Journal of Medical Physics
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Year : 2022  |  Volume : 47  |  Issue : 4  |  Page : 367-373

Quality Improvement Process with Incident Learning Program Helped Reducing Transcriptional Errors on Telecobalt Due to Mismatched Parameters in Different Generations

Department of Radiation Oncology and Medical Physics, Tata Memorial Center; HBNI, Mumbai, Maharashtra, India

Correspondence Address:
Dr. Rahul Krishnatry
Department of Radiation Oncology and Medical Physics, Tata Memorial Center, E Borges Road, Parel, Mumbai, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmp.jmp_74_22

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Purpose: Higher frequency of transcriptional errors in the radiotherapy electronic charts for patients on telecobalt was noted. We describe the impact of the quality improvement (QI) initiative under the department's incident learning program (ILP). Materials and Methods: The multidisciplinary quality team under ILP was formed to identify the root cause and introduce methods to reduce (smart goal) the current transcription error rate of 40% to <5% over 12 months. A root cause analysis including a fishbone diagram, Pareto chart, and action prioritization matrix was done to identify key drivers and interventions. Plan-Do-Study-Act (PDSA) Cycle strategy was undertaken. The primary outcome was percentage charts with transcriptional errors per month. The balancing measure was “new errors” due to interventions. All errors were identified and corrected before patient treatment. Results: The average baseline error rate was 44.14%. The two key drivers identified were education of the workforce involved and mechanical synchronization of various machine parameters. PDSA cycle 1 consisted of an education program and sensitization of the staff, post which the error rates dropped to 5.4% (t-test P = 0.03). Post-PDSA cycle 2 (synchronization of machine parameters), 1, 3, and 6 months and 1 year, the error rates were sustained to 5%, 4%, 3%, and 4% (t-test P > 0.05) with no new additional errors. Conclusions: With various generations of machines and technologies that are not synchronized, the proneness of transcription errors can be very high which can be identified and corrected with a typical QI process under ILP.

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