儿科诊断性X线扫描家属知情与接受现状调查

目的:了解建德市基层医院儿科门诊进行X线扫描治疗中,家属对X射线检查的知情同意与接受现状。方法:随机抽取建德市属3家公立综合性医院共60位接受X线扫描的儿童患者家属为调查对象,发放自制调查问卷并统计分析。结果:55位(91.67%)患者表示治疗前主治医生只告知了X射线是诊断性检查,没有具体告知X射线的危害性;18位(30%)的患者家属不清楚辐射会对人体有损害,42位(70%)不清楚或不注意辐射警示标志,更不会主动要求防护措施,学历水平较高者及有从事医务工作背景者接受X射线检查的为12人(20%),明显低于学历水平低者或没有从事医务工作背景者的46人(76.67%),不知可否接受的有2人(3.33%)(P<0.001)。结论:基层医院儿科诊断性X射线扫描前的知情同意告知仍需加强,儿科放射检查偏多,应避免并加强宣传和教育。     

The art of consent: visual materials help adult patients make informed choices about surgical care

Abstract

Supporting patients in making informed healthcare decisions is a cornerstone of ethical medical practice. Surgeons frequently draw for and show images to patients when consenting them for operations but the value of this practice in informed decision-making is unclear. An audit was conducted in a General Surgery Department. 244 patients completed questionnaires on the value of visual materials when giving consent for surgery. The complexity of the operations was classified into “simple”, “moderate” or “complex”. 100% of patients felt they had given informed consent to surgery. 62% of patients received at least one form of visual material during the consenting process. All patients who received a drawing, and 99% of those provided with other images, valued these resources. Visual materials were considered more useful to patients when giving consent for moderate or complex operations than simple ones. Approximately one third of patients who did not receive visual materials would have appreciated these when making an informed decision. This research highlights the value of surgeons drawing for, and providing other visual resources to, their patients as part of the consent process. There is a role for further research and training materials in drawing skills for surgeons.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

Electronic health records and support for primary care teamwork

Objective Consensus that enhanced teamwork is necessary for efficient and effective primary care delivery is growing. We sought to identify how electronic health records (EHRs) facilitate and pose challenges to primary care teams as well as how practices are overcoming these challenges.Methods Practices in this qualitative study were selected from those recognized as patient-centered medical homes via the National Committee for Quality Assurance 2011 tool, which included a section on practice teamwork. We interviewed 63 respondents, ranging from physicians to front-desk staff, from 27 primary care practices ranging in size, type, geography, and population size.Results EHRs were found to facilitate communication and task delegation in primary care teams through instant messaging, task management software, and the ability to create evidence-based templates for symptom-specific data collection from patients by medical assistants and nurses (which can offload work from physicians). Areas where respondents felt that electronic medical record EHR functionalities were weakest and posed challenges to teamwork included the lack of integrated care manager software and care plans in EHRs, poor practice registry functionality and interoperability, and inadequate ease of tracking patient data in the EHR over time.Discussion Practices developed solutions for some of the challenges they faced when attempting to use EHRs to support teamwork but wanted more permanent vendor and policy solutions for other challenges.Conclusions EHR vendors in the United States need to work alongside practicing primary care teams to create more clinically useful EHRs that support dynamic care plans, integrated care management software, more functional and interoperable practice registries, and greater ease of data tracking over time.     

Healing the Professional Culture of Medicine

The past decade has been a time of great change for US physicians. Many physicians feel that the care delivery system has become a barrier to providing high-quality care rather than facilitating it. Although physician distress and some of the contributing factors are now widely recognized, much of the distress physicians are experiencing is related to insidious issues affecting the cultures of our profession, our health care organizations, and the health care delivery system. Culture refers to the shared and fundamental beliefs of a group that are so widely accepted that they are implicit and often no longer recognized. When challenges with culture arise, they almost always relate to a problem with a subcomponent of the culture even as the larger culture does many things well. In this perspective, we consider the role of culture in many of the problems facing our health care delivery system and contributing to the high prevalence of professional burnout plaguing US physicians. A framework, drawn from the field of organizational science, to address these issues and heal our professional culture is considered.     

Quality of decision aids developed for women at average risk of breast cancer eligible for mammographic screening: Systematic review and assessment according to the International Patient Decision Aid Standards instrument

Mammographic screening contributes to a reduction in specific mortality, but it has disadvantages. Decision aids are tools designed to support people's decisions. Because these aids influence patient choice, their quality is crucial. The objective of the current study was to conduct a systematic review of decision aids developed for women eligible for mammographic screening who have an average breast cancer risk and to assess the quality of these aids. The systematic review included articles published between January 1, 1997, and August 1, 2019, in the PubMed, Embase, Cochrane, and PsycInfo databases. The studies were reviewed independently by 2 reviewers. Any study containing a decision aid for women eligible for mammographic screening with an average breast cancer risk was included. Two double-blind reviewers assessed the quality of the selected decision aids using the International Patient Decision Aid Standards instrument, version 3 (IPDASi). Twenty-three decision aids were extracted. Classification of decision aid quality using the IPDASi demonstrated large variations among the decision aids (maximum IPDASi score, 188; mean ± SD score, 132.6 ± 23.8; range, 85-172). Three decision aids had high overall scores. The 3 best-rated dimensions were disclosure (maximum score, 8; mean score, 6.8), focusing on transparency; information (maximum score, 32; mean score, 26.1), focusing on the provision of sufficient details; and probabilities (maximum score, 32; mean score 25), focusing on the presentation of probabilities. The 3 lowest-rated dimensions were decision support technology evaluation (maximum score, 8; mean score, 4.3), focusing on the effectiveness of the decision aid; development (maximum score, 24; mean score, 12.6), evaluating the development process; and plain language (maximum score, 4; mean score, 1.9), assessing appropriateness for patients with low literacy. The results of this review identified 3 high-quality decision aids for breast cancer screening.