Enhancing Laboratory Sample Collection Efficiency through Laboratory Information Systems: Insights into Optimal Despatch Rider Management
Abstract
Effective administration of dispatch riders is crucial for delivering reliable diagnoses and research results by enabling prompt and precise sample collection. Laboratory Information Systems (LIS) offers a solution to enhance the efficiency and organization of dispatch riders in laboratory settings. Thus, the main objective of this study is to provide insights and knowledge on the potential benefits of implementing LIS to optimize the management of dispatch riders and enhance the efficiency of sample collection. An analysis of previous research findings illustrates the varied benefits of using LIS in improving key performance indicators such as turnaround time, mistake rates, and coordination of dispatch riders. The LIS platform serves as a centralized system for managing and allocating sample collection jobs, minimizing scheduling conflicts, and optimizing dispatch rider routes. Real-time tracking capabilities enable laboratory management to monitor dispatch rider locations and sample collection progress, facilitating enhanced coordination and resource allocation. Furthermore, LIS-generated data analytics provide valuable insights into sample collection patterns, enabling proactive management strategies to mitigate potential bottlenecks. Integration of LIS into payroll systems allows for automated compensation calculation based on dispatch rider performance metrics, ensuring fair and transparent pay rates. Studies consistently demonstrate that adequate compensation positively impacts dispatch rider motivation and sample collection effectiveness. This study highlights the vital importance of LIS in enhancing dispatch rider administration for efficient sample collection operations. It provides insights for laboratory managers and policymakers to optimize the potential of LIS to improve operational performance, facilitate patient care, and advance research outcomes.
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Ádám, Wolf, Wolton, D., Trapl, J., Janda, J., Romeder-Finger, S., Gatternig, T., Farcet, J. B., Galambos, P., & Széll, K. (2021). Towards Robotic Laboratory Automation Plug & Play: The LAPP Framework. DOI: https://doi.org/10.1016/j.slast.2021.11.003
Avivar, C. (2012). Strategies for the Successful Implementation of Viral Laboratory Automation. *NCBI*. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.2174/1874357901206010115
Baggethun, A. (2020). Supporting the Logistics of Lab Sample Transportation with Mobile Technology. https://www.duo.uio.no/handle/10852/79603
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. DOI: https://doi.org/10.1177/014920639101700108
Bath, T. G., Bozdag, S., Afzal, V., & Crowther, D. (2011). LimsPortal and BonsaiLIMS: Development of a lab information management system for translational medicine. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1186/1751-0473-6-9
Benini, M., Detti, P., & Zabalo Manrique de Lara, G. (2021). A vehicle routing problem for biological sample transportation in healthcare: Mathematical formulations and a metaheuristic approach.
Burke, M. A., et al. (2017). The impact of integrated laboratory information systems on workflow and turnaround time: A systematic review. Journal of Pathology Informatics, 8, 45-54.
Chang, T., Draper, M. M., Van den Bout, A., Kephart, E., Maul-Newby, H., Vasquez, Y., Woodbury, J., Randi, S., Pedersen, M., Nave, M., La, S., Gallagher, N., McCabe, M. M., Dhillon, N., Bjork, I., Luttrell, M., Dang, F., MacMillan, J. B., Green, R., ... Sanford, J. R. (2021). A method for campus-wide SARS-CoV-2 surveillance at a large public university. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1371/journal.pone.0261230
Cheng, P., Jin, J., Chen, L., Lin, X., & Zheng, L. (2021). A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing [Technical Report]. DOI: https://doi.org/10.14778/3476249.3476271
Chiarini, A., & Vagnoni, E. (2017). Lean principles and healthcare management: A review and case studies. International Journal of Services and Operations Management, 28(4), 479-503.
Crawford, J. M., Shotorbani, K., Sharma, G., Crossey, M., Kothari, T., Lorey, T. S., Prichard, J. W., Wilkerson, M., & Fisher, N. (2017). Improving American Healthcare Through “Clinical Lab 2.0”: A Project Santa Fe Report. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1177/2374289517701067
Coetzee L-M, Cassim N, Glencross DK. (2020). Weekly laboratory turn-around time identifies poor performance masked by aggregated reporting. Afr J Lab Med. 2020;9(1), a1102. https://doi. org/10.4102/ajlm.v9i1.1102. DOI: https://doi.org/10.4102/ajlm.v9i1.1102
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. DOI: https://doi.org/10.2307/249008
Dawande, P., Wankhade, R. S., Akhtar, F. I., & Noman, O. (2022). Turnaround Time: An Efficacy Measure for Medical Laboratories. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.7759/cureus.28824
Dawood, P., Breuer, F., Homolya, I., Stebani, J., Gram, M., Jakob, P. M., Zaiss, M., & Blaimer, M. (2024). A novel image space formalism of Fourier domain interpolation neural networks for noise propagation analysis.
de Ruijter, A., Cats, O., Kucharski, R., & van Lint, H. (2021). Evolution of Labour Supply in Ridesourcing. DOI: https://doi.org/10.1080/21680566.2021.2024917
Douthwaite, J. A., Brown, C. A., Ferdinand, J. R., Sharma, R., Elliott, J., Taylor, M. A., Malintan, N. T., Duvoisin, H., Hill, T., Delpuech, O., Orton, A. L., Pitt, H., Kuenzi, F., Fish, S., Nicholls, D. J., Cuthbert, A., Richards, I., Ratcliffe, G., ... Clark, R. (2022). Improving the efficiency and effectiveness of an industrial SARS-CoV-2 diagnostic facility. *NCBI*. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1038/s41598-022-06873-6
Grasas, A., Ramalhinho, H., Pessoa, L. S., Resende, M. G. C., Caballé, I., & Barba, N. (2014). On the improvement of blood sample collection at clinical laboratories. *NCBI*. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1186/1472-6963-14-12
Ikeri, K., Cardona, V. Q., & Menkiti, O. R. (2021). Improving timeliness of newborn screens in the neonatal intensive care unit: A quality improvement initiative. *NCBI*. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1038/s41372-021-00985-z
Islam, M. M., Poly, T. N., & Li, Y. C. J. (2018). Recent advancement of clinical information systems: Opportunities and challenges. *NCBI*. [Link](https://www.ncbi.nlm.nih.gov)
Jacobs, J., Hardy, L., Semret, M., Lunguya, O., Phe, T., Affolabi, D., Yansouni, C., & Vandenberg, O. (2019). Diagnostic Bacteriology in District Hospitals in Sub-Saharan Africa: At the Forefront of the Containment of Antimicrobial Resistance. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.3389/fmed.2019.00205
Johnson, A., Smith, R., & Williams, K. (2021). Enhancing laboratory efficiency through integrated systems. Journal of Laboratory Medicine, 45(3), 210-223.
Kwon, H. K., Gopal, C. B., Kirschner, J., Caicedo, S., & Storey, B. D. (2020). A user-centered approach to designing an experimental laboratory data platform.
Landaverde, L., McIntyre, D., Robson, J., Fu, D., Ortiz, L., Chen, R., Oliveira, S. M. D., Fan, A., Barrett, A., Burgay, S. P., Choate, S., Corbett, D., Doucette-Stamm, L., Gonzales, K., Hamer, D. H., Huang, L., Huval, S., Knight, C., Landa, C., ... Klapperich, C. M. (2022). Buildout and integration of an automated high-throughput CLIA laboratory for SARS-CoV-2 testing on a large urban campus. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1016/j.slast.2022.06.003
Luo, Y. T., Wang, J. H., Zhang, M. M., Wang, Q. Z., Chen, R., Wang, X. L., & Wang, H. L. (2021). COVID?19?another influential event impacts on laboratory medicine management. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1002/jcla.23804
Mohaimenul Islam, M., Nasrin Poly, T., & Jack Li, Y. C. (2018). Recent Advancement of Clinical Information Systems: Opportunities and Challenges. *NCBI*. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1055/s-0038-1667075
Narayanan, S., Lai, Y. W., (2021). Medical Tourism in Malaysia: Growth, Contributions and Challenges. Thailand and The World Economy | Vol. 39, No.1, January - April 2021
Patel, M., et al. (2022). Implementing dispatch management systems in clinical laboratories: A case study. Healthcare Technology Today, 14(2), 134-148.
Poulton, M., Noulas, A., Weston, D., & Roussos, G. (2018). Modelling Metropolitan-area Ambulance Mobility under Blue Light Conditions. DOI: https://doi.org/10.1109/ACCESS.2018.2886852
Puspitasari, N. N., Syah, N. T. Y. R., Indradewa, N. R., & Sunaryanto, N. K. (2024). Marketing Strategy and Marketing Plan in Daksa Laboratory Medik Business Development Project. Journal of Social and Economics Research, 6(1), 1299–1310. https://doi.org/10.54783/jser.v6i1.379 DOI: https://doi.org/10.54783/jser.v6i1.379
Rheingans-Yoo, D., Kominers, S. D., Ma, H., & Parkes, D. C. (2019). Ridesharing with Driver Location Preferences. DOI: https://doi.org/10.24963/ijcai.2019/79
Sadeghi Eshkevari, S., Tang, X., Qin, Z., Mei, J., Zhang, C., Meng, Q., & Xu, J. (2022). Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace. DOI: https://doi.org/10.1145/3534678.3539095
Scholes-Pearson, E., & Mercadillo, N. (2021). Where is my sample? Investigating pre-analytical pathology sampling errors in a psychiatric hospital. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1192/bjo.2021.911
Singh, N., Data, D., George, J., & Diggavi, S. (2019). SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization. DOI: https://doi.org/10.1109/CDC42340.2020.9303828
Singh, V., et al. (2019). Financial management in integrated laboratory systems: Challenges and solutions. Journal of Health Economics, 42(2), 157-165.
Smith, J., & Thompson, D. (2023). Artificial intelligence in laboratory logistics: Optimizing dispatch routes. Clinical Chemistry, 69(5), 341-352.
Srivaastava, S., Gupta, R., Rai, A., & Cheema, A. S. (2014). Electronic Health Records and Cloud-based Generic Medical Equipment Interface.
Tashkandi, S. A., Alenezi, A., Bakhsh, I., AlJuryyan, A., H AlShehry, Z., AlRashdi, S., Guzman, M., Pono, M., Lim, F., Rose Tabudlong, A., Elwan, L., Fagih, M., & Aboabat, A. (2021). Clinical laboratory services for primary healthcare centers in urban cities: A pilot ACO model of ten primary healthcare centers. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1186/s12875-021-01449-1
Ucar, F., Erden, G., Taslipinar, M. Y., Ozturk, G., Ginis, Z., Bulut, E., & Delibas, N. (2016). Greater Efficiency Observed 12 Months Post-Implementation of an Automatic Tube Sorting and Registration System in a Core Laboratory. [Link](https://www.ncbi.nlm.nih.gov)
Usanov, D., Pechina, A., van de Ven, P., & van der Mei, R. (2019). Approximate Dynamic Programming for Real-time Dispatching and Relocation of Emergency Service Engineers.
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. DOI: https://doi.org/10.1111/j.1540-5915.2008.00192.x
Viksna, J., Celms, E., Opmanis, M., Podnieks, K., Rucevskis, P., Zarins, A., Barrett, A., Guha Neogi, S., Krestyaninova, M., McCarthy, I., Brazma, A., & Sarkans, U. (2007). PASSIM – an open-source software system for managing information in biomedical studies. [Link](https://www.ncbi.nlm.nih.gov) DOI: https://doi.org/10.1186/1471-2105-8-52
Villena, F. (2021). LaboRecommender: A crazy-easy-to-use Python-based recommender system for laboratory tests.
Wade, M., & Hulland, J. (2004). Review: The resource-based view and information systems research: Review, extension, and suggestions for future research. MIS Quarterly, 28(1), 107-142. DOI: https://doi.org/10.2307/25148626
Weng, W., & Yu, Y. (2021). Labor-right Protecting Dispatch of Meal Delivery Platforms. DOI: https://doi.org/10.1109/CDC45484.2021.9683147
White, R. R., & Munch, K. (2014). Handling Large and Complex Data in a Photovoltaic Research Institution Using a Custom Laboratory Information Management System. DOI: https://doi.org/10.1557/opl.2014.31
Womack, J. P., & Jones, D. T. (2010). Lean Thinking: Banish Waste and Create Wealth in Your Corporation. Simon & Schuster.
Younes, Nadin, Duaa W. Al-Sadeq, Hadeel AL-Jighefee, Salma Younes, Ola Al-Jamal, Hanin I. Daas, Hadi. M. Yassine, and Gheyath K. Nasrallah. 2020. "Challenges in Laboratory Diagnosis of the Novel Coronavirus SARS-CoV-2" Viruses, 12(6), 582. https://doi.org/10.3390/v12060582 DOI: https://doi.org/10.3390/v12060582
Zhao, B., Bryant, L., Wilde, M., Cordell, R., Salman, D., Ruszkiewicz, D., Ibrahim, W., Singapuri, A., Coats, T., Gaillard, E., Beardsmore, C., Suzuki, T., Ng, L., Greening, N., Thomas, P., Monks, P. S., Brightling, C., Siddiqui, S., & Free, R. C. (2019). LabPipe: An extensible informatics platform to streamline the management of metabolomics data and metadata.
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