A Pre-Protocol/Post-Protocol Quality Improvement Initiative Specific to One Hospital for Security of Pathologic Specimen

ObjectiveThe objective of this study was to reduce errors in a pathologic specimen with the help of a protocol systematizing the pathology specimen management process in the operating room.Materials and methodThis quasi-experimental study was carried out in the operating room unit of a research and training hospital. A protocol systematizing the process of specimen management in secure surgical pathology and prepared in light of the current literature was used as an intervention, and the effectiveness of the protocol was tested.ResultsIt was determined that the rate of adverse events decreased from .3226% (68 of 21,078) to .032% (6 of 18,706) after the protocol systematizing the surgical pathology specimen management process prepared by the researchers, and the protocol was found to be effective by 90% (P = .03).ConclusionBased on the data obtained in this study, we recommend the use of a pathologic specimen management protocol in the operating room.     

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.     

Harnessing Behavioral Economics Principles to Promote Better Surgeon Accountability for Operating Room Cost: A Prospective Study

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