Do neurosurgeons receive more patient complaints than other physicians? Describing who is most at risk and how we can improve

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

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.

Professionalism of Admitting and Consulting Services and Trauma Patient Outcomes

OBJECTIVE

To determine whether trauma patients managed by an admitting or consulting service with a high proportion of physicians exhibiting patterns of unprofessional behaviors are at greater risk of complications or death.

SUMMARY BACKGROUND DATA

Trauma care requires high-functioning interdisciplinary teams where professionalism, particularly modeling respect and communicating effectively, is essential.

METHODS

This retrospective cohort study used data from nine level I trauma centers that participated in a national trauma registry linked with data from a national database of unsolicited patient complaints. The cohort included trauma patients admitted January 1, 2012 through December 31, 2017. The exposure of interest was care by one or more high-risk services, defined as teams with a greater proportion of physicians with high numbers of patient complaints. The study outcome was death or complications within 30 days.

RESULTS

Among the 71,046 patients in the cohort, 9,553 (13.4%) experienced the primary outcome of complications or death, including 1,875 of 16,107 patients (11.6%) with 0 high-risk services, 3,788 of 28,085 patients (13.5%) with one high-risk service, and 3,890 of 26,854 patients (14.5%) with 2+ high-risk services (p < .001). In logistic regression models adjusting for relevant patient, injury, and site characteristics, patients who received care from one or more high-risk services were at 24.1% (95% CI 17.2% to 31.3%; P < 0.001) greater risk of experiencing the primary study outcome.

CONCLUSIONS

Trauma patients who received care from at least one service with a high proportion of physicians modeling unprofessional behavior were at an increased risk of death or complications.

Patient complaints in the postoperative period following spine surgery

OBJECTIVE

Patient complaints are associated with a number of surgical and medical outcomes. Despite high rates of patient complaints regarding spine surgeons and efforts to study patient complaints across medicine and surgery, few studies have analyzed the complaints of patients undergoing spinal surgery. The authors present a retrospective analysis that, to their knowledge, is the first study to directly investigate the complaints of spine surgery patients in the postoperative period.

METHODS

Institutional records were reviewed over a 5-year period (2015-2019) to identify patients who underwent spine surgery and submitted a complaint to the institution's ombudsman's office within 1 year of their surgery. A control group, comprising patients who underwent spine surgery without filing a complaint, was matched to the group that filed complaints by admission diagnosis and procedure codes through propensity score matching. Patient demographic and clinical data were obtained by medical record review and compared between the two groups. Patient complaints were reviewed and categorized using a previously established taxonomy.

RESULTS

A total of 52 patients were identified who submitted a complaint after their spine surgery. There were 56 total complaints identified (4 patients submitted 2 each) that reported on 82 specific issues. Patient complaints were most often related to the quality of care received and communication breakdown between the healthcare team and the patient. Patients who submitted complaints were more likely to be Black or African American, have worse baseline health status, and have had prior spine surgery. After their surgery, these patients were also more likely to have longer hospital stays, experience postoperative complications, and require reoperation.

CONCLUSIONS

Complaints were most often related to the quality of care received and communication breakdown. A number of patient-level demographic and clinical characteristics were associated with an increased likelihood of a complaint being filed after spine surgery, and patients who filed complaints were more likely to experience postoperative complications. Improving communication with patients could play a key role in working to address and reduce postoperative complaints. Further study is needed to better understand patient complaints after spine surgery and investigate ways to optimize the care of patients with risks for postoperative complaints.

Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method

The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed using deep-learning technology for automatic tumor detection, visual assessment, and grading prediction. Specifically, a modified U-Net convolutional neural network was first established to segment tumor images. Subsequently, the segmentations were introduced into rendering algorithms for spatial reconstruction and another DenseNet convolutional neural network for grading prediction. The trained models were integrated as a system, and the robustness was tested based on its performance on an external dataset from the second institution involving different magnetic resonance imaging platforms. The results showed that the segment model represented a noteworthy performance with dice coefficients of 0.920 ± 0.009 in the validation group. With accurate segmented tumor images, the rendering model delicately reconstructed the tumor body and clearly displayed the important intracranial vessels. The DenseNet model also achieved high accuracy with an area under the curve of 0.918 ± 0.006 and accuracy of 0.901 ± 0.039 when classifying tumors into low-grade and high-grade meningiomas. Moreover, the system exhibited good performance on the external validation dataset.

Unsolicited patient complaints among radiation, medical, and surgical oncologists

BACKGROUND

Unsolicited patient complaints (UPCs) about physician practices are nonrandomly associated with malpractice claims and clinical quality. The authors evaluated the distributions and types of UPCs associated with oncologists by specialty and assessed oncologist characteristics associated with UPCs.

METHODS

This retrospective study reviewed UPCs associated with US radiation oncologists (ROs), medical oncologists (MOs), and surgical oncologists (SOs) from 35 health care systems from 2015 to 2018. Average total UPCs were compared by specialty in addition to sex, medical school graduation year, degree, medical school location, residency location, practice setting, and practice region. For continuous variables, linear regression was used to test for an association with total complaints.

RESULTS

The study included 1576 physicians: 318 ROs, 1020 MOs, and 238 SOs. The average number of UPCs per physician was different and depended on the oncologic specialty: ROs had significantly fewer complaints (1.28; 95% confidence interval [CI], 1.02-1.54) than MOs (3.81; 95% CI, 3.52-4.10) and SOs (6.89; 95% CI, 5.99-7.79; P < .0001). In a multivariable analysis, oncologic specialty, recency of graduation, and academic practice were predictive of higher total UPCs (P < .05). UPCs described concerns with care and treatment (42.8%), communication (26.4%), accessibility (17.5%), concern for patient (10.3%), and billing (2.9%).

CONCLUSIONS

ROs had significantly fewer complaints than MOs and SOs and may have a lower risk of malpractice claims as a group. In addition to oncologic specialty, a more recent year of medical school graduation and working at an academic center were independent risk factors for UPCs. Further research is needed to clarify the reasons underlying these associations and to identify interventions that decrease UPCs and associated risks.

LAY SUMMARY

This study of 1576 oncologists found that radiation oncologists had significantly fewer complaints than medical oncologists, who in turn had significantly fewer complaints than surgical oncologists. Other characteristics associated with more patient complaints included recency of medical school graduation and practice in an academic setting. Oncologists' patient complaints provide information that may have practical applications for patient safety and risk management. Understanding and addressing the characteristics that increase the risk for complaints could improve patients' experiences and outcomes.