Laboratory information system using Google Sheets and Telegram: A pilot project in Gandhi Hospital.

INTRODUCTION

With increased awareness of technology throughout the culture and society, there has been an increased demand for paperless data transfer. The medical community has also grown quite a lot in its tech savviness, with the advent of advanced laboratory machines, which enable high-speed biochemical, pathological and microbiological analysis. There has also been development of advanced systems, which enable the transfer of the analysed data directly to the attending physicians, without them having to stand in long queues or face unnecessary inconveniences associated with offline dispatch of lab reports. All systems are designed and implemented as web-based applications. All applications are accessed via the internet website. Most hospital labs (approximately 80%) face frequent complaints about slow turnaround times.^[1] Web applications have increased speed, flexibility, comfortability, and productivity of workflow since 1998.^[2]

However, the costs and infrastructure associated with the implementation of such a system are not devoid of challenges. The present initiative was taken up in a tertiary care government hospital where the Biochemistry lab runs on fully automated equipment, but without a laboratory information system (LIS) configuration and with the dispatch of reports being handled manually. In order to upgrade this system, we came up with a method to issue online reports without the assistance of a 3rd party vendor or service. Consequently, the primary objective of this project was to design an information flow model that provides a high-quality, cost-effective solution for online laboratory report dispatch that is reproducible in any healthcare setting with minimal expertise and infrastructure.

MATERIAL AND METHODS

This interventional study was undertaken by the Department of Clinical Biochemistry at Gandhi Hospital, Secunderabad. The study utilised daily analytical data generated from the Beckman Coulter-AU5800 system, Siemens-ADVIA Centaur XPT system, and Sysmex-CS-1600 CoagulometerAutoanalyser. The core methodology involved the segregation and organisation of data into a user-friendly format using a set of predefined formulae in “Google Sheets,” followed by the dispatch of reports through a “Live” link via the “Telegram” app. This workflow leveraged the unique features of Google Sheets, a web-based application that allows the sharing of spreadsheets via live links that update synchronously across all devices. Special permissions within the application were utilised to control data access and ensure patient data protection. Telegram, a popular social messaging platform, was used to distribute these live links regularly within a private group containing the hospital's physicians.

The system architecture was divided into a “Frontend” and a “Backend” [Figure 1]. The Backend contained the processed data, including Patient IDs and lab results from the auto-analysers, and remained hidden and accessible only to the creator to prevent unauthorised editing [Figure 2]. The Frontend served as the report interface visible to the physicians [Figure 3]. It included elements such as the Date and Batch number, a specific Search Cell for selecting unique Subject IDs via a dropdown list, and columns for Reference Ranges and Methods of Estimation. Integration between the two sheets was achieved using query formulas, such as =QUERY('IB Master List'!A: BK,"SELECT * WHERE A='"&B1&"'",1), which pulled specific data from the master list based on the user's selection [Figure 4]. Data validation rules were set using the “List from range” criteria to create the navigation dropdowns [Figure 5]. Finally, quality indicators, including turnaround time (TAT), short turnaround time (STAT), user complaints, and failure rates, were assessed and compared with the previous manual system and an available commercial LIS over a three-month period.

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Figure 1:

The process or workflow of collecting, processing and dispatching the data.

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Figure 2:

The report format (contains the date & batch, subject ID, search cell, reference ranges, methods of estimation and comment)

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Figure 3:

The private telegram group (only contains doctors).

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Figure 4:

Data validation method in Google Sheets - for creating a dropdown list in the frontend.

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Figure 5:

Comparing the turnaround time (TAT) and short turnaround time (STAT) before and after the implementation of the Google Sheets-based system.

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RESULTS

The analysis of quality indicators demonstrated significant improvements following the implementation of the new system [Table 1]. The TAT, defined as the interval between specimen receipt and verified report dispatch, was found to be halved compared to the pre-implementation phase. However, the STAT remained unchanged, as the laboratory consistently prioritised emergency samples in both the manual and digital systems. Regarding accuracy, transcriptional errors were significantly reduced; under the paper-based system, errors occurred at a rate of up to 10% per day, whereas the Google Sheets-based system reduced this to less than 2.5% per day.

User satisfaction metrics also showed positive trends. Complaints from patients dropped from approximately 15% per day to less than 2.5%, while complaints from clinicians decreased from 12.5% to 3-4% per day. In terms of system reliability, the failure rate for the Google Sheets system was negligible, with only rare interruptions due to internet or computer hardware issues. In contrast, the commercially available LIS, which is dependent on server integrity, was functional for only 84% of the month, exhibiting a 16% monthly breakdown rate [Figure 6].

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Figure 6:

Feedback taken from doctors or physicians of the hospital.

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Feedback regarding access and quality was largely positive. In the initial deployment stages, 70% of clinicians found the system immediately accessible, while minor adaptation issues among the remainder were resolved through educational updates in the Telegram group. Ultimately, 90% of clinicians agreed that reporting errors had decreased compared to the paper-based system. Furthermore, all surveyed clinicians agreed that the reports were more informative, offered added clinical benefit due to improved accuracy, and that the overall TAT had improved compared to the previous system [Table 2].

DISCUSSION

After more than two years of continuous use (commencing 15-05-2022) and implementation in other departments like Pathology and Microbiology, we have observed great success in sustaining this system. The Google Sheets-based system demands very low resources and expertise, making it scalable to institutions of various sizes. This method is particularly advantageous for laboratories in various states of India that face financial or infrastructural hurdles regarding standard LIS installation. The data in this system allows for rapid entry, retrieval, tracking, and sharing, with easy options for archiving and backup.

Our findings regarding the reduction of TAT are consistent with other studies that emphasise the role of automation in laboratory efficiency. For instance, Goswami et al. observed that the implementation of a LIS significantly contributes to the reduction of pre-analytical and post-analytical delays ^[3]. Similarly, our observation of reduced transcriptional errors aligns with the findings of Chauhan et al., who noted that manual transcription is a primary source of laboratory errors and that digitisation is crucial for patient safety.^[4]

While commercial LIS solutions are the standard, they often come with high maintenance costs and server dependencies. Our study showed a 0% failure rate compared to a 16% failure rate of a local server-based LIS. This reliability in resource-constrained settings echoes the work of Varghese et al., who advocated for cloud-based and low-cost digital interventions in developing healthcare infrastructures to bridge the gap between urban and rural diagnostic capabilities.^[5]Furthermore, the use of Telegram for report dissemination parallels the increasing use of mobile instant messaging in healthcare; Panda et al. highlighted that while informal, smartphone-based communication significantly accelerates clinical decision-making, provided that patient confidentiality is maintained, a requirement we addressed through our “Backend/Frontend” permission structure.^[6]

CONCLUSION

The Google Sheets and Telegram-based LIS is a cost-effective, reliable, and user-friendly alternative to commercial systems in resource-limited settings, significantly reducing TAT and transcriptional errors.