The evaluation of rural clinic systems is critical for public health policy, yet methodological rigour in assessing clinical outcomes using longitudinal data remains inconsistent. Panel-data estimations offer robust analytical potential but their application in this specific context lacks systematic review. This meta-analysis aims to critically appraise the methodological application of panel-data models in estimating clinical outcomes within rural clinic systems, identifying common analytical strengths, limitations, and reporting standards. A systematic search identified relevant studies employing panel-data methods. Methodological quality and reporting were assessed using a predefined framework. The core statistical model synthesised is the two-way fixed effects specification: $Y_{it} = \alpha + \beta X_{it} + \mu_i + \lambda_t + \epsilon_{it}$, where $Y_{it}$ denotes clinical outcomes. Inference was based on the distribution of reported standard errors, with a focus on their robustness to clustering. The analysis reveals significant methodological heterogeneity. A predominant theme was the frequent omission of necessary diagnostic tests for model assumptions, particularly concerning serial correlation and cross-sectional dependence. Over 60% of studies failed to report using cluster-robust standard errors at the clinic level, potentially underestimating uncertainty. While panel-data methods are widely adopted, their application is often methodologically incomplete, compromising the validity of estimated effects on clinical outcomes. This undermines evidence-based policy formulation. Future research must adhere to stricter econometric reporting standards, including diagnostic testing and appropriate adjustment for clustering. Funders and journals should mandate detailed methodological appendices. health systems research, econometric evaluation, longitudinal data, fixed effects, public health, methodological review This study provides the first systematic methodological critique of panel-data applications in this domain, establishing a benchmark for analytical rigour and identifying a prevalent underestimation of standard errors as a key threat to inference.