Background and study aims  Addition of a reminder program to conventional indications improves colonoscopy. The aim of this study was to evaluate the effectiveness of a short telephone call reminder (STCR) on a patient’s first colonoscopy. Patients and methods  One day before colonoscopy, we made a STCR of < 10 minutes to 141 randomly selected patients of 258 recruited. The STCRs informed patients about the procedure date, indications for taking laxatives, and dietetic requirements. Questions were clarified only when patients asked directly. We evaluated bowel preparation, quality indicators, and patient satisfaction. Data were expressed as mean ± SD and percentages. Statistical differences were evaluated by Student’s t and Chi squared tests; alpha = 0.05. All authors had access to the study data and reviewed and approved the final manuscript. Results  The STCR group had better bowel preparation which was demonstrated by higher completion frequency (97.16 % vs. 82.05 %), in less time (4.52 ± 3.06 vs. 5.38 ± 3.03 hours) intake of laxative, and higher Boston’s scale (7.66 ± 2.42 vs. 5.2 ± 1.65). Quality indicators of colonoscopy were better in patients that received a STCR [cecal intubation rate: 100.00 % vs. 87.18 %; polyp detection: 42.55 % vs. 9.4 %; and cecal arrival time (min): 12.09 ± 3.62 vs. 15.09 ± 5.02]. STCR patients were more satisfied (97.87 % vs. 55.56 %) and would repeat colonoscopy (21.99 % vs. 11.11 %). Conclusions  A simple additional step such as a STCR improves quality of bowel preparation, quality indicators, and satisfaction of patients undergoing their first colonoscopy. Clinical trial registry in Mexico City General Hospital: DI/16/107/3/108.

The importance of data analysis in companies grows every day, with a global market that generates large amounts of transactions. Industry 4.0 is one of the technological trends, which is a set of diverse technologies whose objective is the digitalization and technological connectivity of the entire value chain of organizations. Data analysis and decision making in real time have a positive impact on efficiency. One of the technologies that support this concept is big data, which can support companies to use and manage large volumes of data as support in decision making. In this research project, the computational environment of Apache Hadoop software has been analyzed to create a technological strategy that supports companies in creating a roadmap to know and implement big data technology; as a result, a computer laboratory for big data has been created at the Autonomous University of Coahuila, Mexico to support medium-sized manufacturing companies in their data analysis strategy for decision making.

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