This review examines how laboratory turnaround time affects nursing workflow and patient satisfaction, indicating areas for improvement.
Background: Laboratory turnaround time (TAT), defined as the time between collection of the sample and making the results available for the clinician, is a key indicator in healthcare. TATs can affect nursing workflows and patient satisfaction. Delayed TAT can increase nurse workload, disrupt care delivery, and decrease patient trust, while short turnaround time promotes efficiency, satisfaction, and patient outcomes. Aim: To assess the impact of TAT on nursing care delivery and patient satisfaction, identify potential steps for optimizing TAT, and address the barriers to changing TAT. Methods: This review comprised a synthesis of peer-reviewed literature examining the effect of TAT on nursing workflow, patient satisfaction, and interventions, including point-of-care testing (POCT) and electronic health record (EHR) integration, based on clinical, operational, and patient-focused outcomes. Results: Long laboratory turnaround time can worsen nurse workload, increase stress, and raise error rates, with the worst impact occurring in high acuity settings. Similarly, shorter TAT had an improved workflow by allowing for POCT to remove redundant activities. Long TAT can negatively lower patient satisfaction due to the added anxiety of waiting, while the less time a patient waits, the better. Optimizing TAT improves care delivery and trust with patients. Interventions such as POCT and EHR integration can reduce TAT by about 20-50%. Barriers to optimized TAT can include costs associated with creating an intervention and staffing shortages. Professional collaboration and creating standardized TAT protocols may help to eliminate delays. Conclusion: Improving TAT can improve healthcare efficiency and improve patient satisfaction. Implementation of POCT, electronic health record interface, and standardized protocols will be crucial, and support with education and investigation on cost-effective solutions.
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Alanazi et al. (2024) studied this question.
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