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ORIGINAL ARTICLE
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

Date of Submission08-Aug-2022
Date of Decision30-Sep-2022
Date of Acceptance04-Oct-2022
Date of Web Publication10-Jan-2023

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


DOI: 10.4103/jmp.jmp_74_22

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   Abstract 

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.


Keywords: Incident learning program, quality improvement, telecobalt


How to cite this article:
Krishnatry R, Johnny C, Tahmeed T, Scaria L, Sutar V, Tambe C, Upreti RR, Kinhikar RA, Agarwal JP. Quality Improvement Process with Incident Learning Program Helped Reducing Transcriptional Errors on Telecobalt Due to Mismatched Parameters in Different Generations. J Med Phys 2022;47:367-73

How to cite this URL:
Krishnatry R, Johnny C, Tahmeed T, Scaria L, Sutar V, Tambe C, Upreti RR, Kinhikar RA, Agarwal JP. Quality Improvement Process with Incident Learning Program Helped Reducing Transcriptional Errors on Telecobalt Due to Mismatched Parameters in Different Generations. J Med Phys [serial online] 2022 [cited 2023 Feb 1];47:367-73. Available from: https://www.jmp.org.in/text.asp?2022/47/4/367/367429



   Introduction Top


Preventable medical errors alone add to very high mortality and morbidity each year.[1] Radiotherapy is a complex, multistep process. Unlike other disciplines where there may be a solitary relationship between the patient and the physician, the discipline of radiation oncology differs drastically. There is a complex and dynamic ecosystem involving human–human (caregiver-patient), human–machine, and machine–machine interaction. Even within the “human” category, there are multiple individuals with varying levels of expertise, academic background, and training (oncologists, physicists, and technologists). The human–machine interaction is dictated mostly by the user input at various stages, increasing the potential of transcriptional error at multiple stages. The importance of devising optimal checkpoints at critical steps, mechanisms to review and update the workflows, and safety measures cannot be over-emphasized.

Modern linear accelerators (LA) come with software platforms that directly import radiation treatment plans from the treatment planning system with minimal manual intervention. This considerably decreases the number of human–machine interphases, decreasing the chances of errors. On the other hand, the telecobalt therapy units lack such sophistication and still depend on manual entries and human–machine interphases. The treatment parameters such as field size, gantry angle, and collimator angle are first transcribed from planning stations like simulator or planning systems. Then, these are manually entered or implemented on the machine to deliver the treatment, leading to human–machine interphase at two points of time. This significantly increases the chances of transcriptional errors. Newer units from Theratronics (Best Theratronics Ltd) come with an auto-setup feature,[2] but lack robustness and have a high cost attached to it. Most of the telecobalt units are in the centers across less developed countries that would lack funding for such updating. In the era of higher technologies, clinical acceptance of telecobalt is being questioned by health administrators and care providers. There is a need for improving executions in dose delivery in telecobalt.

Large-volume centers with a combination of both cobalt and LAs as seen in many low- and middle-income countries (LMICs) present a unique challenge. As per the published annual report of 2015–2016, 6235 patients received about 100,000 radiotherapy external beam fractionations and 3590 brachytherapy fractions at our center.[3] The external beam radiotherapy techniques used range from conventional cobalt teletherapy machines (30%) to specialized techniques, such as rotational intensity-modulated radiotherapy, image-guided radiotherapy, and adaptive and stereotactic radiotherapy using high-end LAs.[3] All types of cancer and intent treatment suitable with conventional plan are accommodated in telecobalt machines. This amalgamation of varied technologies creates a marked variation in the workflow at times, making the whole procedure more error prone.

There have been reports that have shown that around 60% of the incidents in radiotherapy are due to human error.[4] Transcriptional errors have been reported to a tune of 29%–42% in Western literature.[5],[6] A preliminary audit report from our institute showed that overall telecobalts units were more prone to errors (78%), where transcriptional errors constituted 52.2%. Following this, an institutional voluntary online incident learning program (ILP) was initiated.[3],[7] To the best of our knowledge, there are no previous studies reporting transcriptional errors due to mismatch of technical parameters, with different make, model, and type of machines spanning over many generations. Coexistence of these machines along with the existing work pool will require incident reporting and a robust quality improvement program (QIP). The preexisting checks at our institution have broadly highlighted in our previous publications.[3],[7]

In the current study, we present the challenges associated with one of the common type of transcriptional errors in telecobalts when they are from various era and are used in concurrence with modern LAs and planning systems. We also show how these errors were successfully addressed with the help of ILS and QIP.


   Materials and Methods Top


This study was conducted under the mandate of an Institutional Radiotherapy Incident Reporting program (XRIP). The radiotherapy errors reported since the inception of the incident reporting program from January 2017 to June 2018 were reviewed and it was noticed that there was not any significant change or improvement in transcriptional errors in telecobalt compared to our previous experience from audit in 2016.[3] Although most (99%) of these transcriptional errors were near miss as they would be corrected at setup, they were unacceptable to accumulate. This being perceived as a major problem; a QIP was planned with a quality task team (QTT) identified by the ILP committee.

The QTT performed a baseline audit of electronic radiation treatment charts (e-RT charts) of all patients on the telecobalt unit on one single day to estimate the real on ground situation without any reporting bias in usual incident reporting program. Usual weekday was selected without any prior intimation to ground staff. The e-RT charts were compared with the planning record (either from approved plans on planning system or simulator portal film printouts/screenshots, as applicable) for transcription errors in treatment plan parameters. These included treatment machine/unit, time dose fractionation, immobilization techniques, source-to-skin distance/source-to-axis distance (type and value), radiotherapy field information (number, orientation, dimensions, weightage, collimation, depth of the point of prescription for each field, treatment time calculations for each field), and additional beam modification devices (bolus, tissue compensator, block and template for marking field block, wedge). All transcriptional errors (incidents or near misses) were collected, and the error rates were calculated as percentage e-RT charts with any error for that audit.

All transcriptional errors thus identified were collated and reviewed by the QTT to decide the smart (specific, measurable, attainable, realistic, and timely) goal. The QTT conducted several “Gemba walks” through the transcription steps of radiation planning and preparation of e-RT charts for telecobalt patients. Various meetings were conducted with the human man force involved (oncologists, physicists, and therapists) for suggestions and discussions to identify and understand the causes of various transcription errors.

Thus, identified causes were collated with the help of a fishbone chart. The Pareto analysis was conducted based on observed frequency and were further shortlisted using the problem prioritization matrix to identify the focused high impact key drivers. Hence, the interventions were decided and implemented one after the other. At each intervention, a snap audit was conducted as described for the baseline audit to document the change in error rate and the impact of interventions. The audits were also supposed to flag up if any new errors occurred due to interventions. Along with this, interviews with human man force were done prior and after each intervention to see if the interventions were acceptable with no serious inconveniences or difficulty in the routine workflow of the department. The audit results were charted using a simple run chart to understand the progress.

Statistical analysis

Descriptive statistics were used for calculating error rates at various audits and for Pareto and prioritization matrix. The results for pre- and postinterventions were analyzed using a Chi-square test. All the statistical tests were run on Statistical Package for the Social Sciences (IBM Corp., IBM SPSS Statistics for Macintosh, version 26.0, Armonk, NY, USA).


   Results Top


Historic and baseline error rates

For the baseline audit, all 111 e-RT charts on four telecobalt units were reviewed, and 49 charts (44.14%) with transcriptional errors were identified. The most common transcriptional error was with incorrect collimation angle (25/49; 51.0%), followed by gantry angle (13/49; 26.5%) and wedge orientation (11/49; 22.5%). All of these errors were near misses, as they were already identified and corrected at the first fraction patient setup on treatment starting day. There were no transcriptional errors identified for treatment machine/unit, time dose fractionation, immobilization techniques, source-to-skin distance/source-to-axis distance (type and value), radiotherapy field information (number, dimensions, weightage, depth of prescription, and treatment time calculations), and additional beam modification devices.

SMART goal

Based on the above findings, a smart goal was devised as “to reduce the current transcriptional error rate in telecobalt of 44.14% to <5% over 1 year.” This would lead to about an 89% reduction in the transcriptional error rate.

Root cause analysis

With an in-depth review of the transcriptional process and discussions with various human workforce involved, 17 causes were identified and plotted on a fishbone chart [Figure 1]. The Pareto analysis was done to identify the six most important causes [Figure 2: lacunae in education, the mismatch between the simulator and telecobalt parameters, a mismatch between treatment planning system parameters with telecobalt parameters, mismatch among the four telecobalt machines for various parameters, casual attitude, and lack of autoimporting of treatment plans attributing to nearly 68% of the causes for errors]. These causes were further prioritized using a problem prioritization matrix [Figure 3] to identify the key drivers.
Figure 1: Fish-bone cause-effect diagram. CT: Computed tomography, 2D: 2 dimensional, 3D: 3 dimensional. RT: Radiation treatment

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Figure 2: Pareto analysis

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Figure 3: Problem prioritization matrix

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The two key drivers shortlisted were education and sensitization of human workforce involved in the transcriptional workflow and synchronization of various machine parameters (various telecobalt machines with each other, telecobalt units with planning simulator, and with treatment planning system). The various mismatches which existed are detailed in [Table 1].
Table 1: The translational discrepancy for simulator, treatment planning system, and various telecobalt machines

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Interventions and outcomes

For the first key driver, “education and sensitization,” a 4-month intensive education session with continued feedback communication and postsession tests were planned and implemented from November 2018 to February 2019. These sessions included basic structure and workflow of each machine, the mismatch between various machine parameters, conversion of parameters one machine to another, and sessions with discussions of past errors and impact on workload and work culture. Each was proceeded by a multiple-choice question based on short tests and discussion, comments from the participants. Postcompletion of the first intervention, an audit was conducted for all 74 eRT-charts on four telecobalt units that day, and a significant drop in error rates to 5.4% (P = 0.03) was seen.

It was realized that the human workforce involved directly in transcriptions were mainly residents (oncologists, physicists, and technologists) and would need long-term education programs to sustain the low error rates. This would also be prone to significant aberration with the arrival of a new pool of residents each year as they will have a learning curve. The translation of machine parameters from one to another needed significant application and may lead to errors due to several other environmental factors as well. Hence, further long-term interventions were planned for the second key driver, “mismatch correction between machines.”

The first intervention for the second key driver was to synchronize the mechanics of the different telecobalt machines and with the simulator machine. At our institute, the various cobalt machines were procured over a period of 30 years. Hence, there are multigeneration telecobalts which have their own minor modifications based on the upgrade at each time point. Having said that the conventional simulator that is currently used in our department was designed to simulate the working of a modern LA. Hence, there is a need to modify the parameters when the plan has to be implemented on older telecobalts. It was identified that one of the telecobalt machine gantry rotations was not correctable due to electronic mechanics and inbuilt universal wedge (Theratron Equinox). Other than this, all other machine parameters for the remaining three telecobalt were synchronized with the simulator over a weekend, so that the routine functioning and treatment of ongoing patients were not disturbed.

The telecobalt manufacturer's engineers with the in-house team of physicists realigned the parameters followed by extensive quality assurance (QA) as per the internationally acceptable standards. The second intervention was the calibration of the treatment planning system for all telecobalt machines as per their current parameters. As there was a residual mismatch only in gantry and resultant wedge orientation between Equinox and the other three machines, only two machine-based planning platforms (Equinox and others) were generated on the treatment planning system. The third intervention was carried as a repeat education and information session with the resident pool to explain the changes done and its impact on the translation of information from planning machines to treatment machines.

Hence, now, they need to be careful only regarding translating wedge orientation for only one machine (Equinox) when the patient was planned on the simulator. Other than this, all parameters of machines matched with the simulator and all parameters on the treatment planning system (including Equinox) were matched with all telecobalt units. After the above interventions, repeat audits were conducted at 1, 3, 6 months, and 1 year to assess the error rates at those times and assurance of sustenance of results. It was seen that the error rates were consistently below 5% in all the above times, as shown in the run chart [Figure 4].
Figure 4: Run chart to show the progress in error rates with interventions

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   Discussion Top


Radiotherapy treatment consists of various steps such as assessment of patient, the decision to treat, simulation, target delineation, prescription of treatment dose, treatment planning, QA, patient setup, treatment verification, and delivery.

According to an estimate from the International Atomic Energy Agency, radiotherapy is not accessible to up to 82% of the world's population living in the less developed world, with only 32% of the available treatment units are located there.[8] There is also a difference in the profile of existing radiotherapy equipment with a rapidly changing trend. Telecobalt machines have been gradually replaced by LAs, with the later number almost doubled while the former remaining almost stable (2827–2268) from 2006 to 2013.[8] Still, a large portion of world in LMIC countries is dependent on telecobalts which will need many indigenous approaches to match the technological demands in the large region.[9],[10]

With this changing trend in technology, especially in LMIC, there is an evolving challenge to match training and workflow in a routine with a vision of safe radiotherapy delivery to patients. This problem evolves over time as the workforce gets more casual with the use of modern LA workflow, where the majority of human interphases are automated. In contrast, telecobalt still need rigorous attention while transcribing information from one platform to another (from planning to paper and from paper to machine for delivery). Systems with students in training (physician, physicist, and technologist) are prone to errors, especially when these trainees undergo various rotations through the department, which may or may not keep them well versed with all the different aspects of machine specifications. Although this issue is never directly addressed in literature before, there are important clues available on how advancing technology does improve the safety of radiotherapy delivery.[7]

One of the recent studies suggested that implementation of checklist on telecobalt could decrease frequency of errors while routine workflow.[10] The review of the same checklist can suggest repetitive errors in the system, and if a systematic cause can be identified, it can be addressed successfully, as in our study (transcriptional).[11] In the current study, we presented a small QIP under the umbrella project of the radiotherapy ILP.

Once a systematic problem point is identified, deeper study and root cause analysis can lead to a formidable solution through proper QI approach. The standard education and training as the first intervention was quite successful in mitigating the errors, but for permanent sustainability, the second intervention of mechanically correcting the mismatch was necessary. As more centers with existing telecobalts may install newer telecobalts or LAs, the simple mechanical differences in various machines may precipitate similar errors as our experience.

Before the current exercise, the transcriptions errors were routinely identified and corrected at the day 1 setups by the team of treating physicians and therapists. Without verification form treating physicians, no patients would be initiated on treatment. This way, all transcription errors were routinely corrected and were of near-miss type. Unfortunately, the overwhelming number of these near-miss types would create distress among therapists and physicians alike. The errors in transcription by physicians could at times lead to long times for verification as they may get occupied in clinics. This would also at times delay the patient treatment. The mistrust for the written prescription/transcription was a major stressor between physicians and therapists. Overall, a sense of dissatisfaction and mistrust was developing. This was also leading to an increased proneness of the system to actual incidents as so much of energy was being routinely spent to correct errors which in first place should not have existed.

We feel as various centers across the globe as they move to LAs era when retaining their functional telecobalt units, they may face similar challenges. These centers may be in south or Southeast Asia, Africa, or South America. We are hopeful that our study result would guide them through. The second key driver of synchronization of various machine parameters was the key to the maintenance of error rates below the threshold.

A study conducted by Clark et al. showed that reporting of incidents irrespective of the outcomes has led to an increased culture of safety and also as an effective learning tool.[12] This is very similar to our voluntary reporting program, which is an ongoing incidence learning program. Even after eliminating the apparent sources of errors, the human factor must always be factored in, and vigilance is required at every step of the planning process. Yeung et al. suggested that the only way to minimize these errors is by a robust QA program.[13] The need for a voluntary reporting program is also emphasized by many academic centers.[14],[15] Another valuable tool will be the development of a quality control program that identifies common errors and does more in-depth study to identify error-prone steps and develop measures to mitigate them.[16]

Limitations

The limitations of the current study include its applicability to limited to centers that are currently in our transition phase or would move to the same in the future. Many centers that may transit without an overlapping period of technologies would not face similar challenges. The current study focuses only on the first human–machine interphase of transcribing planning information. The second interphase of committing transcribed information to the machine is not addressed. This is not feasible in old generation telecobalt units as there is no available automated machine record to verify actual delivery parameters.

During the routine education and sensitization, sessions of 1–2-h duration were held for the therapists, residents, physicists, and other clinicians on Saturdays after routine work hours. These sessions during work hours may compromise routine work and if conducted outside work hours, may increase overtime, and may have financial implications for the institutes if compensated appropriately. These issues were beyond the scope of current article.


   Conclusions Top


Nevertheless, we feel this study shows a very novel and practical method of reducing errors in the transitioning overlapping technology radiotherapy practice representing less developed world centers. Simple interventions made in our study have resulted in a drastic reduction of errors without affecting the general workflow. Thus, an internal audit of the general workflow and planning process would lead to a greater understanding of underlying issues right from the grass-root level. After completely understanding the root cause, specific interventions could greatly benefit in rectification and prevention of errors.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
   References Top

1.
Rodziewicz TL, Hipskind JE. Medical Error Prevention. Treasure Island (FL): StatPearls; 2018.  Back to cited text no. 1
    
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Ltd BT. 2008-2018. Available from: http://www.theratronics.ca/product_mlc.html. [Last accessed on 2022 Jul 10].  Back to cited text no. 2
    
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Agarwal JP, Krishnatry R, Panda G, Pathak R, Vartak C, Kinhikar RA, et al. An audit for radiotherapy planning and treatment errors from a low-middle-income country centre. Clin Oncol (R Coll Radiol) 2019;31:e67-74.  Back to cited text no. 3
    
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Shafiq J, Barton M, Noble D, Lemer C, Donaldson LJ. An international review of patient safety measures in radiotherapy practice. Radiother Oncol 2009;92:15-21.  Back to cited text no. 4
    
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Shoffren O, Tsang Y, Kudhail J. Implementation of a paperless workflow in radiotherapy; Reducing transcription. Radiother Oncol 2017;123:S563-S4.  Back to cited text no. 5
    
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Ho PE, O'Connor C, Smoke M, Farrell T. A comparison of perceptual and reported errors in radiation therapy. J Med Imaging Radiat Sci 2013;44:23-30.  Back to cited text no. 6
    
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Manjali JJ, Krishnatry R, Palta JR, Agarwal JP. Quality and safety with technological advancements in radiotherapy: An overview and journey narrative from a low- and middle-income country institution. JCO Glob Oncol 2022;8:e2100367.  Back to cited text no. 7
    
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IAEA. Radiotherapy in Cancer Care: Facing the Global Challenge. Available from: https://www-pub.iaea.org/MTCD/Publications/PDF/P1638_web.pdf. [Last accessed on 2022 Jul 10].  Back to cited text no. 8
    
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Laskar SG, Sinha S, Krishnatry R, Grau C, Mehta M, Agarwal JP. Access to Radiation therapy: From local to global and equality to equity. JCO Glob Oncol 2022;8:e2100358.  Back to cited text no. 9
    
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Maitre P, Krishnatry R, Chopra S, Gondhowiardjo S, Likonda BM, Hussain QM, et al. Modern radiotherapy technology: Obstacles and opportunities to access in low- and middle-income countries. JCO Glob Oncol 2022;8:e2100376.  Back to cited text no. 10
    
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Mukundan H, Singh S, Lohia N, Semwal MK, Kumar V, Bhatnagar S, et al. Use of a checklist approach on a telecobalt in an attempt to reduce human errors in radiotherapy delivery and improve therapeutic ratio. J Med Phys 2021;46:1-6.  Back to cited text no. 11
  [Full text]  
12.
Clark BG, Brown RJ, Ploquin J, Dunscombe P. Patient safety improvements in radiation treatment through 5 years of incident learning. Pract Radiat Oncol 2013;3:157-63.  Back to cited text no. 12
    
13.
Yeung TK, Bortolotto K, Cosby S, Hoar M, Lederer E. Quality assurance in radiotherapy: Evaluation of errors and incidents recorded over a 10 year period. Radiother Oncol 2005;74:283-91.  Back to cited text no. 13
    
14.
Kalapurakal JA, Zafirovski A, Smith J, Fisher P, Sathiaseelan V, Barnard C, et al. A comprehensive quality assurance program for personnel and procedures in radiation oncology: Value of voluntary error reporting and checklists. Int J Radiat Oncol Biol Phys 2013;86:241-8.  Back to cited text no. 14
    
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Rahn DA 3rd, Kim GY, Mundt AJ, Pawlicki T. A real-time safety and quality reporting system: Assessment of clinical data and staff participation. Int J Radiat Oncol Biol Phys 2014;90:1202-7.  Back to cited text no. 15
    
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Konski A, Movsas B, Konopka M, Ma C, Price R, Pollack A. Developing a radiation error scoring system to monitor quality control events in a radiation oncology department. J Am Coll Radiol 2009;6:45-50.  Back to cited text no. 16
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
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