Electronic clinical records in primary care for estimating disease burden and management. An example of COPD

Chronic obstructive pulmonary disease (COPD) is a significant health problem in developed countries. We aimed to estimate the prevalence of COPD in a single Spanish healthcare area. We also aimed to assess if there are any differences in prevalence and spirometry use among primary care services by utilizing already registered information. We designed a cross-sectional study to determine the prevalence of COPD and the performance of spirometries in each primary care service. A total of 8,444 patients were diagnosed with COPD, with a prevalence of 2.6% for individuals older than 39 years. The prevalence increased with age and was much higher in men. Significant heterogeneity was found in the prevalence of COPD and spirometry use among primary care services. COPD was underdiagnosed and there was wide variability in spirometry use in our area. Greater efforts are needed to diagnose COPD in order to improve its clinical outcomes and to refine registries so that they can be used as reliable sources of information     

Optimizing access and configuration of trauma centre care in New South Wales

IntroductionGetting the right patient, to the right place, at the right time is dependent on a multitude of modifiable and non-modifiable factors. One potentially modifiable factor is the number and location of trauma centres (TC). Overabundance of TC dilutes volumes and could be associated with worse outcomes. We describe a methodology that evaluates trauma system reconfiguration without reductions in potential access to care. We used the mature trauma system of New South Wales (NSW) as a model given the perceived overabundance of urban major trauma centres (MTC).MethodsWe first evaluated potential access to TC care via ground and air transport through the use of geographic information systems (GIS) network analysis. Potential access was defined as the proportion of the population living within 60-min transport time from a potential scene of injury to a TC by ground or rotary-wing aircraft. Sensitivity analyses were carried out in order to account for potential pre-hospital interventions and/or transport delays; travel times of 15-, 30-, 45-, 60-, and 90-min were also analyzed. We then evaluated if the current configuration of the system (number of urban MTS in the Sydney basin) could be optimized without reductions in potential access to care using two GIS methodologies: location-allocation and individual removal of MTC.Results86% of the NSW population has potential access to a TC within 60 min ground travel time; potential access improves to 99% with rotary-wing transport. The 1% of the population without potential TC access lives in 48% of the land area (>384,000km2). Utilizing two different methodologies we identified that there was no change in potential access by ground transport after removing 1 or 2 MTC in the Sydney basin at the 30-, 45-, and 60-min transport times. However, 0.02% and 0.5% of the population would not have potential access to MTC care at 15 min after removing one and two MTC respectively.DiscussionRedistribution of the number of MTC in the Sydney basin could be achieved without a significant impact on potential access to care. Our approach can be utilized as an initial tool to evaluate a trauma system where overabundance of coverage is present.     

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.