Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis
BMJ 2019; 366 doi: https://doi.org/10.1136/bmj.l4185 (Published 17 July 2019) Cite this as: BMJ 2019;366:l4185Linked editorial
Preventable harm: getting the measure right
All rapid responses
Rapid responses are electronic comments to the editor. They enable our users to debate issues raised in articles published on bmj.com. A rapid response is first posted online. If you need the URL (web address) of an individual response, simply click on the response headline and copy the URL from the browser window. A proportion of responses will, after editing, be published online and in the print journal as letters, which are indexed in PubMed. Rapid responses are not indexed in PubMed and they are not journal articles. The BMJ reserves the right to remove responses which are being wilfully misrepresented as published articles or when it is brought to our attention that a response spreads misinformation.
From March 2022, the word limit for rapid responses will be 600 words not including references and author details. We will no longer post responses that exceed this limit.
The word limit for letters selected from posted responses remains 300 words.
Dear Editors,
Panagioti et al have done a detailed analysis and have presented interesting findings. It is extremely important that healthcare associated harms are recognized and reduced to the minimum possible.
We would like to draw readers' attention to an under-recognized, under-reported and under-treated challenge which has not been documented enough in the scientific literature and hence has not been appropriately represented in various databases.
Prevalence estimates report older hospitalized patients can spend anything up to 95% of their time in bed or chair, during their hospitalization. The physiological effects of bed rest start to take place with-in the first 24 hours whilst some patients may still be in a trolley in the emergency department. Interventions such as intravenous infusion, catheterisation, bedrails, naso-gastric tube, etc. can further reduce mobility and progressively worsen the physiological deterioration. Data from the Acute Frailty Network suggests that impact of bed rest in first 24 hours includes reduced muscle power by 2-5% and reduced circulatory volume by up to 5%. Impact in the first 7 days includes reduced circulatory volume by up to 25%, reduced VO2 max by upto 8-15%, reduced muscle strength by up to 5-10%, reduced Functional Residual Capacity (FRC) by up to 15-30%, reduced skin integrity, reduced dignity, self-confidence, independence, choice and quality.
There is thus substantial evidence that prolonged bed rest in older people can be harmful.
Additional under-reported harms could include increased incidence of delirium, immobility, incontinence, loss of self-esteem, poorly planned discharge, delayed care transitions/co-ordination/communication, leading to premature decisions about future care needs in the wrong setting and worse outcomes, and premature institutionalisation.
It is in this context that the term ‘deconditioning syndrome’ has been increasingly used and understood in recent years. We define ‘deconditioning syndrome’ as the syndrome of physical, psychological and functional decline that occurs as a result of prolonged bed rest and associated loss of muscle strength, commonly experienced through hospitalisation. Though it can affect people of any age, the effect on older people can be more rapid, severe, and can often be irreversible.
The Oxford dictionary defines the term deconditioning as 'Cause to lose fitness or muscle tone, especially through lack of exercise’ and ‘sedentary lifestyles that decondition their bodies’. (1)
There is also enough evidence that activity and exercise help in recovery and therefore can contribute to reduced length of stay in hospitals and improve fitness therefore potentially impacting on self-care, independence and care needs. (2) The evidence around strategies for mass implementation has been relatively less. The current work by the authors provide some of this much required evidence.
The ‘National Deconditioning Awareness and Prevention Campaign (UK) -Get Up, Get Dressed Get Moving’ (3) and #EndPJParalysis (4) have generated widespread support nationally (5,6) and internationally as an approach to generate awareness and understanding and to prevent the detrimental effects caused by deconditioning in hospitals, care homes and those living alone. Whilst understanding the pitfalls and qualms about use of social media, the huge popularity of endpjparalysis on social media (and beyond) for this work has demonstrated how this concept of getting patients dressed and moving connects usefully with healthcare staff and patients/families alike.
What can be done to prevent deconditioning at scale?
Methods to prevent deconditioning could involve multiple approaches. These could include creating individualized care plans for activity and exercise based on individuals’ abilities, group exercise and self-care tasks, deconditioning ‘care bundles’, incorporating deconditioning in all root cause analysis (aka DATIX), prescribing personalized exercises depending upon individuals’ abilities, educating staff and public, busting myths and highlighting facts; common dining and activities. Some of these have been tried with varying successes in various settings but equally one needs to be careful of not under-mining those who may feel less able to participate than others at a certain time in their illness.
To further raise awareness, it is essential that education on physical activity, exercise and deconditioning syndrome is implemented into all health care professionals’ curricula, and supported by charities and patient groups. A move to include the importance of physical activity is currently under way with all UK medical, nursing and pharmacy schools. It remains to be seen how the prevention of deconditioning work may be better integrated nationally and despite staffing shortages, whether adequate training and deconditioning awareness will be enough to tackle this important challenge.
With a rapidly shifting population demographic deconditioning syndrome must be addressed more robustly across the board. It has significant implications not least on quality of life, dignity and mortality but also in the number of occupied hospital beds and in reducing health care associated unintended harms. All groups of staff--namely, receptionists, therapists, porters, healthcare assistants, nurses, therapists, doctors and others--have an important role in patient care and therefore in preventing deconditioning. The current challenge lies in developing and implementing effective strategies to prevent deconditioning in hospitals and care homes. Reporting and measuring such harm will be the first step in this journey. Older people deserve no less.
Ref:
1. Oxford Dictionaries. Oxford Dictionary of English. 7th Revised Edition. Oxford. Oxford University Press. 2012
2. Getting hospital patients up and moving shortens stay and improves fitness. Published on 16 April 2019. doi: 10.3310/signal-000759
3. Arora A. Blog – Time to move: Get up, get dressed and keep moving. [Internet] 2017 [Cited 2017 November 22] Available from: https://www.england.nhs.uk/blog/amit-arora/
4. O’Hanlan S. Editorial – British Geriatrics Society. [Internet] 2017 [Cited 2017 November 22] Available from: http://www.bgs.org.uk/nursepublications/newsletter/jun17-news/jun17-edit...
5. https://www.economist.com/britain/2018/06/14/why-britains-hospitals-are-... (accessed 18.08.2019)
6. Jane Cummings. Blog- We should all support #EndPJparalysis [Internet] 2017 [Cited 2017 February 23] Available from: https://www.england.nhs.uk/blog/jane-cummings-32/
Competing interests: Working with colleagues, I created the 'National Deconditioning Awareness and Prevention Campaign: Get Up Get Dressed Get Moving' and work closely with endPJparalysis campaign to generate awareness about harms associated with inappropriately prolonged bed rest in NHS. The work is supported by NHS England/NHS Improvement and British Geriatrics Society and has been shortlisted for a BMJ award previously. There are no financial or pharmaceutical gains or payments. The resources produced are freely available on internet for patient benefit.
The systematic review and meta-analysis by Panagioti et al. (1) on preventable patient harm raise important, general issues concerning how to promote resilient health care organization and to preserve public trust in health care. Preventable harm by diagnosis was defined as “Missed, wrong, delayed or inappropriate diagnostic incidents resulting from failure to capture documented signs, symptoms, and laboratory tests, not ordering and indicated diagnostic test or not undertaking adequate patient assessment.” (eTable 1). Among the 66 studies included in their review, diagnostic errors contributed to 16% of preventable harm. A Medline search for “diagnostic errors” had 38,362 hits. Although the diagnosis of child maltreatment was not part of this comprehensive review, diagnostic precision in detection of child maltreatment is of the utmost importance to avoid false positive as well as false negative diagnoses, as in both cases the consequences can be most severe.
We performed a Medline search for “diagnostic errors child maltreatment” and found 381 hits in the period 1975-2019. Of relevance were 147 contributions representing observational hospital studies, comments, letters, and case reports. These addressed correct diagnosis by discussing false negatives and false positives where medical conditions had been mistaken for abuse and how to improve the diagnostic procedure by medical technology such as imaging. However, there is no gold standard to diagnose child maltreatment, especially infant physical abuse; it is still an area of evolving knowledge.
Population register studies addressing infant maltreatment diagnosis might give clues how to improve patient safety, both when a correct diagnosis can be lifesaving, but also for parents seeking health care when maltreatment is not the case. We have demonstrated that subdural haemorrhage, skull fracture, retinal haemorrhage, fractures of rib and long bones are all strongly correlated with infant abuse diagnosis, and the use of this diagnosis shows a substantial increase over time with pronounced regional disparities (2), and an increase in babies removed from their parents to out-of-home care (3). On the other hand, on a population level, a minor proportion of those having a subdural haemorrhage had a diagnosis of infant abuse, most were associated to short falls, male sex, and also to prematurity, multiple births, and small-for gestational age (4). On a population level we have further shown that rib or long bone fractures during infancy are strongly associated with metabolic bone disease (5). However, specificity and sensitivity of medical markers for a correct maltreatment diagnosis cannot be addressed by population register studies.
The need to build an evidence based diagnostic work-up for infant maltreatment addressed by the systematic review of the Swedish agency for health technology assessment and assessment of social services concluded that “There is insufficient scientific evidence on which to assess the diagnostic accuracy of the triad in identifying traumatic shaking (very low quality evidence).”
(6).
We do agree with the conclusion of Panagioti et al., “Improving the assessment and reporting standards of preventability in future studies is critical for reducing patient harm in medical care settings”. Clinical studies on the diagnosis of infant abuse, derived from updated knowledge, are warranted and overdue.
Ulf Högberg, MD, PhD, Department of Women’s and Children’s Health, Uppsala University, Sweden
Waney Squier, FRCP, FRCPath, Formerly Department of Neuropathology, Oxford University John Radcliffe Hospital, United Kingdom
Ingemar Thiblin, MD, PhD, Forensic Medicine, Department of Surgical Sciences, Uppsala University, Sweden
Vineta Fellman MD, PhD, Department of Clinical Sciences, Lund, Pediatrics, Lund University, Lund, Sweden, Children’s Hospital, University of Helsinki and Folkhälsan Research Center, Helsinki, Finland
Göran Högberg, MD, PhD, Formerly Department of Women’s and Children’s Health, Child and Adolescent Psychiatric Unit, Karolinska Institutet, Stockholm, Sweden
Jacob Andersson, MD, Forensic Medicine, Department of Surgical Sciences, Uppsala University, Sweden
Knut Wester, MD, PhD, Department of Clinical Medicine, University of Bergen and Department of Neurosurgery, Haukeland University Hospital, Bergen, Norway
References
1. Panagioti M, Khan K, Keers RN, Abuzour A, Phipps D, Kontopantelis E, et al. Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. Bmj. 2019;366:l4185. doi: 10.1136/bmj.l4185
2. Högberg U, Lampa E, Högberg G, Aspelin P, Serenius F, Thiblin I. Infant abuse diagnosis associated with abuse head trauma criteria: incidence increase due to overdiagnosis? Eur J Publ Health. 2018;28(4):641-6. doi:10.1093/eurpub/cky062
3. Högberg U SR, Wester K, Högberg G, Andersson J, Thiblin I. Medical diagnoses among infants at entry in out-of-home care: A Swedish population-register study. Health Science Report. 2019;e133:1-12. doi: 10.1002/hsr2.133
4. Högberg U, Andersson J, Squier W, Högberg G, Fellman, Thiblin I, Wester K. Epidemiology of subdural haemorrhage during infancy: a population-based register study. PLoS One. 2018. doi: 10.1371/journal.pone.0206340
5. Högberg U, Andersson J, Högberg G, Thiblin I Metabolic bone disease risk strongly contributing to long bone and rib fractures during early infancy: A population register study PLoS One. 2018;13(12):e0208033. doi: 10.1371/journal.pone.0208033
6. Elinder G, Eriksson A, Hallberg B, Lynoe N, Sundgren PM, Rosen M, et al. Traumatic shaking: The role of the triad in medical investigations of suspected traumatic shaking. Acta Paediatr. 2018;107 Suppl 472:3-23. doi:10.1111/apa.14473
Competing interests: No competing interests
Patient safety prevention or mitigation, lessons from studies: A good question raised, too many answers needed!
Re: Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. Panagioti M: BMJ 2019;366:l4185. doi: 10.1136/bmj.l4185.
Today, a big public health challenge we must face is understanding and mitigating preventable patient harm. Harmful patient incidents have become a large financial burden for the fragile healthcare system and a bad experience for patient perceptions with physicians, which threaten the relationship between patients and clinicians and increase the practice of defensive medicine (1). This systematic review and meta-analysis by Panagioti M et al (2) reported that the pooled prevalence for preventable patient harm was 6% and a pooled proportion of 12% of preventable patient harm was severe or led to death. Furthermore, incidents related to drugs (25%) and other treatments (24%) accounted for the largest proportion of preventable patient harm (2).
The good news of this study is that “half of patient harm is preventable” along with the bad news is “less evidence is available for specific medical specialties” (2). How to benefit the most? As a doctor major in cardiology, the key point is to find the vulnerable patients and treat them with available evidence-based medicine and technologies. Harmful patient incidents widely exist in medical and health care system and some of them are actually hard to be completely avoided. Thus vulnerable patients detection, risk stratification and precision medicine implementation are a round continuum in the cardiovascular disease management. In the cardiovascular system, having titin-truncating variants was associated with a higher risk of receiving appropriate implanted cardioverter defibrillator (ICD) therapy for fibrillation or anti-tachycardia pacing (3). Even so, other patients who implanted an ICD or a CRT-D and experience an improvement of left ventricular function during follow-up, appear still to be at risk of major ventricular arrhythmias (4), not to mention there was a significant residual risk of appropriate implantable ICD therapy in the second generator life even among patients with advanced age and with a full prior generator period without any appropriate ICD events (5).
Fortunately, a couple of technologies including checklists, structured communication, scripted rounding, and electronic alerts have already been used to manage risk of harm incurred to patients. Even in the current healthcare system, these technologies are now deeply sedimented in managerial and professional discourses (6). The commitment to create a continuously improving culture of safety is imperative to optimize patient care and their outcomes. With an improved culture of safety, errors can be reduced and the overall progression of healthcare quality can be realized (7).
As stated in the study by Panagioti M et al (2), the heterogeneity of study methods and contents do have great impact on the interpretation and utilization of their conclusions. The heterogeneity behind these studies or literatures at least includes the following: assessment methods, harm severity definition, participants from different countries with diversified economic and health care quality status, participants from children, adults to older adults of in- and outpatients, different settings from primary care, emergency units to internal or surgical departments, different etiology from cancer, cardiovascular disease, trauma, behavior, psychiatry to drug usage, medical management as interventional or surgical procedures and so on. What we should do now? Yes, getting the measure right (8)! Obviously, more RCT trials focused on these heterogeneities are needed and too many new problems have already appeared!
Thank you for considering our views.
Dr. Zhu Yanrong
MD phD Chen Zhong (zhongchen7498@hotmail.com)
Department of Cardiology, Affiliated Sixth People’s Hospital East, Shanghai Jiao Tong University, Shanghai University of Medicine and Health Sciences, No. 222 Huanhu Xisan Road, Shanghai 201306, P.R.China
July 28, 2019
Competing interests: no competing interests.
Reference:
1. Lyu HG, Cooper MA, Mayer-Blackwell B, et al. Medical Harm: Patient Perceptions and Follow-up Actions. J Patient Saf 2017;13(4):199-201. doi: 10.1097/PTS.0000000000000136.
2. Panagioti M, Khan K, Keers RN, et al. Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. BMJ 2019;366:l4185. doi: 10.1136/bmj.l4185.
3. Corden B, Jarman J, Whiffin N, et al. Association of Titin-Truncating Genetic Variants With Life-threatening Cardiac Arrhythmias in Patients With Dilated Cardiomyopathy and Implanted Defibrillators. JAMA Netw Open. 2019;2(6):e196520. doi: 10.1001/jamanetworkopen.2019.6520.
4. Artico J, Ceolin R, Franco S, et al. ICD replacement in patients with intermediate left ventricular dysfunction under optimal medical treatment. Int J Cardiol 2019. pii: S0167-5273(19)32357-5. doi: 10.1016/j.ijcard.2019.06.072. [Epub ahead of print]
5. Ruwald MH, Ruwald AC, Johansen JB, et al. Incidence of appropriate implantable cardioverter-defibrillator therapy and mortality after implantable cardioverter-defibrillator generator replacement: results from a real-world nationwide cohort. Europace 2019. pii: euz121. doi: 10.1093/europace/euz121. [Epub ahead of print]
6. Hutchinson M, Jackson D, Wilson S. Technical rationality and the decentring of patients and care delivery: A critique of 'unavoidable' in the context of patient harm. Nurs Inq. 2018;25(2):e12225. doi: 10.1111/nin.12225.
7. Lawson C, Predella M, Rowden A. Assessing the culture of safety in cardiovascular perfusion: attitudes and perceptions. Perfusion 2017;32(7):583-590. doi: 10.1177/0267659117699056.
8. Irene Papanicolas, Jose F Figueroa. Preventable harm: getting the measure right. BMJ 2019; 366: l4611
Competing interests: No competing interests
Pangioti et al (1) are on a hiding to nothing when they hope to measure the extent of preventable therapeutic harm. To measure something you need to recognise it. Unfortunately the nature of adverse events in therapy is that they can be invisible, even in plain sight. I’d like to tell you how frequent invisible adverse events are, but their invisibility renders that impossible.
This is a central problem in evaluating the net benefits of treatment. It is easy to measure the intended therapeutic outcome of a treatment because it is (usually) unimodal and predictable. You apply a treatment in order to achieve a specific outcome. If the outcome is achieved, the treatment is successful (having used a control to account for spontaneous remission). If it doesn’t, the treatment is ineffective. Not all effective treatments are totally successful, of course, and you may have to measure the outcome on a scale, but generally there is one scale and the measured criterion is obvious.
This is not so of adverse events, because:
1. They may not relate to the pathology or even the system… or sometimes the individual treated (remember thalidomide?).
2. They may result from the interaction of other factors not related to the problem or its treatment, such as other pathology, diet, substance use, environmental hazards, change in weight, pregnancy etc.
3. They may be multiple.
4. They may not relate to the known mechanism of action of the intervention.
5. They may be disconnected in time from the treatment, appearing months or years later.
6. The patient may not recognise that they are caused by treatment.
7. The patient may not report them.
8. The therapist might not spot them.
9. The therapist might spot them but not recognise them as treatment-related.
10. The adverse effect in question may never have been reported, and therefore dismissed as non-treatment related.
11. The adverse effect(s) may be indistinguishable from non-treatment-related pathology.
It would be nice to imagine that only relatively minor preventable harms go unrecognised. But I am always brought back to the following story:
An elderly man had assumed that his recent inability to complete the Times Crossword related to his advancing years. He was unable to take his blood pressure pills for a few days. He was able to complete the crossword! A switch of pills allowed him to continue to solve it.
The upshot of this is that all measures of preventable harm (or even of unpreventable harm) resulting from therapy underestimate the prevalence. This is not in due to inadequate science or the shortage of studies. It is inherent in the nature of the problem.
Finding the exact incidence of preventable harm is therefore impossible. This is not a reason not to be vigilant. But, as always, when contemplating or discussing treatment, it is important to allow for the unknown unknown.
By the way, Papanicolas and Figuero (2) (perhaps inadvertently) use the word “error” when describing preventable harm. I think this is unfortunate. Most preventable harms do not result from “errors”. They are just unfortunate happenstance that occurs even with painstaking clinicians who adopt standard guidelines.
Arnold Zermansky
Hon Senior Visiting Research Fellow
University of Leeds
REFERENCES:
(1) Panagioti M, Khan K, Keers RN et al. BMJ 2019; 366:l4185
(2) Papanicolas I and Figueroa JF. BMJ 2019; 366:l4611
Competing interests: No competing interests
There has been quite a bit of work on “the patient safety issue” here in Australia and is raising its ugly head at The Royal Commission into Aged Care Quality and Safety (https://agedcare.royalcommission.gov.au/Pages/default.aspx) which is currently sitting. Some years ago I recall observations that indicated that an appendectomy in a Perth Hospital had in excess of a 10% greater incidence of preventable harm than did a similar hospital in Sydney. Hope this is not the case today. The issue related to non-standard procedures.
In my own case, I have prevented drug-to-drug negative interactions in emergency twice as the junior doctor was unaware. Patients need to be much more involved in discussions and actions around safety. After all, we pay the cost, suffer the trauma and carry the pain.
Competing interests: No competing interests
Dear Editor,
This is a timely and interesting piece, but unfortunately, it has conclusions that are, generally, not supported by the data presented.
Firstly, the headline figure - 1 in 20 patients suffer from preventable harm - is a classic example of conflating individual risk with population risk. The vast majority of patients in healthcare are in primary care, with a much smaller number in secondary (outpatient) care, then a smaller number again admitted, with an even smaller number requiring surgery or intensive care. As the authors note, the bulk of the risk of preventable harm applies in those two fields, so one cannot simply apply this risk across the whole healthcare setting.
Could the authors repeat the analysis excluding those two specialities, and provide a risk estimate that reflects the vast majority of patients?
Another problem relates to timing, an issue the authors briefly mention. Clearly, the 1 in 20 figure might make sense for an inpatient admission, but how this is measured in outpatients is less clear. Is this 1 in 20 risk applied across each individual consultation (this seems unlikely, suggesting that most GP's would cause preventable death or disability a few times a week, which is clearly wrong).
So how long is this? Is this across a patients whole exposure to healthcare? In which case, the 1 in 20 figure might seem more realistic, but given most multimorbid patients (those at the most risk of preventable harm) have hundreds, if not thousands of consultations, this makes the risk of preventable harm per consultation extremely low, limiting the potential benefit of any individual intervention.
For example, a prescribing system that leads to a ten second increase in consultation time for a 10% relative reduction in risk of harm per consultation might make sense if each consultation had a 1 in 20 risk of preventable harm, but not if the risk was 1 in 20,000 (assuming 1,000 consultations per lifetime).
Without knowing the denominator (risk per consultation? per admission? per year?), the interpretation of this figure is likely to lead to a significant overestimate of the benefit of harm prevention programmes, assuming a risk per consultation or process that is much higher than reality.
Fergus Hamilton
SpR in Microbiology and Infectious Disease
NIHR Academic Clinical Fellow
Competing interests: No competing interests
Herewith, we thank Panagioti and colleagues for their tremendous work on pooled analyses of the prevalence, severity, and nature of preventable patient harm in different medical settings.[1] This topic is of heightened interest to all clinical health professionals, especially we anesthesiologists. However, we express deep concern about the accuracy based on the principles of performing a meta-analysis.
One crucial finding of this study was that preventable patient harm was more prevalent in advanced specialties (intensive care or surgery) which may entail high-risk patients and more work pressures. We do agree with these convincible explanations. However, we cannot accept the notion that “Surgery” can stand for “Anesthesia”, though we anesthesiologists are always suffering more work overload or even sudden death than our counterparts.2 Even if “Surgery” encompasses the anesthesia setting, it seems that the authors may have missed some articles focusing on anesthesia-associated patient harm according to the inclusion criteria.3-5 Particularly, taking into account the important principle of comprehensive data collection when performing a meta-analysis, missing some articles might have an impact on pooled effects and sometimes the results can even go in the contrary direction.
In addition, the choice of the model involved seems to be inappropriate. Generally speaking, the model is selected based on the heterogeneity. If the I2 > 50% was considered indicative of statistical heterogeneity, in which case the random-effects model would be adopted, otherwise a fixed-effects model would be considered.
Overall, we suggest the authors could include anesthesia-associated preventable patient harm, which is a crucial part and dates back to the 1970s.
1 Panagioti M, Khan K, Keers RN, et al. Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. BMJ 2019; 366: l4185. doi: 10.1136/bmj.l4185
2 Huang J, Lee J. Causes of sudden death of young anesthesiologists in China: Response to Zhang and colleagues: Rising sudden death among anaethesiologists in China. Br J Anaesth 2017; 119: 548-549. doi: 10.1093/bja/aex288
3 Curatolo CJ, McCormick PJ, Hyman JB, Beilin Y. Preventable Anesthesia-Related Adverse Events at a Large Tertiary Care Center: A Nine-Year Retrospective Analysis. Jt Comm J Qual Patient Saf 2018; 44: 708-718. doi: 10.1016/j.jcjq.2018.03.013
4 Wanderer JP, Gratch DM, Jacques PS, Rodriquez LI, Epstein RH. Trends in the Prevalence of Intraoperative Adverse Events at Two Academic Hospitals After Implementation of a Mandatory Reporting System. Anesth Analg 2018; 126: 134-140. doi: 10.1213/ANE.0000000000002447
5 Lobaugh L, Martin LD, Schleelein LE, Tyler DC, Litman RS. Medication Errors in Pediatric Anesthesia: A Report From the Wake Up Safe Quality Improvement Initiative. Anesth Analg 2017; 125: 936-942. doi: 10.1213/ANE.0000000000002279
Competing interests: No competing interests
The excellent paper by Panagioti et al. [1] reminds us of the significance impact of preventable iatrogenic harm, affecting around 6% of patients. More than half the harm is due to mismanagement of prescribed medicines and other therapeutic management incidents. Further research is unlikely to substantially alter this estimate [2], and these findings should catalyze change in practice, policy and the prevailing zeitgeist.
In 2017, the WHO’s 3rd Patient Safety Challenge ‘Medication Without Harm’ called for action to strengthen monitoring systems, facilitate improvements in monitoring practices, and reduce medicines-related harm by 50% by 2022.[3, 4]. Unfortunately, little has changed. The previous WHO Global Patient Safety Challenge was effectively met by a checklist approach [5]. There are, of course, many arenas in which action is needed. Our own work in the area of residential care and older people seeks to build a consensus around a similar communications approach to enhance the systems and practices of medication optimisation. Relatively little work on iatrogenic harm has been undertaken in primary care or with older adults [1], but our work suggests that only pro-active checks by nurses or carers to identify and ameliorate adverse side effects can ensure that people in care homes are not sedated by antipsychotics or anxiolytics or kept awake by risperidone given at bedtime or confused by anti-muscarinics or left in pain [6, 7]. Comprehensive patient monitoring by nurses, juxtaposed with medicines charts, addresses this preventable, low-level iatrogenic harm, previously undetected or attributed to ‘age’ or ‘illness’, but which, if not addressed, can escalate to serious adverse drug reactions [8].
Panagiota and colleagues’ meta-analysis [1] may be the stimulus needed for healthcare systems to empower professionals to devote sufficient time and learning to person-centred monitoring and checking for potential adverse side effects, however mundane [9].
1. Panagioti Maria, Khan Kanza, Keers Richard N, Abuzour Aseel, Phipps Denham, Kontopantelis Evangelos et al. Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis BMJ 2019; 366 :l4185
2. Abbasi Kamran. First do no harm: the impossible oath BMJ 2019; 366 :l4734
3. WHO 2017 WHO launches global effort to halve medication-related errors in 5 years. Geneva/ Bonn. Available from: http://www.who.int/mediacentre/news/releases/2017/medication-related-err... (accessed 20 August 2017).
4. WHO. Medication without harm 2017. Available from: http://apps.who.int/iris/bitstream/10665/255263/1/WHO-HIS-SDS-2017.6-eng... (accessed 20 August 2017).
5. de Vries EN, Ramrattan MA, Smorenburg SM, et al. The incidence and nature of in-hospital adverse events: a systematic review. Qual Saf Health Care 2008;17:216-23. doi: 10.1136/qshc.2007.023622 [published Online First: 2008/06/04]
6. Jordan S, Gabe-Walters ME, Watkins A, et al. Nurse-Led Medicines' Monitoring for Patients with Dementia in Care Homes: A Pragmatic Cohort Stepped Wedge Cluster Randomised Trial. PLoS One 2015;10:e0140203. doi: 10.1371/journal.pone.0140203 [published Online First: 2015/10/16]
7. ADRE – THE ADVERSE DRUG REACTION PROFILE: HELPING TO MONITOR MEDICINES http://www.swansea.ac.uk/adre/ (accessed 17 July 2019)
8. Jones R, Moyle C, Jordan S. Nurse-led medicines monitoring: a study examining the effects of the West Wales Adverse Drug Reaction Profile. Nurs Stand 2016;31:42-53. doi: 10.7748/ns.2016.e10447 [published Online First: 2016/12/03]
9. Jordan S, Logan PA, Panes G, Vaismoradi M, Hughes D. Adverse Drug Reactions, Power, Harm Reduction, Regulation and the ADRe Profiles. Pharmacy (Basel). 2018 Sep 18;6(3). pii: E102. doi: 10.3390/pharmacy6030102. PubMed PMID: 30231573. http://www.mdpi.com/2226-4787/6/3/102/pdf
Competing interests: No competing interests
Re: Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis
I would like to thank all authors who submitted responses to this systematic review and meta-analysis.1 Their positive remarks as well as criticism encourage a fruitful debate about the methodological challenges in examining the prevalence of preventable patient harm in medical care settings.
Hamilton makes a number of valid queries reflecting key methodological challenges, which we had to resolve while conducting and reporting this systematic review and meta-analysis.
There are certainly variations in the number of patients treated across primary care, general hospitals and other medical care specialities. However, this is a meta-analysis of international studies with a varying flow of patients across different medical care settings/specialities. Distinguishing the proportion of patients in each medical care setting would be impossible due to these country-level variations. Excluding studies conducted in intensive care and surgical care units would indicate selective reporting.
Instead, the following two strategies were applied in this meta-analysis. First, the pooled prevalence estimates of preventable patient harm in each medical care setting/speciality are reported in Figure 2 to highlight such variations (see Figure 2). Second, because the prevalence of preventable patient harm was expected to vary from very small to large across studies, the Freeman-Tukey Double Arcsine transformation was used to stabilise the variances2 and then perform a random effects meta-analysis implementing the DerSimonian-Laird method.
The pooled estimate of the prevalence of preventable patient harm is primarily driven by data from general hospitals (where the vast majority of studies were based) rather than data from intensive care or the surgical care units. The headline conclusion that 1 in 20 patients experience patient harm stands even if intensive care and surgical care units are excluded from the analyses (although this exclusion is not justified). We also highlight the need to increase the evidence on the prevalence, nature and severity of preventable patient harm in primary care and other busy medical care settings such as psychiatry.
The denominator in this meta-analysis is the number of patients or unique patient consultations. Studies which did not provide data on the number of patients exposed to harms or number of unique consultations containing harms (or we were unable to calculate/obtain this figure) were ineligible for inclusion in this meta-analysis. We have purposively chosen this approach to avoid double counting of patients who may have had more than one consultations and experience more than one preventable harms.
The timing of studies was assessed as a possible covariate in the meta-regression analyses. Large timing variations were found across studies. For example, in some studies the medical records of patients were reviewed for only one week whereas a 12-month review of medical records were undertaken in other studies. The classification of the various timings reported across studies into a manageable number of covariate values was not possible. According to the recommendations of Thompson and Higgins, each covariate value should be based on a minimum of 8-10 studies.3 Consequently, we did not formally include timing as a covariate in the meta-regression analyses but we outlined this important methodological variation across studies as a limitation in the discussion.
Zermansksy highlights the challenges in detecting and assessing the causality of patient harm. Assessing the prevalence of overall patient harm is a scientifically important question and future triangulation of methods could improve accuracy e.g. by providing a better framework or using a combination of methods (e.g. mixed-method approaches with parallel involvement of patients as partners).4-7 However, despite efforts and progress, some patient harms are undetectable and particularly challenging to assess their causality. By definition, preventable harm occurs as a result of an identifiable modifiable cause, and/or its future recurrence can be avoided by reasonable adaptation to a process, or adherence to guidelines. 8 Thus, the fundamental advantage of focusing on preventable patient harm is that improvement strategies can specifically target those types of harms that are identifiable and avoidable.
References
1. Panagioti M, Khan K, Keers RN, et al. Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. 2019;366:l4185. doi: 10.1136/bmj.l4185 %J BMJ
2. Freeman MF, Tukey JW. Transformations Related to the Angular and the Square Root. Ann Math Stat 1950;21(4):607-11. doi: DOI 10.1214/aoms/1177729756
3. Thompson SG, Higgins JPT. How should meta-regression analyses be undertaken and interpreted? Stat Med 2002;21(11):1559-73. doi: 10.1002/sim.1187
4. Vincent C, Taylor-Adams S, Stanhope N. Framework for analysing risk and safety in clinical medicine. 1998;316(7138):1154-57. doi: 10.1136/bmj.316.7138.1154 %J BMJ
5. Hignett S, Lang A, Pickup L, et al. More holes than cheese. What prevents the delivery of effective, high quality and safe health care in England? Ergonomics 2018;61(1):5-14. doi: 10.1080/00140139.2016.1245446 [published Online First: 2016/10/08]
6. Jylha V, Saranto K, Bates DW. Preventable adverse drug events and their causes and contributing factors: the analysis of register data. International journal for quality in health care : journal of the International Society for Quality in Health Care 2011;23(2):187-97. doi: 10.1093/intqhc/mzq085 [published Online First: 2011/01/19]
7. Sujan MA, Ingram C, McConkey T, et al. Hassle in the dispensary: pilot study of a proactive risk monitoring tool for organisational learning based on narratives and staff perceptions. BMJ quality & safety 2011;20(6):549-56. doi: 10.1136/bmjqs.2010.048348 [published Online First: 2011/03/15]
8. Nabhan M, Elraiyah T, Brown DR, et al. What is preventable harm in healthcare? A systematic review of definitions. Bmc Health Serv Res 2012;12 doi: 10.1186/1472-6963-12-128
Competing interests: No competing interests