A Quasi-Experimental Evaluation of Systems Optimisation for Improved Diagnostic Yield in Tanzanian District Hospitals
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2011
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| _version_ | 1866901280745586688 |
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| author | Mfinanga, Juma Mwakyusa, Grace |
| author_facet | Mfinanga, Juma Mwakyusa, Grace |
| contents | <p>{ "background": "Diagnostic yield in sub-Saharan African district hospitals is often suboptimal, constrained by fragmented and under-resourced clinical systems. Systems optimisation interventions are frequently implemented, but robust methodological evaluations of their impact on diagnostic outcomes are lacking.", "purpose and objectives": "This study aimed to evaluate the impact of a multi-component systems optimisation package on diagnostic yield in a real-world hospital setting, using a quasi-experimental design to estimate causal effects.", "methodology": "A controlled interrupted time-series analysis was conducted across four matched district hospitals. Two hospitals received the intervention, which integrated laboratory workflow re-engineering, clinical decision support algorithms, and targeted staff training. The primary outcome was the monthly proportion of conclusive diagnoses from admitted patients. The effect was estimated using a generalised linear mixed model: $\\logit(\\pi{it}) = \\beta0 + \\beta1 Tt + \\beta2 X{it} + \\beta3 (Tt \\times X{it}) + ui + \\epsilon{it}$, where $\\pi{it}$ is the probability of a conclusive diagnosis in hospital $i$ at time $t$, $Tt$ is the post-intervention period, and $X{it}$ is the intervention indicator. Robust standard errors were clustered at the hospital level.", "findings": "The intervention was associated with a significant increase in diagnostic yield. The adjusted odds ratio for a conclusive diagnosis was 2.45 (95% CI: 1.78 to 3.37). The absolute improvement in the intervention hospitals was 18.7 percentage points (from a baseline of 54.3% to 73.0%) post-implementation, a change not observed in control sites.", "conclusion": "A structured systems optimisation package can substantially improve diagnostic conclusiveness in resource-constrained hospital settings. The findings demonstrate that synergistic adjustments to laboratory, clinical, and human resource subsystems can effectively mitigate diagnostic delays and uncertainties.", "recommendations": "Health policymakers</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18954258 |
| institution | Zenodo |
| language | eng |
| publishDate | 2011 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Quasi-Experimental Evaluation of Systems Optimisation for Improved Diagnostic Yield in Tanzanian District Hospitals Mfinanga, Juma Mwakyusa, Grace Health systems strengthening Diagnostic yield Quasi-experimental design Sub-Saharan Africa District hospitals Tanzania Clinical systems optimisation <p>{ "background": "Diagnostic yield in sub-Saharan African district hospitals is often suboptimal, constrained by fragmented and under-resourced clinical systems. Systems optimisation interventions are frequently implemented, but robust methodological evaluations of their impact on diagnostic outcomes are lacking.", "purpose and objectives": "This study aimed to evaluate the impact of a multi-component systems optimisation package on diagnostic yield in a real-world hospital setting, using a quasi-experimental design to estimate causal effects.", "methodology": "A controlled interrupted time-series analysis was conducted across four matched district hospitals. Two hospitals received the intervention, which integrated laboratory workflow re-engineering, clinical decision support algorithms, and targeted staff training. The primary outcome was the monthly proportion of conclusive diagnoses from admitted patients. The effect was estimated using a generalised linear mixed model: $\\logit(\\pi{it}) = \\beta0 + \\beta1 Tt + \\beta2 X{it} + \\beta3 (Tt \\times X{it}) + ui + \\epsilon{it}$, where $\\pi{it}$ is the probability of a conclusive diagnosis in hospital $i$ at time $t$, $Tt$ is the post-intervention period, and $X{it}$ is the intervention indicator. Robust standard errors were clustered at the hospital level.", "findings": "The intervention was associated with a significant increase in diagnostic yield. The adjusted odds ratio for a conclusive diagnosis was 2.45 (95% CI: 1.78 to 3.37). The absolute improvement in the intervention hospitals was 18.7 percentage points (from a baseline of 54.3% to 73.0%) post-implementation, a change not observed in control sites.", "conclusion": "A structured systems optimisation package can substantially improve diagnostic conclusiveness in resource-constrained hospital settings. The findings demonstrate that synergistic adjustments to laboratory, clinical, and human resource subsystems can effectively mitigate diagnostic delays and uncertainties.", "recommendations": "Health policymakers</p> |
| title | A Quasi-Experimental Evaluation of Systems Optimisation for Improved Diagnostic Yield in Tanzanian District Hospitals |
| topic | Health systems strengthening Diagnostic yield Quasi-experimental design Sub-Saharan Africa District hospitals Tanzania Clinical systems optimisation |
| url | https://doi.org/10.5281/zenodo.18954258 |