We thank the French Society of Emergency Medicine for such a large study. However it is difficult to conclude regarding a number of limitations due to the observational cross-sectional study scheme.
The main critics concern the 3 measurements tools allowing to decide appropriateness of ED visits or not. Appropriate Use Score Method: the authors decide a cut-off based on Italian litterature data which is not French sounded; Possible GP Use Method : it would be more accurate to have an external evaluation instead of a judge and part one ; Resource Utilisation Method : the elephant in the room is the result of consultation and the final diagnostic as well.
I don’t discuss here the relevance of a number of items as distance between home and ED Departments, medical density and so far.
Specifically I doubt it is possible to interpret socioeconomic and territorial factors results without diagnostics and diagnostic frequency on June, 11, 2013
We are looking forward for more studies informing use of ED departments.
As a non-physician often working as a researcher with physicians, I didn't know whether to laugh or cry at the explanation in this article as to why certain specialties might get lower patient satisfaction scores than internists. Yes, it may well be that the episodic nature of the encounter with a non-chosen physician leads to lower satisfaction. But given the long history of jokes within medicine based on the personality type differences among specialties, and, one would think, given the authors' personal experiences in say, their last dermatology appointment, might not differences among doctors rather than patient issues be at least be presented as a hypothesis? The briefest of Googling finds a post in the KevinMD blog, directed towards physicians, on "remarkably distinct" cultures. (The blog is here: https://www.kevinmd.com/blog/2018/07/what-personality-type-fits-your-med.... One of the literature links is here: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2923.2011.04099.x)
I admire the question and rigor the researchers used in answering it. But, in the Discussion, simply assuming "differences in perception must be due to the patient" fails to meet the test of common sense.
We can only reiterate the points brought forward by Carl Macrae in “The problem with incident reporting”. In our own work with incident reporting systems in the UK and Austria we observe that an estimated 50% of incident reports are eliminated without further consideration due to their non-clinical nature, even if they may affect patient safety. The remaining clinical reports are then pigeonholed to fit existing medical categories of expertise for further investigation. Hence, current incident reporting practices do not truly reflect the systemic complexities of medical errors, which are composed of both clinical and non-clinical elements. Incident reporting systems in healthcare need to either use more stringent reporting criteria to exclude non-clinical incidents upfront (even if they affect patient safety), but this would mean cutting themselves short of opportunities for whole system improvement. Alternatively, non-clinical reports should be further investigated to determine their potential contribution to (un)safe practice. This is likely to require the inclusion of non-clinical, organisational experts in the analysis of incident reports.
We appreciate the concerns raised and agree that accurately presenting the findings of our study regarding patient-reported possible PICC-related complications is important. We took several steps to ensure transparency in how we presented our data. First, we were vigilant about consistently defining our outcomes as “possible complications” in all key areas of the paper including the main outcomes and conclusions section of the abstract, study measures section in the methods, main findings in the results section and in the discussion. The fact that the term “possible” or “potential” was not always used or some terms appeared to be used interchangeably was to improve readability of the article. In general, however, we were conscientious about clearly noting that we asked patients about signs or symptoms of a potential complication or adverse effect. Second, we purposefully chose to not present the data as complication rates (implying that these were actual events), but as the percentage of patients reporting a given event – thus we faithfully represented what we were told by patients during follow-up assessments. Third, we took these approaches because our primary objective was to accurately present the data collected from our patients; if something mattered enough to a patient to tell us about it, then it should matter to us as healthcare professionals regardless of internal standards that we may use to define serious medical complications. While that’s not to say that f...
We appreciate the concerns raised and agree that accurately presenting the findings of our study regarding patient-reported possible PICC-related complications is important. We took several steps to ensure transparency in how we presented our data. First, we were vigilant about consistently defining our outcomes as “possible complications” in all key areas of the paper including the main outcomes and conclusions section of the abstract, study measures section in the methods, main findings in the results section and in the discussion. The fact that the term “possible” or “potential” was not always used or some terms appeared to be used interchangeably was to improve readability of the article. In general, however, we were conscientious about clearly noting that we asked patients about signs or symptoms of a potential complication or adverse effect. Second, we purposefully chose to not present the data as complication rates (implying that these were actual events), but as the percentage of patients reporting a given event – thus we faithfully represented what we were told by patients during follow-up assessments. Third, we took these approaches because our primary objective was to accurately present the data collected from our patients; if something mattered enough to a patient to tell us about it, then it should matter to us as healthcare professionals regardless of internal standards that we may use to define serious medical complications. While that’s not to say that future work to better understand how patient reported outcomes relate to ascertainable or medically defined outcomes isn’t needed, our reporting was accurate and achieved the primary objective of this study - which was to highlight PICC-associated patient concerns and opportunities for care improvement.
"What is needed now is an understanding of what is the threshold of professional nurse staff and complementary staff needed to achieve optimal outcomes, and how are these levels influenced by patient nursing acuity and the education, experience, organisation and work environment of the nurse workforce." --- I agree with you. Further, this study may be approached by Park's Optimized Nurse Staffing (Sweet Spot) Estimation Theory (Park, 2017): https://onlinelibrary.wiley.com/doi/full/10.1111/jan.13284. I am doing the research now.
Krein, et al (Patient-Reported Complications Related to Peripherally Inserted Central Catheters: A Multicenter Prospective Cohort Study; Feb 2019) should be commended for sharing the results of this very interesting study. After reading it a few times, I am compelled to share the following concerns with you and the research team. The knowledge regarding PICC-related complications is indeed incomplete, but I am not sure if the main outcome(s) of your study are clearly represented to the reader.
The word “possible” is critical to correctly interpreting the results of this study. The term “complication” implies a medical diagnosis or medical confirmation – which your study attempted to do by conducting the chart reviews to confirm the presence or absence of a PICC complication, with limited success. Terms such as signs, symptoms, issues, adverse effects, and complications are used interchangeably throughout the paper to describe the patients’ self-reported experience, but without the benefit of operational definitions. These are not synonyms. Definitions help us to have a common understanding of a word or topic; they help us get on the same page when reading about an issue.
The word “possible” seems appropriate in the main outcome(s) statement, but is curiously missing from the report title. The phrase “medical complications” is used in the title of Table 2 – which clearly reports predominantly patient self-reported symptoms. The same bias is exhibited...
Krein, et al (Patient-Reported Complications Related to Peripherally Inserted Central Catheters: A Multicenter Prospective Cohort Study; Feb 2019) should be commended for sharing the results of this very interesting study. After reading it a few times, I am compelled to share the following concerns with you and the research team. The knowledge regarding PICC-related complications is indeed incomplete, but I am not sure if the main outcome(s) of your study are clearly represented to the reader.
The word “possible” is critical to correctly interpreting the results of this study. The term “complication” implies a medical diagnosis or medical confirmation – which your study attempted to do by conducting the chart reviews to confirm the presence or absence of a PICC complication, with limited success. Terms such as signs, symptoms, issues, adverse effects, and complications are used interchangeably throughout the paper to describe the patients’ self-reported experience, but without the benefit of operational definitions. These are not synonyms. Definitions help us to have a common understanding of a word or topic; they help us get on the same page when reading about an issue.
The word “possible” seems appropriate in the main outcome(s) statement, but is curiously missing from the report title. The phrase “medical complications” is used in the title of Table 2 – which clearly reports predominantly patient self-reported symptoms. The same bias is exhibited in the paragraph titled “Change in patient-reported complications over time”. The study limitations clearly note that the signs and symptoms reported could not be interpreted within the context of comorbidities, nor could the complications be confirmed without evidence in the medical record.
Study titles and terms will influence the readers – who in this case are clinicians and patients. It seems irresponsible to use the term “complication” without the qualifying word “possible” to accurately represent the findings and import of this study.
Krein et al have the best of intentions as they try to develop the body of knowledge around vascular access devices. However, an analysis of self-reported symptoms should not be represented as a complication rate.
O’Reilly-Shah et al. present a novel approach to quality improvement in anaesthesia by attempting to elicit change in practice using ‘nudge theory’ derived from the field of behavioural economics [1]. Translating new research evidence into common clinical practice is an important quality issue and cheap and effective strategies to achieve this are of interest. O’Reilly-Shah et al. hypothesised that using ‘nudge-type’ interventions, an audit-feedback dashboard as well as changes to mechanical ventilator default settings, might improve anaesthesia provider compliance with this ‘lung protective’ ventilation strategy in the general operating theatre environment.
I disagree with the conclusion that the authors have drawn, that these interventions might improve clinical and financial outcomes. My disagreement stems from the clinical rationale of the intervention, which overlaps onto the assumptions built into the ‘nudge theory’ of behavioural economics itself.
Sunstein & Thaler, who have influenced large scale policy making in several countries, describe nudge theory as a form of choice architecture that “alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives” [2, 3]. Their philosophy is described as “libertarian paternalism”, as it influences choices that make people better off as judged by themselves, while preserving their freedom to choose otherwise. This is presumably what the interve...
O’Reilly-Shah et al. present a novel approach to quality improvement in anaesthesia by attempting to elicit change in practice using ‘nudge theory’ derived from the field of behavioural economics [1]. Translating new research evidence into common clinical practice is an important quality issue and cheap and effective strategies to achieve this are of interest. O’Reilly-Shah et al. hypothesised that using ‘nudge-type’ interventions, an audit-feedback dashboard as well as changes to mechanical ventilator default settings, might improve anaesthesia provider compliance with this ‘lung protective’ ventilation strategy in the general operating theatre environment.
I disagree with the conclusion that the authors have drawn, that these interventions might improve clinical and financial outcomes. My disagreement stems from the clinical rationale of the intervention, which overlaps onto the assumptions built into the ‘nudge theory’ of behavioural economics itself.
Sunstein & Thaler, who have influenced large scale policy making in several countries, describe nudge theory as a form of choice architecture that “alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives” [2, 3]. Their philosophy is described as “libertarian paternalism”, as it influences choices that make people better off as judged by themselves, while preserving their freedom to choose otherwise. This is presumably what the interventions chosen by O’Reilly-Shah et al. were based on, that changing ventilator defaults did not force anaesthetists to select a certain tidal volume, and audit-feedback dashboards might foster change through an increase in awareness of peer group behaviour.
Assessing preferences and costs may not be straightforward, as described in a critique of nudge theory by Berg & Davidson [3]. They argue that many choice decisions implicitly have a trade-off, and it is difficult to predict in advance which direction the balance tips. Furthermore, selecting a particular tidal volume (a continuous variable) for a particular patient during an operation might be a complex decision and not a binary option, which makes the cost or indeed the opportunity cost, of a particular choice difficult to ascertain, in economic parlance.
A study much vaunted by the New York Times Magazine demonstrating improvement in hand hygiene rates amongst clinicians after the introduction of screen savers depicting bacterial cultures from physicians’ hands, failed to be replicated in another study using a similar methodology, which suggests that audit-feedback dashboard techniques may not be robust [4, 5]. O’Reilly-Shah et al. state that most practitioners accepted the target ventilation strategy without debate but they also admit that there is equipoise in the anaesthetic literature regarding the benefit of a low-tidal volume strategy for elective surgical patients [1]. As such, the increased compliance rate might not have reflected the provider’s ‘better preference’ per se, but a trend towards the default lower tidal volume as it may not have been an important factor either way.
A better example using the authors’ approach might be the default setting of ‘pressure-controlled’ ventilation as opposed to ‘volume-controlled’ ventilation in paediatric patients undergoing general anaesthesia, to avoid the danger of delivering overtly dangerous large tidal volumes. This is sensible practice in paediatric, but in hospitals with mixed adult and paediatric populations, this may not always be appropriate. It would then represent a binary decision and the benefit would be more obvious.
References
1. O'Reilly-Shah VN, Easton GS, Jabaley CS, Lynde GC. Variable effectiveness of stepwise implementation of nudge-type interventions to improveprovider compliance with intraoperative low tidal volume ventilation. BMJ Qual Saf. 2018 May 18
2. Thaler RH, Sunstein CR. Nudge: Improving Decisions About Health, Wealth, And Happiness. New Haven : Yale University Press, 2008, p5-6.
3. Berg C, Davidson S. Nudging, calculation, and utopia, Journal of Behavioral Economics for Policy, Vol. 1, Special Issue, 49-52, 2017
4. Dubner SJ, Levitt SD. Selling soap. The New York Times Magazine, September 24, 2006.
5. Charters M, Cheng AL, Esaki RK, Kuo AS, Neal J, Thawani JP, Chenoweth C. The impact of a screensaver in promoting hand hygiene. Am J Infect Control. 2009 Dec;37(10):867-8
Conventional statistical process control (SPC) has limitations when used with hospital averse event (AE) data. Much data, especially hospital infections like bacteraemias, arise in complex systems.1 These differ from the simple or complicated industrial systems that produce data that are analysed with such success with conventional SPC. AE data arising in complex systems are often nonlinear. Expected values are often unknown. There is often delay in obtaining the AE data e.g. with bacteraemia data – the patient has symptoms, a blood sample is obtained, it is sent to pathology for culture, analysis and reporting and is finally placed in a suitable database then analysed (one of the benefits of conventional SPC is in providing rapid feedback so an industrial process that is going out of control is promptly identified). Most hospital AEs are relatively uncommon and alert staff such as those in Infection Management will frequently detect a change well before a statistical analysis. However, analysis using a time-series chart is still desirable. It can add confirmation to the observations of Infection Management and Quality Improvement staff. A hospital department can summarise its performance with a chart. Management and the public can be informed. A problem is devising control limits about an often non-existent expected value using a linear mean value that may be atypical of much of the data.
How may this dilemma be overcome? The often changing predicted mean value can...
Conventional statistical process control (SPC) has limitations when used with hospital averse event (AE) data. Much data, especially hospital infections like bacteraemias, arise in complex systems.1 These differ from the simple or complicated industrial systems that produce data that are analysed with such success with conventional SPC. AE data arising in complex systems are often nonlinear. Expected values are often unknown. There is often delay in obtaining the AE data e.g. with bacteraemia data – the patient has symptoms, a blood sample is obtained, it is sent to pathology for culture, analysis and reporting and is finally placed in a suitable database then analysed (one of the benefits of conventional SPC is in providing rapid feedback so an industrial process that is going out of control is promptly identified). Most hospital AEs are relatively uncommon and alert staff such as those in Infection Management will frequently detect a change well before a statistical analysis. However, analysis using a time-series chart is still desirable. It can add confirmation to the observations of Infection Management and Quality Improvement staff. A hospital department can summarise its performance with a chart. Management and the public can be informed. A problem is devising control limits about an often non-existent expected value using a linear mean value that may be atypical of much of the data.
How may this dilemma be overcome? The often changing predicted mean value can be shown well using a generalised additive model (GAM). Its confidence limits can be useful as they delimit the range of the predicted mean supported by the GAM analysis. Trends and change-points may be identified. However, this fails to address potentially outlying individual observations such as large monthly counts and rates. It is suggested that the supported ranges identified by the confidence intervals about the individual monthly values can help identify outliers. Finally, a seasonal trend test can help identify the trends and change-points that frequently accompany determined efforts to improve or that complicate a deteriorating process. Admittedly, these observations apply chiefly to rate data such as bacteraemias and new isolates of antibiotic-resistant organisms. However, there are also binary data to which they apply.
Reference
1. Morton, A., Whitby, M., Tierney, N., Sibanda, N. and Mengersen, K. 2016. Statistical Methods for Hospital Monitoring. Wiley StatsRef: Statistics Reference Online. 1–8.
Hemming et al. (“Ethical Implications of Excessive Cluster Sizes in Cluster Randomized Trials,” 20 February 2018) cite the FIRST Trial as an example of a “higher risk” cluster-randomized trial in which large cluster sizes pose unjustifiable excess risk. The authors state, “[t]he obvious way to reduce the cluster size in this study is to reduce the duration of the trial…”
We believe this to be an inappropriate recommendation stemming from an inaccurate appraisal of the FIRST Trial.
The FIRST Trial was designed to inform a potential policy change in U.S. resident duty hours. In the Statistical Analysis Plan, which was made available at www.nejm.org, we clearly and prospectively stated that “[t]his study is a trial-based evaluation of potential policy effects on patient safety and resident wellbeing... this study is intended to inform real-world policy decision-making with respect to resident duty hours regulation.”[1] The SAP and Supplemental Appendix (www.nejm.org) also provides all assumptions for our power calculations and cluster sizes, which were not large in the case of resident outcomes.[2]
As such, it was important that the trial closely resemble real-world conditions in which residency training occurs and duty hour policies are implemented. A shorter study would pose risks owing to non-standard, multiple policy shifts and would increase administrative/organizational bu...
Hemming et al. (“Ethical Implications of Excessive Cluster Sizes in Cluster Randomized Trials,” 20 February 2018) cite the FIRST Trial as an example of a “higher risk” cluster-randomized trial in which large cluster sizes pose unjustifiable excess risk. The authors state, “[t]he obvious way to reduce the cluster size in this study is to reduce the duration of the trial…”
We believe this to be an inappropriate recommendation stemming from an inaccurate appraisal of the FIRST Trial.
The FIRST Trial was designed to inform a potential policy change in U.S. resident duty hours. In the Statistical Analysis Plan, which was made available at www.nejm.org, we clearly and prospectively stated that “[t]his study is a trial-based evaluation of potential policy effects on patient safety and resident wellbeing... this study is intended to inform real-world policy decision-making with respect to resident duty hours regulation.”[1] The SAP and Supplemental Appendix (www.nejm.org) also provides all assumptions for our power calculations and cluster sizes, which were not large in the case of resident outcomes.[2]
As such, it was important that the trial closely resemble real-world conditions in which residency training occurs and duty hour policies are implemented. A shorter study would pose risks owing to non-standard, multiple policy shifts and would increase administrative/organizational burdens of staff workflow coordination. Moreover, a shorter period would threaten external validity, rendering evidence from the study potentially uninformative for policy decisions that could affect the lives of millions. Thus, a shorter study would be of little social benefit while imposing unjustifiable risks and burden. A planned interim analysis was independently reviewed by a Data Safety Monitoring Board out of consideration for patient safety, but termination was unwarranted.[3]
If the policy intervention is ethically unacceptable, the study should not be done under any time frame. Yet, the debate over U.S. resident duty hours is marked by equipoise: it is not at all clear that flexible duty hours pose greater risk than duty hour regulations that interrupt critical patient care (e.g., leaving during an operation) or lead to more patient care hand offs.[4] Considerable literature demonstrates adverse effects on patient outcomes of excessive care transitions [5,6] along with residents’ concerns that training may suffer with reductions in duty hours.[7-10]
Hemming et al. are well-intentioned in articulating ethical concerns arising from gratuitously large clusters. However, in trials designed to inform policy, the importance of external validity may justify ostensibly “large” cluster sizes.
Respectfully yours,
Karl Y. Bilimoria, M.D., M.S.
Jeanette W. Chung, Ph.D.
Larry V. Hedges, Ph.D.
[4] Philibert I, Nasca T, Brigham T, Shapiro J. Duty-hour limits and patient care and resident outcomes: can high-quality studies offer insight into complex relationships? Ann Rev Med. 2013;64:467-483
[5] Denson JL, Jensen A, Saag HS, et al. Association between end-of-rotation resident transition in care and mortality among hospitalized patients. JAMA. 2016;316(21):2204-2213
[6] Denson JL, McCarty M, Fang Y, et al. Increased mortality rates during resident handoff periods and the effect of ACGME duty hour regulations. Am J Med. 2015;128(9):994-1000
[7] Ahmed N, Devitt KS, Kshet I, et al. A systematic review of the effects of resident duty hour restrictions in surgery: impact on resident wellness, training, and patient outcomes. Ann Surg. 2014;259(6):1041-1053
[8] Drolet BC, Spalluto LB, Fischer SB. Residents’ perspectives on ACGME regulation of supervision and duty horus – a national survey. N Engl J Med. 2010;363:e34
[9] Drolet BC, Christopher DA, Fischer SA. Residents’ response to duty-hour regulations – a follow-up national survey. N Engl J Med. 2012;366:e35
[10] Fargen KM, Chakraborty A, Friedman WA. Results of a national neurosurgery resident survey on duty hour regulations. Neurosurgery. 2011;69(6):1162-1170
[This is a revision of a submission from earlier today that contains references.]
To the editor:
We read Weenink, et al.’s review of remediation and rehabilitation programs for healthcare professionals with interest.1 It is among the most systematic and certainly the most internationally focused reviews to date. The article noted, “the aim of these programs is two-fold: to help professionals with problems and to protect patients from professionals who are unable to perform adequately.” This important point is in direct alignment with the Federation of State Physician Health Program’s (FSPHP) philosophy of supporting our member programs in their mission of early detection of potentially impairing illness. As members of the leadership of the Federation of State Physician Health Programs (FSPHP), we laud this review and believe additional commentary is worthwhile.
In the U.S. and Canada, each Physician Health Program (PHP) is unique in its scope of services, funding and the types of healthcare professionals served.2 In the U.S., we trace our roots back to a seminal paper that appeared in the Journal of the American Medical Association (AMA) in 1973.3 As Weenink, et al. noted, all programs provide services for professionals with substance use disorders and other mental health conditions. PHPs do not provide treatment, rather, we provide care coordination and monitoring for health professionals with impairing illness. The FSPHP brings together PHPs in the U....
[This is a revision of a submission from earlier today that contains references.]
To the editor:
We read Weenink, et al.’s review of remediation and rehabilitation programs for healthcare professionals with interest.1 It is among the most systematic and certainly the most internationally focused reviews to date. The article noted, “the aim of these programs is two-fold: to help professionals with problems and to protect patients from professionals who are unable to perform adequately.” This important point is in direct alignment with the Federation of State Physician Health Program’s (FSPHP) philosophy of supporting our member programs in their mission of early detection of potentially impairing illness. As members of the leadership of the Federation of State Physician Health Programs (FSPHP), we laud this review and believe additional commentary is worthwhile.
In the U.S. and Canada, each Physician Health Program (PHP) is unique in its scope of services, funding and the types of healthcare professionals served.2 In the U.S., we trace our roots back to a seminal paper that appeared in the Journal of the American Medical Association (AMA) in 1973.3 As Weenink, et al. noted, all programs provide services for professionals with substance use disorders and other mental health conditions. PHPs do not provide treatment, rather, we provide care coordination and monitoring for health professionals with impairing illness. The FSPHP brings together PHPs in the U.S. and Canada. We are working to expand and improve services we provide to healthcare workers.
As noted, multiple studies confirm the PHP model’s remarkable remission rates from substance use disorders. The combination of sufficient initial treatment, often amongst a cohort of peers, with chronic disease management (comprised of frequent random substance screening, ongoing support group attendance and needed adjuvant pharmacotherapy, psychotherapy and psychiatric care) results in a five-year remission rate of approximately 80 percent. Remarkably this chronic disease management model shows exactly the same efficacy in opioid use disorders as other substance problems.4 This stands in sharp contrast to the suboptimal outcomes seen in the general population as we struggle to find solutions to the international problem of opioid use disorders. The outcomes achieved by PHPs have led to assertions this model of treatment should be considered for all addiction disorders, including opioid use disorders.5
Outcomes for other behavioral health conditions have been less clear, with few papers addressing outcomes in this area.6 We believe this is partly due to the lack of a uniform systematic disease management model for treating depressive illnesses, stress and burnout among physicians and other healthcare workers. The FSPHP is the North American forum of evolving concepts in in the management of chronic mental health conditions.
PHPs in the U.S. and Canada are less directly involved with medical skill dyscompetence per se, except in cases where the dyscompetence arises from, or is exacerbated by, interpersonal difficulties, behavioral or medical illness. In North America, such problems are more often addressed through credentialing bodies and medical boards (US) and colleges (Canada). A network of evaluators assess physician competency and assist PHPs, medical boards and medical colleges in determining individual needs and remediation strategies.
Maintaining a healthy and competent healthcare workforce requires a collaborative effort by organized medicine inclusive of the multiple stakeholders that oversee and/or represent health professionals. We see Weenink, et al.’s fine paper as a call to action to do more. The FSPHP is eager to answer this call by sharing our efforts collaboratively with our international partners to enhance, expand and explore current and future pathways to a safe healthcare workforce around the world. Surely, we owe all those we serve in the day to day practice of medicine the “globalization” of effective models of remediation and rehabilitation for healthcare professionals.
Paul H. Earley, M.D., DFASAM
Brad Hall, M.D., DABAM, DFASAM, MROCC, AAMRO
Chris Bundy, MD, MPH
References
1. J.-W. Weenink, R. B. Kool, R. H. Bartels, and G. P. Westert. "Getting Back on Track: A Systematic Review of the Outcomes of Remediation and Rehabilitation Programmes for Healthcare Professionals with Performance Concerns." British Medical Journal Quality & Safety 26, no. 12 (2017): 1004.
2. R. DuPont, A. McLellan, G. Carr, M. Gendel, and G. Skipper. "How Are Addicted Physicians Treated? A National Survey of Physician Health Programs." J Subst Abuse Treat 37, no. 1 (2009): 1-7.
3. AMA Council on Mental Health. "The Sick Physician: Impairment by Psychiatric Disorders, Including Alcoholism and Drug Dependence." JAMA 223, no. 6 (1973): 684-87.
4. K. Domino, T. F. Hornbein, N. L. Polissar, G. Renner, J. Johnson, S. Alberti, and L. Hankes. "Risk Factors for Relapse in Health Care Professionals with Substance Use Disorders." JAMA 293, no. 12 (2005): 1453-60.
5. R. L. DuPont, W. M. Compton, and A. T. McLellan. "Five-Year Recovery: A New Standard for Assessing Effectiveness of Substance Use Disorder Treatment." J Subst Abuse Treat 58 (2015): 1-5.
6. J. R. Knight, L. T. Sanchez, L. Sherritt, L. R. Bresnahan, and J. A. Fromson. "Outcomes of a Monitoring Program for Physicians with Mental and Behavioral Health Problems." J Psychiatr Pract 13, no. 1 (2007): 25-32.
We thank the French Society of Emergency Medicine for such a large study. However it is difficult to conclude regarding a number of limitations due to the observational cross-sectional study scheme.
The main critics concern the 3 measurements tools allowing to decide appropriateness of ED visits or not. Appropriate Use Score Method: the authors decide a cut-off based on Italian litterature data which is not French sounded; Possible GP Use Method : it would be more accurate to have an external evaluation instead of a judge and part one ; Resource Utilisation Method : the elephant in the room is the result of consultation and the final diagnostic as well.
I don’t discuss here the relevance of a number of items as distance between home and ED Departments, medical density and so far.
Specifically I doubt it is possible to interpret socioeconomic and territorial factors results without diagnostics and diagnostic frequency on June, 11, 2013
We are looking forward for more studies informing use of ED departments.
As a non-physician often working as a researcher with physicians, I didn't know whether to laugh or cry at the explanation in this article as to why certain specialties might get lower patient satisfaction scores than internists. Yes, it may well be that the episodic nature of the encounter with a non-chosen physician leads to lower satisfaction. But given the long history of jokes within medicine based on the personality type differences among specialties, and, one would think, given the authors' personal experiences in say, their last dermatology appointment, might not differences among doctors rather than patient issues be at least be presented as a hypothesis? The briefest of Googling finds a post in the KevinMD blog, directed towards physicians, on "remarkably distinct" cultures. (The blog is here: https://www.kevinmd.com/blog/2018/07/what-personality-type-fits-your-med.... One of the literature links is here: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2923.2011.04099.x)
I admire the question and rigor the researchers used in answering it. But, in the Discussion, simply assuming "differences in perception must be due to the patient" fails to meet the test of common sense.
We can only reiterate the points brought forward by Carl Macrae in “The problem with incident reporting”. In our own work with incident reporting systems in the UK and Austria we observe that an estimated 50% of incident reports are eliminated without further consideration due to their non-clinical nature, even if they may affect patient safety. The remaining clinical reports are then pigeonholed to fit existing medical categories of expertise for further investigation. Hence, current incident reporting practices do not truly reflect the systemic complexities of medical errors, which are composed of both clinical and non-clinical elements. Incident reporting systems in healthcare need to either use more stringent reporting criteria to exclude non-clinical incidents upfront (even if they affect patient safety), but this would mean cutting themselves short of opportunities for whole system improvement. Alternatively, non-clinical reports should be further investigated to determine their potential contribution to (un)safe practice. This is likely to require the inclusion of non-clinical, organisational experts in the analysis of incident reports.
We appreciate the concerns raised and agree that accurately presenting the findings of our study regarding patient-reported possible PICC-related complications is important. We took several steps to ensure transparency in how we presented our data. First, we were vigilant about consistently defining our outcomes as “possible complications” in all key areas of the paper including the main outcomes and conclusions section of the abstract, study measures section in the methods, main findings in the results section and in the discussion. The fact that the term “possible” or “potential” was not always used or some terms appeared to be used interchangeably was to improve readability of the article. In general, however, we were conscientious about clearly noting that we asked patients about signs or symptoms of a potential complication or adverse effect. Second, we purposefully chose to not present the data as complication rates (implying that these were actual events), but as the percentage of patients reporting a given event – thus we faithfully represented what we were told by patients during follow-up assessments. Third, we took these approaches because our primary objective was to accurately present the data collected from our patients; if something mattered enough to a patient to tell us about it, then it should matter to us as healthcare professionals regardless of internal standards that we may use to define serious medical complications. While that’s not to say that f...
Show More"What is needed now is an understanding of what is the threshold of professional nurse staff and complementary staff needed to achieve optimal outcomes, and how are these levels influenced by patient nursing acuity and the education, experience, organisation and work environment of the nurse workforce." --- I agree with you. Further, this study may be approached by Park's Optimized Nurse Staffing (Sweet Spot) Estimation Theory (Park, 2017): https://onlinelibrary.wiley.com/doi/full/10.1111/jan.13284. I am doing the research now.
Krein, et al (Patient-Reported Complications Related to Peripherally Inserted Central Catheters: A Multicenter Prospective Cohort Study; Feb 2019) should be commended for sharing the results of this very interesting study. After reading it a few times, I am compelled to share the following concerns with you and the research team. The knowledge regarding PICC-related complications is indeed incomplete, but I am not sure if the main outcome(s) of your study are clearly represented to the reader.
The word “possible” is critical to correctly interpreting the results of this study. The term “complication” implies a medical diagnosis or medical confirmation – which your study attempted to do by conducting the chart reviews to confirm the presence or absence of a PICC complication, with limited success. Terms such as signs, symptoms, issues, adverse effects, and complications are used interchangeably throughout the paper to describe the patients’ self-reported experience, but without the benefit of operational definitions. These are not synonyms. Definitions help us to have a common understanding of a word or topic; they help us get on the same page when reading about an issue.
The word “possible” seems appropriate in the main outcome(s) statement, but is curiously missing from the report title. The phrase “medical complications” is used in the title of Table 2 – which clearly reports predominantly patient self-reported symptoms. The same bias is exhibited...
Show MoreO’Reilly-Shah et al. present a novel approach to quality improvement in anaesthesia by attempting to elicit change in practice using ‘nudge theory’ derived from the field of behavioural economics [1]. Translating new research evidence into common clinical practice is an important quality issue and cheap and effective strategies to achieve this are of interest. O’Reilly-Shah et al. hypothesised that using ‘nudge-type’ interventions, an audit-feedback dashboard as well as changes to mechanical ventilator default settings, might improve anaesthesia provider compliance with this ‘lung protective’ ventilation strategy in the general operating theatre environment.
Show MoreI disagree with the conclusion that the authors have drawn, that these interventions might improve clinical and financial outcomes. My disagreement stems from the clinical rationale of the intervention, which overlaps onto the assumptions built into the ‘nudge theory’ of behavioural economics itself.
Sunstein & Thaler, who have influenced large scale policy making in several countries, describe nudge theory as a form of choice architecture that “alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives” [2, 3]. Their philosophy is described as “libertarian paternalism”, as it influences choices that make people better off as judged by themselves, while preserving their freedom to choose otherwise. This is presumably what the interve...
Conventional statistical process control (SPC) has limitations when used with hospital averse event (AE) data. Much data, especially hospital infections like bacteraemias, arise in complex systems.1 These differ from the simple or complicated industrial systems that produce data that are analysed with such success with conventional SPC. AE data arising in complex systems are often nonlinear. Expected values are often unknown. There is often delay in obtaining the AE data e.g. with bacteraemia data – the patient has symptoms, a blood sample is obtained, it is sent to pathology for culture, analysis and reporting and is finally placed in a suitable database then analysed (one of the benefits of conventional SPC is in providing rapid feedback so an industrial process that is going out of control is promptly identified). Most hospital AEs are relatively uncommon and alert staff such as those in Infection Management will frequently detect a change well before a statistical analysis. However, analysis using a time-series chart is still desirable. It can add confirmation to the observations of Infection Management and Quality Improvement staff. A hospital department can summarise its performance with a chart. Management and the public can be informed. A problem is devising control limits about an often non-existent expected value using a linear mean value that may be atypical of much of the data.
How may this dilemma be overcome? The often changing predicted mean value can...
Show MoreHemming et al. (“Ethical Implications of Excessive Cluster Sizes in Cluster Randomized Trials,” 20 February 2018) cite the FIRST Trial as an example of a “higher risk” cluster-randomized trial in which large cluster sizes pose unjustifiable excess risk. The authors state, “[t]he obvious way to reduce the cluster size in this study is to reduce the duration of the trial…”
We believe this to be an inappropriate recommendation stemming from an inaccurate appraisal of the FIRST Trial.
The FIRST Trial was designed to inform a potential policy change in U.S. resident duty hours. In the Statistical Analysis Plan, which was made available at www.nejm.org, we clearly and prospectively stated that “[t]his study is a trial-based evaluation of potential policy effects on patient safety and resident wellbeing... this study is intended to inform real-world policy decision-making with respect to resident duty hours regulation.”[1] The SAP and Supplemental Appendix (www.nejm.org) also provides all assumptions for our power calculations and cluster sizes, which were not large in the case of resident outcomes.[2]
As such, it was important that the trial closely resemble real-world conditions in which residency training occurs and duty hour policies are implemented. A shorter study would pose risks owing to non-standard, multiple policy shifts and would increase administrative/organizational bu...
Show More[This is a revision of a submission from earlier today that contains references.]
To the editor:
Show MoreWe read Weenink, et al.’s review of remediation and rehabilitation programs for healthcare professionals with interest.1 It is among the most systematic and certainly the most internationally focused reviews to date. The article noted, “the aim of these programs is two-fold: to help professionals with problems and to protect patients from professionals who are unable to perform adequately.” This important point is in direct alignment with the Federation of State Physician Health Program’s (FSPHP) philosophy of supporting our member programs in their mission of early detection of potentially impairing illness. As members of the leadership of the Federation of State Physician Health Programs (FSPHP), we laud this review and believe additional commentary is worthwhile.
In the U.S. and Canada, each Physician Health Program (PHP) is unique in its scope of services, funding and the types of healthcare professionals served.2 In the U.S., we trace our roots back to a seminal paper that appeared in the Journal of the American Medical Association (AMA) in 1973.3 As Weenink, et al. noted, all programs provide services for professionals with substance use disorders and other mental health conditions. PHPs do not provide treatment, rather, we provide care coordination and monitoring for health professionals with impairing illness. The FSPHP brings together PHPs in the U....
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