Medical Malpractice: Reform for Today's Patients and Clinicians

Clinical applications of artificial intelligence in radiology

The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI's adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists' liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.

Medical Dispute Committees in the Netherlands: a qualitative study of patient expectations and experiences

BACKGROUND

Health care incidents, such as medical errors, cause tragedies all over the world. Recent legislation in the Netherlands has established medical dispute committees to provide for an appeals procedure offering an alternative to civil litigation and to meet the needs of clients. Dispute committees incorporate a hybrid procedure where one can file a complaint and a claim for damages resulting in a verdict without going to court. The procedure is at the crossroads of complaints law and civil litigation. This study seeks to analyze to what extent patients and family members' expectations and experiences with dispute committees match the goals of the new legislation.

METHODS

This qualitative, retrospective research includes in-depth, semi-structured, face-to-face interviews with patients or family members who filed a complaint with a dispute committee in the Netherlands. The researchers conducted an inductive, thematic analysis of the qualitative data.

RESULTS

A total of 26 interviews were held with 30 patients and family members. The results showed that participants particularly felt the need to be heard and to make a positive impact on health care. Some wished to be financially compensated, for others money was the last thing on their mind. The results demonstrated the existence of unequal power relationships between participants and both the defendant and dispute committee members. Participants reported the added value of (legal) support and expressed the need for dialogue at the hearing. Participants sometimes experienced closure after the proceedings, but often did not feel heard or felt a lack of a practical outcome and a tangible improvement.

CONCLUSIONS

This study shows that participants' expectations and experiences were not always met by the current set up of the dispute committee proceedings. Participants did not feel heard, while they did value the potential for monetary compensation. In addition, some participants did not experience an empowered position but rather a feeling of a power misbalance. The feeling of a power misbalance and not being heard might be explained by existing epistemic injustice, which is a concept that should be carefully considered in processes after health care incidents.

'Artificial intelligence in Barrett's Esophagus'

Despite advances in endoscopic imaging modalities, there are still significant miss rates of dysplasia and cancer in Barrett's esophagus. Artificial intelligence (AI) is a promising tool that may potentially be a useful adjunct to the endoscopist in detecting subtle dysplasia and cancer. Studies have shown AI systems have a sensitivity of more than 90% and specificity of more than 80% in detecting Barrett's related dysplasia and cancer. Beyond visual detection and diagnosis, AI may also prove to be useful in quality control, streamlining clinical work, documentation, and lessening the administrative load on physicians. Research in this area is advancing at a rapid rate, and as the field expands, regulations and guidelines will need to be put into place to better regulate the growth and use of AI. This review provides an overview of the present and future role of AI in Barrett's esophagus.

Defensive medicine practice in different specialties among junior physicians in kasralainy hospitals, Egypt

Background:

Defensive medicine has great impact on medical practice and population health. It may provide enhanced quality of services with good explanations to patients resulting in increased satisfaction. On the other hand, it might include unnecessary investigations, prescription of unnecessary treatments which may be expensive or dangerous for patients.

Aim of Work:

This study aims to evaluate awareness and practice of defensive medicine among junior doctors in Cairo University Hospital.

Methods:

This cross-sectional study includes 261 junior physicians by interviewing them using a structured questionnaire.

Results:

Defensive medicine practice is highly affected by sociodemographic characteristics of study population. Almost half the female doctors are always giving extra details about the medication use (56%) P < 0.001. Around 90% of both specialties have not been involved in medical litigation.

Conclusions:

Defensive medicine is highly prevalent among junior physicians. Following clinical standards and fear of legal actions by patients are considered main causes of practice of defensive medicine.

Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit

BACKGROUND AND AIMS

Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of GI endoscopy in areas ranging from lesion detection and classification to quality metrics and documentation. Progress in this field will be measured by whether AI implementation can lead to improved patient outcomes and more efficient clinical workflow for GI endoscopists. The aims of this article are to report the findings of a multidisciplinary group of experts focusing on issues in AI research and applications related to gastroenterology and endoscopy, to review the current status of the field, and to produce recommendations for investigators developing and studying new AI technologies for gastroenterology.

METHODS

A multidisciplinary meeting was held on September 28, 2019, bringing together academic, industry, and regulatory experts in diverse fields including gastroenterology, computer and imaging sciences, machine learning, computer vision, U.S. Food and Drug Administration, and the National Institutes of Health. Recent and ongoing studies in gastroenterology and current technology in AI were presented and discussed, key gaps in knowledge were identified, and recommendations were made for research that would have the highest impact in making advances and implementation in the field of AI to gastroenterology.

RESULTS

There was a consensus that AI will transform the field of gastroenterology, particularly endoscopy and image interpretation. Powered by advanced machine learning algorithms, the use of computer vision in endoscopy has the potential to result in better prediction and treatment outcomes for patients with gastroenterology disorders and cancer. Large libraries of endoscopic images, "EndoNet," will be important to facilitate development and application of AI systems. The regulatory environment for implementation of AI systems is evolving, but common outcomes such as colon polyp detection have been highlighted as potential clinical trial endpoints. Other threshold outcomes will be important, as well as clarity on iterative improvement of clinical systems.

CONCLUSIONS

Gastroenterology is a prime candidate for early adoption of AI. AI is rapidly moving from an experimental phase to a clinical implementation phase in gastroenterology. It is anticipated that the implementation of AI in gastroenterology over the next decade will have a significant and positive impact on patient care and clinical workflows. Ongoing collaboration among gastroenterologists, industry experts, and regulatory agencies will be important to ensure that progress is rapid and clinically meaningful. However, several constraints and areas will benefit from further exploration, including potential clinical applications, implementation, structure and governance, role of gastroenterologists, and potential impact of AI in gastroenterology.

Determination of legal responsibility in shared airway management between anesthesiology and otolaryngology

BACKGROUND

Malpractice litigation remains an important point of contention in the United States. Airway management often sees multidisciplinary teams of anesthesiologists and otolaryngologists. This report analyzes lawsuits affecting both teams in airway management.

METHODS

The Westlaw legal database (West Publishing Co., St. Paul, MN) was used to search for malpractice cases involving failed airway management, where both anesthesiology and otolaryngology were involved.

RESULTS

Among the 28 cases analyzed, otolaryngology and anesthesiology were most commonly sued together (46.4%). When sued together, defendants were less likely to win and average award amounts ($4, 558 716) were higher. These cases most commonly occurred in the operating room (78.6%), involved a difficult/improper intubation (39.3%), alleged a failure to follow standard of care (57%), and resulted in death (60.7%).

CONCLUSION

These cases primarily cited failure to follow standard of care and communication failures. Efforts should be directed toward multidisciplinary airway management protocols and effective communication.

Current Applications and Future Impact of Machine Learning in Radiology

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.

Outcomes of medical malpractice claims in assisted reproductive technology over a 10-year period from a single carrier

OBJECTIVE

Medical malpractice claims vary by specialty. Contributory factors to malpractice in reproductive endocrinology and infertility (REI) are not well defined. We sought to determine claims' frequency, basis of claims, and outcomes of settled claims in REI.

DESIGN

This is a retrospective, descriptive review of 10 years of claims.

SETTING

The setting is private practices.

MATERIALS AND METHODS

Claims were monitored within one malpractice carrier between 2006 and 2015 covering 10 practices and 184,015 IVF cycles. Total claims, basis of claims, and indemnity paid were evaluated.

RESULTS

There were 176 incidents resulting in 30 settled claims with indemnity payments in 21. Categories of claims settled included misdiagnosis (N = 4), lack of informed consent (N = 5), embryology errors (N = 8), and surgical complications (N = 4). Total and average awards were $15,062,000 and $717,238, respectively. Misdiagnosis and lack of informed consent had highest total award amount at $11,583,000 accounting for 76% of award dollars. The two highest awards were $4.5 million and $3.0 million for cancer and genetic misdiagnosis, respectively. Excluding these two awards, payments totaled $7,562,000, ranged from $6000 to $900,000 and averaged $170,363. Errors in handling of embryos were highest in frequency accounting for 38% of claims paid for a total of $1,593,000 with average payment of $199,188. Settlements for surgical complications totaled $1,855,000 and averaged $463,750 per claim.

CONCLUSIONS

Misdiagnosis and lack of informed consent are the highest award categories. Embryology lab errors are the most frequent causes of claims with the lowest award per settlement. The average cost for claims settled is relatively high compared to settlements in other specialties.