In Harrison’s Principles of Internal Medicine, human understanding is emphasized as one of three necessary characteristics that a physician must have. Inflammation, which is caused by inflammatory inducers (inf-ids), is a fundamental feature of disease at the cellular and molecular levels. Inflammation protects the body, but excessive or prolonged inflammation can be damaging and can cause disease. Humans are repeatedly exposed to external and internal environmental factors that generate inf-ids throughout their lives. External environmental factors include microbial and non-microbial inf-ids, as well as stressors that inevitably arise during social interactions. Internal environmental factors include the adaptive physiological response that is present from birth. Inf-ids may also be produced by the four-step habit loop, which consists of a cue (e.g., stressor), emotions, routine act (adaptive response), and a reward. Immune cells in the circulatory system and in tissues may have positive and negative effects in inflammatory responses. However, low-grade inflammation may be difficult to detect. We propose a model of disease development that integrates external and internal environmental factors from the perspective of human understanding.

Background: The preanalytical phase is more vulnerable to errors. This study aimed to establish preanalytical quality indicators (QIs) suitable for Korean clinical laboratories and investigate the current status of preanalytical phase performance monitoring in Korea using these QIs. Methods: We reviewed previous studies investigating preanalytical QIs including the International Federation of Clinical Chemistry (IFCC) model of QIs, to establish a set of QIs for Korean clinical laboratories. An e-mail survey consisting of this QI set was sent to 90 clinical laboratories. The collected data were analyzed, and performance measures were evaluated according to the quality specifications defined by the IFCC and the sigmascale method. Results: A model consisting of 23 preanalytical phase QIs was established.Approximately 47% (42/90) of clinical laboratories responded to the survey.The average result submission rate for each QI was 56% (standard deviation, 26%). The QIs with the highest and lowest result submission rates were “rejected samples due to hemolysis” (95%) and “recollected sample due to errors caused outside the laboratory” (17%). The QIs with the highest and lowest error rates were “hemolyzed sample detected by hemolytic index” (median, 0.546%; sigma performance level, “good”) and “samples not received” (median, 0.001%; sigma performance level, “very good”), respectively. Conclusions This survey findings on preanalytical phase QIs could serve as a foundation for developing an external quality assessment program for clinical laboratories in Korea.

In Harrison’s Principles of Internal Medicine, human understanding is emphasized as one of three necessary characteristics that a physician must have. Inflammation, which is caused by inflammatory inducers (inf-ids), is a fundamental feature of disease at the cellular and molecular levels. Inflammation protects the body, but excessive or prolonged inflammation can be damaging and can cause disease. Humans are repeatedly exposed to external and internal environmental factors that generate inf-ids throughout their lives. External environmental factors include microbial and non-microbial inf-ids, as well as stressors that inevitably arise during social interactions. Internal environmental factors include the adaptive physiological response that is present from birth. Inf-ids may also be produced by the four-step habit loop, which consists of a cue (e.g., stressor), emotions, routine act (adaptive response), and a reward. Immune cells in the circulatory system and in tissues may have positive and negative effects in inflammatory responses. However, low-grade inflammation may be difficult to detect. We propose a model of disease development that integrates external and internal environmental factors from the perspective of human understanding.

Background: The preanalytical phase is more vulnerable to errors. This study aimed to establish preanalytical quality indicators (QIs) suitable for Korean clinical laboratories and investigate the current status of preanalytical phase performance monitoring in Korea using these QIs. Methods: We reviewed previous studies investigating preanalytical QIs including the International Federation of Clinical Chemistry (IFCC) model of QIs, to establish a set of QIs for Korean clinical laboratories. An e-mail survey consisting of this QI set was sent to 90 clinical laboratories. The collected data were analyzed, and performance measures were evaluated according to the quality specifications defined by the IFCC and the sigmascale method. Results: A model consisting of 23 preanalytical phase QIs was established.Approximately 47% (42/90) of clinical laboratories responded to the survey.The average result submission rate for each QI was 56% (standard deviation, 26%). The QIs with the highest and lowest result submission rates were “rejected samples due to hemolysis” (95%) and “recollected sample due to errors caused outside the laboratory” (17%). The QIs with the highest and lowest error rates were “hemolyzed sample detected by hemolytic index” (median, 0.546%; sigma performance level, “good”) and “samples not received” (median, 0.001%; sigma performance level, “very good”), respectively. Conclusions This survey findings on preanalytical phase QIs could serve as a foundation for developing an external quality assessment program for clinical laboratories in Korea.

Background: The preanalytical phase is more vulnerable to errors. This study aimed to establish preanalytical quality indicators (QIs) suitable for Korean clinical laboratories and investigate the current status of preanalytical phase performance monitoring in Korea using these QIs. Methods: We reviewed previous studies investigating preanalytical QIs including the International Federation of Clinical Chemistry (IFCC) model of QIs, to establish a set of QIs for Korean clinical laboratories. An e-mail survey consisting of this QI set was sent to 90 clinical laboratories. The collected data were analyzed, and performance measures were evaluated according to the quality specifications defined by the IFCC and the sigmascale method. Results: A model consisting of 23 preanalytical phase QIs was established.Approximately 47% (42/90) of clinical laboratories responded to the survey.The average result submission rate for each QI was 56% (standard deviation, 26%). The QIs with the highest and lowest result submission rates were “rejected samples due to hemolysis” (95%) and “recollected sample due to errors caused outside the laboratory” (17%). The QIs with the highest and lowest error rates were “hemolyzed sample detected by hemolytic index” (median, 0.546%; sigma performance level, “good”) and “samples not received” (median, 0.001%; sigma performance level, “very good”), respectively. Conclusions This survey findings on preanalytical phase QIs could serve as a foundation for developing an external quality assessment program for clinical laboratories in Korea.

In Harrison’s Principles of Internal Medicine, human understanding is emphasized as one of three necessary characteristics that a physician must have. Inflammation, which is caused by inflammatory inducers (inf-ids), is a fundamental feature of disease at the cellular and molecular levels. Inflammation protects the body, but excessive or prolonged inflammation can be damaging and can cause disease. Humans are repeatedly exposed to external and internal environmental factors that generate inf-ids throughout their lives. External environmental factors include microbial and non-microbial inf-ids, as well as stressors that inevitably arise during social interactions. Internal environmental factors include the adaptive physiological response that is present from birth. Inf-ids may also be produced by the four-step habit loop, which consists of a cue (e.g., stressor), emotions, routine act (adaptive response), and a reward. Immune cells in the circulatory system and in tissues may have positive and negative effects in inflammatory responses. However, low-grade inflammation may be difficult to detect. We propose a model of disease development that integrates external and internal environmental factors from the perspective of human understanding.