Impact of Laboratory Quality Indicators on Patient Safety and Healthcare Outcomes in a Tertiary Hospital
Background: Laboratory quality indicators (LQIs) are essential to measuring the effectiveness of the total testing process and ensuring good patient outcomes in tertiary healthcare settings. Delays in turnaround time (TAT), specimen rejection, blood culture contamination, and other patient safety events are all examples of preventable system failures. The cumulative clinical burden of these failures remains poorly understood in resource-intensive hospital environments. Objective: To measure major LQIs – TAT, specimen rejection, blood culture contamination rates, and pre- and post-analytical delays – over 12 months and their relationship with patient safety events in a tertiary hospital. Methods: This retrospective cross-sectional study was carried out at King Fahad Medical City, Riyadh, Saudi Arabia, from January to December 2023. Data were extracted from the laboratory information system for all specimens processed (N = 167,850) across six clinical departments. Quality indicators were compared against IFCC/ISO 15189 standards. Descriptive statistics, Pearson correlation, the Kruskal-Wallis test, and multiple linear regression were used for analysis, performed in SPSS v28 and Python 3.12. Results: Mean overall specimen rejection rate was 3.18% (range: 2.0-4.5%), declining significantly from 4.2% to 2.0% over the study period. Insufficient sample volume (33.3%) and hemolysis (27.7%) were the leading causes of rejection. Median routine TAT fell from 38 to 25 minutes. Blood culture contamination averaged 2.43%, exceeding the 2.0% CLSI threshold during the first seven months. A total of 210 patient safety events were attributed to LQI failures, with a 62.0% reduction from Q1 to Q4. Pearson correlation showed strong positive relationships between rejection rate and safety events (r = 0.995, p < 0.001), TAT and safety events (r = 0.958, p < 0.001), and contamination rate and safety events (r = 0.995, p < 0.001). Multiple linear regression identified the composite LQI model as a significant predictor of safety events (R2 = 0.994, F = 432.03, p < 0.001). Conclusion: LQIs showed strong, statistically significant correlations with patient safety events in this tertiary hospital. Sustained quality improvement initiatives – staff training, standardized collection protocols, and information technology optimization – produced clinically meaningful reductions across all measured indicators. Accreditation frameworks should mandate systematic monitoring of LQIs.