Facilitators and Barriers for Telemedicine Systems in India from Multiple Stakeholder Perspectives and Settings: A Systematic Review.

Background: Telemedicine is viewed as a crucial tool for addressing the challenges of limited medical resources at health care facilities. However, its adoption in health care is not entirely realized due to perceived barriers. This systematic review outlines the critical facilitators and barriers that influence the implementation of telemedicine in the Indian health care system, observed at the infrastructural, sociocultural, regulatory, and financial levels, from the perspectives of health care providers, patients, patient caregivers, society, health organizations, and the government. Methods: This review complies with the current Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols 2015. A total of 2,706 peer-reviewed studies published from December 2016 to September 2023 in the PubMed, Cochrane, Scopus, Web of Science, CINAHL, MEDLINE, and PsycInfo databases were considered for the title and abstract screening, after which 334 articles were chosen for the full-text review. In the end, 46 studies were selected for data synthesis. Results: Analysis of the literature revealed key barriers such as data privacy and security concerns, doctor and patient resistance to information and communications technology (ICT), infrastructure issues, and ICT training gaps. Facilitators included reduced health care delivery costs, enhanced patient access to health care in remote areas, and shorter patient wait times. The real-world experiences of Indian telemedicine practitioners and pioneers are also explored to complement literature-based perspectives on telemedicine implementation. Both stress the need for reliable internet connectivity, technological adoption, comprehensive ICT training, positive sociocultural attitudes, stringent data privacy measures, and viable business models as crucial for effective telemedicine adoption, with experts emphasizing practical adaptability alongside the literature-recognized facilitators.

Leveraging multi-tier healthcare facility network simulations for capacity planning in a pandemic.

The COVID-19 pandemic has placed severe demands on healthcare facilities across the world, and in several countries, makeshift COVID-19 centres have been operationalised to handle patient overflow. In developing countries such as India, the public healthcare system (PHS) is organised as a hierarchical network with patient flows from lower-tier primary health centres (PHC) to mid-tier community health centres (CHC) and downstream to district hospitals (DH). In this study, we demonstrate how a network-based modelling and simulation approach utilising generic modelling principles can (a) quantify the extent to which the existing facilities in the PHS can effectively cope with the forecasted COVID-19 caseload; and (b) inform decisions on capacity at makeshift COVID-19 Care Centres (CCC) to handle patient overflows. We apply the approach to an empirical study of a local PHS comprising ten PHCs, three CHCs, one DH and one makeshift CCC. Our work demonstrates how the generic modelling approach finds extensive use in the development of simulations of multi-tier facility networks that may contain multiple instances of generic simulation models of facilities at each network tier. Further, our work demonstrates how multi-tier healthcare facility network simulations can be leveraged for capacity planning in health crises.

Category-specific uncertainty modeling in clinical laboratory measurement processes.

BACKGROUND: A statement of measurement uncertainty describes the quality of a clinical assay analysis result, and uncertainty models of clinical assays can be used to evaluate and optimize laboratory protocols designed to minimize the measurement uncertainty associated with an assay. In this study, we propose a methodology to lend systematic structure to the uncertainty modeling process. METHODS: Clinical laboratory assays are typically classified based on the chemical reaction involved, and therefore, based on the assay analysis methodology. We use this fact to demonstrate that uncertainty models for assays within the same category are structurally identical in all respects except for the values of certain model parameters. This is accomplished by building uncertainty models for assays belonging to two categories--substrate assays based on optical absorbance analysis of endpoint reactions, and ion selective electrode (ISE) assays based on potentiometric measurements of electromotive force. RESULTS: Uncertainty models for the substrate assays and the ISE assays are built, and for each category, a general mathematical framework for the uncertainty model is developed. The parameters of the general framework that vary from assay to assay for each category are identified and listed. CONCLUSIONS: Estimates of measurement uncertainty from the models were compared with estimates of uncertainty from quality control data from the clinical laboratory. We demonstrate that building a general modeling framework for each assay category and plugging in parameter values for each assay is sufficient to generate uncertainty models for an assay within a given category.

Healthcare seeking behavior among patients visiting public primary and secondary healthcare facilities in an urban Indian district: A cross-sectional quantitative analysis.

In this work, we examined healthcare seeking behavior (HSB) of patients visiting public healthcare facilities in an urban context. We conducted a cross-sectional survey across twenty-two primary and secondary public healthcare facilities in the South-west Delhi district in India. The quantitative survey was designed to ascertain from patients at these facilities their HSB-i.e., on what basis patients decide the type of healthcare facility to visit, or which type of medical practitioner to consult. Based on responses from four hundred and forty-nine participants, we observed that factors such as wait time, prior experience with care providers, distance from the facility, and also socioeconomic and demographic factors such as annual income, educational qualification, and gender significantly influenced preferences of patients in choosing healthcare facilities. We used binomial and multinomial logistic regression to determine associations between HSB and socioeconomic and demographic attributes of patients at a 0.05 level of significance. Our statistical analyses revealed that patients in the lower income group preferred to seek treatment from public healthcare facilities (OR = 3.51, 95% CI = (1.65, 7.46)) irrespective of the perceived severity of their illness, while patients in the higher income group favored directly consulting specialized doctors (OR = 2.71, 95% CI = (1.34, 5.51)). Other factors such as having more than two children increased the probability of seeking care from public facilities. This work contributes to the literature by: (a) providing quantitative evidence regarding overall patient HSB, especially at primary and secondary public healthcare facilities, regardless of their presenting illness, (b) eliciting information regarding the pathways followed by patients visiting these facilities while seeking care, and (c) providing operational information regarding the surveyed facilities to facilitate characterizing their utilization. This work can inform policy designed to improve the utilization and quality of care at public primary and secondary healthcare facilities in India.

Prevalence of highly actionable mutations among Indian patients with advanced non-small cell lung cancer: A systematic review and meta-analysis.

BACKGROUND: Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality in India. To clarify rates of actionable mutations, and thereby identify opportunities to improve the delivery of best available care for a large volume of patients, a comprehensive review of available data is warranted. METHODS: Studies that reported prevalence of any actionable gene variant among adult Indian patients with advanced NSCLC were selected from three databases (PubMed, EMBASE, and Cochrane Library). Ranges in actionable variant prevalence were reported. Meta-analysis of proportions was completed among studies specifically evaluating mutational prevalence within ALK or EGFR. Sensitivity analyses were undertaken among populations sharing high heterogeneity. RESULTS: Twenty-six studies were selected. Ranges in actionable mutational prevalence among NSCLC patients were as follows: ALK: 4.1-21.4%, BRAF: 1.5-3.5%, EGFR: 11.9-51.8%, HER2: 0-1.5%, KRAS: 4.5-6.4%, NTRK: 0-.7%, and ROS-1: 3.5-4.1%. Following sensitivity analysis, pooled ALK mutational prevalence rates were 8.3% (95% CIs: 6.6-10.4%) and 4.01% (95% CIs: 2.3-7.0) for adenocarcinoma and NSCLC patients, respectively. Pooled EGFR mutational prevalence rates were 28.7% (95% CIs: 23.5-34.6%) and 24.2% (95% CIs: 19.9-29.1%) for adenocarcinoma and NSCLC patients, respectively. CONCLUSIONS: Nearly 40% of Indian patients with advanced adenocarcinoma and 30% with NSCLC share an actionable mutation in ALK or EGFR. Approximately one-half of adenocarcinoma patients have an actionable variant. Efforts should be directed toward efficiently identifying candidates for targeted agents and delivering such treatments.

Application of mathematical models of system uncertainty to evaluate the utility of assay calibration protocols.

BACKGROUND: Laboratory protocols used to calibrate commercial clinical chemistry systems affect test result quality. Mathematical models of system uncertainty can be developed using performance parameters provided by the manufacturer for various subsystems. These models can be used to evaluate protocols for specific laboratory operations. METHODS: A mathematical model was developed to estimate the uncertainty inherent in the Roche Diagnostics P-Modular system, and included uncertainties associated with the sample and reagent pipettes, spectrometer and the calibration process. The model was then used to evaluate various alternate calibration protocols: calibration based on mean of replicate measurements (n=1-6) and calibration based on conditional acceptance when the following quality control specimen was within one standard deviation of target. The effect of calibrator concentrations on assay measurement uncertainty was also studied, and calibrator concentrations that minimize uncertainty at a specific concentration were identified. RESULTS: The simulation model produced uncertainty estimates of 3.5% for the serum cholesterol assay and identified sample pipette (40%) and spectrometer (21%) as the largest contributors to measurement uncertainty. Each additional replicate calibrator measurements result in diminishing reductions in measurement uncertainty, with maximum reductions (19%) achieved with five replicate measurements. The conditional acceptance of calibration only when the control was within 1s resulted in an 18% reduction. CONCLUSIONS: The model can be used to evaluate the utility of laboratory protocols and establish realistic assay performance targets. The model also can help instrument manufacturers and laboratorians identify major contributors to assay measurement uncertainty, which helps improve performance in future assay systems.

A discrete-event simulation model of the kidney transplantation system in Rajasthan, India.

We present a discrete-event simulation model of the kidney transplantation system in an Indian state, Rajasthan. Organs are generated across the state based on the organ donation rate among the general population, and are allocated to patients on the kidney transplantation waitlist. The organ allocation algorithm is developed using official guidelines published for kidney transplantation, and model parameters were estimated using publicly available data to the extent possible. Transplantation outcomes generated by the model include: (a) the probabilities of a patient receiving an organ within one to 5 years of registration and (b) the average number of deaths per year due to lack of donated organs. Simulation experiments involving observing the effect of increasing the organ arrival rate and establishing additional transplantation centres on transplantation outcomes are also conducted. We also demonstrate the use of such a model to optimally locate additional transplantation centres using simulation optimisation methods.

A model-based investigation into urban-rural disparities in tuberculosis treatment outcomes under the Revised National Tuberculosis Control Programme in India.

In this study, we investigate differences in tuberculosis (TB) treatment outcomes between urban and rural India and estimate their impact on epidemiological outcomes such as TB incidence, prevalence and mortality using a mathematical model of TB transmission dynamics. Publicly available district-level treatment outcomes data for new and previously treated TB cases was analyzed in conjunction with census data providing the proportion of urban population in each district to determine the effect of urbanity/rurality on treatment outcomes. Districts were grouped in clusters based on the proportion of urban population in each district, wherein the clusters were identified by applying machine learning methods. Regression analyses revealed that average treatment success rates among both new and previously treated cases decline with increase in the proportion of urban population in a district cluster, with substantially sharper declines in treatment success rates with degree of urbanity observed for previously treated cases. The impact of differences in treatment outcomes on epidemiological outcomes was estimated using a dynamic transmission model developed for this purpose. For example, the cluster with highest treatment success rates is projected to have an average of 3.2% fewer deaths per 100,000 population in comparison with the national average across 2019-24, and the cluster with the lowest treatment success rates has an average of 4.5% more deaths per 100,000 in comparison with the national average. We anticipate that these disparities in TB treatment outcomes and epidemiology between urban and rural India may motivate investigations into the associated causes and their redressal.

An economic analysis of tolvaptan compared with fluid restriction among hospitalized patients with hyponatremia.

OBJECTIVE: The vasopressin-receptor antagonist tolvaptan is used for the treatment of hyponatremia (HN) in hospitalized patients with congestive heart failure (CHF) or syndrome of inappropriate antidiuretic hormone secretion (SIADH). The objective of this economic modeling study was to assess the potential cost and health outcomes associated with tolvaptan in comparison with fluid restriction (FR). METHODS: A decision-analytic model was developed to estimate potential cost and health outcomes associated with tolvaptan compared with FR among hospitalized CHF and SIADH patients with severe HN (serum sodium [SS] levels < 125 mEq/L). The model, which was populated with data from the published literature, assumes that response to treatment influences hospital length of stay, probability of an intensive care unit (ICU) admission, and probability of a 30-day all-cause hospital readmission. One-way and probabilistic sensitivity analyses (PSAs) assessed the influence of parameter uncertainty on model results. RESULTS: Model results suggest that, among hospitalized CHF patients with severe HN, the use of tolvaptan compared with FR may lead to reductions of 7.2% and 4.6% in ICU admissions and 30-day readmissions, respectively. Compared with FR, tolvaptan may result in total cost-savings of $156 per hospitalized CHF patient. Among hospitalized SIADH patients with severe HN, the model suggested reductions of 14.6% and 5.1% in ICU admissions and 30-day readmissions, respectively. Compared with FR, tolvaptan may result in total cost-savings of $135 per hospitalized SIADH patient. PSAs found that the probabilities of net cost-savings from the use of tolvaptan compared with FR were 64% and 59% among patients with severe HN with CHF and SIADH, respectively. CONCLUSIONS: Decision-analytic modeling based on published data for hospitalized CHF and SIADH patients with severe HN, indicates that tolvaptan compared with FR has the potential to improve health outcomes and produce cost-savings that more than offset the cost of tolvaptan.

Cost-effectiveness analysis of universal noninvasive testing for post-treatment confirmation of Helicobacter pylori eradication and the impact of patient adherence.

BACKGROUND: The treatment failure rate for Helicobacter pylori eradication therapy is ~20% due to poor patient compliance and increased antibiotic resistance. This analysis assessed the cost-effectiveness of universal post-treatment testing to confirm eradication of H. pylori infection in adults. METHODS: Decision-analytic models evaluated the cost-effectiveness of universal post-treatment testing (urea breath test [UBT] or monoclonal fecal antigen test [mFAT]) vs no testing (Model 1), and UBT vs mFAT after adjusting for patient adherence to testing (Model 2) in adults who previously received first-line antimicrobial therapy. Patients testing positive received second-line quadruple therapy; no further action was taken for those testing negative or with no testing (Model 1) or for those nonadherent to testing (Model 2). In addition to testing costs, excess lifetime costs and reduced quality-adjusted life-years (QALYs) due to continuing H. pylori infection were considered in the model. RESULTS: Expected total costs per patient were higher for post-treatment testing (UBT: US$325.76; mFAT: US$242.12) vs no testing (US$182.41) in Model 1 and for UBT (US$336.75) vs mFAT (US$326.24) in Model 2. Expected QALYs gained per patient were 0.71 and 0.72 for UBT and mFAT, respectively, vs no testing (Model 1), and the same was 0.37 for UBT vs mFAT (Model 2). The estimated incremental costs per QALY gained for post-treatment testing vs no testing were US$82.90-US$202.45 and, after adjusting for adherence, US$28.13 for UBT vs mFAT. CONCLUSION: Universal post-treatment testing was found to be cost-effective for confirming eradication of H. pylori infection following first-line therapy. Better adherence to UBT relative to mFAT was the key to its cost-effectiveness.

Impact of patient adherence on the cost-effectiveness of noninvasive tests for the initial diagnosis of Helicobacter pylori infection in the United States.

OBJECTIVE: Previous US-based economic models of noninvasive tests for diagnosis of Helicobacter pylori infection did not consider patient adherence or downstream costs of continuing infection. This analysis evaluated the long-term cost-effectiveness of the urea breath test (UBT), fecal antigen test (FAT), and serology for diagnosis of H. pylori infection after incorporating information regarding test adherence. MATERIALS AND METHODS: A decision-analytic model incorporating adherence information evaluated the cost-effectiveness of the UBT, FAT, and serology for diagnosis of H. pylori infection. Positive test results led to first-line triple therapy; no further action was taken for nonadherence or negative results. Excess lifetime costs and reduced quality-adjusted life-years (QALYs) were estimated for patients with continuing H. pylori infection. RESULTS: In the base-case scenario with estimated adherence rates of 86%, 48%, and 86% for the UBT, monoclonal FAT, and serology, respectively, corresponding expected total costs were US$424.99, $466.41, and $404.98/patient. Test costs were higher for the UBT, but were fully or partially offset by higher excess lifetime costs for the monoclonal FAT and serology. The QALYs gained/patient with the UBT vs monoclonal FAT and serology were 0.86 and 0.27, respectively. The UBT was dominant vs the monoclonal FAT, leading to lower costs and higher QALYs; the UBT was cost-effective vs serology (incremental cost/QALY gained $74). CONCLUSION: Based on a comprehensive modeled analysis that included consideration of patient test adherence and long-term consequences resulting from continuing H. pylori infection, the UBT provided the greatest economic value among noninvasive tests for diagnosis of H. pylori infection, because of high patient adherence and excellent test performance.