Skip to main content
NCBI home page
International Journal for Quality in Health Care logoLink to International Journal for Quality in Health Care
. 2023 Dec 29;36(1):mzad114. doi: 10.1093/intqhc/mzad114

Risk identification and prediction of complaints and misconduct against health practitioners: a scoping review

Yufeng Wang 1,*,2, Sanyogita (Sanya) Ram 2,2, Shane Scahill 3,2
PMCID: PMC10791111  PMID: 38155372

Abstract

Identifying the risk and predicting complaints and misconduct against health practitioners are essential for healthcare regulators to implement early interventions and develop long-term prevention strategies to improve professional practice and enhance patient safety. This scoping review aims to map out existing literature on the risk identification and prediction of complaints and misconduct against health practitioners. This scoping review followed Arksey and O’Malley’s five-stage methodological framework. A comprehensive literature search was conducted on MEDLINE, EMBASE, and CINAHL databases and finished on the same day (6 September 2021). Articles meeting the eligibility criteria were charted and descriptively analysed through a narrative analysis method. The initial search generated 5473 articles. After the identification, screening, and inclusion process, 81 eligible studies were included for data charting. Three key themes were reported: methods used for identifying risk factors and predictors of the complaints and misconduct, synthesis of identified risk factors and predictors in eligible studies, and predictive tools developed for complaints and misconduct against health practitioners. The findings reveal that risk identification and prediction of complaints and misconduct are complex issues influenced by multiple factors, exhibiting non-linear patterns and being context specific. Further efforts are needed to understand the characteristics and interactions of risk factors, develop systematic risk prediction tools, and facilitate the application in the regulatory environment.

Keywords: risk identification, risk prediction, complaints, misconduct, health practitioners

Introduction

In recent years, health regulators have shown an increasing inclination towards proactive and data-driven approaches to oversight [1]. Complaints and misconduct are recognized as valuable resources for understanding patient dissatisfaction and harm, as well as detecting problematic practitioner practices [2, 3]. Regulatory agencies worldwide consider complaints and misconduct as crucial components of their risk management frameworks. For instance, in the USA, the Vanderbilt Center for Patient and Professional Advocacy (CPPA) has developed a Patient Advocacy Reporting System (PARS) complaint programme in collaboration with hospitals and medical groups to identify physicians at high risk of complaints effectively [4]. Similarly, national schemes have been implemented in Australia and New Zealand to regulate health professions, with a particular focus on complaints and disciplinary data [5]. These developments were noted as offering ‘opportunities for researchers, policymakers and regulators to move towards evidence-based regulation’ [6].

In related fields, a growing number of studies have explored complaints and misconduct against health practitioners. Numerous investigations have revealed that the occurrence of such complaints and misconduct is not entirely random, with a small proportion of practitioners accounting for up to 50% of claims [7–9]. Certain research has identified specific characteristics associated with high-risk health practitioners, including demographics such as gender, age, and speciality [10, 11]. Moreover, beyond these “personal” or “human” factors, practice or system characteristics, such as workload [12] and practice setting [11], have emerged as discernible indicators of medico-legal matters. Despite the growing body of research in this field, there is a lack of reviews that comprehensively examine the risk of complaints and misconduct against health practitioners, especially when viewed through a predictive and prospective lens.

This study adopts a broad perspective with the goal of providing a comprehensive understanding of existing literature on the risk and prediction of complaints and misconduct against health practitioners. To achieve this, a broad scoping review methodology was employed due to its advantages in mapping a wide range of available evidence, identifying key characteristics or factors related to a concept, and conveying the breadth and depth of a field [13–15]. This study seeks to address the following three specific questions: (i) what methods have been used to identify risk factors and predictors of complaints and misconduct against health practitioners? (ii) What are the patterns and taxonomy of risk factors and predictors of complaints and misconduct against health practitioners? (iii) What predictive models or tools have been developed to identify risks for complaints or misconduct against health practitioners prospectively?

Methods

Study design

This study utilized the well-established five-stage methodological framework proposed by Arksey and O’Malley [13], which is widely recognized and commonly used in scoping reviews. The five stages include (i) identifying the research question, (ii) identifying relevant studies, (iii) study selection, (iv) charting the data, and (v) collating, summarizing, and reporting the results. To ensure rationality and integrity, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews (PRISMA-ScR) checklist was also applied to report the findings [16].

Research question

The research question was formulated by following the Population, Concept, and Context framework [17]. In this study, the Population encompasses all health practitioners. The Concept focuses on the identification and prediction of risks associated with complaints and misconduct. Within the scope of this study, the term “complaints and/or misconduct” is a broad term that encompasses various related concepts, including complaints, misconducts, disciplinary matters, malpractices, grievances, misbehaviours, medico-legal cases, medical errors, disputes, criminal cases, and claims that are against health practitioners. There are no geographic or contextual restrictions. Consequently, the following research question was posited: what is known from the existing literature about the identification and prediction of risks associated with complaints and misconduct against health practitioners?

Search strategy

The search strategy was developed over several research team meetings based on three sets of primary concepts: risk identification or prediction, health practitioners, and complaints and/or misconduct. A search of literature was conducted on 6 September 2021, using three databases: Medline via PubMed (1946 to the present), EMBASE via Ovid (1980 to the present), and CINHAL Plus via EBSCOhost (1937 to the present). All searches were subject to date and language limitations, with only English articles published since 2000 being included. Although the search was pre-planned in advance, no formal protocol was registered for this review. A full search strategy for each database is available in the supplementary file (Supplementary Appendix A).

Screening and selection process

The screening and selection process was conducted in three stages. Firstly, the search results were imported into Endnote software. Duplicates were removed using the software’s built-in function, followed by further manual checking. Subsequently, the titles and abstracts of all imported studies were screened based on the inclusion criteria outlined in Table 1. Additionally, the reference lists of review papers were searched for additional relevant publications (i.e. backward snowballing) [13, 18], ensuring comprehensive coverage of evidence. Finally, exclusion criteria were developed for full-text screening, and the final decision regarding the inclusion of the remaining articles was made based on the refined eligibility criteria.

Table 1.

Inclusion and exclusion criteria.

Inclusion criteria Exclusion criteria
1. Original research articles describing experimental or observational investigations or review articles using formal methods to explore the identification or prediction of the risk of complaints and misconduct against health practitioners.
2. The primary focus of the study was on complaints, misconducts, disciplinary matters, malpractices, grievances, misbehaviours, medico-legal cases, medical errors, disputes, criminal cases, and claims against health practitioners (collectively referred to as “complaints and/or misconduct”).
3. Published in English since 2000.		 1. Studies that focused on:
(i) health complaints that referred to patients’ bodily disorders, diseases, symptoms, or distress;
(ii) defensive medicine.
2. The following types of prediction studies:
(i) did not target health practitioners or only focused exclusively on veterinarians;
(ii) focused solely on predicting specific outcomes of complaints or misconduct (such as payment or liability);
(iii) only analysed patient or complainant-related risk factors.
3. Publications in the form of commentary, editorials, letters, notes, news, communications, books, case reports, theoretical essays, conference abstracts, and oral presentations.
4. Non-English studies or publications before 2000.
5. The completed text was not available through the university library service.
Open in a new tab

To expedite the process, a rapid review approach “single screening” was adopted for abstract and full-text screening [19]. This widely accepted rapid review approach offers a more efficient analysis and quicker generation of evidence portfolio. However, it does carry the risk of potentially missing relevant studies. Nevertheless, given the extensive number of studies identified through the searching and screening processes, any missing studies are expected to have minimal impact on the overall summarizing and dissemination of these research findings [20, 21].

Throughout the searching, screening, and assessing process, the research team held regular meetings to discuss the eligible criteria, key concepts, and observations. One reviewer initially assessed each article, with any uncertainties resolved through discussions with two advisors who had expertise in pharmacy, organization management, and law until a consensus was reached.

Data extraction, analysis, and report

An Excel® sheet was created to extract the following information: author(s), title, journal, year of publication, location, methodology, data source, year of the study conducted, involved professional(s), aims, key findings, and conclusions. A narrative synthesis analysis method recommended by Arksey and O’Malley (2005) was then performed, including numerical statistical summaries, textual commentaries, and tabular and graphical representations [22].

Results

Search results and characteristics

The database search initially yielded 5473 articles. After removing 2212 duplicates, a total of 3261 articles were reviewed in title and abstract, and 292 were retained for full-text screening. Also included were 62 articles through screening the reference list of eight systematic reviews and traditional literature reviews. Through the full-text screening of 354 articles, 81 articles were finally included for data charting. The complete identification, screening, and inclusion process is illustrated in the PRISMA-ScR flowchart (Fig. 1). The supplementary file (Supplementary Appendix B) provides basic information on eligible studies.

Figure 1.

Figure 1

Open in a new tab

PRISMA flowchart for study selection.

As seen in Table 2, the number of identified articles has been increasing in recent years, with two peaks observed between 2011–2013 (n = 16) [7, 23–37] and 2018–2021(n = 27) [1, 2, 4, 9–12, 38–57]. Over three-quarters (77.78%) of these studies were conducted in the USA (n = 37, 45.68%) [4, 9, 24, 25, 29, 31, 36, 41, 43, 49, 52–55, 57–79], Australia (n = 14, 17.28%) [1, 2, 7, 8, 34, 37, 44, 45, 51, 56, 80–83], Canada (n = 6, 7.41%) [26, 28, 47, 84–86], and Denmark (n = 6, 7.41%) [27, 30, 32, 46, 48, 50]. Quantitative methods were predominantly used (73, 90.12%) [2, 4, 7–9, 12, 23–38, 41–85, 87–92], with no pure qualitative studies identified. The majority of studies (70, 86.42%) focused on physicians or doctors [2, 4, 7–12, 23, 25–27, 29, 30, 32, 34–41, 43, 44, 47–50, 52–79, 81–87, 89–94], while fewer studies targeted on pharmacists (n = 1) [33], nurses (n = 1) [31], and dentists (n = 3) [24, 42, 45]. Additionally, six studies considered broad or unspecified health practitioners [1, 28, 46, 51, 80, 88].

Table 2.

Quantitative description of identified publications.

Category N %
Publication year
2000 2 2.47
2002 2 2.47
2003 4 4.94
2004 3 3.70
2005 3 3.70
2006 2 2.47
2007 2 2.47
2008 2 2.47
2009 4 4.94
2010 2 2.47
2011 5 6.17
2012 5 6.17
2013 6 7.41
2014 4 4.94
2015 4 4.94
2016 2 2.47
2017 2 2.47
2018 8 9.88
2019 7 8.64
2020 7 8.64
2021(September) 5 6.17
Locations from where the research data collected
USA 37 45.68
Australia 14 17.28
Canada 6 7.41
Denmark 6 7.41
Othersa 5 6.17
UK 4 4.94
China 4 4.94
Italy 1 1.23
Norway 1 1.23
Japan 1 1.23
New Zealand 1 1.23
Greece 1 1.23
Study design
Quantitative 73 90.12
Mixed method 3 3.70
Review 5 6.17
Focused health professions
Physicians/doctors 70 86.42
Broad/unspecified health practitioners 6 7.41
Dentists 3 3.70
Nurses 1 1.23
Pharmacists 1 1.23
Total 81 100
Open in a new tab
a

Review articles that collected data from more than one country.

Methods used for identifying risk factors and predictors

The majority of the eligible studies (n = 76) adopt statistical analysis methods to identify risk factors or predictors of complaints and misconduct against health practitioners, encompassing 73 quantitative studies and 3 mixed-method studies. Specifically, 19 studies compared the characteristics and identified high-risk characteristics of complaints and misconduct cases by conducting significance tests such as the Chi-square test, Student’s t-test, and Mann–Whitney test. Additionally, 57 studies further examine the risk factors associated with complaints or misconduct through regression models, such as Poisson regression [47], logistical regression, recurrent-event survival analysis, and generalized estimating equation. In five other eligible review articles, three employed traditional narrative review analysis, with two utilizing meta-analysis methods for analysis for the identification of risk factors.

Synthesis of identified risk factors and predictors

From the eligible 81 studies, three main categories of risk factors and predictors have been identified: health practitioners’ factors, system and environment factors, and complaint/misconduct issues. The synthesises and detailed findings for each eligible study are provided in Supplementary Appendix C and D.

Health practitioners’ factors were the most frequently mentioned, encompassing various aspects related to their basic demographics (age, gender, ethnicity, place of birth, social class, marital status, and family background), education characteristics (location of education, graduation school, medical degree, graduation time, previous behaviour at school, trainee status, and continuous education), competence or health (emotional intelligence and physician impairment), professional backgrounds (medical speciality, profession, practice experience, and employment characteristics), and work practice (operational characteristics, workload, interaction with patients, and teamwork). The specific risk factors and predictors extracted from eligible studies are presented in Fig. 2. However, it should be noted that only a limited number of factors were universally validated in more than three studies. These included older age [1, 2, 7, 8, 26, 31, 44, 45, 47, 51, 56, 60, 65, 70, 90], male gender [1, 2, 7, 8, 11, 29, 34, 35, 43–45, 47, 51, 54–56, 60, 62, 64, 65, 67, 68, 71, 75, 81–83, 85, 87, 89, 90, 93, 94], poor performance in examinations [47, 63, 73], low scores [63, 73, 77, 85], specific specialties (surgery [1, 8, 11, 34, 65, 68, 70, 71, 81, 85, 90, 91], obstetrics-gynaecology [1, 7, 8, 47, 60, 62, 65, 70, 81, 90], and general practice [1, 11, 12, 35, 60, 62, 68, 89, 93]), the professions of family practitioners [47, 60, 62], longer practice time [27, 32, 34, 47, 48, 52, 59, 62, 64, 66, 78], one or more previous claims history [1, 7, 8, 31, 36, 67, 71, 83, 95], not being board certified [60–62], greater patient volume/clinical activity [31, 32, 49, 50, 52, 67, 71], working longer hours [47, 81, 82], and solo practice [35, 47, 64, 68].

Figure 2.

Figure 2

Open in a new tab

Risk factors for predicting complaints and misconduct against health practitioners.

Note: Risk factors in bold were identified in the literature more than three times.

System and environment risk factors pertain to the factors related to the system, organization, or the broad environment in which the health practitioner practices. System factors included specific practice settings, practice size, facility type, and organization characteristics. One predominant system factor identified in the studies was the academic/university settings. Regarding environmental factors, several studies conducted in the USA [29, 31, 55, 68], Australia [1, 2, 51, 56], Denmark [30], and Canada [85] examined the relationship between geographic regions and the risk of complaints and misconduct. In addition, a Danish study found that General Practitioners serving lower-income patients were more likely to receive complaints or disciplinary board criticisms [50].

In terms of the issue of complaints and misconduct, four studies reported specific issues that pose a high risk. An Australian study found that complaints related to substance use, honesty, mental health, sexual boundaries, and medication use and supply were strongly recurrent [1]. In a dental school setting, appointments had a high complaint rate, with communication being the second-highest complaint type overall [24]. In the USA, one study revealed that plaintiffs overwhelmingly alleged diagnosis and procedural errors [43]. Another American study identified dissatisfaction with treatment and accessibility as the two most common complaint types among high-risk otolaryngologists [54].

Predictive tools for complaints and misconduct analysis

Three practical predictive tools have been developed for regulating health practitioners (Table 3). The first tool is the CPPA PARS database developed in the USA [55]. This database generates a risk score and establishes a complaint-type profile for each physician through analysis of the complaint data, allowing institutions to identify physicians at higher risk [55, 58, 76]. The other tool is the Predicted Risk of New Event (PRONE) score, proposed and tested by Spittal et al. in Australia [1, 83]. It uses a 22-point scoring system consisting of four predictors: speciality, gender, previous complaint numbers, and the time since the last complaint. This scoring system helps to assess a doctor’s future likelihood of complaint [83]. In 2019, an updated Predicted Risk of New Event for Health Practitioners (PRONE-HP) version was released, incorporating two additional predictors: practice location and complaint issues [1].

Table 3.

Predictive model and tools for complaints and misconduct analysis.

Predictive tools Objective Setting Variables Risk score measurement
CPPA PARS [55] Physician The USA Complaint types Each unique complaint embedded in a complaint report is assigned to one of the six complaint categories: care and treatment, communication, access and availability, concern for patient and family, safety of environment, and billing.
The complaint-type profile for each physician reflects the distribution of complaints across these categories.
The PARS system analyses these complaints and generates a risk score for each physician. The specific calculation method has not been disclosed.
PRONE score [83] Doctor Australia A doctor’s specialty, sex, the number of previous complaints, and the time since the last complaint Risk increased monotonically with PRONE score. Doctors with a score between 0 and 2 had a 2-year complaint risk of 14.2%. Doctors with PRONE scores between 15 and 17 had a subsequent complaint risk of 87.8%.
Specialty: Anaesthesia (0), radiology (0), other specialties (0), internal medicine (1), ophthalmology (2), general practice (2), psychiatry (3), orthopaedic surgery (3), other surgery (3), general surgery (3), obstetrics and gynaecology(3), dermatology (4), and plastic surgery(6)
Sex: Female (0) and male (2)
Number of previous complaints: 1 (0), 2 (1), 3 (2), 4 (4), 5–6 (5), 7–8 (6), 9 (7), and 10+ (11)
Time since the last complaint: 1–2 years (0), 6 months to 1 year (1), <6 months (2)
PRONE-HP score [1] Health practitioners, including doctors, nurses and midwives, dental practitioners, psychologists, pharmacists, chiropractors, optometrists, osteopaths, physiotherapists, podiatrists, aboriginal and Torres-Strait Islander (ATSI) health practitioners, Chinese medicine practitioners, medical radiation practitioners, and occupational therapists Australia Sex, age, profession and specialty, practice location, number of prior complaints, and complaint issue Practitioners with a score ≤4 had a 1% chance of a complaint within 24 months and those with a score ≥35 had a >85% chance.
Sex: Female (0) and male (2)
Age: ≤25 years (0), 26–35 years (0), 36–45 years (3), 46–55 years (4), 56–65 years (4), and 66–75 years (4)
Profession/specialty: Doctor: general practice (14), surgery (16), obstetrics and gynaecology (17), physician (13), psychiatry (17), anaesthesia (10), radiology (11), emergency and intensive care unit (ICU) (10), non-clinical (9), non-specialist (12); nurse: registered nurse (4), enrolled nurse (4); midwife (0); dental: dentists and dental prosthetist (15), other dental practitioners (7); psychologist (11); pharmacist (10); ATSI practitioner (9); Chinese medicine practitioner (7); chiropractor (11); medical radiation practitioner (0); occupational therapist (2); optometrist (7); osteopath (6); physiotherapist (3); podiatrist (9)
Practice location: Major cities of Australia (0), inner/outer regional Australia (1), and remote/very remote Australia (2)
Number of prior complaints: 0 (0), 1 (6), 2 (8), 3 (10), 4–6 (12), ≥7 (15)
Complaint issue:
Health: physical health (3), mental health (7), substance use (9)
Conduct: Records & reports (3), use or supply of medications (5), honesty (8), fees and servicing (3), interpersonal behaviour (3), sexual boundaries (5), compliance with conditions (2), and other conduct issues (4)
Performance: Prescribing or dispensing (3), procedures (3), and treatment, communication, and other clinical issues (2)
Open in a new tab

Discussion

Statement of principal findings

The principal findings of this scoping review provide a comprehensive overview of the existing literature on risk identification and prediction related to complaints and misconduct against health practitioners. Although the included studies demonstrate a broad geographic and professional diversity, 60% were conducted in the USA and Australia, and 86% primarily focused on physicians. This observation suggests a need for more diverse and inclusive research encompassing a wider range of health practitioners in different contexts.

The identified risk factors and predictors fall into three categories: health practitioners’ factors, system and environment factors, and issues of complaints/misconduct. Health practitioners’ characteristics were the most frequently reported risk factors. Our analysis consistently found certain characteristics recurring as risk factors in more than three studies, such as older age, male gender, poor performance at school, complaints or misconduct history, longer in practice, and higher workloads. However, when investigating specific risk factors, it becomes evident that these factors are context specific and profession dependent. For example, studies vary in findings on age’s impact on complaints and misconduct against health practitioners [1, 4, 76]. Furthermore, different analytical perspectives reveal diverse patterns. Physician-level analysis shows higher operative volume heightens liability risk due to potential adverse events in every patient interaction, while institutionally, greater clinical volume correlates with lower mortality rates for some surgeries [49]. This highlights the need for targeted and comprehensive studies to enhance the specific field across different settings and professions.

It is worth noting that environment and organization characteristics play an important role in risk assessment and prediction. While our review has included isolated findings mentioning these factors as potential risk factors for complaints and misconduct, few studies systematically examined them as distinct categories of risk factors, with only two exceptions [50, 65]. One repeatedly validated factor in this realm is the presence of academic/university settings. Factors contributing to this might involve treating patients with complex diagnoses, additional research, teaching and administration responsibilities, and complex organizational structures [55].

Another noteworthy finding is that the issue of complaints or misconduct itself has emerged as a risk factor, suggesting that specific issues can indicate different levels of risk. While this aspect was not extensively emphasized in eligible research, complaint issues have proven significant in identifying and predicting complaints and misconduct risks in practical predictive tools. Our findings reveal that two identified predicting tools incorporate complaint types as indicators—the CPPA PARS system profiles complaint types to reflect complaint distribution related to individuals [55]. Conversely, the PRONE-HP score system provides a summary of the overall complaint-type distribution among health practitioners, revealing strong associations between complaints related to mental health, substance use, sexual boundaries, and honesty with recurrence [1]. However, these models have not integrated external system or environmental factors, making it a potential area for further explorations.

Strengths and limitations

To our knowledge, this study is the first comprehensive review that synthesizes the issue of risk identification and prediction in the broad field of health practitioners. This expansive research scope enabled us to acquire a thorough understanding of all medico-legal matters related to complaints and misconduct, thereby showcasing a comprehensive landscape within the relevant field. The inclusion of all health practitioners allowed us to gain insights into current research patterns and identify sub-areas that require further exploration. Moreover, this study goes beyond the research progress but also elucidates the practical application, enriching the significant practical value of this research.

However, this study has a few limitations to consider when interpreting the results. Firstly, the language and publication date restrictions may have resulted in missing studies from non-English speaking countries as well as older studies, potentially limiting the scope of the review. Additionally, using a rapid review approach may have introduced some bias into the review process. However, the involvement of the two experts in all screening and assessment processes as much as possible helped to mitigate this potential bias to some extent.

Interpretation within the context of the wider literature

The findings of this study are in alignment with previous review articles regarding some category of risk factors associated with complaints or misconduct [10, 11]. A systematic review identified risk factors associated with malpractice claims and impaired performance in medical practitioners and categorized these factors as demographic and workplace related, encompassing practice-related elements such as training location, speciality as well as system, organization, and environmental factors related to clinical workload, clinical practice setting, and geographical location [10]. Another focused mapping review synthesized the priori risk factors associated with medical practitioners, placing emphasis on the influence of “human factors” and system factors [11]. This study expands the findings of existing reviews by highlighting the predictive function of complaints/misconduct issues, which have received little attention and were not explicitly suggested until 2019 [1].

In practice, most existing techniques used to analyse patient complaints are complex and pose challenges when implemented in frontline supervision. This is largely due to the lack of technical capacity among regulatory agencies [83]. While the two tools developed so far have addressed this issue to some extent, they still come with some limitations, as both were primarily tailored for specific contexts, limiting their broader applicability. Furthermore, these models do not incorporate system and environment factors. Regulators may face complexity when trying to evaluate these factors across their entire caseload, making them unsuitable candidates for routine-use risk calculators. Given the importance of these contextual factors, future investigations should explore strategies to include them as variables in practical risk measurement [83].

Implications for policy, practice, and research

This study offers valuable insights for policymakers, educators, regulators, and practitioners, shedding light on the risk areas that need to be addressed, enabling the development of targeted regulatory and educational strategies. The findings also point towards several directions for future research. While robust evidence has been generated from quantitative analysis, we believe that incorporating qualitative approaches would be beneficial in gaining a deeper understanding of the nature, meaning, and mechanisms underlying high-risk and predictive characteristics of complaint and misconduct cases. In addition, the complexity observed in this study and previous research highlights the need to explore the interrelationships among these factors and the potential underlying mechanisms in different contexts. Furthermore, developing more comprehensive and user-friendly predictive tools that can convert research findings into a scoring system to guide interventions is essential. Testing the effectiveness of current prediction models in diverse health settings might also be likely to benefit from refining these predictive tools in practice applications.

Conclusion

This scoping review advances our understanding of the risk identification and prediction of complaints and misconduct against health practitioners. The findings highlight currently understudied areas, particularly related to qualitative analysis of risk factors, the indicative roles of complaint issues, and the development of practical predictive tools. Furthermore, predicting the complaint risk is multifactorial, non-linear, and context specific. Future in-depth explorations in specific areas will significantly contribute to developing more proactive and data-driven regulations.

Supplementary Material

mzad114_Supp
Click here for additional data file. (400.9KB, zip)

Acknowledgements

This research was carried out as part of the first authors’ PhD project. We would like to acknowledge the China Scholarship Council for funding the first author’s PhD study (CSC No. 202109210031).

Contributor Information

Yufeng Wang, School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Level 3, Building 503, 85 Park Road, Grafton, Auckland 1023, New Zealand.

Sanyogita (Sanya) Ram, School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Level 3, Building 503, 85 Park Road, Grafton, Auckland 1023, New Zealand.

Shane Scahill, School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Level 3, Building 503, 85 Park Road, Grafton, Auckland 1023, New Zealand.

Author contributions

Yufeng Wang: Conceptualization, formal analysis, investigation, methodology, visualization, writing – original draft. Sanyogita (Sanya) Ram: Conceptualization, methodology, supervision, writing – review & editing. Shane Scahill: Methodology, supervision, writing – review & editing.

Supplementary data

Supplementary data is available at IJQHC online.

Conflict of interest

None declared.

Ethics and other permissions

Not applicable.

Funding

No funding sources were utilized.

Data availability

Data relevant to the study are included in the article or uploaded as supplemental information.

References

  • 1. Spittal  MJ, Bismark  MM, Studdert  DM. Identification of practitioners at high risk of complaints to health profession regulators. BMC Health Serv Res  2019;19:380. 10.1186/s12913-019-4214-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Ryan  AT, Too  LS, Bismark  MM. Complaints about chiropractors, osteopaths, and physiotherapists: a retrospective cohort study of health, performance, and conduct concerns. Chiropr Man Therap  2018;26:12. 10.1186/s12998-018-0180-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Mattarozzi  K, Sfrisi  F, Caniglia  F  et al.  What patients’ complaints and praise tell the health practitioner: implications for health care quality. A qualitative research study. Int J Qual Health Care  2017;29:83–9. 10.1093/intqhc/mzw139 [DOI] [PubMed] [Google Scholar]
  • 4. Fathy  CA, Pichert  JW, Domenico  H  et al.  Association between ophthalmologist age and unsolicited patient complaints. JAMA Ophthalmol  2018;136:61–7. 10.1001/jamaophthalmol.2017.5154 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Bismark  MM, Fletcher  M, Spittal  MJ  et al.  A step towards evidence-based regulation of health practitioners. Aust Health Rev  2015;39:483–5. 10.1071/AH14222 [DOI] [PubMed] [Google Scholar]
  • 6. Millbank  J. Serious misconduct of health professionals in disciplinary tribunals under the National Law 2010–17. Aust Health Rev  2020;44:190–9. 10.1071/AH18239 [DOI] [PubMed] [Google Scholar]
  • 7. Bismark  MM, Spittal  MJ, Gurrin  LC  et al.  Identification of doctors at risk of recurrent complaints: a national study of healthcare complaints in Australia. BMJ Qual Saf  2013;22:532–40. 10.1136/bmjqs-2012-001691 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Studdert  DM, Bismark  MM, Mello  MM  et al.  Prevalence and characteristics of physicians prone to malpractice claims. N Engl J Med  2016;374:354–62. 10.1056/NEJMsa1506137 [DOI] [PubMed] [Google Scholar]
  • 9. Dambrino  RJ, Zuckerman  SL, Guidry  BS  et al.  Do neurosurgeons receive more patient complaints than other physicians? Describing who is most at risk and how we can improve. J Neurosurg  2021;134:1990–7. 10.3171/2020.4.JNS20870 [DOI] [PubMed] [Google Scholar]
  • 10. Austin  EE, Do  V, Nullwala  R  et al.  Systematic review of the factors and the key indicators that identify doctors at risk of complaints, malpractice claims or impaired performance. BMJ Open  2021;11:e050377. 10.1136/bmjopen-2021-050377 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Croft  E, Clark  MT, Efstathiou  N  et al.  A focused mapping review and synthesis of a priori risk factors associated with medical misconduct. BMJ Open Qual  2019;8:e000538. 10.1136/bmjoq-2018-000538 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Bratland  SZ, Baste  V, Steen  K  et al.  Physician factors associated with increased risk for complaints in primary care emergency services: a case–control study. BMC Fam Pract  2020;21:201. 10.1186/s12875-020-01272-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Arksey  H, O’Malley  L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol  2005;8:19–32. 10.1080/1364557032000119616 [DOI] [Google Scholar]
  • 14. Levac  D, Colquhoun  H, O’Brien  KK. Scoping studies: advancing the methodology. Implement Sci  2010;5:69. 10.1186/1748-5908-5-69 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Munn  Z, Peters  MDJ, Stern  C  et al.  Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol  2018;18:143. 10.1186/s12874-018-0611-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Tricco  AC, Lillie  E, Zarin  W  et al.  PRISMA extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med  2018;169:467–73. 10.7326/M18-0850 [DOI] [PubMed] [Google Scholar]
  • 17. Peters  MDJ, Godfrey  C, McInerney  P  et al.  Chapter 11: Scoping Reviews (2020 version). In: Aromataris  E and Munn  Z (eds), JBI Manual for Evidence Synthesis. JBI, 2020. 10.46658/JBIMES-20-12 [DOI] [Google Scholar]
  • 18. Heeren  P, Hendrikx  A, Ceyssens  J  et al.  Structure and processes of emergency observation units with a geriatric focus: a scoping review. BMC Geriatr  2021;21:95. 10.1186/s12877-021-02029-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Shemilt  I, Khan  N, Park  S  et al.  Use of cost-effectiveness analysis to compare the efficiency of study identification methods in systematic reviews. Syst Rev  2016;5:140. 10.1186/s13643-016-0315-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Tricco  AC, Antony  J, Zarin  W  et al.  A scoping review of rapid review methods. BMC Med  2015;13:224. 10.1186/s12916-015-0465-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Waffenschmidt  S, Knelangen  M, Sieben  W  et al.  Single screening versus conventional double screening for study selection in systematic reviews: a methodological systematic review. BMC Med Res Methodol  2019;19:132. 10.1186/s12874-019-0782-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Austin  EE, Blakely  B, Tufanaru  C  et al.  Strategies to measure and improve emergency department performance: a scoping review. Scand J Trauma Resusc Emerg Med  2020;28:55. 10.1186/s13049-020-00749-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Lyu  S-Y, Liao  C-K, Chang  K-P  et al.  Analysis of medical litigation among patients with medical disputes in cosmetic surgery in Taiwan. Aesthetic Plast Surg  2011;35:764–72. 10.1007/s00266-011-9684-1 [DOI] [PubMed] [Google Scholar]
  • 24. Sachdeo  A, Konfino  S, Icyda  RU  et al.  An analysis of patient grievances in a dental school clinical environment. J Dent Educ  2012;76:1317–22. 10.1002/j.0022-0337.2012.76.10.tb05386.x [DOI] [PubMed] [Google Scholar]
  • 25. Kynes  JM, Schildcrout  JS, Hickson  GB  et al.  An analysis of risk factors for patient complaints about ambulatory anesthesiology care. Anesth Analg  2013;116:1325–32. 10.1213/ANE.0b013e31827aef83 [DOI] [PubMed] [Google Scholar]
  • 26. Tessler  MJ, Shrier  I, Steele  RJ. Association between anesthesiologist age and litigation. Anesthesiology  2012;116:574–9. 10.1097/ALN.0b013e3182475ebf [DOI] [PubMed] [Google Scholar]
  • 27. Birkeland  S, Depont Christensen  R, Damsbo  N  et al.  Characteristics of complaints resulting in disciplinary actions against Danish GPs. Scand J Prim Health Care  2013;31:153–7. 10.3109/02813432.2013.823768 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. McGregor  MJ, Cohen  M, Stocks-Rankin  C-R  et al.  Complaints in for-profit, non-profit and public nursing homes in two Canadian provinces. Open Med  2011;5:e183–92. [PMC free article] [PubMed] [Google Scholar]
  • 29. Baker  SR, Whang  JS, Luk  L  et al.  The demography of medical malpractice suits against radiologists. Radiology  2013;266:539–47. 10.1148/radiol.12110971 [DOI] [PubMed] [Google Scholar]
  • 30. Nikoghosyan-Bossen  G  1, Hauberg  A, Homøe  P  1. Increased number of ear-nose-throat malpractice complaints in Denmark. Dan Med J  2012;59:1–5. [PubMed] [Google Scholar]
  • 31. Guidera  M, McCool  W, Hanlon  A  et al.  Midwives and liability: results from the 2009 nationwide survey of certified nurse-midwives and certified midwives in the United States. J Midwifery Womens Health  2012;57:345–52. 10.1111/j.1542-2011.2012.00201.x [DOI] [PubMed] [Google Scholar]
  • 32. Birkeland  S, Christensen  RD, Damsbo  N  et al.  Patient complaint cases in primary health care: what are the characteristics of general practitioners involved?  Biomed Res Int  2013;2013:1–5. 10.1155/2013/807204 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Phipps  DL, Noyce  PR, Walshe  K  et al.  Pharmacists subjected to disciplinary action: characteristics and risk factors. Int J Pharm Pract  2011;19:367–73. 10.1111/j.2042-7174.2011.00119.x [DOI] [PubMed] [Google Scholar]
  • 34. Bismark  MM, Spittal  MJ, Studdert  DM. Prevalence and characteristics of complaint‐prone doctors in private practice in Victoria. Med J Aust  2011;195:25–8. 10.5694/j.1326-5377.2011.tb03183.x [DOI] [PubMed] [Google Scholar]
  • 35. Samenow  CP, Yabiku  ST, Ghulyan  M  et al.  The role of family of origin in physicians referred to a CME course. HEC Forum  2012;24:115–26. 10.1007/s10730-011-9171-8 [DOI] [PubMed] [Google Scholar]
  • 36. Mehtsun  WT, Ibrahim  AM, Diener-West  M  et al.  Surgical never events in the United States. Surgery  2013;153:465–72. 10.1016/j.surg.2012.10.005 [DOI] [PubMed] [Google Scholar]
  • 37. Gogos  AJ, Clark  RB, Bismark  MM  et al.  When informed consent goes poorly: a descriptive study of medical negligence claims and patient complaints. Med J Aust  2011;195:340–4. 10.5694/mja11.10379 [DOI] [PubMed] [Google Scholar]
  • 38. Casali  MB, Blandino  A, Del Sordo  S  et al.  Alleged malpractice in orthopaedics. Analysis of a series of medical insurance claims. J Orthop Traumatol  2018;19:7. 10.1186/s10195-018-0500-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Pereira-Lima  K, Mata  DA, Loureiro  SR  et al.  Association between physician depressive symptoms and medical errors: a systematic review and meta-analysis. JAMA Network Open  2019;2:e1916097. 10.1001/jamanetworkopen.2019.16097 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Shouhed  D, Beni  C, Manguso  N  et al.  Association of emotional intelligence with malpractice claims: a review. JAMA Surg  2019;154:250–6. 10.1001/jamasurg.2018.5065 [DOI] [PubMed] [Google Scholar]
  • 41. Welle  D, Trockel  MT, Hamidi  MS  et al.  Association of occupational distress and sleep-related impairment in physicians with unsolicited patient complaints. Mayo Clin Proc  2020;95:719–26. 10.1016/j.mayocp.2019.09.025 [DOI] [PubMed] [Google Scholar]
  • 42. Liu  Z, Zhang  Y, Asante  JO  et al.  Characteristics of medical disputes arising from dental practice in Guangzhou, China: an observational study. BMJ Open  2018;8:e018738. 10.1136/bmjopen-2017-018738 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Panuganti  PL, Hartnett  DA, Eltorai  AEM  et al.  Colorectal cancer litigation: 1988–2018. Am J Gastroenterol  2020;115:1525–31. 10.14309/ajg.0000000000000705 [DOI] [PubMed] [Google Scholar]
  • 44. Veness  BG, Tibble  H, Grenyer  BF  et al.  Complaint risk among mental health practitioners compared with physical health practitioners: a retrospective cohort study of complaints to health regulators in Australia. BMJ Open  2019;9:e030525. 10.1136/bmjopen-2019-030525 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Thomas  LA, Tibble  H, Too  LS  et al.  Complaints about dental practitioners: an analysis of 6 years of complaints about dentists, dental prosthetists, oral health therapists, dental therapists and dental hygienists in Australia. Aust Dent J  2018;63:285–93. 10.1111/adj.12625 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Birkeland  S, Bismark  M, Barry  MJ  et al.  Does greater patient involvement in healthcare decision-making affect malpractice complaints? A large case vignette survey. PLoS One  2021;16:e0254052. 10.1371/journal.pone.0254052 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. De Champlain  AF, Ashworth  N, Kain  N  et al.  Does pass/fail on medical licensing exams predict future physician performance in practice?  J Med Regul  2020;106:17–26. 10.30770/2572-1852-106.4.17 [DOI] [Google Scholar]
  • 48. Birkeland  S, Bogh  SB, Cheng  M. Education trajectories and malpractice complaints—a study among Danish general practitioners. Cogent Educ  2018;5:1473747. 10.1080/2331186X.2018.1473747 [DOI] [Google Scholar]
  • 49. Schaffer  AC, Babayan  A, Yu-Moe  CW  et al.  The effect of clinical volume on annual and per-patient encounter medical malpractice claims risk. J Patient Saf  2021;17:e995–1000. 10.1097/PTS.0000000000000706 [DOI] [PubMed] [Google Scholar]
  • 50. Birkeland  S, Bogh  SB. General practice location and malpractice litigation. Rural Remote Health  2019;19:4663. 10.22605/RRH4663 [DOI] [PubMed] [Google Scholar]
  • 51. Walton  M, Kelly  PJ, Chiarella  EM  et al.  Profile of the most common complaints for five health professions in Australia. Aust Health Rev  2020;44:15–23. 10.1071/AH18074 [DOI] [PubMed] [Google Scholar]
  • 52. Carlson  JN, Foster  KM, Pines  JM  et al.  Provider and practice factors associated with emergency physicians’ being named in a malpractice claim. Ann Emerg Med  2018;71:157–164.e4. 10.1016/j.annemergmed.2017.06.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Schaffer  AC, Yu-Moe  CW, Babayan  A  et al.  Rates and characteristics of medical malpractice claims against hospitalists. J Hosp Med  2021;16:390–6. 10.12788/jhm.3557 [DOI] [PubMed] [Google Scholar]
  • 54. Nassiri  AM, Pichert  JW, Domenico  HJ  et al.  Unsolicited patient complaints among otolaryngologists. Otolaryngol Head Neck Surg  2019;160:810–7. 10.1177/0194599818823706 [DOI] [PubMed] [Google Scholar]
  • 55. Raldow  A, Adefres  B, Warso  M  et al.  Unsolicited patient complaints among radiation, medical, and surgical oncologists. Cancer  2021;127:2350–7. 10.1002/cncr.33513 [DOI] [PubMed] [Google Scholar]
  • 56. Tibble  HM, Broughton  NS, Studdert  DM  et al.  Why do surgeons receive more complaints than their physician peers? Complaints about surgeons and physicians. ANZ J Surg  2018;88:269–73. 10.1111/ans.14225 [DOI] [PubMed] [Google Scholar]
  • 57. Menon  NK, Shanafelt  TD, Sinsky  CA  et al.  Association of physician burnout with suicidal ideation and medical errors. JAMA Network Open  2020;3:e2028780. 10.1001/jamanetworkopen.2020.28780 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58. Stimson  CJ, Pichert  WJ, Ilene  NM  et al.  Medical malpractice claims risk in urology: an empirical analysis of patient complaint data. J Urol  2010;183:1971–6. 10.1016/j.juro.2010.01.027 [DOI] [PubMed] [Google Scholar]
  • 59. Dallal  RM, Pang  J, Soriano  I  et al.  Bariatric-related medical malpractice experience: survey results among ASMBS members. Surg Obes Relat Dis  2014;10:121–4. 10.1016/j.soard.2013.04.015 [DOI] [PubMed] [Google Scholar]
  • 60. Kohatsu  ND, Gould  D, Ross  LK  et al.  Characteristics associated with physician discipline: a case-control study. Arch Intern Med  2004;164:653–8. 10.1001/archinte.164.6.653 [DOI] [PubMed] [Google Scholar]
  • 61. Clay  SW, Conatser  RR. Characteristics of physicians disciplined by the State Medical Board of Ohio. J Am Osteopath Assoc  2003;103:81–8. [PubMed] [Google Scholar]
  • 62. Khaliq  AA, Dimassi  H, Huang  CY  et al.  Disciplinary action against physicians: who is likely to get disciplined?  Am J Med  2005;118:773–7. 10.1016/j.amjmed.2005.01.051 [DOI] [PubMed] [Google Scholar]
  • 63. Papadakis  MA, Teherani  A, Banach  MA  et al.  Disciplinary action by medical boards and prior behavior in medical school. N Engl J Med  2005;353:2673–82. 10.1056/NEJMsa052596 [DOI] [PubMed] [Google Scholar]
  • 64. Virapongse  A, Bates  DW, Shi  P  et al.  Electronic health records and malpractice claims in office practice. Arch Intern Med  2008;168:2362–7. 10.1001/archinte.168.21.2362 [DOI] [PubMed] [Google Scholar]
  • 65. Rolph  JE, Adams  JL, McGuigan  KA. Identifying malpractice-prone physicians. J Empir Leg Stud  2007;4:125–53. 10.1111/j.1740-1461.2007.00084.x [DOI] [Google Scholar]
  • 66. Perlis  CS, Campbell  RM, Perlis  RH  et al.  Incidence of and risk factors for medical malpractice lawsuits among Mohs surgeons. Dermatol Surg  2006;32:79–83. 10.1097/00042728-200601000-00016 [DOI] [PubMed] [Google Scholar]
  • 67. Abbott  RL, Ou  RJ, Bird  M. Medical malpractice predictors and risk factors for ophthalmologists performing LASIK and photorefractive keratectomy surgery. Ophthalmology  2003;110:2137–46. 10.1016/j.ophtha.2003.07.001 [DOI] [PubMed] [Google Scholar]
  • 68. Weycker  DA, Jensen  GA. Medical malpractice among physicians: who will be sued and who will pay?  Health Care Manag Sci  2000;3:269–77. 10.1023/A:1019014028914 [DOI] [PubMed] [Google Scholar]
  • 69. Mangalmurti  S, Seabury  SA, Chandra  A  et al.  Medical professional liability risk among US cardiologists. Am Heart J  2014;167:690–6. 10.1016/j.ahj.2014.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70. Waters  TM, Lefevre  FV, Budetti  PP. Medical school attended as a predictor of medical malpractice claims. Qual Saf Health Care  2003;12:330–6. 10.1136/qhc.12.5.330 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71. Hickson  GB, Federspiel  CF, Pichert  JW  et al.  Patient complaints and malpractice risk. JAMA  2002;287:2951–7. 10.1001/jama.287.22.2951 [DOI] [PubMed] [Google Scholar]
  • 72. Resnick  AS, Mullen  JL, Kaiser  LR  et al.  Patterns and predictions of resident misbehavior—a 10-year retrospective look. Curr Surg  2006;63:418–25. 10.1016/j.cursur.2006.05.004 [DOI] [PubMed] [Google Scholar]
  • 73. Papadakis  MA, Arnold  GK, Blank  LL  et al.  Performance during internal medicine residency training and subsequent disciplinary action by state licensing boards. Ann Intern Med  2008;148:869–76. 10.7326/0003-4819-148-11-200806030-00009 [DOI] [PubMed] [Google Scholar]
  • 74. Jena  AB, Schoemaker  L, Bhattacharya  J  et al.  Physician spending and subsequent risk of malpractice claims: observational study. BMJ  2015;351:h5516. 10.1136/bmj.h5516 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75. Chauhan  SP, Chauhan  VB, Cowan  BD  et al.  Professional liability claims and Central Association of Obstetricians and Gynecologists members: myth versus reality. Am J Obstet Gynecol  2005;192:1820–6. 10.1016/j.ajog.2004.12.058 [DOI] [PubMed] [Google Scholar]
  • 76. Kohanim  S, Sternberg  P, Karrass  J  et al.  Unsolicited patient complaints in ophthalmology: an empirical analysis from a large national database. Ophthalmology  2016;123:234–41. 10.1016/j.ophtha.2015.10.010 [DOI] [PubMed] [Google Scholar]
  • 77. Papadakis  MA, Hodgson  CS, Teherani  A  et al.  Unprofessional behavior in medical school is associated with subsequent disciplinary action by a state medical board. Acad Med  2004;79:244–9. 10.1097/00001888-200403000-00011 [DOI] [PubMed] [Google Scholar]
  • 78. Boyll  P, Kang  P, Mahabir  R  et al.  Variables that impact medical malpractice claims involving plastic surgeons in the United States. Aesthet Surg J  2017;38:785–92. 10.1093/asj/sjx182 [DOI] [PubMed] [Google Scholar]
  • 79. Adamson  TE, Baldwin  DC  Jr, Oppenberg  A  et al.  The virtuous orthopaedist has fewer malpractice suits. Clin Orthop Relat Res  2000;378:104–9. 10.1097/00003086-200009000-00017 [DOI] [PubMed] [Google Scholar]
  • 80. Taylor  DM, Wolfe  RS, Cameron  PA. Analysis of complaints lodged by patients attending Victorian hospitals, 1997–2001. Med J Aust  2004;181:31–5. 10.5694/j.1326-5377.2004.tb06157.x [DOI] [PubMed] [Google Scholar]
  • 81. Nash  LM, Kelly  PJ, Daly  MG  et al.  Australian doctors’ involvement in medicolegal matters: a cross-sectional self-report study. Med J Aust  2009;191:436–40. 10.5694/j.1326-5377.2009.tb02879.x [DOI] [PubMed] [Google Scholar]
  • 82. Nash  L, Daly  M, Johnson  M  et al.  Personality, gender and medico-legal matters in medical practice. Australas Psychiatry  2009;17:19–24. 10.1080/10398560802085359 [DOI] [PubMed] [Google Scholar]
  • 83. Spittal  MJ, Bismark  MM, Studdert  DM. The PRONE score: an algorithm for predicting doctors’ risks of formal patient complaints using routinely collected administrative data. BMJ Qual Saf  2015;24:360–8. 10.1136/bmjqs-2014-003834 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84. Liu  JJ, Alam  AQ, Goldberg  HR  et al.  Characteristics of internal medicine physicians disciplined by professional colleges in Canada. Medicine (Baltimore)  2015;94:e937–e937. 10.1097/MD.0000000000000937 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85. Tamblyn  R, Abrahamowicz  M, Dauphinee  D  et al.  Physician scores on a national clinical skills examination as predictors of complaints to medical regulatory authorities. JAMA  2007;298:993–1001. 10.1001/jama.298.9.993 [DOI] [PubMed] [Google Scholar]
  • 86. Ambady  N, LaPlante  D, Nguyen  T  et al.  Surgeons’ tone of voice: a clue to malpractice history. Surgery  2002;132:5–9. 10.1067/msy.2002.124733 [DOI] [PubMed] [Google Scholar]
  • 87. Yates  J, and James  D. Risk factors at medical school for subsequent professional misconduct: multicentre retrospective case-control study. BMJ  2010;340:c2040. 10.1136/bmj.c2040 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88. Otaki  Y, Ishida  MD, Saito  Y  et al.  Analysis of closed claims in the clinical management of rheumatoid arthritis in Japan. Chin Med J Engl  2017;130:1454–8. 10.4103/0366-6999.207479 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89. Unwin  E, Woolf  K, Wadlow  C  et al.  Disciplined doctors: does the sex of a doctor matter? A cross-sectional study examining the association between a doctor’s sex and receiving sanctions against their medical registration. BMJ Open  2014;4:e005405. 10.1136/bmjopen-2014-005405 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90. Wu  CY, Lai  HJ, Chen  RC. Medical malpractice experience of Taiwan: 2005 versus 1991. Intern Med J  2009;39:237–42. 10.1111/j.1445-5994.2009.01801.x [DOI] [PubMed] [Google Scholar]
  • 91. Wu  CY, Lai  HJ, Chen  RC. Patient characteristics predict occurrence and outcome of complaints against physicians: A study from a medical center in central Taiwan. J Formos Med Assoc  2009;108:126–34. 10.1016/S0929-6646(09)60043-7 [DOI] [PubMed] [Google Scholar]
  • 92. Tsimtsiou  Z, Kirana  PS, Hatzimouratidis  K  et al.  What is the profile of patients thinking of litigation? Results from the hospitalized and outpatients’ profile and expectations study. Hippokratia  2014;18:139–43. [PMC free article] [PubMed] [Google Scholar]
  • 93. Cunningham  W, Crump  R, and Tomlin  A. The characteristics of doctors receiving medical complaints: a cross-sectional survey of doctors in New Zealand. N Z Med J  2003;116:1183. [PubMed] [Google Scholar]
  • 94. Unwin  E, Woolf  K, Wadlow  C  et al.  Sex differences in medico-legal action against doctors: a systematic review and meta-analysis. BMC Med  2015;13:172–172. 10.1186/s12916-015-0413-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95. Joffe  AM, Aziz  MF, Posner  KL  et al.  Management of difficult tracheal intubation: a closed claims analysis. Anesthesiology  2019;131:818–29. 10.1097/ALN.0000000000002815 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mzad114_Supp
Click here for additional data file. (400.9KB, zip)

Data Availability Statement

Data relevant to the study are included in the article or uploaded as supplemental information.

Articles from International Journal for Quality in Health Care are provided here courtesy of Oxford University Press

RESOURCES