Role of digital health interventions in strengthening human resources for primary healthcare systems in India: A systematic review

Abstract

Inlow- and middle-income countries (LMICs) like India, Human Resources for Health (HRH) constitute the backbone of a health system, providing critical services to ensure equitable, accessible, and high-quality healthcare for all. However, they are often overburdened, which hinders their performance and impact. The digital health interventions can appear as a boon to assist and enhance the performance of HRH, thereby delivering better healthcare services. To evaluate the impact and the effectiveness of digital health interventions on healthcare workforce capacity building in primary health care settings in India. A systematic review of published research was conducted as per the PRISMA guidelines. Online databases (PubMed and Google Scholar) were explored to identify studies written in English from January 2015 to March 2025 that discuss digital health interventions within Primary Healthcare settings or involve human resources in those settings in the Indian context. The review included all forms of messaging, voice calls, and interactive voice responses as interventions. A total of 22 studies were included (cRCTs: 6; RCTs: 5; interventional: 4; Quasi-experimental: 3; pre-post: 3; pseudo-RCT: 1). In terms of health conditions, the most studied area was Maternal and Child Health, addressed in 9 studies. The studies reported positive outcomes resulting from mobile health interventions. The digital health interventions were found to strengthen adherence to clinical protocols, improve maternal education, and enhance treatment outcomes, reinforcing their potential role in shaping future public health strategies.

Introduction

In low- and middle-income countries (LMICs) like India, Human Resources for Health (HRH) constitute the backbone of a health system, providing critical services to ensure equitable, accessible, and high-quality healthcare for all. Community health workers in villages consistently played a crucial role in making healthcare accessible, fair, and effective.

As countries strive toward universal health coverage (UHC) and the SDGs, it is essential to have a well-trained, well-prepared, and equitably distributed health workforce, adequately supported. This should be more than a mere technicality, as any health system could never exist without the humans that hold it together.[1,2]

Service quality and equity in rural and hard-to-reach areas are mainly due to workforce scarcity, skill gaps, and a lack of training. The HRH, which serves as the backbone of the healthcare system, is often burdened by the work that hinders their performance and impacts the overall service delivery. Other challenges include accessibility and connectivity issues that not only burden hardworking health professionals but also threaten the sustainability of health system progress.[3,4]

A wide range of ongoing efforts to reduce inequities in access to healthcare workers, like incentives and alternative training approaches, are in place. Yet, digital interventions such as telemedicine can also be explored as a potential mechanism to make healthcare services available to underserved communities. The swift integration of digital technologies in health systems, particularly within the HRH sector, is a novel approach that can fundamentally alter the development of healthcare workforces. Digital health interventions, such as mobile health (mHealth) applications, electronic immunization registries (EIRs), geo-location-enabled case management tools, clinical decision support tools, and checklists, provide innovative solutions for skill enhancement, accessibility, and quality of health service delivery.

To meet emerging health needs, the National Digital Health Mission (NDHM) was launched in 2020 to create a nationwide digital health ecosystem.[5] Under this ecosystem, the e-Sanjeevani platform enables remote consultations, and U-WIN Portal has digitized the Universal Immunisation Programme.[6] These digital interventions have the scope to improve service delivery in hard-to-reach areas. These have created a new avenue for community members to access specialist services without bearing the direct and indirect costs.[7] Digital interventions could potentially turn around service delivery by facilitating capacity building of the workforce, simplifying management processes, and thereby enhancing care quality.[8]

This study aims to evaluate the impact and the effectiveness of novel digital health interventions on healthcare workforce capacity building in primary health care settings in India.

Materials and Methods

Study design and setting: A comprehensive systematic review of published research and literature on digital health interventions to strengthen Human Resources for Primary Healthcare systems in India was conducted. This analysis was grounded in the PRISMA guidelines.

Study participants and sampling: All the studies with digital health interventions provided in primary health care settings in India by HRH were included. We considered articles written in English from January 2015 to March 2025. The publication geography was set to be in India only. We included Randomised Control Trials, Quasi Experimental Studies, Pre-post study design, and Pseudo trials that reported any kind of interventions on the effectiveness, usability, acceptability, feasibility, and implementation of eHealth solutions in improving service delivery. The review included all forms of messaging, voice calls, and interactive voice responses as interventions.

Data collection tool and technique: Online databases (PubMed and Google Scholar) were explored to find studies discussing the digital health interventions within Primary Healthcare settings or involving the human resources in those settings in the Indian context. Additionally, we searched supplementary articles examining references and citations of the studies.

Ethical consideration: This systematic review has been registered in the PROSPERO registry vide registration no. CRD42025645228 dated 14^th February 2025.[9]

Statistical analysis method: Due to the heterogeneity of the studies, only descriptive analysis of the included studies was done, and meta-analysis was not planned.

Results

The initial database search identified 1047 records, and after removing 14 duplicates, 1033 records were retained for screening. The initial screening eliminated 940 articles, and 93 articles were further screened based on their titles and abstracts. Among these, 70 articles did not meet the inclusion criteria. A total of 23 studies were screened for full-text exploring various digital health interventions across Indian healthcare settings. At the end, 21 articles were included, and an additional study was identified through back-referencing, making the total number 22[10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31] [Figure 1].

Figure 1.

PRISMA chart for the Study

Risk of bias was assessed using the Cochrane Risk of Bias tool. Of the 22 included studies, 5 were judged to be at low risk of bias, 8 had some concerns, and 9 were at high risk [Figure 2].

Figure 2.

Risk assessment of the included studies

Study characteristics

1. Study Design: Cluster Randomised Controlled Trials (Cluster RCTs) were the most used design, featured in 6 studies.[13,17,22,25,26,29] This was followed by individual-level Randomised Controlled Trials (RCTs) in 5 studies.[12,16,20,23,30] Other designs included interventional designs (4 studies),[11,14,19,31] quasi-experimental approaches (3 studies),[18,27,28] pre-post studies (3 studies),[10,15,21] and one pseudo-RCT.[24]

2. Study Geography: Geographically, the studies were conducted across 15 Indian states, with Andhra Pradesh (3 studies)[21,26,28] and Bihar (3 studies)[10,13,31] contributing the highest number. Other states included Maharashtra (2),[24,29] Gujarat (2),[22,30] Himachal Pradesh (2),[11,14] Rajasthan (2),[19,25] and one study each from Kerala,[12] Uttarakhand,[20] Puducherry,[23] Goa,[16] Madhya Pradesh,[17] Telangana,[15] Jharkhand,[18] and Uttar Pradesh.[27] This spread indicates a broad geographic coverage with both urban and rural representation.

3. Health Condition: In terms of health conditions, the most studied area was Maternal and Child Health (MCH), addressed in 9 studies.[10,13,18,23,24,25,27,31] Other prevalent topics included diabetes mellitus alone (2 studies),[19,28] diabetes and hypertension (2 studies),[11,15] cardiovascular diseases (2 studies),[14,26] and one study each focusing on emergency care,[20] stroke,[12] tuberculosis,[16] HIV/AIDS,[29] mental health,[21] tobacco use,[17] and primary health care services.[30] This shows a balanced emphasis on both communicable and non-communicable diseases in digital health interventions.

4. Duration: Five studies had shorter durations of intervention ranging from 1 month[19] to 6 months.[12,16,18,23] Another nine studies lasted between 8 and 12 months,[10,13,14,17,25,27,28,30,31] seven studies extended between 18 and 24 months,[11,15,20,21,24,26,29] and one study lasted 36 months,[22] reflecting a mix of short-term and long-term interventions including longitudinal follow-ups.

5. Study Settings: Most studies (12) were done in a community-based settings,[15,18,19,21,24,25,26,27,28,29,31] while 9 studies were facility-based,[10,11,12,13,14,16,22,23,30] and only one study used a home-based care model.[20] The study settings included rural communities or remote villages (9 studies),[15,17,18,19,21,25,26,27,28] Primary health centres (5),[11,13,14,22,30] tertiary care hospitals (4),[12,16,20,23] focus or high-burden districts (2),[29,31] nursing schools (1),[10] and urban slums (1).[24]

6. Sample size: The smallest study included 92[10] participants, while the largest enrolled 62,254[26] individuals. Five studies had sample sizes under 200,[10,16,23,25,30] 6 studies included between 200 and 999 participants,[12,14,17,22,28,29] 7 studies ranged from 2000 to 4999 participants,[13,15,18,19,20,24,27] and 4 studies involved 5000 or more individuals.[11,21,26,31] [Table 1].

Table 1.

Characteristics of the included study (n=22)

Author, Year		Place		Study design		Disease/Condition		Duration (months)		Facility/Community		Study setting		State Population/Age group		Sample size
Rani et al.,[28]		Andhra Pradesh		Quasi-experimental		Diabetes Mellitus		12		Community Based		Rural Community		18 years or older adults, mobile users		545
Mahala et al.,[20]		Rishikesh		RCT		Emergency		24		Home Based		Tertiary care hospital		60 years and above		2000
Babu et al.,[12]		Kerala		RCT		Stroke		6		Facility Based		Tertiary care hospital		Adult stroke patients (within 1 month)		209
Joshi et al.,[19]		Jodhpur		Interventional design		Diabetes Mellitus		1		Community Based		Community level		Adult 35 years or older		4000
Mohan et al.,[23]		Puducherry		RCT		Maternal & Child Health		6		Facility based		Hospital Setting		Nurse midwives		140
Verma et al.,[30]		Gujarat		RCT		Primary Health care services		12		Facility based		HWCs		All Patients		104
Fernandes et al.,[16]		Goa		RCT		Tuberculosis		6		Facility based		Tertiary care hospital		TB Patients		162
Hejjaji et al., 2021[17]		Madhya Pradesh		Cluster RCT		Tobacco & Smoking		12		Community Based		Community level		18–70 Years		560
Ward et al.,[31]		Bihar		Interventional design		Maternal & Child Health		12		Community Based		Focus Districts		ANC, PNC Mother		6471
Suryavanshi et al.,[29]		Maharashtra		Cluster RCT		AIDS		24		Community Based		High Burden Districts		HIV pregnant/postpartum women		884
Murthy et al., 2019[24]		Maharashtra		Pseudo RCT		Maternal & Child Health		24		Community Based		Urban Slum		Reproductive age group women		2016
Modi et al.,[22]		Gujarat		Cluster RCT		Maternal & Child Health		36		Facility Based		PHCs		Mothers of 1 to 4-year-old infants		560
Peiris et al.,[26]		Andhra Pradesh		Cluster RCT		CVD		24		Community Based		Villages		Adults >40 years		62254
Carmichael et al., 2019[13]		Bihar		Cluster RCT		Maternal & Child Health		12		Facility Based		Sub Centres		Reproductive age group women		3000
Dandge et al.,[15]		Telangana		Pre-post design		Hypertension, Diabetes		24		Community Based		Rural Community		Adults		2456
Chauhan et al.,[14]		Himachal Pradesh		Interventional design		Acute Coronary Syndrome		12		Facility Based		CHCs		Adults		819
Nagar et al.,[25]		Rajasthan		Cluster RCT		Maternal & Child Health		9		Community Based		Village health camp		Mothers with infants <6 years old		198
Ilozumba et al.,[18]		Jharkhand		Quasi-experimental		Maternal & Child Health		6		Community Based		Rural Community		Pregnant women & mothers (18 to 45)		2200
Prinja et al.,[27]		Uttar Pradesh		Quasi-experimental		Maternal & Child Health		8		Community Based		Rural Community		Women and newborns		2759
Maulik et al.,[21]		Andhra Pradesh		Pre-post design		Mental health		24		Community Based		Remote Villages		Adults		5167
Agarwal et al.[10]		Bihar		Pre-post design		Maternal & Child Health		12		Facility Based		Nursing schools		GNM students		92
Ajay et al.,[11]		Himachal Pradesh		Interventional design		Hypertension, Diabetes		18		Facility Based		PHCs		Adults with NCDs		7532

Key finding

1. Targeted Outcomes: The most common focus of the included studies was on Reproductive, Maternal, Newborn, and Child Health (RMNCH),[18,22,24,31] followed by NCDs such as diabetes, hypertension, and stroke.[11,12,28] Other targeted outcomes included self-monitoring, medication adherence, CVD risk assessment, mental health access, smoking cessation, teleconsultation, and immunization adherence.

2. Nature of the Interventions: The interventions were mobile or digital health (mHealth) interventions, involving mobile apps,[13,15,21,26] voice messages,[18,25,31] teleconsultations,[20,30] or decision-support tools.[11,26] The nature of interventions ranged from teleconsultations,[20,30] mobile-based text[12,17,18] or voice messages,[18,25,31] tablet-based training,[15,26,29] to virtual classroom models[10] and clinical decision support systems (CDSS).[11,26]

3. Providers of Interventions: Intervention delivery was mostly led by Community Health Workers (CHWs) or Accredited Social Health Activists (ASHAs).[19,21,26] Some interventions were self-administered through mobile phones,[16,20] while others were facilitated by nurses, medical officers, ANMs, or nurse educators.

4. Conclusion: The major findings emerged under three thematic areas: Telemedicine and Teleconsultation, Voice and Text Message–Based Interventions, and Clinical Decision Support Systems (CDSS) [Table 2].

Table 2.

Key findings from the included study (n=22)

Author, Year		Targeted Outcome		Name of the Intervention		Nature of Intervention		Person providing intervention		Data tools		Key Finding		Conclusion drawn
Rani et al.,[28]		Diabetes Awareness		Arogya World’s mDiabetes program		Mobile-based voice messages in regional language (Telugu)		via Mobile Phones and ASHAs		KAP tool		Post-intervention, diabetes awareness rose to 97.43%		mHealth improved diabetes awareness and healthy habits
Mahala et al.,[20]		Self-Monitoring of NCDs		NA		Mobile-based Teleconsultation		Self via mobile phones		WHO-QOL BREF		Intervention group had higher mean scores in the physical health domain (11.16 vs. 10.96, P=0.009), psychological domain (11.74 vs. 11.62, P=0.020), and environment domain (12.44 vs. 12.26, P=0.0001)		Teleconsultation significantly improves the quality of life for elderly patients
Babu et al.,[12]		Medication adherence		Medication adherence and management of risk factors for secondary prevention of stroke (MaMoRS)		Mobile-based text messages		Self via mobile phones and study coordinators		WHO STEPS; IPAQ-SF); FADS		Improvement in self-reported healthy diet intake (P=0.003), intake of fruits (P=0.005), and were physically more active (P=0.001). At 6 months, mean fasting blood sugar (P=0.005) and high-density lipoprotein cholesterol higher (P=0.024) in the intervention group		The use of a mobile app is an effective method to improve medication adherence and risk-factor control in stroke survivors and is feasible in LMICs like India
Joshi et al.,[19]		Screening for Diabetes		Standardized Health Education Messages (SHEM)		Mobile-based screening interview		Community health workers		Follow-up Interview		90.50% of high-risk individuals obtained diabetes testing, and of these, 65.67% had a written report showing they tested positive for diabetes or prediabetes, requiring ongoing clinical care		mHealth better identify those at high diabetes risk in the community, how to improve the linkage, from community screening and highrisk identification, to testing and diagnosis and continuing clinical care
Mohan et al.,[23]		Management of pregnancy, labor, and childbirth		E-Midwife		Mobile based training		Nurses		Usability Scale		The overall score of the usability testing of E-Midwife was 82.75±0.68. The total mean score of quality was 3.77±0.86		The E-midwife mobile application has a high usability and quality, which makes its usage effective and efficient
Verma et al.,[30]		e-Prescription with a diagnosis		MyTeleDoc		Mobile-based Teleconsultation		CHOs		Patient’s history and physical exam findings		It was observed that 74% (77/104) of diagnostic concordance and 79.8% (83/104) of concordance in the treatment plan between the in-person and remote doctors		The findings reflect that telemedicine can be a safe and acceptable alternative mode of care, especially in remote rural settings when in-person care is not accessible
Fernandes et al.,[16]		Tele counseling		NA		Mobile-based tele-counseling		Self		Telephonic Interview		Quit rate at six months was 54 (67.5%) in the intervention group versus 34 (42%) in the control group;		Mobile tele-counseling is an effective strategy for smoking cessation among TB patients
												RR 1.60 (95% CI 1.19, 2.16) P=0.001
Hejjaji et al.,[17]		Tobacco Quit Readiness		Project multi-unit kit for tobacco intervention (MUKTI)		Mobile-based text messages; Home-based screening		Self and CHWs		Household surveys by CHWs		At 12 months, 31 (20.5%) participants in the intervention arm and 9 (10.8%) in the control arm quit smoking (absolute risk difference=9.7%; RR=1.69; 95% CI: 0.04–71.33, P=0.74).		CHW-led home-based counseling, supplemented by regular text messages, led to an increase in quit rates for smoking
Ward et al.,[31]		RMNCH health promotion		Mobile Kunji and Dr. Anita’s mHealth tools		Mobile-based Interactive Voice Response for behaviour change		Frontline health workers (FLHWs)		Household surveys		Associated with significantly higher odds of consuming iron-folic acid tablets (odds ratio (OR)=2.3, 95% confidence interval (CI)=1.8–3.1) as well as taking a set of three measures for delivery preparedness (OR=2.8, 95% CI=1.9–4.2) and appropriate infant complementary feeding (OR=1.9, 95% CI=1.0–3.5).		Significant improvements in RMNCHN-related knowledge and behaviours
Suryavanshi et al.,[29]		Prevention of Mother-to-Child Transmission		COMBIND		Tablet-based Behavioural training intervention in Marathi; Home Visits and Counseling		Community health workers		Smart form by CHWs during home visits		Increases uptake of exclusive breastfeeding at two months (adjusted Odds Ratio (aOR), 2.10; 95% CI 1.06 to 4.15) and early infant diagnosis at six weeks (aOR, 2.19; 95% CI 1.05–3.98)		COMBIND was promising to enhance behaviours needing personal motivation (e.g., breastfeeding and EID)
Murthy et al.,[24]		RMNCH-related behaviour		m-Mitra package		Mobile-based voice messages in regional language		Self		Household survey by the project team		Maternal knowledge was significantly better on issues like complementary feeding (OR 1.89, P<0.01), vaccine importance (OR 1.567, P=0.028), and optimum birth weight (OR 2.279, P<0.01)		Mobile voice messages can be an effective tool to improve maternal knowledge and child health practices
Modi et al.,[22]		RMNCH related behaviour		ImTeCHO		Mobile and Web-based job aid to facilitate home visit screening by ASHAs		ASHA		Home visits by ASHAs		The coverage of at least two home visits within the first week of birth was 32.4% in intervention group, compared to 22.9% in the control group.		Use of the ImTeCHO mobile- and web-based application improved coverage and quality of MNCH services in hard-to-reach areas
Peiris et al.,[26]		CVD Risk Assessment		SMART health India		Tablet-based household CVD risk assessments by CHWs using a clinical decision support system (CDSS) application		ASHA and PHC Doctors		Home visits by ASHAs		The change in SBP from baseline was -9.3 mmHg vs -9.2 mmHg (mean difference 0.28, 95% CI −3.58 to 4.13, ICC 0.014) in the intervention vs control periods		The strategy was well implemented with increased treatment rates among high-risk individuals assessed by CHWs
Carmichael et al.,[13]		RMNCH-related behaviour		Information Communication Technology-Continuum of Care Service (ICT-CCS) tool		Mobile phone-based mHealth app for complications, counseling during home visits, and supportive supervision by PHC staff		Frontline health workers (FLHWs)		Surveys, mobile data logs		The intervention also resulted in 13, 12, and 21 percentage point increases in skin-to-skin care (P<0.01), breastfeeding immediately after delivery (P<0.01), and age-appropriate complementary feeding (P<0.01)		Mobile tools can improve FLHW performance and coverage of essential services
Dandge et al.,[15]		Task Shifting		TETRA Project		Telemedicine via tablet apps; Home visits		Non-physician health workers		BP/glucose logs, tele-records		Control of BP and blood sugar was achieved in 54.0 and 34.1% of individuals with hypertension and diabetes, respectively		Telemedicine by trained non-physicians effective for rural NCD care
Chauhan et al.,[14]		Tele consultation		NA		Tele-ECG via smartphone		Primary care physicians		ECG records, hospital admissions		Smartphone-based Tele-ECG support for primary care physicians reduced the hospital-to-aspirin time significantly (P<0.0001)		Tele-ECG an effective, low-cost strategy and is easily replicable anywhere in the world
Nagar et al.,[25]		Immunization Adherence		Near Field Communication		Voice reminder and wearable device (Digital pendant)		Parents/caregivers		Immunization records		Increased adherence to the immunisation schedule		Overall community acceptance of the novel, culturally specific NFC necklace
Ilozumba et al.,[18]		RMNCH-related behaviour		NA		mHealth training for CHWs; MfM (Mobile phone for Mothers); voice-based text		Community health workers		Surveys, in-app data		The MfM intervention group reported higher levels of maternal health knowledge		The CHW mobile app improved maternal awareness and practices
Prinja et al.,[27]		RMNCH-related behaviour		Reducing Maternal and Newborn Deaths (ReMiND)		mHealth support to CHVs for MCH-related counseling		Community health volunteers		Household surveys		Increase in coverage of iron–folic acid supplementation (12.58%), self-reported pregnancy complications (13.11%) and post-delivery (19.6%)		mHealth can increase uptake of maternal and child care services
Maulik et al.,[21]		Task Shifting		SMART Mental Health Project		App-based screening and referral; IVRS		ASHA and PHC Doctors		Screening logs, referral rates		The intervention increased the number of those who went on to use mental health services		Mobile tools improved mental health access in rural areas
Agarwal et al.[10]		RMNCH-related behaviour		NA		Virtual classroom training (72 h standardized training package)		Nurse educators		Pre/post tests		The mean student score increased to 62.0 (95% CI, 60.3–63.7) post-intervention		e-Learning is feasible and effective for MNH capacity building
Ajay et al.,[11]		Task Shifting		mPower Heart Project		Mobile-based clinical decision support tool (CDSS)		Medical officers and ANMs		BP/sugar logs, patient records		Better adherence to protocols		Digital tools improve chronic disease care in PHCs

Telemedicine and Teleconsultation: A statistically significant enhancement in the physical, psychological, and environmental quality-of-life domains among elderly patients was noted through teleconsultation.[20]

Voice and Text Message-Based Interventions: The studies reported positive outcomes such as a marked improvement in diabetes awareness, which increased from 82.75% at baseline to over 99% post-intervention.[28] A notable improvement in dietary intake, physical activity levels, and lipid profiles in stroke survivors receiving mobile-based lifestyle interventions.[12] Moreover, RMNCH-focused studies found that digital health tools contributed to better maternal knowledge and improved newborn care practices.[13,31]

Clinical Decision Support Systems (CDSS): A better adherence to protocols was noted in the intervention group using CDSS.[11]

These findings collectively point to the efficacy of mHealth interventions in driving behavior change and improving health outcomes across various settings.

Discussion

This systematic review synthesized evidence from studies to understand the extent of digital health interventions in strengthening human resources for primary healthcare in India. The findings collectively demonstrate that digital tools, dominantly mobile-based Health applications[32,33] with varying effectiveness, scalability, distinct strengths, and target outcomes. These low-tech solutions are particularly suited for areas with limited smartphone penetration or digital literacy, enabling widespread dissemination of standardized health information to pregnant women, mothers, and patients with chronic conditions.[34,35] Similar results were noted by Punzalan and Punzalan that integrating digital health competencies equips healthcare professionals with the necessary tools to deliver competent, culturally aware, and technologically proficient care.[36]

Clinical Decision Support Systems (CDSS), like those in the mPower Heart Project and SMART Health India, have enabled Community Health Workers to perform complex tasks such as cardiovascular risk assessments and non-communicable disease management, shifting responsibilities from physicians and expanding community care.[37,38] A study by Esfandiar et al. on acceptance of artificial intelligence (AI) in medical centers showed that AI training courses are effective in knowledge and acceptability of AI.[39] It highlights that if proper training is imparted to HRH, CDSS using AI can easily be acceptable and scalable.

Teleconsultation platforms like MyTeleDoc have demonstrated high diagnostic concordance with in-person visits, proving vital for bridging the specialist gap in remote areas and improving outcomes in geriatric and primary care.[20,40] Furthermore, digital job aids and data management tools (e.g., ImTeCHO and ICT-CCS) have streamlined the workflows of frontline workers, enhancing the efficiency and quality of Maternal, Neonatal, and Child Health (MNCH) service delivery in hard-to-reach regions.[22,41] A study by Sharmila et al. in South India highlighted the acceptance and satisfaction with the tele consultation process.[42]

This review shows that digital health tools outperform traditional models for resource management and care. Cluster-randomized trials find that digital aids and mHealth apps help Community Health Workers like ASHAs deliver more comprehensive and higher-quality services, such as postnatal visits and infant feeding counseling, compared to training and paper systems.[22,41]

Similarly, in managing non-communicable diseases, CDSS-equipped health workers facilitated better adherence to clinical protocols and improved risk factor control compared to usual care, showcasing the efficacy of digital tools in enhancing the performance of the primary care workforce where traditional supervision and training often fall short.[11,26]

Limitation

The review included searching only PubMed and Google Scholar databases, which may have left articles published in other databases and gray literature.

Conclusion

The systematic review in this article highlights the role of digital health interventions in reinforcing Human Resources in Health in India’s primary healthcare system. It is evident that mobile applications, teleconsultation platforms, clinical decision support systems, and voice-based messaging services, have increased workforce efficiency, adherence to clinical pathways, and quality of care provided at community and levels in resource-constrained settings. Given a scope of integration in the existing infrastructure, these interventions hold tremendous potential in addressing healthcare challenges in India. Further, the study future studies may plan a meta-analysis to quantify and pool the overall effect of digital health interventions in the Indian context, which can be integrated into the existing infrastructure in the country.

AI use policy

The authors declare that AI technology was used to check for grammatical errors and improve sentence structure. Authors are fully responsible for the content of their manuscript, even those parts produced by an AI tool, and are thus liable for any breach of publication ethics.

Author’s contributions

SA contributed to the conception and design, participated in the review and critique process. SA and SS drafted sections of the manuscript, and revised it critically for intellectual content, acquired articles for review, abstracted findings to tables, and contributed to analysis. SSB, GB, KR participated in the review and critique process, and revised it critically for intellectual content.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.