Clinical risk analysis with failure mode and effect analysis (FMEA) model in a dialysis unit.

BACKGROUND: The aim of clinical risk management is to improve the quality of care provided by health care organizations and to assure patients' safety. Failure mode and effect analysis (FMEA) is a tool employed for clinical risk reduction. We applied FMEA to chronic hemodialysis outpatients. METHODS: FMEA steps: (i) process study: we recorded phases and activities. (ii) Hazard analysis: we listed activity-related failure modes and their effects; described control measures; assigned severity, occurrence and detection scores for each failure mode and calculated the risk priority numbers (RPNs) by multiplying the 3 scores. Total RPN is calculated by adding single failure mode RPN. (iii) Planning: we performed a RPNs prioritization on a priority matrix taking into account the 3 scores, and we analyzed failure modes causes, made recommendations and planned new control measures. (iv) Monitoring: after failure mode elimination or reduction, we compared the resulting RPN with the previous one. RESULTS: Our failure modes with the highest RPN came from communication and organization problems. Two tools have been created to ameliorate information flow: "dialysis agenda" software and nursing datasheets. We scheduled nephrological examinations, and we changed both medical and nursing organization. Total RPN value decreased from 892 to 815 (8.6%) after reorganization. CONCLUSIONS: Employing FMEA, we worked on a few critical activities, and we reduced patients' clinical risk. A priority matrix also takes into account the weight of the control measures: we believe this evaluation is quick, because of simple priority selection, and that it decreases action times.

Comparison of glucose pump test and urea test in measuring blood access flow.

BACKGROUND: The Glucose Pump Test (GPT) is a new method for measuring the access blood flow (Qa) based on a constant glucose infusion (Qi), with known glucose concentration (Ci), in the arterial needle and on glucose determination in two blood samples from the venous needle, the first (C1) in basal conditions, the second (C2) during the infusion. Qa depends on the difference in glucose between the two samples and is computed from the formula: QaGPT = Qi x (Ci-C2)/(C2-C1). METHODS: The new method, previously evaluated by ultrasound dilution and color-Doppler techniques, was compared with the Urea Test (UT) in 20 patients measuring recirculation (R) during reversal of the arterial and venous needles (QaUT = Qb x (1/R -1)). All Qa determinations were done twice by both methods. Glucose and urea were determined respectively two and three times. RESULTS: Mean QaGPT = 841, SD 347 ml/min, mean QaUT = 872, SD 417 ml/min (p = n.s.); mean percent difference QaGPT-QaUT= 16%, SD 14; mean coefficients of variation of paired determinations: 8.1% and 12.1% respectively; Pearson coefficient between the two methods: r= 0.91. CONCLUSIONS: The comparison showed a good correlation between the two methods and similar mean values. The coefficient of variation of the new method was acceptable and lower than with the UT. The GPT is a reliable technique for measuring blood flow in vascular accesses.