OBJECTIVE: To generate, validate and establish a web-based individualised opioid conversion calculator at the University Hospital Basel and to analyse its value and significance. SETTING: A 700-bed university hospital. METHOD: Published data were screened by a Medline search using the keywords: opioid, rotation, switching, potency and dose ratio. Based on this data, the criteria for the conversion calculator were defined. A prospective process validation was performed prior to the approval. Five months after the introduction of the tool, an online survey was performed in order to evaluate its acceptance by users. In the last step, a manual calculation using a written table was compared with the calculator in a cross-over trial with 72 participants in order to assess the findings of the survey. RESULTS: The opioid conversion calculator could be generated for 24 combinations of 6 opioids for 5 different routes of administration. The validation of the calculator was performed successfully without discovering any major or critical defects. The proportion of correct answers increased from 68% using the table to 81% using the calculator (P < 0.001), the median time necessary to answer the ten questions was 8.9 min using the table and 4.8 min using the calculator. CONCLUSION: A web-based opioid conversion calculator was planned, generated, validated and established at the hospital. Based on the results of the online survey and the results of our cross-over trial we conclude that the tool saves time compared to manual calculation and may contribute to patient safety by avoiding calculation errors. With this tool we contribute to the optimisation of processes in hospital and patient safety.
OBJECTIVE: To generate, validate and establish a web-based individualised opioid conversion calculator at the University Hospital Basel and to analyse its value and significance. SETTING: A 700-bed university hospital. METHOD: Published data were screened by a Medline search using the keywords: opioid, rotation, switching, potency and dose ratio. Based on this data, the criteria for the conversion calculator were defined. A prospective process validation was performed prior to the approval. Five months after the introduction of the tool, an online survey was performed in order to evaluate its acceptance by users. In the last step, a manual calculation using a written table was compared with the calculator in a cross-over trial with 72 participants in order to assess the findings of the survey. RESULTS: The opioid conversion calculator could be generated for 24 combinations of 6 opioids for 5 different routes of administration. The validation of the calculator was performed successfully without discovering any major or critical defects. The proportion of correct answers increased from 68% using the table to 81% using the calculator (P < 0.001), the median time necessary to answer the ten questions was 8.9 min using the table and 4.8 min using the calculator. CONCLUSION: A web-based opioid conversion calculator was planned, generated, validated and established at the hospital. Based on the results of the online survey and the results of our cross-over trial we conclude that the tool saves time compared to manual calculation and may contribute to patient safety by avoiding calculation errors. With this tool we contribute to the optimisation of processes in hospital and patient safety.
Authors: J Santiago-Palma; N Khojainova; C Kornick; D J Fischberg; L H Primavera; R Payne; P Manfredi Journal: Cancer Date: 2001-10-01 Impact factor: 6.860
Authors: Miguel Angel Benítez-Rosario; Manuel Feria; Antonio Salinas-Martín; Luis Pedro Martínez-Castillo; José Javier Martín-Ortega Journal: Cancer Date: 2004-12-15 Impact factor: 6.860
Authors: G W Hanks; F Conno; N Cherny; M Hanna; E Kalso; H J McQuay; S Mercadante; J Meynadier; P Poulain; C Ripamonti; L Radbruch; J R Casas; J Sawe; R G Twycross; V Ventafridda Journal: Br J Cancer Date: 2001-03-02 Impact factor: 7.640