Digital technologies are transforming the way clinical laboratories operate. Routine tasks are being automated, workflows streamlined, and data management enhanced. These changes have proven to be an effective means of addressing high testing volumes and the demand for rapid turnaround times, as well as minimising operational costs for organisations around the world [1].

One important step in such a transformation is the implementation of digital workflows. The Lab Insights team caught up with Dr Mohd Jamsani bin Mat Salleh, a chemical pathologist at Seberang Jaya Hospital in Penang, Malaysia, to discuss his experiences in applying digital workflows in a clinical lab.

How do you define digital workflow in the context of a clinical lab, and what are the initial steps involved in its implementation?

Digital workflow is a series of electronically completed steps to automate tasks and processes. For organisations, a digital workflow strategy starts with having a comprehensive understanding of one’s laboratory processes. Current processes must be analysed to identify bottlenecks and pain-points, which may include manual data entry, paper-based records, inefficient sample tracking, and delayed result reporting.

Working towards addressing the identified pain-points, organisations must invest in the right technology (e.g., laboratory information systems [LIS], laboratory information management systems [LIMS], and other relevant software solutions) and design efficient workflows. The goal is to streamline operations by reducing the burden of low-impact tasks and prioritising those with the greatest potential return.

Finally, the selected technology solutions should be deployed and configured to match the designed workflows, with comprehensive training provided to laboratory staff to ensure effective use of the new systems.

Can you share specific examples of how digital workflows have improved efficiency in labs?

One notable example is the implementation of an autoverification system.

Manual verification of test results in labs is a time-consuming and error-prone process. Rule-based autoverification systems offer a solution by automating the review of test results. These systems consist of a middleware and LIS and follow a set of algorithms. The algorithms, in turn, are developed based on instrument messages and flags, quality control status, result limit checks, delta checks, critical values, consistency checks, and patient-related clinical information.

The positive impact of autoverification programs on laboratory efficiency, error rates, and patient safety is well supported in published literature [2, 3, 4, 5]. In our lab, specifically, we witnessed a dramatic reduction in in HbA1c testing time when we replaced our old testing system with a new one that features a bi-directional LIS interface and an onboard advisor software. This new system, which runs on a 32-rule autoverification algorithm, automates the review of normal HbA1c chromatograms. It cut turnaround time from 1 hour to less than 5 minutes and processed more than 70 percent of daily samples within 4 hours.

Another is the adoption of a paperless workflow for serum protein electrophoresis (SPE).

As you know, SPE involves the production of physical gels and densitograms. These physical records often require significant physical space and can be difficult to retrieve and share efficiently. Additionally, manual data entry and interpretation are prone to human error.

Our lab was able to successfully transition from paper-based to an entirely digital workflow by using specialised software and information systems that allow digital image capture and analysis. This led to faster turnaround times, particularly for new diagnoses and same-day immunosubtraction.

Beyond the shortened turnaround time, the digital workflow also yielded other benefits. First, the elimination of physical gels and paper records has minimised environmental impact, a boon in an era of growing sustainability concerns. Second, digital images and reports have facilitated seamless exchange of information between healthcare professionals. Third, a digital workflow offers greater flexibility by allowing for individual sample processing, rather than relying on batch processing, which causes delays.

We’re interested in how organisations balance the need for digital transformation and the risks and costs involved. What do you think are the biggest challenges organisations face when implementing digital workflow in their labs, and how can these challenges be overcome?

Financial challenges. To ensure that digital transformation projects, including digital workflows, yield optimal returns, a careful consideration of the Return on Investment (ROI) is crucial. Organisations should perform meticulous examination of all costs associated with the project, from software acquisition to infrastructure upgrades. They should also account for the potential gains, looking at whether increased efficiency, improved data accuracy, and faster turnaround times can translate to significant cost savings in the long run.

Technological challenges. First and foremost among these is data security. The interconnected nature of digital systems makes laboratories vulnerable to cyberattacks, which can have severe consequences, including data breaches, system disruptions, and reputational damage. To mitigate this risk, organisations should implement robust cybersecurity measures, including data encryption, access controls, and regular security audits that prevent unauthorised people from accessing private data.

Another is data usability and interoperability. The significant amounts of data laboratories generate during total testing process offer opportunities for organisations to gain valuable insights into their operations, identify areas for improvement, and make informed decisions. However, research conducted by Forrester Research revealed that up to 73 percent of the data collected by organisations goes unused for analytics [6]. This challenge partly arises from the inability of different systems and devices to communicate and exchange data seamlessly. A way to address these is by standardising data formats and leveraging artificial intelligence to maximise the value of data [7, 8]. It may also help to modernise existing systems, move to cloud-based solutions, and develop application programming interfaces [9].

Regulatory challenges. Last but not least, the regulation of digital technologies and the collection and use of data. Organisations can ensure compliance by: [10]

• • • Staying informed about evolving regulations and industry standards;
    • Actively engaging with relevant regulatory authorities to understand requirements and seek guidance; and
    • Implementing robust compliance frameworks, including data privacy policies, security measures, and incident response plans.

What advice would you give to organisations that are just starting their digital transformation journey? How can they ensure successful implementation?

Digital transformation is imperative in clinical laboratories — streamlined workflows, reduced errors, and faster turnaround times all spell a win for both patients and healthcare providers as well as organisations themselves. However, navigating the path from analogue to digital is not easy and requires careful planning and execution. This transformation is best implemented in phases, guided by a clear strategy.

Ultimately, successful digital transformation is not just about process and technology but also about people. It is important to overcome resistance to change among employees in the organisation. This requires effective communication, empathy, and a clear vision of the future. By involving employees in the process, providing training, and recognising their contributions, organisations can foster a culture of innovation and adaptability.

References:

[1] Plebani, M. Clinical laboratories: production industry or medical services? Clinical Chemistry and Laboratory Medicine (CCLM). 2015;53(7):995-1004. doi:10.1515/cclm-2014-1007

[2] Gungoren MS. Crossing the chasm: strategies for digital transformation in clinical laboratories. Clinical Chemistry and Laboratory Medicine (CCLM). 2023;61(4):570-575. doi:10.1515/cclm-2022-1229

[3] Gao R, Zhao F, Xia L, et al. Establishment and application of autoverification system for HbA1c testing. Biochem Med (Zagreb). 2024;34(3):030705. doi:10.11613/BM.2024.030705

[4] Wang Z, Peng C, Kang H, et al. Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory. BMC Med Inform Decis Mak. 2019;19(1):123. doi:10.1186/s12911-019-0848-2

[5] Christy A L, et al. B-128 Delivering Predictable Operational efficiency gains at our core central reference laboratory with strategic deployment of navify® Lab Operation informatics automation solution. Clinical Chemistry. 2024;70(Supplement_1):hvae106.489. doi:10.1093/clinchem/hvae106.489

[6] Barrett, J., “Up to 73 Percent of Company Data Goes Unused for Analytics. Here’s How to Put It to Work”. https://www.inc.com/jeff-barrett/misusing-data-could-be-costing-your-business-heres-how.html

[7] Hulsen T, et al. From big data to better patient outcomes. Clinical Chemistry and Laboratory Medicine (CCLM). 2023;61(4):580-586. doi:10.1515/cclm-2022-1096

[8] Ebubekir B, et al. Automation in the clinical laboratory: integration of several analytical and intralaboratory pre- and post-analytical systems. Turkish Journal of Biochemistry. 2017;42(1):1-13. doi:10.1515/tjb-2016-0234

[9] Gao R, Zhao F, Xia L, et al. Establishment and application of autoverification system for HbA1c testing. Biochem Med (Zagreb). 2024;34(3):030705. doi:10.11613/BM.2024.030705

[10] Jovičić SŽ, Vitkus D. Digital transformation towards the clinical laboratory of the future. Perspectives for the next decade. Clin Chem Lab Med. 2023;61(4):567-569. Published 2023 Jan 11. doi:10.1515/cclm-2023-0001