Assessment of utilization of automated systems and laboratory information management systems in clinical microbiology laboratories in Thailand - Preeyarach Klaytong; Panida Chamawan; Voranadda Srisuphan; Krittiya Tuamsuwan; Phairam Boonyarit; Adisak Sangchankoom; Archawin Rojanawiwat; Aekkawat Unahalekhaka; Kulsumpun Krobanan; Pimrata Leethongdee; Orapan Sripichai; Vanaporn Wuthiekanun; et al. (PLoS ONE, 2025)
Introduction Clinical microbiology laboratories are essential for diagnosing and monitoring antimicrobial resistance (AMR). Here, we assessed the systems involved in generating, managing and analyzing blood culture data in these laboratories in an upper-middle-income country. Methods From October 2023 to February 2024, we conducted a survey on the utilization of automated systems and laboratory information management systems (LIMS) for blood culture specimens in 2022 across 127 clinical microbiology laboratories (one each from 127 public referral hospitals) in Thailand. We categorized automated systems for blood culture processing into three steps: incubation, bacterial identification, and antimicrobial susceptibility testing (AST). Results Of the 81 laboratories that completed the questionnaires, the median hospital bed count was 450 (range, 150-1,387), and the median number of blood culture bottles processed was 17,351 (range, 2,900-80,330). All laboratories (100%) had an automated blood culture incubation system. Three-quarters of the laboratories (75%, n = 61) had at least one automated system for both bacterial identification and AST, about a quarter (22%, n = 18) had no automated systems for either step, and two laboratories (3%) outsourced both steps. The systems varied and were associated with the hospital level. Many laboratories utilized both automated systems and conventional methods for bacterial identification (n = 54) and AST (n = 61). For daily data management, 71 laboratories (88%) used commercial microbiology LIMS, three (4%) WHONET, three (4%) an in-house database software and four (5%) did not use any software. Many laboratories manually entered data of incubation (73%, n = 59), bacterial identification (27%, n = 22) and AST results (25%, n = 20) from their automated systems into their commercial microbiology LIMS. The most common barrier to data analysis was ‘lack of time’, followed by ‘lack of staff with statistical skills’ and ‘difficulty in using analytical software’. Conclusion In Thailand, various automated systems for blood culture and LIMS are utilized. However, barriers to data management and analysis are common. These challenges are likely present in other upper-middle-income countries. We propose that guidance and technical support for automated systems, LIMS and data analysis are needed.