ABDM made registration easier and was user-friendly. However, only 55% felt comfortable with data privacy protection. Major barriers included preference for in-person healthcare (85.6%), high data plan costs (81.9%), difficulty using health apps (65.6%), language difficulties (64.0%) and hesitation to share one time password (OTP) (60.0%). Notably, 88% expressed need for training and support, while 58.6% lacked adequate knowledge about ABDM despite visiting participating facilities.

Conclusion

While ABDM demonstrates operational efficiency and moderate patient user satisfaction, adoption of ABHA registration remains limited in comparison to conventional OPD registrations, with significant barriers related to digital literacy, awareness, economic constraints, and privacy concerns. Substantial gender, age, and educational disparities exist in digital health literacy. To achieve equitable and sustainable adoption, targeted interventions are essential, including community-based digital literacy programs, simplified multilingual interfaces, strengthened privacy safeguards, subsidized data access, and on-site support through trained facilitators particularly for vulnerable populations including women, older adults, and those with lower educational attainment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-026-14314-7.

Introduction

The global adoption of digital health platforms and electronic health records has gained significant momentum to enhance access, quality, efficiency and cost effectiveness of healthcare delivery. The COVID-19 pandemic further spotlighted the urgency of leveraging health tech to build resilient systems that can continue uninterrupted services during a crisis [1]. The World Health Organization’s Global Strategy on Digital Health 2020–2025 outlines key priority areas, including promoting global collaboration and technical assistance for digital health, ensuring ethical use of data, enhancing digital health literacy and skills, regulating digital technologies, and integrating digital interventions into health systems [2]. The WHO also established the Global Observatory for Digital Health and its efforts to develop a Global Maturity Model for assessing country readiness. Key global guidelines and frameworks have emerged to support the development of people-centric national digital health systems. This includes the ITU-WHO National eHealth Strategy Toolkit, the International Organization for Standardization (ISO) health informatics standards, and the WHO-ITU National eHealth Strategy Toolkit [3–5]. Adoption of digital health is also embedded in the United Nations Sustainable Development Goals and identified as an enabler for achieving Universal Health Coverage.

However, global adoption displays disparities based on geographical regions and countries, income level and analysis of WHO data from 125 countries found that 91% of high-income countries reported having a national electronic health record system compared to only 23% of low-income countries [6]. Sub-Saharan Africa and South-East Asia have the lowest rates globally. In India, the National Health Stack blueprint, developed in 2018, laid the foundations for a national digital health ecosystem [7]. It envisioned developing the building blocks for data storage, exchange, and consent-based access through initiatives like Health ID, Personal Health Records, Electronic Medical Records, and Registries. This digital health strategy evolved into the National Digital Health Mission, formally launched in 2020. In 2021, it was renamed as the Ayushman Bharat Digital Mission (ABDM), aligning with other national policies [8]. The NITI Aayog and the National Health Authority provide overall guidance for implementing ABDM.

ABDM aims to develop the digital health infrastructure for nationwide portability in patient care and other health services. A few key objectives outlined are to provide equitable access to quality healthcare by improving accessibility, affordability, and reducing fragmentation, develop the backend digital health data systems required for a smooth digital health experience, enable linkages between stakeholders for seamless data access and exchange, promote the adoption of digital health standards and regulations for data security and privacy, create a system of Personal Health Records accessible to individuals, strengthen existing public health programs by digitizing operations [8]. A core component of the ABDM is the Ayushman Bharat Health Account (ABHA), which provides citizens with a unique 14-digit health ID that facilitates linkages between patients, doctors, insurance companies, and other stakeholders [9].

As on 28 January 2026 8.79 Crore ABHA ID created under ABDM in 2025–26 financial year [10]. However, usage of ABHA ID remains relatively low [11]. A national survey in 2021 found only 10% of users engaged with the ABHA platform, despite 60% being aware of it [12]. This reveals gaps in turning awareness into adoption among vulnerable groups. The Unified Health Interface (UHI) allows individuals to discover and access healthcare services across public and private ABDM-registered providers through web or mobile apps seamlessly [13]. The Open Network for Digital Commerce (ONDC) integration enables citizens to connect to an interoperable network of health service providers, compare costs, and book services [14].

Strategic linkage of ABDM with the government’s flagship health insurance scheme, Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), allows its beneficiaries to access paperless, cashless healthcare by authenticating via their ABHA number [15, 16]. This integration seeks to close equity gaps in access and healthcare expenditures for at-risk populations.

Although India has led extensive digital health initiatives, concrete evidence regarding fair adoption and effects is still scarce but beginning to emerge [17, 18]. Evaluating usage patterns, acceptability, satisfaction, obstacles, and integration into the health system is essential for effective adoption within priority populations. The current situation emphasizes the need and the chance to create robust empirical data on the uptake of ABDM initiatives among prospective beneficiaries. Implementation research is essential to develop tailored strategies that synchronize digital health policies with the requirements and situations of at-risk groups. Addressing digital gaps can advance India’s goals of achieving universal health coverage, better population health outcomes, and health equity. This study is one of the first to evaluate India’s Ayushman Bharat Digital Mission (ABDM) through a real-time, ground-level assessment conducted in a busy government outpatient department. Using a comprehensive, multi-dimensional approach, it combines data with patient-reported outcomes such as satisfaction and digital health literacy measured through the DHLI scale. This provides a holistic understanding of ABHA ID adoption beyond registration numbers by assessing how ABHA functions as a foundational digital identity that links patients to multiple ABDM-enabled services rather than as a standalone registration outcome. The study captures real user experiences at the point of care, revealing operational challenges, behavioral patterns, and system-level barriers faced during ABHA registration and use. By linking policy intent with on-ground implementation and examining ABDM alongside existing schemes like AB-PMJAY, it offers practical, evidence-based recommendations to strengthen digital health initiatives and ensure improved access, efficiency, and equity in healthcare delivery.

Aim

To study the adoption and outcome of populations on the Ayushman Bharat Digital Mission among OPD patients in a tertiary care hospitals.

Objectives

1. To estimate the adoption of ABHA ID registration services enabled by Ayushman Bharat Digital Mission for the population.

2. To assess patient satisfaction, digital literacy levels, and to identify barriers, gaps, and limitations faced in accessing healthcare through Ayushman Bharat Digital Mission.

Material and method

The study was designed as an observational cross-sectional study, including patients attending OPD registration, while excluding those unable to provide consent or participate in interviews due to health conditions or other reasons. For the sample size calculation, the following formula was used:

n = Z²(pq) / d², where: “n” is the calculated sample; “Z” is the standardized normal variable associated with the confidence level, which is 1.96; “p” is the prevalence, in the absence of a single, reliable prevalence estimate applicable to all study outcomes, a prevalence (p) of 50% was assumed for sample size calculation. This conservative approach was adopted to ensure the maximum sample size and adequate power, as the study simultaneously evaluated multiple outcomes with varying prevalence, including ABHA adoption, digital health literacy, patient satisfaction, and barriers to digital health access, and “d” is the sampling error, using a sampling error of 5% and a 95% confidence level. A sample size of 385 was obtained, adding a 10% non-response rate, the final sample size calculated was 423, which was then rounded up to 425.

Calculation: n= Z²(pq) / d²

(1.96)² × (50 × 50)/ (5)² = 385

By adding 10% non-response rate it came 423, which was rounded off to 425.

Study setting

The study was conducted in the outpatient department (OPD) of three government tertiary care hospitals in Agra, where a large and diverse patient population from both urban and rural areas of the district and nearby regions. The facility was selected due to its implementation of the Ayushman Bharat Digital Mission (ABDM), availability of both digital (ABHA ID-based) and conventional OPD registration systems, and its established role in providing secondary and tertiary care.

Data collection procedure

This hospital-based cross-sectional study was conducted to assess adoption, satisfaction, and barriers associated with the Ayushman Bharat Digital Mission (ABDM). Adoption rates were assessed by examining hospital records of the main tertiary care hospital over eight months (September 2024 to April 2025), in line with the data collection timeframe, to ascertain the percentage of patients who finished ABHA ID registration compared to the total OPD registrations which also included conventional OPD registrations. In contrast, data on digital health literacy (DHLI), patient satisfaction, and perceived barriers, gaps and limitations were collected through primary interviews conducted across all three study sites included in the study. Structured interviews were carried out with patients utilizing a pre-validated and pre-tested questionnaire to evaluate patient satisfaction, digital literacy, and barriers encountered during the process. The questionnaire was administered through interviewer-assisted interviews, where items and response options were read aloud in a standardized manner to ensure comprehension. A multistage random sampling technique was used in this hospital-based cross-sectional study to ensure representativeness while maintaining operational feasibility. In the first stage all three government tertiary care hospitals in Agra, selected based on early implementation of the Ayushman Bharat Digital Mission (ABDM), with an equal distribution of participants from each hospital. Within each hospital, OPD days served as the second stage in which two OPD days per week were randomly selected using the lottery method. OPD records from the corresponding day of the previous week were reviewed solely to estimate average patient flow; these records were not used for participant selection. On each selected OPD day, 10 eligible patients were selected using a random number table at the final stage. This approach was done to minimize selection bias. This procedure was repeated in hospitals consecutively until the required sample size was achieved. The interviews took place in a secluded area close to the OPD to minimize response bias. The questionnaire was only for patients who completed ABHA ID registration. It captured socio-demographic characteristics such as age, gender, education, occupation, and area of residence, as well as sections on digital literacy levels, patient satisfaction, and barriers faced, such as technical errors, poor internet connectivity, lack of operator support, or difficulty in understanding digital procedures.

Study tool

A pilot study was conducted on 10% of the total sample (n = 42) to test the questionnaire before the main study. It helped to see if the questions were clear, easy to understand, and in the right order. A dummy table was made to check how the data and if all answers could be properly recorded. Based on the findings, small changes were made, and the final questionnaire was prepared for the main study. The DHLI has 7 separate digital health literacy skills as shown in Table 1 [19]. These include operational skills for computer and Internet browser usage capabilities, navigation skills for web navigation and orientation abilities, and information searching skills for the application of effective search strategies. Additionally, the instrument measure skills for evaluating reliability through assessment of general information credibility, determining Self-Efficacy, one’s ability to manage digital tools for health needs, content generation involving adding content to web-based applications, and privacy protection focusing on safeguarding and respecting privacy during Internet use [19].

Table 1.

Interpretation of the DHLI domains

DHLI Domain	Interpretation
Operational Skills	Ability to use digital devices and browsers.
Navigation Skills	Ease in moving across websites and platforms.
Information Searching	Ability to search for health information online.
Evaluating Reliability	Confidence in judging the trustworthiness of online information.
Adding Content	Ease in interacting and contributing content online.
Protecting Privacy	Confidence in the safe handling of personal health information online.
Self-Efficacy	Belief in one’s ability to manage digital tools for health needs.

The self-report component consists of 21 items total, with 3 items per skill category measured on a 4-point Likert scale. Response options range from “very easy” to “very difficult.” The scoring system is reverse-scored so that higher scores indicate higher digital health literacy levels. Sub scores for each domain were computed as the mean of the three items within that domain. For each participant, the DHLI score was calculated by summing the responses to all items and dividing the total by 21, yielding an individual mean score ranging from 1 to 4. The total mean DHLI score was calculated as the sum of individual DHLI scores divided by the total number of participants, representing the average of digital health literacy. Internal consistency was assessed through Cronbach’s alpha, indicating the weighted average correlation among items in the scale. The generally accepted threshold of α = 0.7-0.8 is considered satisfactory for research applications, making the DHLI suitable for empirical studies investigating digital health literacy competencies and ensuring reliable measurement of participant abilities [19, 20].

The study tool used was a structured questionnaire to assess patient satisfaction and perceptions which reflected experiences based on this direct interaction with Ayushman Bharat Digital Mission (ABDM) services. It included ten statements covering key aspects such as usability, accessibility, data privacy, efficiency, and overall satisfaction. The statements were: “The platform was user-friendly,” “ABDM improved care coordination,” “ABDM made registration easier,” “I understand ABDM and its benefits,” “ABDM made access to healthcare easier,” “I am comfortable sharing data on ABDM,” “I am satisfied with ABDM support,” “ABDM can reduce healthcare costs,” “ABDM improved service efficiency,” and “I’m comfortable with data privacy in ABDM.” Each statement was rated on a Likert scale, Strongly Disagree, Disagree, Agree, and Strongly Agree, to capture participants’ level of agreement and satisfaction with different aspects of the digital platform [Additional File 1]. The questions on barriers, gaps, and limitations were developed to systematically assess user-reported challenges and unmet needs [Additional File 1].

Statistical analysis

Data collection was carried out using Google Forms, and all responses were cleaned, coded, and compiled in Microsoft Excel for further processing. The data were analyzed in accordance with the study’s objectives using Microsoft Excel, Jamovi software version 2.3.28, and R software version 4.5.1, Microsoft Excel was used for data cleaning and compilation, Jamovi for descriptive and non-parametric inferential analyses, and R software for result verification and graphical visualization [21–23]. Monthly ABHA ID adoption rates were analyzed using descriptive statistics. Patient satisfaction, digital literacy, and barriers to ABDM services were assessed using both descriptive and inferential analyses. Mean, median, standard deviation, and standard error were calculated for DHLI domains, along with frequency distributions for satisfaction and barriers. As the data were non-normal (Shapiro–Wilk test), non-parametric tests were applied, including the Mann–Whitney U test for gender differences and Spearman’s rank correlation for associations between DHLI scores, age, and education.

Results

The study sample comprised 425 participants. The age distribution showed that the majority of participants (33.6%, n = 143) were in the 30–44 years age group, followed by those aged 45–59 years (28.5%, n = 121) and 15–29 years (23.3%, n = 99). Participants aged 60–74 years constituted 12.7% (n = 54) of the sample, while older adults aged 75 years and above represented only 1.4% (n = 6). The age group (0–14 years) had minimal representation with just 0.5% (n = 2). Gender distribution of females comprising slightly more than half of the sample 51.5% (n = 219) in comparison to males 48.5% (n = 206).

According to occupational status, the largest occupational category was semi-skilled workers (37.4%, n = 159), followed by unskilled workers (24.2%, n = 103) and unemployed individuals (23.1%, n = 98). Skilled workers represented 12.5% (n = 53), while semi-professionals constituted only 2.8% (n = 12). The most common educational level was primary school completion (32.0%, n = 136), followed by middle school education (26.1%, n = 111). A substantial proportion of participants were illiterate (20.2%, n = 86), while 12.2% (n = 52) had completed high school education. Intermediate education was reported by 6.4% (n = 27) of participants, and only 3.1% (n = 13) held graduate degrees. Socioeconomic status distribution indicated that the largest proportion of participants belonged to the lower-middle socioeconomic category (37.6%, n = 160), followed by those in the lower socioeconomic status group (31.5%, n = 134). Middle socioeconomic status was represented by 25.2% (n = 107) of participants. Upper-middle and high socioeconomic status groups had limited representation, accounting for 4.9% (n = 21) and 0.7% (n = 3) of the sample, respectively.

Table 2 summarizes the monthly ABHA registration figures alongside total OPD attendances from September 2024 through April 2025. Over these eight months, a total of 537,278 OPD visits were recorded, of which 129,007 completed ABHA registrations, yielding a below average adoption rate of 8 months which was 23.96%.

Table 2.

Distribution of Ayushman Bharat Health Account (ABHA) registration adoption rates across study months (September 2024–April 2025)

Month	Total OPD Patients	ABHA Registrations	ABHA Adoption Rate (%)
Sep-24	71,776	20,973	29.22
Oct-24	68,934	22,526	32.68
Nov-24	68,130	14,442	21.2
Dec-24	69,078	13,292	19.24
Jan-25	62,841	13,989	22.26
Feb-25	67,649	15,676	23.17
Mar-25	70,386	14,445	20.52
Apr-25	58,484	13,664	23.36

The distribution of ABHA versus traditional OPD registrations over the eight-month study period exhibits descriptive monthly variations, as seen in Fig. 1. Early adoption of the digital system was demonstrated by the fact that, in the immediate post-implementation phase, ABHA registrations accounted for 29.2% of all visits in September 2024 and peaked at 32.7% in October 2024. After then, when conventional registration reclaimed its lead (78.8% and 80.8%, respectively), the percentage of ABHA use decreased to 21.2% in November and 19.2% in December primarily attributable due to reduced OPD attendance and service delivery disruptions during the festive and year-end period, including public holidays. January 2025 had a rebound of 22.3%, and February 2025 saw a recovery of 23.2%, indicating that digital processes had stabilized. ABHA registration was 20.52% in March 2025 showing a decrease due to public festive holidays. By April, the percentage had dropped to 23.4%, returning to the January–February distribution.

Fig. 1.

Monthly comparison of ABHA and conventional OPD registrations (September 2024–April 2025)

Descriptive statistics for each DHLI domain among 425 participants are shown in Table 3, which demonstrates consistently moderate levels of digital health literacy (scale 1–4). Participants scored highest in Operational Skills (M = 2.38, SD = 0.64) and Navigation Skills (M = 2.30, SD = 0.63), indicating relative confidence in basic device use and moving through online platforms. Information Searching (M = 2.31, SD = 0.64), Adding Self-Generated Content (M = 2.32, SD = 0.66), and Self-Efficacy (M = 2.34, SD = 0.64) also fell solidly in the moderate range. In contrast, the lowest domain scores were found for Evaluating Reliability (M = 2.18, SD = 0.66) and Protecting Privacy (M = 2.22, SD = 0.65), highlighting key gaps in critical appraisal and privacy protection. The overall total DHLI mean score of 2.94 reflects a moderate level of digital health literacy among participants.

Table 3.

Descriptive statistics of digital health literacy instrument domains

DHLI Domains	N	Mean	Median	SD	SE
Operational skills	425	2.38	2.33	0.638	0.0310
Navigation Skills	425	2.30	2.33	0.626	0.0304
Information Searching	425	2.31	2.33	0.636	0.0308
Evaluating Reliability	425	2.18	2.00	0.662	0.0321
Adding Self-Generated Content	425	2.32	2.33	0.663	0.0322
Protecting Privacy	425	2.22	2.33	0.648	0.0314
Self-Efficacy	425	2.34	2.33	0.637	0.0309
Total Mean DHLI Score	425	2.94	3.06	0.715	0.0347

*N – number of participants; Mean – average score on a 4-point scale; Median – middle value of responses; SD (Standard Deviation) – variability of responses; SE (Standard Error) – precision of the mean estimate. The total DHLI score was calculated as a composite score by summing responses across all seven DHLI domains (21 items)

In Table 4, Mann–Whitney U test was conducted, as the data were not normally distributed (confirmed by the Shapiro–Wilk test). The analysis showed a statistically significant difference in total DHLI scores between male and female participants (U = 15,922, p < .001). This indicated that the distribution of digital health literacy scores differs significantly by gender. The rank biserial correlation of 0.294 indicates a moderate effect size, suggesting significant difference in digital health literacy scores between male and female participants. Given the significance level (p < .001), we reject the null hypothesis (H₀: µ₁ = µ₂) and accept the alternative hypothesis (Hₐ: µ₁ ≠ µ₂), confirming that digital health literacy levels vary meaningfully between males and females in the study population.

Table 4.

Mann-Whitney U test comparing male and female digital health literacy instrument scores

Mann-Whitney U Test
		Statistic	p		Effect Size
Total DHLI Mean Score	Mann-Whitney U	15,922	< 0.001*	Rank biserial correlation	0.294

*Note. Hₐ µ _{1] Male} ≠ µ _{2] Female;} p < .001 indicates a highly significant difference

Figure 2 graphically depicts the mean total DHLI scores for males and females, with Males demonstrating higher total DHLI scores compared to females, with a mean score of approximately 3.16 (median: 3.25), while females showed a mean score of around 2.73 (median: 2.79). The confidence intervals, represented by the vertical blue lines, indicate the precision of these estimates and show minimal overlap between the two groups, suggesting a statistically significant difference in digital health literacy scores between genders. The relatively tight confidence intervals suggest reasonable sample sizes and consistency within each group. Overall, the data indicate that male participants in this study exhibited moderately higher digital health literacy levels than their female counterparts.

Fig. 2.

Gender comparison of mean digital health literacy instrument scores

Figure 3 depicts a clear positive relationship between education level and digital health literacy among the 425 participants. Individuals with no formal education scored the lowest (M = 2.40), while those completing primary school scored 2.83. Middle school graduates achieved a mean score of 3.07, entering the “good” range, and further increases were observed for high school (M = 3.39) and intermediate education (M = 3.47). Participants with a college degree demonstrated the highest digital health literacy (M = 3.68). The small standard error bars confirm the precision of these estimates. These results underscore the importance of formal education in enhancing digital health competencies.

Fig. 3.

Mean digital health literacy instrument score by education level

As shown in Table 5, a Spearman’s correlation analysis was conducted to assess the relationship between total Digital Health Literacy (DHLI) scores and selected sociodemographic variables. A moderate positive correlation was observed between educational level and DHLI scores (ρ = 0.480, p < .001), indicating that higher education is associated with higher digital health literacy. Occupation also had moderate positive correlation with DHLI (ρ = 0.335, p < .001), suggesting that individuals with higher occupation tend to have better digital health literacy. In contrast, a weak negative correlation was observed between age and DHLI (ρ = −0.230, p < .001), suggesting that younger participants showed higher digital health literacy than older participants. Whereas, socioeconomic status no correlation with digital health literacy instrument scores (ρ = 0.081, p value = 0.081).

Table 5.

Correlation of Digital Health Literacy Instrument (DHLI) score with sociodemographic variables among study participants

Sociodemographic Variables		DHLI Score
Age	Spearman’s rho	-0.230***
	df	423
	p-value	< 0.001
Education Level	Spearman’s rho	0.480***
	df	423
	p-value	< 0.001
Occupation	Spearman’s rho	0.335***
	df	423
	p-value	< 0.001
Socioeconomic Status	Spearman’s rho	0.085
	df	423
	p-value	0.081

Note. spearman correlation * p < .05, ** p < .01, *** p < .001

Figure 4 summarizes responses to the patient satisfaction questionnaire (n = 425), with participants rating their agreement from “Strongly Disagree” to “Strongly Agree” on ten ABDM-related statements. Overall, satisfaction was high: more than 80% of respondents agreed or strongly agreed that “the platform was user-friendly” and that “ABDM made registration easier.” Similarly strong positive ratings (over 75% agreement) were given to the statements “ABDM improved care coordination,” “ABDM improved service efficiency,” and “I understand ABDM and its benefits,” indicating broad recognition of ABDM’s operational advantages. Agreement was also robust (approximately 65–70%) for “I am satisfied with ABDM support” and “ABDM made access to healthcare easier,” reflecting solid but slightly less unanimous approval of the support services and access improvements. Areas of relative concern include data privacy and cost reduction: only about 55% felt comfortable with data privacy on ABDM, and roughly 60% believed ABDM could reduce healthcare costs. These results suggest that while users are generally satisfied with ABDM’s usability and its impact on workflow and access, targeted efforts to bolster confidence in data security and cost-related benefits may enhance overall patient satisfaction.

Fig. 4.

Patient satisfaction with Ayushman Bharat Digital Mission (ABDM) services

Figure 5 shows that “Prefer In-Person Healthcare” was 85.6% yes and 14.4% no; “Cost of Data Plan” was 81.9% yes and 18.1% no; “Difficulty Using Health Apps” was 65.6% yes and 34.4% no; “Language Difficulty” was 64.0% yes and 36.0% no; “Hesitant to Share OTP” was 60.0% yes and 40.0% no; “SIM Recharge Expensive” was 51.3% yes and 48.7% no; “Privacy/Security Concerns” was 49.6% yes and 50.4% no; “Insecure Scanning QR” was 43.3% yes and 56.7% no; and “Cost of Smartphone Main Barrier” was 26.1% yes and 73.9% no.

Fig. 5.

Perceived barriers to accessing Ayushman Bharat Digital Mission (ABDM) services among study participants (n = 425)

Figure 6 shows that 88% of the participants highlight the “Need Training/Support” to use ABDM. About 58.6% reported having “Knowledge of ABDM”, while 41.4% did not. Nearly Half of the participants 48.5% reported that they “Rely on Others” for help using digital device and 51.5% did not.

Fig. 6.

Perceived gaps in Ayushman Bharat Digital Mission (ABDM) services among study participants (n = 425)

Figure 7 shows the limitations affecting use of digital health services. While “Storage Space Issues” was 35.1% yes and 64.9% no; “Poor Internet Connectivity” was 32.7% yes and 67.3% no; “Access SIM Recharge Location” was 28.2% yes and 71.8% no; “Difficulty Charging” was 24.0% yes and 76.0% no; and “Owns Smartphone” was 71.1% yes and 28.9% did not.

Fig. 7.

Perceived limitations in Ayushman Bharat Digital Mission (ABDM) services among study participants (n = 425)

Discussion

This study examined digital literacy levels and adoption patterns, patient satisfaction, barriers, gaps and limitations of Ayushman Bharat Digital Mission among 425 participants of outpatient department. Adoption of ABHA registration remained limited in comparison with conventional offline OPD registration continuing as the preferred mode. Overall digital health literacy was moderate having better operational skills but weaker abilities in evaluating online information and protecting privacy. Higher education, younger age and male gender were associated with better digital health literacy. While participants expressed moderate satisfaction with ABDM services, major barriers included preference for in-person care, high data plan costs, language difficulties, and the need for training and support.

The average DHLI score of 2.94 in our study showed a moderate level of digital health literacy, similar to findings by Asmit A et al. (2024) [24], who found that 45% of adults in Muzaffarpur, Bihar, had moderate and 25% had high digital health literacy [24]. This suggested that moderate literacy levels are common across India, showing that people are still adjusting to digital health use. Ishikawa H et al. (2025) [25] in their comparative study between university students in Japan, the United States and India showed that DHLI scores were significantly higher in the United States (3.10 ± 0.38) than in India (2.29 ± 0.38) and Japan (2.89 ± 0.42) [25]. Based on Kim SM et al. (2025) [26], digital health literacy included four main areas: using devices, understanding health information, applying it for decisions, and willingness to use digital health tools [26]. Our participants demonstrated moderate operational (2.38) and navigational (2.30) skills, but lower confidence in reliability evaluation (2.18) and privacy protection (2.22), suggesting that while basic digital use is improving, critical appraisal and privacy skills remain relatively weak. This means that while people can use digital tools, they are less able to check if the information is trustworthy or keep their data safe. Similar results were seen by Arjun MC et al. (2024), where people using eSanjeevani and Aarogya Setu worried about data privacy [27]. Kamath R et al. (2025) [28] also found low digital literacy and privacy concerns even among postgraduate health students [28]. Zhao et al. (2024) [29] also reported with a mean DHLI score of 2.69 ± 0.61, indicating a moderate level of digital health literacy [29]. This showed the need for digital health education that also teaches critical thinking and privacy protection, not just basic use. There is limited published literature, with very few studies available to compare these findings in populations.

Education emerged as the strongest predictor (ρ = 0.480, p < .001), with DHLI scores increasing from 2.40 among the uneducated to 3.68 among graduates, aligning with Gogoi A et al. (2025) [30], who showed that structured training significantly improved digital literacy though a quasi-experimental study in which the intervention arm showed 90% better knowledge in comparison to control arm of 41.6% [30]. In our study participants showed low educational attainment (20.2% illiterate; 3.1% graduates) similar to challenges noted by Mishra U et al. (2024) [31], where states with low literacy (e.g., Bihar 13.9%, UP 14.7% ABHA coverage) lag in ABDM implementation, suggesting that educational disparities drive digital health inequities in India [31]. Zhao et al. (2024) [29] showed similar results with higher educational attainment was positively associated with DHLI scores, including bachelor’s or associate degrees (β = 0.255, p = .002) and master’s degree or higher (β = 0.256, p < .001) [29].

Our study found a significant gender gap in DHLI scores (U = 15,922, p < .001), with men showing higher digital health literacy than women. This was similar with Gogoi A et al. (2025) [30], who showed that social, cultural, and economic barriers limit women’s access to digital tools and education in India [30]. This gender gap has important implications for ABDM adoption, as women constitute a substantial proportion of healthcare consumers, particularly for maternal and child health services [30]. The finding that females in our study scored lower across all DHLI domains suggests that gender-sensitive interventions are essential. Such interventions should address not only technical skills but also socio-cultural barriers that limit women’s engagement with digital health platforms, including restricted access to smartphones, limited privacy for digital transactions, and lower confidence in using technology independently. Since women are major users of health services, especially in maternal and child health, this gap can slow ABDM adoption [30].

A negative correlation between age and DHLI (ρ = −0.230, p < .001) showed that younger people have better digital skills. Older participants also had lower education levels (ρ = −0.433, p < .001), which further reduced their digital literacy. Zhao et al. (2024) [29] reported similar findings in which analysis showed that older age was significantly associated with lower DHLI scores, particularly in the age groups 35–49 years (β = −0.08, p = .033), 50–64 years (β = −0.161, p < .001), and 65 years and above (β = −0.138, p < .001) [29]. Kim SM et al. (2025) [26] noted similar findings among adults aged 55–75 years [26]. However, Blondino CT et al. (2024) [32] found that with proper training and support, even older community health workers could effectively use digital tools, suggesting that age barriers can be overcome with tailored training [32].

About 58.6% of participants in our study lacked adequate knowledge about the ABDM platform, even after visiting facilities where it was available. This was similar to Arjun MC et al. (2024) [27], who found that only 8% of urban patients in Bengaluru had heard of ABDM, despite knowing other apps like eSanjeevani and Aarogya Setu [27]. This showed weak IEC (Information, Education, and Communication) efforts. Kamath R et al. (2025) [28] also found poor understanding of ABDM among postgraduate health students, showing that even health professionals need better awareness before they can guide patients [28]. Economic barriers were the most common, with 81.9% citing costly data plans and 51.3% citing expensive recharges. This was similar to Blondino CT et al. (2024) [32], who found cost to be a major reason for low digital tool use among community health workers [32]. Since 69.1% of our participants were from lower-income groups, high data plan costs limit their sustained use. Subsidized plans or low-bandwidth solutions could help. Usability and language issues were also major barriers 65.6% found apps hard to use, and 64% struggled with language. Kumaragurubaran P et al. (2024) [33] noted similar problems but found that user-friendly designs improved satisfaction [33]. Chandak A et al. (2025) [34] in his systematic review showed that ten studies met inclusion criteria (1 RCT, 2 observational studies, 7 reviews) [34]. Telehealth interventions reduced costs by USD 223-3,846 per event, with up to 94% savings in low-income settings. Satisfaction was generally high and comparable to in-person care. Older adults aged ≥ 80 faced more barriers due to digital literacy and usability challenges [34].

Samudyatha U C et al. (2023) [35] emphasized the need for local-language communication and culturally appropriate materials [35]. Low digital confidence was evident, with 49.4% uncomfortable using apps, and 88% wanted training. Panigrahi S et al. (2025) [36] in their qualitative study found out that the significance of health literacy in the management of TB multimorbidity [36]. Furthermore, low-literacy patients often faced cultural and semantic challenges, and they relied on community networks; however, the high-literacy patients trusted digital information [36]. Bevilacqua R et al. (2025) [37] showed similar findings in which mean eHealth literacy scores increased significantly after training, with a moderate effect size (paired t-test, p < .05) [37]. Repeated-measures analysis demonstrated sustained improvements in health literacy and self-efficacy at follow-up (p < .05) [37]. The literacy levels many times shape the care plan and patient navigation pathways. The navigation difficulties indicate the possible use of community health workers as trusted guides. They further recommended use of visual tools, improved provider training, and integrated IEC materials addressing multimorbidity, with clear implications for policy and practical healthcare interventions in India to enhance accessibility, trust, and patient-centred care in India [36].

In our study only 55% of participants felt comfortable about ABDM data privacy, and 49.6% expressed concerns about data misuse in our study. Similar concerns were seen by Arjun MC et al. (2024) [27] and Kamath R et al. (2025) [28], showing a widespread trust deficit [27, 28]. Even though the Digital Personal Data Protection Act aims to address these issues, awareness remains low [38]. Our low “Protecting Privacy” score (2.22) showed a weak understanding of data safety. As Sharma (2024) [39] notes, ABDM’s patient-centered design assumes users can manage their own data, an assumption not yet realistic [39].

Our findings reported 71.1% owned smartphones with poor internet access and charging issues. These findings are similar to Mishra U et al. (2024) [31] and Narayan A et al. (2024) [40], who reported wide state-level differences in ABDM coverage and infrastructure [31, 40]. Success stories like CoWIN and eSanjeevani show that when infrastructure and support are strong, adoption grows quickly, something ABDM needs for long-term success About 60% of users hesitated to share OTPs and 48.5% needed help using digital services. Kamath et al. (2025) [28] found similar issues among students, while Kumaragurubaran P et al. (2024) [33] reported that trained staff viewed digital tools as efficient but often faced technical issues [28, 33]. As Bhargo L et al. (2014) [41] noted, poor workflow integration can reduce efficiency and discourage use, highlighting the need for well-trained staff and smooth digital processes [41].

The preference for in-person healthcare expressed by 85.6% of our participants reflects the enduring value of traditional health services and the limitations of digital platforms in replicating the human connection and reassurance provided by face-to-face consultations. Jilani AQ et al. (2023) [42] documented low-to-moderate satisfaction levels with online psychiatric outpatient services during COVID-19, with only 18% of patients reporting higher satisfaction [42]. Datta A et al. (2025) [12] also showed patients reported high satisfaction with ABHA registration (satisfaction index (SI) = 87.9), perceiving it as simple and efficient [12]. This suggests that digital health platforms must be designed as complements to, rather than replacements for, traditional healthcare services, particularly for complex or emotionally sensitive health needs.

Our study found that more than 80% of participants agreed that ABDM made registration easier and that the platform was user-friendly. Our findings align with Sharda S et al. (2026) [43] digital health initiative in India, the digital supportive supervision (DiSS) where user-friendly digital tool improved service delivery in maternal and child healthcare service utilisation [43]. Similarly, most participants in our study felt that ABDM made registration easier and was easy to use, suggesting that simple and well-integrated digital platforms encourage acceptance. However, 58.6% of respondents still lacked enough knowledge about the platform. Similar study of Datta A et al. (2025) [12] also reported awareness of ABHA and other ABDM-related digital features remained low with 35% awareness among a substantial proportion of participants [12]. This showed that while people who use ABDM generally have a good experience, many others face barriers in awareness and adoption. This highlighted that digital health services need to be carefully adapted to each clinical area.

The study showed that ABHA ID registration adoption was influenced not only by service availability but also by digital health literacy, patient awareness, and trust. Strengthening digital literacy support, simplifying registration processes, and addressing privacy protection concerns and cost concerns are essential for effective and equitable implementation of the Ayushman Bharat Digital Mission.

Limitations

This study provided useful insights into digital health literacy and the adoption of ABDM however, several limitations need to be acknowledged. The research was conducted in a single geographic area which may limit the generalizability of the findings to other regions with different population characteristics or implementation contexts. The study was cross-sectional it captures participants’ perceptions at one point in time and cannot establish causality or track changes over time. The eight-month observation period, though informative, reflects only short-term trends and may not capture the long-term patterns of digital health adoption or the impact of evolving strategies.

Recommendations

Based on the findings of the present study, several measures can be suggested to improve the adoption and effective implementation of the Ayushman Bharat Digital Mission (ABDM). There is a need to enhance the adoption of ABHA ID registration by increasing awareness about digital health services through targeted Information, Education, and Communication (IEC) activities at both community and facility levels. Special attention should be given to rural populations, individuals with low literacy, and those from lower socioeconomic backgrounds. Simplifying the ABHA registration process and providing assisted registration services at healthcare facilities can help improve accessibility and uptake. Strengthening digital infrastructure, including reliable internet connectivity and availability of devices, is also essential to support the wider use of digital health services.

Improving digital health literacy and patient satisfaction is crucial for the sustained use of digital health platforms. Community-based programs should be conducted to enhance digital health literacy, particularly among vulnerable groups such as the elderly and illiterate. Healthcare providers and frontline workers, including ASHAs and ANMs, should be trained to support patients in navigating digital health systems. Digital platforms should be designed to be simple, user-friendly, and available in local languages to improve accessibility and acceptability. Efforts should also be made to build trust in digital health systems by addressing concerns related to data privacy and security. Establishing feedback mechanisms can help in understanding patient experiences and improving service delivery. Despite being registered under ABHA, many individuals may still prefer in-person healthcare, indicating the need to promote the benefits and usability of digital health services.

In addition, the study highlights several barriers and gaps that need to be addressed for effective implementation. These include lack of awareness, low digital literacy, technical challenges, and issues related to internet connectivity. Bridging the digital divide by improving access to smartphones, internet services, and digital support systems is essential. Ensuring data privacy and confidentiality through strict adherence to consent-based data sharing practices is also important. Regular monitoring and evaluation should be undertaken to identify gaps and guide improvements in the system.

Future research should focus on longitudinal studies to assess trends in adoption and sustained use of digital health services over time. Qualitative studies are needed to explore user experiences, perceptions, and barriers in greater depth. Implementation research can help identify effective strategies for improving digital health literacy and system utilization. In addition, economic evaluations should be conducted to assess the cost-effectiveness of ABDM, and outcome-based studies should evaluate its impact on healthcare access, quality of care, and patient satisfaction.

Conclusion

This study demonstrated that the Ayushman Bharat Digital Mission (ABDM) has achieved minimal adoption and was generally perceived as efficient and user-friendly, fulfilling its goal of enabling digital health service access. Patient satisfaction with core ABDM services, particularly ABHA registration, was high, and digital registration significantly reduced patient waiting time compared to conventional OPD processes. However, despite these operational advantages, digital health literacy levels among participants remained moderate to low, with notable disparities across gender, age, and educational status. The study identified several barriers and gaps limiting optimal use of ABDM, including preference for in-person care, limited awareness of ABDM features, language difficulties, concerns regarding data privacy, high cost of internet data, and a substantial need for training and user support. Vulnerable groups especially women, older adults, and individuals with lower educational attainment were disproportionately affected, highlighting the risk of a digital divide in healthcare access.

To ensure equitable and sustainable adoption of ABDM, targeted and multi-pronged strategies are essential. These include community-based digital literacy programs, simplified and multilingual user interfaces, strengthened privacy safeguards with transparent user controls, subsidized or free data access for health services, and on-site support through trained healthcare facilitators. Addressing these gaps is critical for ABDM to achieve its vision of inclusive, patient-centred, and universally accessible digital healthcare in India.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

ABDM

Ayushman Bharat Digital Mission

ABHA

Ayushman Bharat Health Account

AB

PMJAY–Ayushman Bharat Pradhan Mantri Jan Arogya Yojana

ANM

Auxiliary Nurse Midwife

ASHAs

Accredited Social Health Activists

CI

Confidence Interval

COVID

19–Coronavirus Disease 2019

DHLI

Digital Health Literacy Instrument

DiSS

Digital Supportive Supervision

DPDP Act

Digital Personal Data Protection Act

EHR

Electronic Health Record

IEC

Information, Education and Communication

ISO

International Organization for Standardization

ITU

International Telecommunication Union

NHA

National Health Authority

NITI Aayog

National Institution for Transforming India

ONDC

Open Network for Digital Commerce

OPD

Outpatient Department

OTP

One–Time Password

QR Code

Quick Response Code

RCT

Randomized Controlled Trial

SD

Standard Deviation

SE

Standard Error

UHI

Unified Health Interface

WHO

World Health Organization

Author contributions

AR, GS, HS contributed to the study conception. Data collection and analysis were performed by AR. The first draft of the manuscript was written by AR. All co-authors contributed to the subsequent stages of manuscript writing, including critical review, editing, and refinement of the content. Each author provided intellectual input, approved the final version of the manuscript. All authors read and approved the final manuscript after critically reviewing.

Funding

Ayushi Ranjan (AR) was supported by a Department of Health Research - Indian Council of Medical Research (DHR-ICMR) grant (HRD/DHR-ICMR/PG-2024/0380). The listed funding represents educational support only; the funders had no role in the conceptualization or writing of the manuscript or in the decision to submit it for publication.

Data availability

The dataset of this study are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.31399953.

Declarations

Ethical approval and consent to participate

All procedures were performed in accordance with the Declaration of Helsinki and the study was approved by Institutional Ethics Committee, S.N Medical College, Agra (reference no.-ECR/1409/Inst/UP/2020, (DHR)-ECR/NEW/Inst/2023/3504). Prior to participation, all individuals were provided with information about the study objectives, confidentiality measures, and informed consent was taken from all participants before enrolment.

Consent for publication

Not applicable.

Competing interests

The author (MS) is currently serving as Joint Director at the Ayushman Bharat Digital Mission (ABDM), Lucknow, which is responsible for the development and implementation of digital health initiatives. MS provided limited technical inputs related to understanding the ABDM ecosystem during the early implementation phase but had no role in the formulation of research questions, development of the study instrument, data collection, data analysis, or interpretation of results, and did not influence the decision to submit the manuscript for publication. All other authors declare that they have no competing interests.