Economic Feasibility and Budget Impact of the OncoCare mHealth Application for Digital Cancer Care in Karnataka India

Economic Feasibility and Budget Impact of the OncoCare mHealth Application for Digital Cancer Care in Karnataka India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Economic Feasibility and Budget Impact of the OncoCare mHealth Application for Digital Cancer Care in Karnataka India G Hari Prakash, Sunil Kumar D, Kiran PK, Vanishri Arun, Deepika Yadav, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9304808/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Cancer represents a significant health and economic burden in India, and catastrophic health expenditure affects 70% of cancer patients. Mobile health interventions offer promising solutions for cancer care coordination; however, comprehensive economic evaluation data remain limited in resource-constrained settings. Objective To conduct a micro-costing analysis of the OncoCare mHealth application development and implementation in Karnataka, India, and assess its fiscal impact within existing cancer care program budgets. Methods A comprehensive micro-costing analysis was conducted using a top-down approach from the provider’s perspective for the 2023-24 fiscal year. Cost data were systematically collected and categorised into direct development, stakeholder engagement, and training costs. The analysis included a sensitivity analysis and a fiscal impact assessment against Karnataka’s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke, with a budget allocation of ₹10.78 crores. Results The total development and implementation cost was ₹1,00,372 (US $ 1,205), distributed across direct development costs (53.8%, ₹54,000), stakeholder engagement costs (37.0%, ₹37,111), and training costs (9.2%, ₹9,261). Mobile application development represented the largest single component (₹40,000, 39.9%). The intervention’s cost represents only 0.093% of Karnataka’s NPCDCS budget, with estimated annual maintenance costs of ₹15,000 (0.014% of the state budget). Sensitivity analysis revealed a total cost variation of ± 8.0%, with app development as the primary cost driver. Conclusion The OncoCare application demonstrates exceptional economic feasibility with minimal budgetary impact and substantial scalability potential. These findings provide compelling evidence for integrating digital health interventions in cancer care programs within resource-constrained settings. micro-costing mHealth cancer care digital health economic evaluation India NPCDCS budget impact analysis healthcare technology cost analysis Figures Figure 1 Introduction Cancer represents one of the most pressing global health challenges of the 21st century, with an estimated 19.3 million new cases and 10.0 million deaths worldwide in 2020( 1 ). India faces a particularly acute cancer burden, with approximately 1.39 million new cancer cases reported in 2020, representing 8.7% of global cancer incidence ( 2 ). The age-standardised incidence rate of cancer in India is 100.4 per 100,000 population, with significant variations across different states and regions. The economic burden of cancer care in India is substantial and multifaceted, encompassing direct medical costs, indirect costs due to productivity losses, and intangible costs related to pain and suffering. Studies indicate that the average cost of cancer treatment in India ranges from ₹50,000 to ₹15,00,000, depending on the type, stage, and treatment modality, with chemotherapy cycles alone costing between ₹10,000 to ₹1,00,000 per cycle ( 3 ). Catastrophic health expenditure affects approximately 70% of cancer patients in India, pushing many families below the poverty line ( 4 ). The National Sample Survey Office reported that cancer treatment accounts for 25% of all hospitalisations in India, with an average per-episode cost of ₹58,763 for cancer care. Cancer care complexity extends beyond direct treatment costs, encompassing significant challenges in care coordination, medication adherence, side effect management, and long-term follow-up. Studies demonstrate that 30–50% of cancer patients experience medication non-adherence, leading to suboptimal treatment outcomes and increased healthcare utilisation ( 5 ). Poor care coordination results in treatment delays, duplicated tests, and fragmented care delivery, contributing to increased morbidity and mortality. Side effects from chemotherapy affect 80–90% of patients, with inadequate monitoring and management leading to treatment discontinuation in 15–20% of cases. These challenges are particularly pronounced in resource-constrained settings where healthcare infrastructure is limited and patient-to-provider ratios are suboptimal. The integration of mobile health (mHealth) technologies has emerged as a promising solution to address these challenges in cancer care delivery. Systematic reviews indicate that mHealth applications in oncology demonstrate effectiveness in symptom management, medication adherence, appointment scheduling, and patient-provider communication ( 6 ). Global evidence suggests that mHealth interventions can improve medication adherence by 15–25%, reduce hospital readmissions by 20–30%, and enhance patient-reported outcomes through real-time monitoring and support ( 7 ). In low- and middle-income countries, where smartphone penetration exceeds 70%, and healthcare access is often limited, mHealth interventions offer promise for scaling cancer care support services. Despite growing evidence of mHealth effectiveness in cancer care, a significant gap remains in economic evaluation data, particularly regarding implementation costs and fiscal impact assessments. Studies from high-income countries report development costs ranging from US $ 10,000 to US $ 500,000 for comprehensive cancer care applications; however, these estimates may not apply to resource-constrained settings with different labour costs, infrastructure requirements, and regulatory environments ( 8 ). The lack of standardised costing methodologies and comprehensive economic evaluations limits evidence-based decision-making for mHealth implementation in cancer care programs. India’s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) represents a significant policy framework for addressing challenges in cancer care, with a total budget allocation of ₹3,000 crores for the period 2017–2020 ( 9 ). The program emphasises strengthening cancer screening, early detection, and treatment services while promoting innovative approaches to improve the efficiency of care delivery. However, the economic feasibility of integrating mHealth interventions into existing program budgets remains unclear, highlighting the need for comprehensive cost analyses. Karnataka state, with a population of 67.5 million and a cancer incidence rate of 106.2 per 100,000 population, represents an important setting for evaluating mHealth interventions in cancer care. The state’s NPCDCS allocation of ₹10.78 crores for FY 2023-24 provides a specific context for assessing the fiscal impact of digital health innovations. The relatively high smartphone penetration rate and existing digital health infrastructure make Karnataka an ideal setting for implementing and evaluating mHealth solutions for cancer care( 10 ). Understanding the economic implications of mHealth implementation is crucial for informing policy decisions, allocating resources effectively, and developing scaling strategies. Micro-costing analysis provides a detailed examination of all resources required for intervention development and implementation, enabling accurate cost estimation and comparative analysis with alternative approaches ( 11 ). This methodology has increasingly been recognised as the gold standard for the economic evaluation of digital health interventions, particularly in resource-constrained settings where precise cost information is essential for sustainability planning. The present study addresses this critical knowledge gap by conducting a comprehensive micro-costing analysis of the OncoCare mHealth application, designed to provide comprehensive cancer care support for patients and healthcare providers in Karnataka, India( 12 ). Through detailed cost categorisation, stakeholder engagement assessment, and fiscal impact analysis, this research aims to provide evidence-based insights for policymakers, healthcare administrators, and researchers considering similar digital health interventions for improving cancer care. Methodology Study Design and Setting A micro-costing analysis using a top-down approach was conducted to determine the economic costs of developing and implementing the OncoCare mHealth application for comprehensive cancer care management and patient support. This approach was selected to capture all resources utilised in the development process, allowing for a detailed examination of cost components and their relative contributions to the total cost. The study was conducted in Karnataka, India, from 2023 to 2024. Application Development and Features The OncoCare mobile application was developed using the Flutter framework, with Firebase serving as the backend infrastructure, to provide comprehensive support for cancer care to patients and healthcare providers. OncoCare serves as “Your Comprehensive Cancer Care Companion”, designed to empower both patients and doctors throughout the cancer treatment journey. Key features included: ( 1 ) chemotherapy scheduling and management system with automated appointment reminders; ( 2 ) comprehensive treatment records and progress tracking with user-friendly record-keeping system; ( 3 ) side effects management and monitoring with personalised insights and coping strategies; ( 4 ) notification center with real-time updates about appointments, medication reminders, and important healthcare information; ( 5 ) doctor-patient collaboration platform enabling seamless communication and shared treatment monitoring; and ( 6 ) secure user authentication with robust privacy protection measures. The application architecture incorporated Firebase Authentication for secure login, Cloud Firestore for patient data storage, Firebase Real-time Database for treatment tracking and progress monitoring, and advanced security measures to ensure confidentiality of health information. The OncoCare application was designed for and deployed among cancer patients and healthcare providers in Karnataka, where the project was actively implemented. The app aimed to support patients in taking control of their cancer journey through accessible digital tools, enhanced care coordination, and comprehensive treatment management( 13 ). Costing Framework The current study employed a provider perspective to assess the costs of developing and implementing the OncoCare application. The analysis time horizon encompassed the full development cycle and the initial one-year implementation period. All costs were calculated in Indian Rupees (INR) for the financial year 2023-24, during which the actual development and implementation took place. Cost data were systematically collected and categorised into direct development, stakeholder engagement, and training costs. Data Collection Cost data were collected using a microcosting tool, adapted from validated digital health costing instruments, to capture all direct and indirect costs systematically. Primary data sources included project financial records, meeting minutes, purchase receipts, salary information, and semi-structured interviews with key personnel involved in the development process. For shared resources, appropriate apportioning techniques were employed to allocate costs accurately to the OncoCare project. Cost Categorisation and Measurement Direct development costs encompassed mobile application development by the technical team, gap assessment activities, and implementation materials (including printing, information, education, and communication materials). Stakeholder engagement costs included three key project meetings: project initiation and requirements gathering, progress review and feedback incorporation, and final validation and implementation planning. At each meeting, costs were further categorised into human resources, equipment, furniture, and space. Training costs comprised human resources, furniture and equipment, space, and computer equipment for comprehensive training sessions across urban and rural healthcare settings. For capital items and durable goods with a lifespan exceeding 1 year, costs were annualised using a linear depreciation method with a 3% discount rate, in line with standard health economic evaluation practices. Fiscal Impact Analysis The fiscal impact of implementing the OncoCare application within the existing National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) was assessed by comparing the development and maintenance costs to the Karnataka state budget allocation for NPCDCS (₹10.78 crores for FY 2023-24). This analysis helped determine the budgetary feasibility of integrating the application into the state’s cancer care program and assessed the potential for scaling within existing resource allocations( 10 ). Sensitivity Analysis A one-way sensitivity analysis was conducted to assess the robustness of the costing results to variations in key cost parameters. Each cost component (app development, stakeholder engagement costs, implementation materials, training costs, and gap assessment) was independently varied by ± 20% while holding all other parameters constant. Results were presented in a tornado diagram to visually illustrate the relative impact of each parameter on the total cost. This analysis helped identify the most influential cost drivers and assess the stability of the cost estimates under different assumptions. Data Analysis All costs were calculated in Indian Rupees (INR) for the 2023-24 fiscal year, during which the actual development and implementation took place. Data were analysed using Microsoft Excel 2019 for cost calculations and Python 3.8 (matplotlib 3.5.1, pandas 1.4.2) for visualisations of sensitivity analysis. Results were reported as total costs and percentages by category, with detailed breakdowns of each cost component to ensure transparency and reproducibility. Ethical Considerations The study was approved by the Institutional Ethics Committee of JSS Medical College, JSS Academy of Higher Education & Research, Mysuru (IEC reference number: JSSMC/IEC/05012022/36NCT/2021-22). All stakeholders involved in providing cost information gave informed consent for their participation. Written informed consent to participate was obtained from all participants involved in the study before data collection. The privacy and confidentiality of all cost-related data were maintained throughout the study period, and the data were stored securely in accordance with institutional guidelines. Results The micro-costing analysis of the Oncocare mHealth cancer care application development and implementation revealed a total cost of ₹1,00,372 (US $ 1,205). As shown in Table 1 , this total was distributed across three primary cost categories: direct development costs (53.8%), stakeholder engagement costs (37.0%), and training costs (9.2%). Table 1 Total costs of Oncocare mHealth application development and implementation Cost Category Resource Description Unit Quantity Unit Cost (INR) Total Cost (INR) Development Phase Software Development Mobile application development by the technical team Fixed cost 1 40,000 40,000 Pre-Implementation Phase Human Resources Data collectors for gap assessment (2 persons) Person-trips 2 2,000 4,000 Implementation Phase Materials & Communication IEC materials, forms, printing, and dissemination Fixed cost 1 10,000 10,000 Total Setup Costs 54,000 Direct Development Costs Direct development costs accounted for the largest share of the total budget, at ₹54,000 (53.8%). As detailed in Table 2 , these costs primarily comprised mobile application development (₹40,000), implementation materials and communication (₹10,000), and gap assessment activities (₹4,000). Table 2 Breakdown of direct development costs Item Cost (INR) Percentage Description App Development 40,000 74.1% One-time mobile application development by the technical team Gap Assessment 4,000 7.4% Data collectors for gap assessment (2 persons, travel + work) Implementation Materials 10,000 18.5% IEC materials, forms, printing, and dissemination Total 54,000 100% Mobile application development accounted for the largest single cost component, reflecting the technology-intensive nature of the intervention. The relatively modest cost compared to commercial app development can be attributed to the academic setting and the utilisation of open-source frameworks. Implementation costs for information, education, and communication materials demonstrated a balanced approach between technology development and user education components. Stakeholder Engagement Costs Stakeholder engagement costs totalled ₹37,111 (37.0% of total costs) and were distributed across three strategic project meetings, as shown in Table 3 . Project initiation (Meeting 1) and final validation (Meeting 3) accounted for the highest investments at 40.2% and 40.0%, respectively, while the progress review meeting (Meeting 2) accounted for 19.8%. Table 3 Breakdown of stakeholder engagement meeting costs Meeting Purpose Cost (INR) Percentage Meeting 1 Project Initiation & Requirements Gathering 14,900 40.2% Meeting 2 Progress Review and Feedback Incorporation 7,340 19.8% Meeting 3 Final Validation and Implementation Planning 14,870 40.0% Total 37,111 100% Each meeting’s costs included multiple components, with human resources accounting for the largest share. For Meeting 1 (Project Initiation & Requirements Gathering), human resources accounted for 92.3% (₹13,750) of the total meeting cost, comprising contributions from an IT Professor, a Professor of Community Medicine, and a Medical Oncologist. The remaining costs were distributed among furniture (3.9%, ₹587), equipment (2.8%, ₹413), and space (1.0%, ₹150), with all capital items appropriately annualised using a 3% discount rate. This cost structure was representative of all three project meetings, highlighting the knowledge-intensive nature of mHealth application development for cancer care. Table 4 Detailed cost analysis - Primary stakeholder meeting (Meeting 1) Cost Component Amount (INR) Percentage (%) Description Annualization Method Human Resources 13,750 92.3% IT Professor, Community Medicine Professor, Medical Oncologist N/A Furniture 587 3.9% AC, computer, chairs, table, fan, LED lights Annual depreciation Space 150 1.0% Meeting room facility costs Annual rental equivalent Equipment 413 2.8% Laptop and presentation equipment Annual depreciation Total Meeting 1 14,900 100.0% Comprehensive stakeholder engagement - Training Costs Training costs comprised ₹9,261 (9.2% of total costs), as detailed in Table 5 . These costs primarily consisted of human resources (₹7,950), representing 85.9% of training expenses, with smaller allocations for furniture and equipment (₹650), space (₹360), and computer equipment (₹301). Table 5 Training program cost analysis Cost Component Amount (INR) Percentage (%) Description Human Resources 7,950 85.9% Six trainers (oncologists, nurses, and medical social workers) across urban and rural locations Furniture & Equipment 650 7.0% Annualised costs for LED TV, chairs, tables, fans, and lights Space 360 3.9% Annualised cost for the training room Equipment 301 3.2% Annualised cost of computer systems Total Training Costs 9,261 100.0% Comprehensive training program The training program demonstrated cost-effectiveness for comprehensive staff training across urban and rural healthcare settings. Human resources accounted for the majority of costs, reflecting the specialised nature of cancer care technology training and the need for qualified clinical instructors with expertise in both oncology and digital health platforms. The geographic distribution strategy, which employs trainers across both urban and rural locations, ensures equitable access to training while maintaining cost efficiency. Fiscal Impact Analysis Implementing the Oncocare mHealth cancer care application aligns with the objectives of the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke (NPCDCS). It could be efficiently integrated within its existing budgetary framework. Based on the Karnataka state budget allocation for NPCDCS (₹10.78 crores for FY 2023-24), the total development cost of Oncocare (₹1,00,372) would represent merely 0.093% of the annual budget . Table 6 Fiscal Impact Analysis - Karnataka NPCDCS Program Parameter Value Karnataka NPCDCS Budget (FY 2023-24) ₹10.78 crores (₹107,800,000) Oncocare Development Cost ₹100,372 Budget Impact (%) 0.093% Annual Maintenance Cost (estimated)* ₹15,000 Maintenance as % of NPCDCS Budget 0.014% Cost per 1,000 beneficiaries** ₹1,004 *Estimated based on 15% of development cost for annual maintenance and updates. **Based on an estimated 100 direct beneficiaries in the pilot implementation The exceptionally low budget impact demonstrates that the Oncocare intervention represents a minimal financial burden while potentially achieving significant improvements in cancer care coordination and patient outcomes. If implemented through the State-specific Initiatives and Innovation component of NPCDCS, the application could enhance program effectiveness while requiring negligible additional financial resources. The annual maintenance costs (estimated at ₹15,000, representing 0.014% of the state NPCDCS budget) compare favourably with traditional cancer care coordination systems. This minimal ongoing cost structure, combined with the preventive and supportive focus on cancer care management, suggests potential for substantial long-term cost savings through improved treatment adherence, reduced hospital readmissions, and enhanced care coordination. Cost Structure Analysis The cost distribution reveals several important characteristics of digital health intervention economics in cancer care: Technology-Development Focus (53.8%) : The predominance of direct development costs reflects the one-time nature of application creation, with subsequent implementations requiring minimal additional development investment. Stakeholder-Centred Approach (37.0%) : The substantial investment in stakeholder engagement demonstrates the importance of participatory design in healthcare technology development, ensuring user acceptance and clinical relevance. Efficient Training Model (9.2%) : The relatively low training costs suggest an efficient knowledge transfer model, with potential to cascade training effects to additional healthcare workers. Scalability Potential : The fixed-cost structure (90.8% of total costs) indicates substantial economies of scale, enabling additional users to be accommodated at minimal incremental cost. The Oncocare cost structure compares favourably with traditional cancer care support systems while offering enhanced functionality, including patient monitoring, medication adherence tracking, and care coordination. The comprehensive digital platform provides 24/7 accessibility, standardised care protocols, and real-time data collection capabilities at a fraction of the cost of conventional support systems. Sensitivity Analysis A one-way sensitivity analysis was conducted using a tornado diagram (Fig. 1 ) to assess the robustness of the cost estimates and identify cost drivers with the highest potential impact. A uniform variation of ± 20% was applied to each key cost component. This range was chosen based on standard practice in economic evaluations when empirical uncertainty bounds are unavailable, and it represents a conservative estimate commonly used in public health cost studies and micro-costing analyses. Table 7 One-way sensitivity analysis results for the Oncocare mHealth application Cost Component Low (-20%) (INR) High (+ 20%) (INR) Impact Range (INR) Contribution to Variance (%) App Development 92,371 108,371 16,000 39.9% Meeting 1 (Project Initiation) 97,391 103,351 5,960 14.8% Meeting 3 (Final Validation) 97,397 103,345 5,948 14.8% Implementation Materials 98,371 102,371 4,000 10.0% Training - Human Resources 98,781 101,961 3,180 7.9% Meeting 2 (Progress Review) 98,903 101,839 2,936 7.3% Gap Assessment 99,571 101,171 1,600 4.0% Training - Furniture & Equipment 100,241 100,501 260 0.6% Training - Space 100,299 100,443 144 0.4% Training - Equipment 100,311 100,431 120 0.3% Base Case Total: ₹100,371 The sensitivity analysis reveals that the total cost of the Oncocare mHealth application shows varying degrees of sensitivity across its cost components, with the overall cost ranging from ₹92,371 to ₹108,371, representing a ± 8.0% variation from the base case estimate. App development costs emerged as the dominant cost driver, accounting for 39.9% of the total cost variance and causing a fluctuation of ₹16,000 (±₹8,000) from the base case. This finding highlights the crucial importance of effective development methodologies, precise technical specifications, and robust project management practices in controlling overall intervention costs. Stakeholder engagement activities, particularly the initial project initiation meeting (Meeting 1) and the final validation meeting (Meeting 3), ranked second and third among the most influential cost drivers, contributing 14.8% each to the total variance, with impact ranges of ₹5,960 and ₹5,948, respectively. The substantial impact of these meetings underscores the value of thorough planning, comprehensive requirements gathering, and effective stakeholder coordination to minimise costly revisions in later development phases. The moderate impact of Meeting 2 (Progress Review) at 7.3% of total variance suggests that mid-project adjustments, while important for quality assurance, have a more contained effect on overall costs. Implementation materials demonstrated moderate cost sensitivity, accounting for 10.0% of total variance and an impact range of ₹4,000. This component’s influence emphasises the importance of strategic procurement planning and efficient resource utilisation for educational materials, printing, and dissemination activities. Training-related human resources, although representing a high absolute cost, exhibited relatively contained cost sensitivity, accounting for 7.9% of total variance, suggesting that variations in trainer compensation or training duration have manageable impacts on overall project costs. Notably, infrastructure-related training components demonstrated exceptional cost stability, with furniture, equipment, space, and computer equipment collectively accounting for only 1.3% of the total cost variance. The minimal impact ranges of ₹260, ₹144, and ₹120, respectively, indicate that these components exhibit remarkable price stability, suggesting that financial planning can accommodate reasonable variations in training infrastructure without substantially affecting overall project budgets. The gap assessment component, which accounts for 4.0% of the total variance and has an impact range of ₹1,600, represents a moderate but manageable source of cost variation. This finding suggests that while pre-implementation assessment activities are important for intervention design, their cost impact remains within acceptable bounds for project planning. These sensitivity analysis results provide several strategic insights for implementation planning and scale-up considerations. First, the predominance of app development costs as the primary cost driver suggests that investments in front-end development planning, technical architecture design, and quality assurance processes yield disproportionate returns in cost control. Organisations considering similar interventions should prioritise robust development partnerships, clear technical specifications, and agile development methodologies to optimise this critical cost component. Second, the substantial impact of stakeholder engagement meetings indicates that comprehensive upfront planning and stakeholder alignment can significantly influence overall project costs. The relatively greater impact of initial and final meetings compared to progress reviews suggests that strategic investment in thorough requirement-gathering and validation processes can prevent costly mid-project modifications and scope changes. Third, the remarkable stability of training infrastructure costs provides confidence for budget planning and scale-up scenarios. The minimal variation in these components suggests that training programs can be reliably costed and scaled across different implementation sites without significant uncertainty in infrastructure requirements. Overall, the sensitivity analysis demonstrates that while the Oncocare intervention exhibits some cost sensitivity to key development and engagement activities, the total cost variation of ± 8.0% falls within acceptable bounds for health intervention planning. Identifying app development and stakeholder engagement as primary cost drivers provides clear targets for cost-optimisation efforts and supports evidence-based resource-allocation decisions for similar digital health implementations in resource-constrained settings. Discussion This comprehensive micro-costing analysis of the OncoCare mHealth application provides valuable insights into the economic feasibility of implementing digital cancer care interventions in resource-constrained settings. The total development and implementation cost of ₹1,00,372 (approximately US $ 1,205) represents a remarkably cost-effective approach to enhancing cancer care delivery, with significant implications for policymakers and healthcare administrators considering similar digital health investments. The cost distribution observed in this study demonstrates a well-balanced approach to mHealth development, with direct development costs accounting for 53.8% of the total budget. This finding aligns closely with previous mHealth microcosting studies from India, including the landmark ReMiND program evaluation by Prinja et al., which reported that development and technology costs accounted for approximately 60% of total implementation expenses. ( 14 ). Our results are notably lower than international benchmarks; Kumar et al. reported mHealth development costs ranging from US $ 10,000 to US $ 500,000 in high-income countries, highlighting the cost advantages achievable in resource-constrained settings through strategic use of local expertise and open-source technologies ( 8 ). The predominance of stakeholder engagement costs (37.0%) reflects best practices in participatory design, emphasising the critical importance of user-centred development in healthcare technology. This investment pattern mirrors findings from Prinja et al.’s microcosting analysis of healthcare delivery systems, which identified stakeholder engagement as a key determinant of intervention sustainability and user adoption ( 15 ). The relatively modest training costs (9.2%) suggest an efficient knowledge transfer model, comparable to the 8–12% training allocation reported in other Indian mHealth implementations ( 16 ). The fiscal impact analysis reveals exceptional affordability within existing healthcare budgets. The OncoCare implementation cost represents merely 0.093% of Karnataka’s NPCDCS budget allocation, demonstrating substantial scope for scaling without significant budgetary strain. This finding is particularly significant when compared to traditional cancer care interventions; Chauhan et al. reported that comprehensive cancer screening programs in India require 2–5% of state health budgets, making OncoCare a highly attractive complementary intervention ( 3 ). The estimated annual maintenance cost of ₹15,000 (0.014% of the state budget) compares favourably with international digital health sustainability models. Systematic reviews of mHealth cost-effectiveness in developing countries report maintenance costs typically ranging from 10–25% of initial development investments, positioning OncoCare within optimal sustainability parameters ( 17 ). This cost structure suggests potential for significant return on investment through improved treatment adherence, reduced hospital readmissions, and enhanced care coordination. While this study focused on implementation costs rather than full cost-effectiveness analysis, the results can be contextualised within broader mHealth economic evaluation literature. Prinja et al.’s cost-effectiveness analysis of the ReMiND mHealth intervention demonstrated an incremental cost-effectiveness ratio of ₹12,993 (US $ 205) per DALY averted ( 18 ). Extrapolating from similar digital health interventions, OncoCare’s cost structure suggests potential for highly favourable cost-effectiveness ratios, particularly given cancer’s substantial burden of disease and the intervention’s comprehensive approach to care coordination. International evidence supports the economic value proposition of cancer-focused mobile health (mHealth) interventions. A systematic review by Gentili et al. identified mobile health applications for chronic disease management, including cancer, as consistently cost-effective with cost per QALY ratios well below established thresholds ( 19 ). The OncoCare application’s multifunctional approach, encompassing appointment scheduling, medication adherence, side effect monitoring, and care coordination, positions it favourably within this evidence base. The cost structure analysis reveals substantial scalability potential through economies of scale. With 90.8% of costs classified as fixed development expenses, marginal costs for additional users remain minimal. This finding aligns with the economic theory of digital goods and mirrors patterns observed in other successful mHealth scale-up initiatives. The ImTeCHO program in Gujarat, India, demonstrated similar scalability advantages, with per-beneficiary costs declining by 60–70% as implementation expanded from pilot to state-wide deployment ( 20 ). The geographic distribution of training costs across urban and rural settings provides important insights for equitable implementation. The balanced approach adopted in OncoCare training (₹9,261 total with rural components) reflects recognition of India’s diverse healthcare landscape. This strategy aligns with recommendations from Prinja et al.’s analysis of primary healthcare delivery costs, which emphasised the importance of tailored approaches for different healthcare settings (21). Policy Implications and Future Directions The findings have significant implications for health policy and resource allocation decisions. The minimal budgetary impact (0.093% of the NPCDCS allocation) suggests that mHealth interventions, such as OncoCare, can be implemented without crowding out other essential health services. This finding supports the National Digital Health Mission’s emphasis on technology-enabled healthcare delivery and provides concrete evidence for decision-makers considering digital health investments. Future research should focus on comprehensive cost-effectiveness analyses that incorporate clinical outcomes, patient-reported outcome measures, and long-term healthcare utilisation patterns. Additionally, multi-state implementation studies would strengthen the generalizability of economic findings and provide insights into regional cost variations. Methodological Strengths and Limitations The micro-costing methodology employed in this study follows established health economic evaluation standards, ensuring transparency and reproducibility. The use of a provider perspective with detailed cost categorisation aligns with international guidelines for digital health economic evaluation (22). The incorporation of sensitivity analysis strengthens the robustness of findings, with the ± 8.0% cost variation falling within acceptable bounds for health intervention planning. However, several limitations warrant acknowledgement. The study was conducted in a single state context, which may limit its generalizability to other Indian states with different healthcare infrastructures and cost structures. The one-year implementation timeframe may not capture long-term maintenance and sustainability costs, though international evidence suggests that annual maintenance typically remains within the 15% range estimated in this study. Additionally, the analysis did not include effectiveness outcomes, precluding a full cost-effectiveness assessment. Conclusion This micro-costing analysis demonstrates that comprehensive mHealth applications for cancer care can be developed and implemented at remarkably low cost in resource-constrained settings. The OncoCare intervention’s cost structure, characterised by modest total investment, minimal budgetary impact, and substantial scalability potential, provides a compelling economic case for digital health integration in cancer care programs. These findings provide valuable evidence to the growing literature on mHealth economics in developing countries and support policy initiatives that promote technology-enabled healthcare delivery. The study’s results, when viewed alongside international evidence on mHealth cost-effectiveness, suggest that well-designed digital health interventions offer a promising pathway to improve cancer care accessibility and quality while maintaining fiscal sustainability. As healthcare systems worldwide grapple with rising cancer burdens and resource constraints, the OncoCare model provides a replicable framework for leveraging technology to enhance care delivery efficiency and effectiveness. Declarations Acknowledgements: The authors gratefully acknowledge the cancer patients and healthcare providers who participated in this study and provided valuable insights for the development of the OncoCare application. Funding: This study was supported by theJSS Academy of Higher Education and Research (JSSAHER/REG/RES/URG/54/2023-24/7931). Ethical approval: The study was approved by the Institutional Ethics Committee of JSS Medical College, JSS Academy of Higher Education and Research, Mysuru (IEC reference number: JSSMC/IEC/05012022/36NCT/2021-22). Consent to participate: Written informed consent was obtained from all participants in the study before data collection. Data availability statement: The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Consent to publish: Not applicable. Competing interests: The authors declare that they have no competing interests. Dual publication: This manuscript is original, has not been published previously, and is not under consideration for publication elsewhere. Authorship : All authors made substantial contributions to the conception and design of the study, acquisition, analysis, or interpretation of data. All authors were involved in drafting the manuscript or revising it critically for important intellectual content, approved the final version, and agree to be accountable for all aspects of the work. Open access : The authors agree to the journal’s open access policy and understand that, if accepted, the article will be published under the applicable open access licence. Third-party material: No third-party material has been used in this manuscript. References Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. Mathur P, Sathishkumar K, Chaturvedi M, et al. 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Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. 2013;45(2):228–36. Ministry of Health and Family Welfare. National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke (NPCDCS): Operational Guidelines. New Delhi: Government of India; 2017. Drummond MF, Sculpher MJ, Claxton K, et al. Methods for the economic evaluation of health care programmes. 4th ed. Oxford: Oxford University Press; 2015. Prinja S, Gupta A, Bahuguna P, Nimesh R. Cost analysis of implementing mHealth intervention for maternal, newborn & child health care through community health workers: assessment of ReMIND program in Uttar Pradesh, India. BMC Pregnancy Childbirth. 2018;18(1):390. Kumar S, Nilsen WJ, Abernethy A, et al. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. 2013;45(2):228–36. Prinja S, Chauhan AS, Karan A, Kaur G, Kumar R. Impact of publicly financed health insurance schemes on healthcare utilisation and financial risk protection in India: a systematic review. PLoS ONE. 2017;12(2):e0170996. Faujdar DS, Prinja S, Singh T, Sahay S, Kumar R. Costing analysis of an information & communications technology-enabled primary healthcare facility in India. Indian J Med Res. 2023;157(4):231–8. Iribarren SJ, Cato K, Falzon L, Stone PW. What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions. PLoS ONE. 2017;12(2):e0170581. Prinja S, Bahuguna P, Gupta A, et al. Cost effectiveness of mHealth intervention by community health workers for reducing maternal and newborn mortality in rural Uttar Pradesh, India. Cost Eff Resour Alloc. 2018;16:25. Gentili A, Failla G, Melnyk A, et al. The cost-effectiveness of digital health interventions: A systematic review of the literature. Front Public Health. 2022;10:787135. Modi D, Saha S, Vaghela P, et al. Costing and cost-effectiveness of a mobile health intervention (ImTeCHO) in improving infant mortality in tribal areas of Gujarat, India: cluster randomised controlled trial. JMIR Mhealth Uhealth. 2020;8(5):e17066. Chauhan AS, Prinja S, Selvaraj S, et al. Cost of delivering primary healthcare services through public sector in India. Indian J Med Res. 2022;156(3):372–80. Drummond MF, Sculpher MJ, Claxton K, et al. Methods for the economic evaluation of health care programmes. 4th ed. Oxford: Oxford University Press; 2015. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9304808","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634249732,"identity":"08f3c4f1-08aa-438e-8747-166b7ee9b2be","order_by":0,"name":"G Hari 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Research","correspondingAuthor":false,"prefix":"","firstName":"Prakash","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2026-04-02 15:08:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9304808/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9304808/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108950258,"identity":"299de29b-5594-4d27-b3ee-ca2691bec6a4","added_by":"auto","created_at":"2026-05-11 07:04:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTornado diagram showing the results of a one-way sensitivity analysis for the cost components of the Oncocare mHealth application.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9304808/v1/6206dddf73951049658d84e2.png"},{"id":108950287,"identity":"951a9b3d-b63b-4d15-9faa-59cf2d1e3c33","added_by":"auto","created_at":"2026-05-11 07:04:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":441821,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9304808/v1/325c4a9c-9274-42f6-99b6-d54d9f26da3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Economic Feasibility and Budget Impact of the OncoCare mHealth Application for Digital Cancer Care in Karnataka India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer represents one of the most pressing global health challenges of the 21st century, with an estimated 19.3\u0026nbsp;million new cases and 10.0\u0026nbsp;million deaths worldwide in 2020(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). India faces a particularly acute cancer burden, with approximately 1.39\u0026nbsp;million new cancer cases reported in 2020, representing 8.7% of global cancer incidence (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The age-standardised incidence rate of cancer in India is 100.4 per 100,000 population, with significant variations across different states and regions.\u003c/p\u003e \u003cp\u003eThe economic burden of cancer care in India is substantial and multifaceted, encompassing direct medical costs, indirect costs due to productivity losses, and intangible costs related to pain and suffering. Studies indicate that the average cost of cancer treatment in India ranges from ₹50,000 to ₹15,00,000, depending on the type, stage, and treatment modality, with chemotherapy cycles alone costing between ₹10,000 to ₹1,00,000 per cycle (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Catastrophic health expenditure affects approximately 70% of cancer patients in India, pushing many families below the poverty line (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The National Sample Survey Office reported that cancer treatment accounts for 25% of all hospitalisations in India, with an average per-episode cost of ₹58,763 for cancer care.\u003c/p\u003e \u003cp\u003eCancer care complexity extends beyond direct treatment costs, encompassing significant challenges in care coordination, medication adherence, side effect management, and long-term follow-up. Studies demonstrate that 30\u0026ndash;50% of cancer patients experience medication non-adherence, leading to suboptimal treatment outcomes and increased healthcare utilisation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Poor care coordination results in treatment delays, duplicated tests, and fragmented care delivery, contributing to increased morbidity and mortality. Side effects from chemotherapy affect 80\u0026ndash;90% of patients, with inadequate monitoring and management leading to treatment discontinuation in 15\u0026ndash;20% of cases. These challenges are particularly pronounced in resource-constrained settings where healthcare infrastructure is limited and patient-to-provider ratios are suboptimal.\u003c/p\u003e \u003cp\u003eThe integration of mobile health (mHealth) technologies has emerged as a promising solution to address these challenges in cancer care delivery. Systematic reviews indicate that mHealth applications in oncology demonstrate effectiveness in symptom management, medication adherence, appointment scheduling, and patient-provider communication (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Global evidence suggests that mHealth interventions can improve medication adherence by 15\u0026ndash;25%, reduce hospital readmissions by 20\u0026ndash;30%, and enhance patient-reported outcomes through real-time monitoring and support (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In low- and middle-income countries, where smartphone penetration exceeds 70%, and healthcare access is often limited, mHealth interventions offer promise for scaling cancer care support services.\u003c/p\u003e \u003cp\u003eDespite growing evidence of mHealth effectiveness in cancer care, a significant gap remains in economic evaluation data, particularly regarding implementation costs and fiscal impact assessments. Studies from high-income countries report development costs ranging from US\u003cspan\u003e$\u003c/span\u003e10,000 to US\u003cspan\u003e$\u003c/span\u003e500,000 for comprehensive cancer care applications; however, these estimates may not apply to resource-constrained settings with different labour costs, infrastructure requirements, and regulatory environments (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The lack of standardised costing methodologies and comprehensive economic evaluations limits evidence-based decision-making for mHealth implementation in cancer care programs.\u003c/p\u003e \u003cp\u003eIndia\u0026rsquo;s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) represents a significant policy framework for addressing challenges in cancer care, with a total budget allocation of ₹3,000 crores for the period 2017\u0026ndash;2020 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The program emphasises strengthening cancer screening, early detection, and treatment services while promoting innovative approaches to improve the efficiency of care delivery. However, the economic feasibility of integrating mHealth interventions into existing program budgets remains unclear, highlighting the need for comprehensive cost analyses.\u003c/p\u003e \u003cp\u003eKarnataka state, with a population of 67.5\u0026nbsp;million and a cancer incidence rate of 106.2 per 100,000 population, represents an important setting for evaluating mHealth interventions in cancer care. The state\u0026rsquo;s NPCDCS allocation of ₹10.78 crores for FY 2023-24 provides a specific context for assessing the fiscal impact of digital health innovations. The relatively high smartphone penetration rate and existing digital health infrastructure make Karnataka an ideal setting for implementing and evaluating mHealth solutions for cancer care(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding the economic implications of mHealth implementation is crucial for informing policy decisions, allocating resources effectively, and developing scaling strategies. Micro-costing analysis provides a detailed examination of all resources required for intervention development and implementation, enabling accurate cost estimation and comparative analysis with alternative approaches (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This methodology has increasingly been recognised as the gold standard for the economic evaluation of digital health interventions, particularly in resource-constrained settings where precise cost information is essential for sustainability planning.\u003c/p\u003e \u003cp\u003eThe present study addresses this critical knowledge gap by conducting a comprehensive micro-costing analysis of the OncoCare mHealth application, designed to provide comprehensive cancer care support for patients and healthcare providers in Karnataka, India(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Through detailed cost categorisation, stakeholder engagement assessment, and fiscal impact analysis, this research aims to provide evidence-based insights for policymakers, healthcare administrators, and researchers considering similar digital health interventions for improving cancer care.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eA micro-costing analysis using a top-down approach was conducted to determine the economic costs of developing and implementing the OncoCare mHealth application for comprehensive cancer care management and patient support. This approach was selected to capture all resources utilised in the development process, allowing for a detailed examination of cost components and their relative contributions to the total cost. The study was conducted in Karnataka, India, from 2023 to 2024.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eApplication Development and Features\u003c/h3\u003e\n\u003cp\u003eThe OncoCare mobile application was developed using the Flutter framework, with Firebase serving as the backend infrastructure, to provide comprehensive support for cancer care to patients and healthcare providers. OncoCare serves as \u0026ldquo;Your Comprehensive Cancer Care Companion\u0026rdquo;, designed to empower both patients and doctors throughout the cancer treatment journey. Key features included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) chemotherapy scheduling and management system with automated appointment reminders; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) comprehensive treatment records and progress tracking with user-friendly record-keeping system; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) side effects management and monitoring with personalised insights and coping strategies; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) notification center with real-time updates about appointments, medication reminders, and important healthcare information; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) doctor-patient collaboration platform enabling seamless communication and shared treatment monitoring; and (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) secure user authentication with robust privacy protection measures. The application architecture incorporated Firebase Authentication for secure login, Cloud Firestore for patient data storage, Firebase Real-time Database for treatment tracking and progress monitoring, and advanced security measures to ensure confidentiality of health information. The OncoCare application was designed for and deployed among cancer patients and healthcare providers in Karnataka, where the project was actively implemented. The app aimed to support patients in taking control of their cancer journey through accessible digital tools, enhanced care coordination, and comprehensive treatment management(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCosting Framework\u003c/h3\u003e\n\u003cp\u003eThe current study employed a provider perspective to assess the costs of developing and implementing the OncoCare application. The analysis time horizon encompassed the full development cycle and the initial one-year implementation period. All costs were calculated in Indian Rupees (INR) for the financial year 2023-24, during which the actual development and implementation took place. Cost data were systematically collected and categorised into direct development, stakeholder engagement, and training costs.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eCost data were collected using a microcosting tool, adapted from validated digital health costing instruments, to capture all direct and indirect costs systematically. Primary data sources included project financial records, meeting minutes, purchase receipts, salary information, and semi-structured interviews with key personnel involved in the development process. For shared resources, appropriate apportioning techniques were employed to allocate costs accurately to the OncoCare project.\u003c/p\u003e\n\u003ch3\u003eCost Categorisation and Measurement\u003c/h3\u003e\n\u003cp\u003eDirect development costs encompassed mobile application development by the technical team, gap assessment activities, and implementation materials (including printing, information, education, and communication materials). Stakeholder engagement costs included three key project meetings: project initiation and requirements gathering, progress review and feedback incorporation, and final validation and implementation planning. At each meeting, costs were further categorised into human resources, equipment, furniture, and space. Training costs comprised human resources, furniture and equipment, space, and computer equipment for comprehensive training sessions across urban and rural healthcare settings.\u003c/p\u003e \u003cp\u003eFor capital items and durable goods with a lifespan exceeding 1 year, costs were annualised using a linear depreciation method with a 3% discount rate, in line with standard health economic evaluation practices.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFiscal Impact Analysis\u003c/h2\u003e \u003cp\u003eThe fiscal impact of implementing the OncoCare application within the existing National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) was assessed by comparing the development and maintenance costs to the Karnataka state budget allocation for NPCDCS (₹10.78 crores for FY 2023-24). This analysis helped determine the budgetary feasibility of integrating the application into the state\u0026rsquo;s cancer care program and assessed the potential for scaling within existing resource allocations(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eA one-way sensitivity analysis was conducted to assess the robustness of the costing results to variations in key cost parameters. Each cost component (app development, stakeholder engagement costs, implementation materials, training costs, and gap assessment) was independently varied by \u0026plusmn;\u0026thinsp;20% while holding all other parameters constant. Results were presented in a tornado diagram to visually illustrate the relative impact of each parameter on the total cost. This analysis helped identify the most influential cost drivers and assess the stability of the cost estimates under different assumptions.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eAll costs were calculated in Indian Rupees (INR) for the 2023-24 fiscal year, during which the actual development and implementation took place. Data were analysed using Microsoft Excel 2019 for cost calculations and Python 3.8 (matplotlib 3.5.1, pandas 1.4.2) for visualisations of sensitivity analysis. Results were reported as total costs and percentages by category, with detailed breakdowns of each cost component to ensure transparency and reproducibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003eThe study was approved by the Institutional Ethics Committee of JSS Medical College, JSS Academy of Higher Education \u0026amp; Research, Mysuru (IEC reference number: JSSMC/IEC/05012022/36NCT/2021-22). All stakeholders involved in providing cost information gave informed consent for their participation. Written informed consent to participate was obtained from all participants involved in the study before data collection. The privacy and confidentiality of all cost-related data were maintained throughout the study period, and the data were stored securely in accordance with institutional guidelines.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe micro-costing analysis of the Oncocare mHealth cancer care application development and implementation revealed a total cost of ₹1,00,372 (US\u003cspan\u003e$\u003c/span\u003e1,205). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this total was distributed across three primary cost categories: direct development costs (53.8%), stakeholder engagement costs (37.0%), and training costs (9.2%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal costs of Oncocare mHealth application development and implementation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResource Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuantity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnit Cost (INR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal Cost (INR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevelopment Phase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoftware Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMobile application development by the technical team\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-Implementation Phase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData collectors for gap assessment (2 persons)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerson-trips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImplementation Phase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterials \u0026amp; Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEC materials, forms, printing, and dissemination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Setup Costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e54,000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDirect Development Costs\u003c/h2\u003e \u003cp\u003eDirect development costs accounted for the largest share of the total budget, at ₹54,000 (53.8%). As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, these costs primarily comprised mobile application development (₹40,000), implementation materials and communication (₹10,000), and gap assessment activities (₹4,000).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBreakdown of direct development costs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCost (INR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApp Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOne-time mobile application development by the technical team\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGap Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData collectors for gap assessment (2 persons, travel\u0026thinsp;+\u0026thinsp;work)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImplementation Materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIEC materials, forms, printing, and dissemination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e54,000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMobile application development accounted for the largest single cost component, reflecting the technology-intensive nature of the intervention. The relatively modest cost compared to commercial app development can be attributed to the academic setting and the utilisation of open-source frameworks. Implementation costs for information, education, and communication materials demonstrated a balanced approach between technology development and user education components.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStakeholder Engagement Costs\u003c/h2\u003e \u003cp\u003eStakeholder engagement costs totalled ₹37,111 (37.0% of total costs) and were distributed across three strategic project meetings, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Project initiation (Meeting 1) and final validation (Meeting 3) accounted for the highest investments at 40.2% and 40.0%, respectively, while the progress review meeting (Meeting 2) accounted for 19.8%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBreakdown of stakeholder engagement meeting costs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeeting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost (INR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeeting 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProject Initiation \u0026amp; Requirements Gathering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeeting 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProgress Review and Feedback Incorporation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeeting 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinal Validation and Implementation Planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e37,111\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEach meeting\u0026rsquo;s costs included multiple components, with human resources accounting for the largest share. For Meeting 1 (Project Initiation \u0026amp; Requirements Gathering), human resources accounted for 92.3% (₹13,750) of the total meeting cost, comprising contributions from an IT Professor, a Professor of Community Medicine, and a Medical Oncologist. The remaining costs were distributed among furniture (3.9%, ₹587), equipment (2.8%, ₹413), and space (1.0%, ₹150), with all capital items appropriately annualised using a 3% discount rate. This cost structure was representative of all three project meetings, highlighting the knowledge-intensive nature of mHealth application development for cancer care.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed cost analysis - Primary stakeholder meeting (Meeting 1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmount (INR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnualization Method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT Professor, Community Medicine Professor, Medical Oncologist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFurniture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAC, computer, chairs, table, fan, LED lights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnual depreciation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeeting room facility costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnual rental equivalent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLaptop and presentation equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnual depreciation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Meeting 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14,900\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eComprehensive stakeholder engagement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTraining Costs\u003c/h2\u003e \u003cp\u003eTraining costs comprised ₹9,261 (9.2% of total costs), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. These costs primarily consisted of human resources (₹7,950), representing 85.9% of training expenses, with smaller allocations for furniture and equipment (₹650), space (₹360), and computer equipment (₹301).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTraining program cost analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmount (INR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSix trainers (oncologists, nurses, and medical social workers) across urban and rural locations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFurniture \u0026amp; Equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnualised costs for LED TV, chairs, tables, fans, and lights\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnualised cost for the training room\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnualised cost of computer systems\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Training Costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9,261\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eComprehensive training program\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe training program demonstrated cost-effectiveness for comprehensive staff training across urban and rural healthcare settings. Human resources accounted for the majority of costs, reflecting the specialised nature of cancer care technology training and the need for qualified clinical instructors with expertise in both oncology and digital health platforms. The geographic distribution strategy, which employs trainers across both urban and rural locations, ensures equitable access to training while maintaining cost efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFiscal Impact Analysis\u003c/h2\u003e \u003cp\u003eImplementing the Oncocare mHealth cancer care application aligns with the objectives of the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke (NPCDCS). It could be efficiently integrated within its existing budgetary framework. Based on the Karnataka state budget allocation for NPCDCS (₹10.78 crores for FY 2023-24), the total development cost of Oncocare (₹1,00,372) would represent merely \u003cb\u003e0.093% of the annual budget\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFiscal Impact Analysis - Karnataka NPCDCS Program\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKarnataka NPCDCS Budget (FY 2023-24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹10.78 crores (₹107,800,000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOncocare Development Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹100,372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBudget Impact (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.093%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual Maintenance Cost (estimated)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹15,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintenance as % of NPCDCS Budget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost per 1,000 beneficiaries**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹1,004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e*Estimated based on 15% of development cost for annual maintenance and updates. **Based on an estimated 100 direct beneficiaries in the pilot implementation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe exceptionally low budget impact demonstrates that the Oncocare intervention represents a minimal financial burden while potentially achieving significant improvements in cancer care coordination and patient outcomes. If implemented through the State-specific Initiatives and Innovation component of NPCDCS, the application could enhance program effectiveness while requiring negligible additional financial resources.\u003c/p\u003e \u003cp\u003eThe annual maintenance costs (estimated at ₹15,000, representing 0.014% of the state NPCDCS budget) compare favourably with traditional cancer care coordination systems. This minimal ongoing cost structure, combined with the preventive and supportive focus on cancer care management, suggests potential for substantial long-term cost savings through improved treatment adherence, reduced hospital readmissions, and enhanced care coordination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCost Structure Analysis\u003c/h2\u003e \u003cp\u003eThe cost distribution reveals several important characteristics of digital health intervention economics in cancer care:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTechnology-Development Focus (53.8%)\u003c/b\u003e: The predominance of direct development costs reflects the one-time nature of application creation, with subsequent implementations requiring minimal additional development investment.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStakeholder-Centred Approach (37.0%)\u003c/b\u003e: The substantial investment in stakeholder engagement demonstrates the importance of participatory design in healthcare technology development, ensuring user acceptance and clinical relevance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEfficient Training Model (9.2%)\u003c/b\u003e: The relatively low training costs suggest an efficient knowledge transfer model, with potential to cascade training effects to additional healthcare workers.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eScalability Potential\u003c/b\u003e: The fixed-cost structure (90.8% of total costs) indicates substantial economies of scale, enabling additional users to be accommodated at minimal incremental cost.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe Oncocare cost structure compares favourably with traditional cancer care support systems while offering enhanced functionality, including patient monitoring, medication adherence tracking, and care coordination. The comprehensive digital platform provides 24/7 accessibility, standardised care protocols, and real-time data collection capabilities at a fraction of the cost of conventional support systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eA one-way sensitivity analysis was conducted using a tornado diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to assess the robustness of the cost estimates and identify cost drivers with the highest potential impact. A uniform variation of \u0026plusmn;\u0026thinsp;20% was applied to each key cost component. This range was chosen based on standard practice in economic evaluations when empirical uncertainty bounds are unavailable, and it represents a conservative estimate commonly used in public health cost studies and micro-costing analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-way sensitivity analysis results for the Oncocare mHealth application\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (-20%) (INR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh (+\u0026thinsp;20%) (INR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImpact Range (INR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eContribution to Variance (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApp Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeeting 1 (Project Initiation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97,391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeeting 3 (Final Validation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97,397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImplementation Materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining - Human Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98,781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101,961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeeting 2 (Progress Review)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98,903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101,839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGap Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99,571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining - Furniture \u0026amp; Equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100,501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining - Space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100,299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100,443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining - Equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100,311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100,431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBase Case Total: ₹100,371\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sensitivity analysis reveals that the total cost of the Oncocare mHealth application shows varying degrees of sensitivity across its cost components, with the overall cost ranging from ₹92,371 to ₹108,371, representing a\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0% variation from the base case estimate. App development costs emerged as the dominant cost driver, accounting for 39.9% of the total cost variance and causing a fluctuation of ₹16,000 (\u0026plusmn;₹8,000) from the base case. This finding highlights the crucial importance of effective development methodologies, precise technical specifications, and robust project management practices in controlling overall intervention costs.\u003c/p\u003e \u003cp\u003eStakeholder engagement activities, particularly the initial project initiation meeting (Meeting 1) and the final validation meeting (Meeting 3), ranked second and third among the most influential cost drivers, contributing 14.8% each to the total variance, with impact ranges of ₹5,960 and ₹5,948, respectively. The substantial impact of these meetings underscores the value of thorough planning, comprehensive requirements gathering, and effective stakeholder coordination to minimise costly revisions in later development phases. The moderate impact of Meeting 2 (Progress Review) at 7.3% of total variance suggests that mid-project adjustments, while important for quality assurance, have a more contained effect on overall costs.\u003c/p\u003e \u003cp\u003eImplementation materials demonstrated moderate cost sensitivity, accounting for 10.0% of total variance and an impact range of ₹4,000. This component\u0026rsquo;s influence emphasises the importance of strategic procurement planning and efficient resource utilisation for educational materials, printing, and dissemination activities. Training-related human resources, although representing a high absolute cost, exhibited relatively contained cost sensitivity, accounting for 7.9% of total variance, suggesting that variations in trainer compensation or training duration have manageable impacts on overall project costs.\u003c/p\u003e \u003cp\u003eNotably, infrastructure-related training components demonstrated exceptional cost stability, with furniture, equipment, space, and computer equipment collectively accounting for only 1.3% of the total cost variance. The minimal impact ranges of ₹260, ₹144, and ₹120, respectively, indicate that these components exhibit remarkable price stability, suggesting that financial planning can accommodate reasonable variations in training infrastructure without substantially affecting overall project budgets.\u003c/p\u003e \u003cp\u003eThe gap assessment component, which accounts for 4.0% of the total variance and has an impact range of ₹1,600, represents a moderate but manageable source of cost variation. This finding suggests that while pre-implementation assessment activities are important for intervention design, their cost impact remains within acceptable bounds for project planning.\u003c/p\u003e \u003cp\u003eThese sensitivity analysis results provide several strategic insights for implementation planning and scale-up considerations. First, the predominance of app development costs as the primary cost driver suggests that investments in front-end development planning, technical architecture design, and quality assurance processes yield disproportionate returns in cost control. Organisations considering similar interventions should prioritise robust development partnerships, clear technical specifications, and agile development methodologies to optimise this critical cost component.\u003c/p\u003e \u003cp\u003eSecond, the substantial impact of stakeholder engagement meetings indicates that comprehensive upfront planning and stakeholder alignment can significantly influence overall project costs. The relatively greater impact of initial and final meetings compared to progress reviews suggests that strategic investment in thorough requirement-gathering and validation processes can prevent costly mid-project modifications and scope changes.\u003c/p\u003e \u003cp\u003eThird, the remarkable stability of training infrastructure costs provides confidence for budget planning and scale-up scenarios. The minimal variation in these components suggests that training programs can be reliably costed and scaled across different implementation sites without significant uncertainty in infrastructure requirements.\u003c/p\u003e \u003cp\u003eOverall, the sensitivity analysis demonstrates that while the Oncocare intervention exhibits some cost sensitivity to key development and engagement activities, the total cost variation of \u0026plusmn;\u0026thinsp;8.0% falls within acceptable bounds for health intervention planning. Identifying app development and stakeholder engagement as primary cost drivers provides clear targets for cost-optimisation efforts and supports evidence-based resource-allocation decisions for similar digital health implementations in resource-constrained settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis comprehensive micro-costing analysis of the OncoCare mHealth application provides valuable insights into the economic feasibility of implementing digital cancer care interventions in resource-constrained settings. The total development and implementation cost of ₹1,00,372 (approximately US\u003cspan\u003e$\u003c/span\u003e1,205) represents a remarkably cost-effective approach to enhancing cancer care delivery, with significant implications for policymakers and healthcare administrators considering similar digital health investments.\u003c/p\u003e \u003cp\u003eThe cost distribution observed in this study demonstrates a well-balanced approach to mHealth development, with direct development costs accounting for 53.8% of the total budget. This finding aligns closely with previous mHealth microcosting studies from India, including the landmark ReMiND program evaluation by Prinja et al., which reported that development and technology costs accounted for approximately 60% of total implementation expenses. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Our results are notably lower than international benchmarks; Kumar et al. reported mHealth development costs ranging from US\u003cspan\u003e$\u003c/span\u003e10,000 to US\u003cspan\u003e$\u003c/span\u003e500,000 in high-income countries, highlighting the cost advantages achievable in resource-constrained settings through strategic use of local expertise and open-source technologies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe predominance of stakeholder engagement costs (37.0%) reflects best practices in participatory design, emphasising the critical importance of user-centred development in healthcare technology. This investment pattern mirrors findings from Prinja et al.\u0026rsquo;s microcosting analysis of healthcare delivery systems, which identified stakeholder engagement as a key determinant of intervention sustainability and user adoption (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The relatively modest training costs (9.2%) suggest an efficient knowledge transfer model, comparable to the 8\u0026ndash;12% training allocation reported in other Indian mHealth implementations (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fiscal impact analysis reveals exceptional affordability within existing healthcare budgets. The OncoCare implementation cost represents merely 0.093% of Karnataka\u0026rsquo;s NPCDCS budget allocation, demonstrating substantial scope for scaling without significant budgetary strain. This finding is particularly significant when compared to traditional cancer care interventions; Chauhan et al. reported that comprehensive cancer screening programs in India require 2\u0026ndash;5% of state health budgets, making OncoCare a highly attractive complementary intervention (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe estimated annual maintenance cost of ₹15,000 (0.014% of the state budget) compares favourably with international digital health sustainability models. Systematic reviews of mHealth cost-effectiveness in developing countries report maintenance costs typically ranging from 10\u0026ndash;25% of initial development investments, positioning OncoCare within optimal sustainability parameters (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This cost structure suggests potential for significant return on investment through improved treatment adherence, reduced hospital readmissions, and enhanced care coordination.\u003c/p\u003e \u003cp\u003eWhile this study focused on implementation costs rather than full cost-effectiveness analysis, the results can be contextualised within broader mHealth economic evaluation literature. Prinja et al.\u0026rsquo;s cost-effectiveness analysis of the ReMiND mHealth intervention demonstrated an incremental cost-effectiveness ratio of ₹12,993 (US\u003cspan\u003e$\u003c/span\u003e205) per DALY averted (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Extrapolating from similar digital health interventions, OncoCare\u0026rsquo;s cost structure suggests potential for highly favourable cost-effectiveness ratios, particularly given cancer\u0026rsquo;s substantial burden of disease and the intervention\u0026rsquo;s comprehensive approach to care coordination.\u003c/p\u003e \u003cp\u003eInternational evidence supports the economic value proposition of cancer-focused mobile health (mHealth) interventions. A systematic review by Gentili et al. identified mobile health applications for chronic disease management, including cancer, as consistently cost-effective with cost per QALY ratios well below established thresholds (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The OncoCare application\u0026rsquo;s multifunctional approach, encompassing appointment scheduling, medication adherence, side effect monitoring, and care coordination, positions it favourably within this evidence base.\u003c/p\u003e \u003cp\u003eThe cost structure analysis reveals substantial scalability potential through economies of scale. With 90.8% of costs classified as fixed development expenses, marginal costs for additional users remain minimal. This finding aligns with the economic theory of digital goods and mirrors patterns observed in other successful mHealth scale-up initiatives. The ImTeCHO program in Gujarat, India, demonstrated similar scalability advantages, with per-beneficiary costs declining by 60\u0026ndash;70% as implementation expanded from pilot to state-wide deployment (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe geographic distribution of training costs across urban and rural settings provides important insights for equitable implementation. The balanced approach adopted in OncoCare training (₹9,261 total with rural components) reflects recognition of India\u0026rsquo;s diverse healthcare landscape. This strategy aligns with recommendations from Prinja et al.\u0026rsquo;s analysis of primary healthcare delivery costs, which emphasised the importance of tailored approaches for different healthcare settings (21).\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePolicy Implications and Future Directions\u003c/h2\u003e \u003cp\u003eThe findings have significant implications for health policy and resource allocation decisions. The minimal budgetary impact (0.093% of the NPCDCS allocation) suggests that mHealth interventions, such as OncoCare, can be implemented without crowding out other essential health services. This finding supports the National Digital Health Mission\u0026rsquo;s emphasis on technology-enabled healthcare delivery and provides concrete evidence for decision-makers considering digital health investments.\u003c/p\u003e \u003cp\u003eFuture research should focus on comprehensive cost-effectiveness analyses that incorporate clinical outcomes, patient-reported outcome measures, and long-term healthcare utilisation patterns. Additionally, multi-state implementation studies would strengthen the generalizability of economic findings and provide insights into regional cost variations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMethodological Strengths and Limitations\u003c/h2\u003e \u003cp\u003eThe micro-costing methodology employed in this study follows established health economic evaluation standards, ensuring transparency and reproducibility. The use of a provider perspective with detailed cost categorisation aligns with international guidelines for digital health economic evaluation (22). The incorporation of sensitivity analysis strengthens the robustness of findings, with the \u0026plusmn;\u0026thinsp;8.0% cost variation falling within acceptable bounds for health intervention planning.\u003c/p\u003e \u003cp\u003eHowever, several limitations warrant acknowledgement. The study was conducted in a single state context, which may limit its generalizability to other Indian states with different healthcare infrastructures and cost structures. The one-year implementation timeframe may not capture long-term maintenance and sustainability costs, though international evidence suggests that annual maintenance typically remains within the 15% range estimated in this study. Additionally, the analysis did not include effectiveness outcomes, precluding a full cost-effectiveness assessment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis micro-costing analysis demonstrates that comprehensive mHealth applications for cancer care can be developed and implemented at remarkably low cost in resource-constrained settings. The OncoCare intervention\u0026rsquo;s cost structure, characterised by modest total investment, minimal budgetary impact, and substantial scalability potential, provides a compelling economic case for digital health integration in cancer care programs. These findings provide valuable evidence to the growing literature on mHealth economics in developing countries and support policy initiatives that promote technology-enabled healthcare delivery.\u003c/p\u003e \u003cp\u003eThe study\u0026rsquo;s results, when viewed alongside international evidence on mHealth cost-effectiveness, suggest that well-designed digital health interventions offer a promising pathway to improve cancer care accessibility and quality while maintaining fiscal sustainability. As healthcare systems worldwide grapple with rising cancer burdens and resource constraints, the OncoCare model provides a replicable framework for leveraging technology to enhance care delivery efficiency and effectiveness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the cancer patients and healthcare providers who participated in this study and provided valuable insights for the development of the OncoCare application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by theJSS Academy of Higher Education and Research (JSSAHER/REG/RES/URG/54/2023-24/7931).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Institutional Ethics Committee of JSS Medical College, JSS Academy of Higher Education and Research, Mysuru (IEC reference number: JSSMC/IEC/05012022/36NCT/2021-22).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Written informed consent was obtained from all participants in the study before data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDual publication:\u003c/strong\u003e This manuscript is original, has not been published previously, and is not under consideration for publication elsewhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship\u003c/strong\u003e: All authors made substantial contributions to the conception and design of the study, acquisition, analysis, or interpretation of data. All authors were involved in drafting the manuscript or revising it critically for important intellectual content, approved the final version, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen access\u003c/strong\u003e: The authors agree to the journal’s open access policy and understand that, if accepted, the article will be published under the applicable open access licence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThird-party material:\u003c/strong\u003e No third-party material has been used in this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathur P, Sathishkumar K, Chaturvedi M, et al. Cancer statistics, 2020: Report from National Cancer Registry Programme, India. JCO Glob Oncol. 2020;6:1063\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan AS, Prinja S, Srinivasan R, et al. Cost-effectiveness of strategies for cervical cancer prevention in India. PLoS ONE. 2020;15(9):e0238291.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKankeu HT, Saksena P, Xu K, Evans DB. The financial burden from non-communicable diseases in low- and middle-income countries: a literature review. Health Res Policy Syst. 2013;11:31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreer JA, Amoyal N, Nisotel L, et al. A systematic review of adherence to oral antineoplastic therapies. Oncologist. 2016;21(3):354\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMata J, Pecorelli N, Kaneva P, et al. A systematic review of mobile applications for cancer patients. J Surg Oncol. 2018;118(8):1367\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitehead L, Seaton P. The effectiveness of self-management mobile phone and tablet apps in long-term condition management: a systematic review. J Med Internet Res. 2016;18(5):e97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Nilsen WJ, Abernethy A, et al. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. 2013;45(2):228\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health and Family Welfare. National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke (NPCDCS): Operational Guidelines. New Delhi: Government of India; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrummond MF, Sculpher MJ, Claxton K, et al. Methods for the economic evaluation of health care programmes. 4th ed. Oxford: Oxford University Press; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrinja S, Gupta A, Bahuguna P, Nimesh R. Cost analysis of implementing mHealth intervention for maternal, newborn \u0026amp; child health care through community health workers: assessment of ReMIND program in Uttar Pradesh, India. BMC Pregnancy Childbirth. 2018;18(1):390.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Nilsen WJ, Abernethy A, et al. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. 2013;45(2):228\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrinja S, Chauhan AS, Karan A, Kaur G, Kumar R. Impact of publicly financed health insurance schemes on healthcare utilisation and financial risk protection in India: a systematic review. PLoS ONE. 2017;12(2):e0170996.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaujdar DS, Prinja S, Singh T, Sahay S, Kumar R. Costing analysis of an information \u0026amp; communications technology-enabled primary healthcare facility in India. Indian J Med Res. 2023;157(4):231\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIribarren SJ, Cato K, Falzon L, Stone PW. What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions. PLoS ONE. 2017;12(2):e0170581.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrinja S, Bahuguna P, Gupta A, et al. Cost effectiveness of mHealth intervention by community health workers for reducing maternal and newborn mortality in rural Uttar Pradesh, India. Cost Eff Resour Alloc. 2018;16:25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGentili A, Failla G, Melnyk A, et al. The cost-effectiveness of digital health interventions: A systematic review of the literature. Front Public Health. 2022;10:787135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModi D, Saha S, Vaghela P, et al. Costing and cost-effectiveness of a mobile health intervention (ImTeCHO) in improving infant mortality in tribal areas of Gujarat, India: cluster randomised controlled trial. JMIR Mhealth Uhealth. 2020;8(5):e17066.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan AS, Prinja S, Selvaraj S, et al. Cost of delivering primary healthcare services through public sector in India. Indian J Med Res. 2022;156(3):372\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrummond MF, Sculpher MJ, Claxton K, et al. Methods for the economic evaluation of health care programmes. 4th ed. Oxford: Oxford University Press; 2015.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"micro-costing, mHealth, cancer care, digital health, economic evaluation, India, NPCDCS, budget impact analysis, healthcare technology, cost analysis","lastPublishedDoi":"10.21203/rs.3.rs-9304808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9304808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCancer represents a significant health and economic burden in India, and catastrophic health expenditure affects 70% of cancer patients. Mobile health interventions offer promising solutions for cancer care coordination; however, comprehensive economic evaluation data remain limited in resource-constrained settings.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo conduct a micro-costing analysis of the OncoCare mHealth application development and implementation in Karnataka, India, and assess its fiscal impact within existing cancer care program budgets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA comprehensive micro-costing analysis was conducted using a top-down approach from the provider\u0026rsquo;s perspective for the 2023-24 fiscal year. Cost data were systematically collected and categorised into direct development, stakeholder engagement, and training costs. The analysis included a sensitivity analysis and a fiscal impact assessment against Karnataka\u0026rsquo;s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke, with a budget allocation of ₹10.78 crores.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe total development and implementation cost was ₹1,00,372 (US\u003cspan\u003e$\u003c/span\u003e1,205), distributed across direct development costs (53.8%, ₹54,000), stakeholder engagement costs (37.0%, ₹37,111), and training costs (9.2%, ₹9,261). Mobile application development represented the largest single component (₹40,000, 39.9%). The intervention\u0026rsquo;s cost represents only 0.093% of Karnataka\u0026rsquo;s NPCDCS budget, with estimated annual maintenance costs of ₹15,000 (0.014% of the state budget). Sensitivity analysis revealed a total cost variation of \u0026plusmn;\u0026thinsp;8.0%, with app development as the primary cost driver.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe OncoCare application demonstrates exceptional economic feasibility with minimal budgetary impact and substantial scalability potential. These findings provide compelling evidence for integrating digital health interventions in cancer care programs within resource-constrained settings.\u003c/p\u003e","manuscriptTitle":"Economic Feasibility and Budget Impact of the OncoCare mHealth Application for Digital Cancer Care in Karnataka India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 07:04:36","doi":"10.21203/rs.3.rs-9304808/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-13T15:01:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135709152465514692688559856750498092786","date":"2026-05-06T06:56:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283789139659300527898610733675826253328","date":"2026-05-04T15:05:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89954886445867952805510197554789407796","date":"2026-05-04T06:32:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T06:24:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T19:00:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T06:17:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-04-15T06:06:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac135139-197c-486b-96e7-25b05df91bac","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-13T15:01:17+00:00","index":73,"fulltext":""},{"type":"reviewerAgreed","content":"135709152465514692688559856750498092786","date":"2026-05-06T06:56:37+00:00","index":69,"fulltext":""},{"type":"reviewerAgreed","content":"283789139659300527898610733675826253328","date":"2026-05-04T15:05:54+00:00","index":68,"fulltext":""},{"type":"reviewerAgreed","content":"89954886445867952805510197554789407796","date":"2026-05-04T06:32:23+00:00","index":56,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T07:04:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 07:04:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9304808","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9304808","identity":"rs-9304808","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}