A comprehensive method for the quantification of medication error probability based on fuzzy SLIM

Fakhradin Ghasemi; Mohammad Babamiri; Zahra Pashootan

doi:10.1371/journal.pone.0264303

Peer Review History

Original SubmissionNovember 16, 2021
10 Dec 2021 Decision Letter - Yang Li, Editor PONE-D-21-36423A comprehensive method for the quantification of medication error probability based on fuzzy SLIMPLOS ONE Dear Dr. Pashootan, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Jan 24 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. 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Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: No Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: This paper presents a comprehensive method for the quantification of medication error probability based on fuzzy SLIM. In general, the topic is interesting, but this work is not well presented. I think that the quality of this paper can be improved if the authors address the following aspects: 1 The main contributions and novelty of this paper should be further summarized. This reviewer suggests the authors exactly mention what is new compared with existing approaches and why the proposed approach is needed to be used instead of the existing methods. This reviewer suggests the authors use bullets (3 bullets is standard) and in each bullet explain one contribution clearly. 2 How scalable is the presented approach? 3 This reviewer would like to suggest the authors add a flowchart of the presented method since more details can enrich the paper. 4 The proposed method might be sensitive to the values of its main controlling parameters. How did you tune the parameters? 5 The novelty and contribution of the present work need further justification. Key results need to contrast and compare with the results of other state-of-the-art methods in the literature. Reviewer #2: This paper is technically sound. There are still some concerns of this reviewer: 1 Author should highlight the research gaps and contribution of the proposed work by comparing the state of the art methods and recent studies. 2 Better justify the selected Fuzzy multiple attributive group decision making (MAGDM) methodology and fuzzy analytical hierarchical process (FAHP). There are so many multiple attribute decision making methods. Why did you choose these two methods, based on what evidence? 3 In the introduction section, the literature review should be strengthened. Authors are recommended to include and review some studies regarding MAGDM and FAHP applications such as [doi.org/10.1371/journal.pone.0251940], [doi.org/10.1155/2016/3965608], [doi.org/10.1371/journal.pone.0239140] to improve the literature survey: 4 Authors have not presented the limitations of this work. How this work can be extended in practical applications? Please elaborate on it. 5 The italic types of symbols in equations and the main text should be in the same expression. 6 Although the manuscript is well written in terms of English, there are some (very few, indeed) grammatical and expression errors. It is suggested to proofread the paper. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0264303.r001
Revision 1
7 Jan 2022 Author Response We would like to thank the reviewers for taking time to review this paper and for their useful and constructive comments which helped us to improve the quality of the paper. Meanwhile, we addressed all comments one by one. All changes are highlighted in yellow in the 'Revised Manuscript with Track Changes' file. Comments for the reviewer #1 and Response of the authors 1-The main contributions and novelty of this paper should be further summarized. This reviewer suggests the authors exactly mention what is new compared with existing approaches and why the proposed approach is needed to be used instead of the existing methods. This reviewer suggests the authors use bullets (3 bullets is standard) and in each bullet explain one contribution clearly. We would like to thank the reviewer for the constructive and helpful comment that helped us to improve the quality of the paper. We add these explanations at the end of the introduction section. The changes are highlighted in yellow and are as follows (lines 103-107): • In this method, all factors supposed to affect medication errors are included, • Th103-107e method is based on the principles of fuzzy logic which is the preferred approach in dealing with uncertain and ambiguous data, • The method is based on a well-accepted foundation methodology for human error analysis, • The method is specialized to assess the probability of medication errors, 2-How scalable is the presented approach? We would like the reviewer for this constructive comment. The technique developed in this study is specialized for the assessment of medication error probability. But the methodology is general and can be applied to other domain. But the whole procedure, including identifying subPSFs, weighting them, and rating them must be repeated. We add this explanation at the discussion section. The changes are highlighted in yellow and are as follows (lines 315-317): The procedure used in this study can be applied to any other domain but all phases, including identifying subPSFs, weighting them, and rating them, must be repeated. 3-This reviewer would like to suggest the authors add a flowchart of the presented method since more details can enrich the paper. We would like to thank the reviewer for this constructive comment. This flowchart was added (Fig 2). 4-The proposed method might be sensitive to the values of its main controlling parameters. How did you tune the parameters? We would like he reviewer for this comment. After weighting subPSFs and PSFs, the rating process should be performed. In this step, a value within 0-10 is assigned to each parameter (subPSFs). For determining these values, the analyst should visit the workplace (hospital in this case) and interview with the related employees. For more clarity, the following paragraph was added into the case study section (lines 293-296): Next, the analyst visited the emergency department and interviewed nurses. Based on the direct observation and information obtained from nurses, the rating of subPSFs was determined. Each subPSF can have a value within a range from 1 to 9 so that higher values are indicative of a better situation of that subPSF. SLI and then HEP were calculated based on the rating of subPSFs. 5 The novelty and contribution of the present work need further justification. Key results need to contrast and compare with the results of other state-of-the-art methods in the literature. We would like to thank the reviewer for this constructive comment. We added a paragraph to the discussion section and address this comment. The changes are highlighted in yellow. Moreover, this paragraph are presented below (lines 316-325): The method developed in this study can evaluate the combined effect of seventeen factors on medication error probability, making it more comprehensive than available HRA techniques. Some of these factors such as workload, availability of work procedures, and safety culture are general to any occupational setting, but some other variables such as patient safety climate, physical health of nurses, and error management culture are unique to healthcare environments. Taking into account these variables is an important advantage of this method over other available techniques. Moreover, the weights calculated in this study are specific to healthcare environments. For example, among organizational factors, patient safety climate had the highest weight, meaning that it is the most important organizational factor affecting the medication error probability. Such a factor with its relative importance has not been considered in any available HRA technique. Comments for the reviewer #2 and Response of the authors 1 Author should highlight the research gaps and contribution of the proposed work by comparing the state of the art methods and recent studies. First, we would like to thank the reviewer for this constructive comment. This comment helped us to improve the quality of the manuscript considerably. We addressed this comment in three section of the manuscript. Two additional paragraphs were added at the end of the introduction section and one paragraph was added to the discussion section. These new paragraphs make it clear why the method presented in this study is the superior one for assessing the medication error probability. All changes are highlighted in yellow. Lines 90-100: Uncertain and ambiguous data and opaque experts’ opinions are other issues in human reliability analysis (HRA). Such data and knowledge can make the results of HRA unreliable. Fuzzy logic theory is specialized for dealing with this issue (26). In contrast to the Boolean logic, the membership degree of a variable to a set can be any real value between zero and one in the Fuzzy environment. Imprecise and vague information obtained in the shape of linguistic terms from domain experts can be manipulated, processed, and represented mathematically using the Fuzzy sets theory and associated Fuzzy operators. Accordingly, Fuzzy logic has found many applications in HRA. For example, techniques such as CREAM, HEART, and SHERPA have been equipped with Fuzzy logic in different ways to reduce subjectivity and enhance the reliability of results (27-30). Consequently, it seems rational, useful, and even necessary to employ Fuzzy logic in developing any new HRA technique. Lines 103-107: • In this method, all factors supposed to affect medication errors are included, • The method is based on the principles of fuzzy logic which is the preferred approach in dealing with uncertain and ambiguous data, • The method is based on a well-accepted foundation methodology for human error analysis, • The method is specialized to assess the probability of medication errors. Lines 316-325: The method developed in this study can evaluate the combined effect of seventeen factors on medication error probability, making it more comprehensive than available HRA techniques. Some of these factors such as workload, availability of work procedures, and safety culture are general to any occupational setting, but some others such as patient safety climate, physical health of nurses, and error management culture are unique to healthcare environments. Taking into account these variables is an important advantage of this method over other available techniques. Moreover, the weights calculated in this study are specific to healthcare environments. For example, among organizational factors, patient safety climate had the highest weight, meaning that it is the most important organizational factor affecting the medication error probability. Such a factor with its relative importance has not been considered in any available HRA technique. 2 Better justify the selected Fuzzy multiple attributive group decision making (MAGDM) methodology and fuzzy analytical hierarchical process (FAHP). There are so many multiple attribute decision making methods. Why did you choose these two methods, based on what evidence? We would like to thank the reviewer for this constructive comment that helped us to improve the quality of the manuscript. We also agree with the reviewer in that there are many FMADM techniques and more justification is needed in the manuscript. Therefore, we added some additional information in the material and methods section. All changes are highlighted in yellow and are as follows: Line 152-164: The main advantage of this method is the use of fuzzy logic in collecting and integrating experts’ opinions. Fuzzy logic is especially helpful in dealing with vague and uncertain information [26]. Accordingly, most multi-criteria decision-making methods have been upgraded based on Fuzzy logic and Fuzzy sets theory [33]. Using Fuzzy logic, the opinions of the experts are gathered in the form of linguistic terms instead of numerical values. Apparently, expressing opinions in terms of linguistic terms is more compatible with the human information processing system and people are more comfortable to express their opinions in terms of linguistic terms instead of crisp values. Moreover, the methodology used in this study benefits from the similarity aggregation method (SAM) for integrating opinions from different experts. SAM is a well-accepted method for making a consensus among opinions gathered from different experts [34]. The method employs a coefficient, known as β, in the aggregation step that provides the analyst or whoever conducts HRA with more dominance and authority over the problem [32]. Moreover, Akyuz [25] used this approach for calculating the weight of PSFs in the SLIM equation. Lines 220-223: The reason behind using fuzzy AHP in this phase of the study was its simplicity and also encompassing the advantages of both AHP and Fuzzy logic (37, 38). Moreover, there were only three PSFs in this step, so the number of required pair-wise comparisons was rationally low. 3 In the introduction section, the literature review should be strengthened. Authors are recommended to include and review some studies regarding MAGDM and FAHP applications such as [doi.org/10.1371/journal.pone.0251940], [doi.org/10.1155/2016/3965608], [doi.org/10.1371/journal.pone.0239140] to improve the literature survey: We would like to thank the reviewer for this helpful comment. These references helped us to improve the theoretical basis of the study. 4 Authors have not presented the limitations of this work. How this work can be extended in practical applications? Please elaborate on it. We would like to thank the reviewer for this constructive comment that helped us to improve the quality of the paper. The limitations of the study was added at the end of the discussion section. The added paragraph as below: Lines 370-374 This study has several limitations which can be the subject of future studies. In this study, the interactions among factors affecting medication errors were not investigated. SLIM cannot capture the interrelationships among a set of PSFs, but other approaches such as Bayesian networks can do this. The weighting process should be repeated in other countries in order to make a comparison about the relative importance of factors affecting medication errors all around the world. 5 The italic types of symbols in equations and the main text should be in the same expression. We would like the reviewer for this constructive comment. All the expressions were made consistent thorough the manuscript. 6 Although the manuscript is well written in terms of English, there are some (very few, indeed) grammatical and expression errors. It is suggested to proofread the paper. We would like to thank the reviewer for this constructive comment. The manuscript was totally revised in this regard. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0264303.r002
9 Feb 2022 Decision Letter - Muhammad Junaid Farrukh, Editor A comprehensive method for the quantification of medication error probability based on fuzzy SLIM PONE-D-21-36423R1 Dear Dr. Zahra Pashootan We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Muhammad Junaid Farrukh Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0264303.r003
Formally Accepted
14 Feb 2022 Acceptance Letter - Muhammad Junaid Farrukh, Editor PONE-D-21-36423R1 A comprehensive method for the quantification of medication error probability based on fuzzy SLIM Dear Dr. Pashootan: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Muhammad Junaid Farrukh Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0264303.r004

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