Focused Ethnography of Diagnosis in Academic Medical Centers
BACKGROUND: Approaches of trainees to diagnosis in teaching hospitals are poorly understood. Identifying cognitive and system-based barriers and facilitators to diagnosis may improve diagnosis in these settings.
METHODS: We conducted a focused ethnography of trainees at two academic medical centers to understand the barriers and facilitators to diagnosis. Field notes regarding the diagnostic process (eg, information gathering, integration and interpretation, working diagnosis) and the work system (eg, team members, organization, technology and tools, physical environment, tasks) were recorded. Following observations, focus groups and interviews were conducted to understand the viewpoints, problems, and solutions to improve diagnosis.
RESULTS: Between January 2016 and May 2016, four teaching teams (4 attendings, 4 senior residents, 9 interns, and 12 medical students) were observed for 168 hours. Observations of diagnosis during care led to identification of the following four key themes: (1) diagnosis is a social phenomenon; (2) data necessary to make diagnoses are fragmented; (3) distractions interfere with the diagnostic process; and (4) time pressures impede diagnostic decision-making. These themes suggest that specific interventions tailored to the academic setting such as team-based discussions of diagnostic workups, scheduling diagnostic time-outs during the day, and strategies to “protect” learners from interruptions might prove to be useful in improving the process of diagnosis. Future studies that implement these ideas (either alone or within a multimodal intervention) appear to be necessary.
CONCLUSION: Diagnosis in teaching hospitals is a unique process that requires improvement. Contextual insights gained from this ethnography may be used to inform future interventions.
© 2018 Society of Hospital Medicine
Our study underscores the importance of social interactions in diagnosis. In contrast, most of the interventions to prevent diagnostic errors target individual providers through practices such as metacognition and “thinking about thinking.”18-20 These interventions are based on Daniel Kahnemann’s work on dual thought process. Type 1 thought processes are fast, subconscious, reflexive, largely intuitive, and more vulnerable to error. In contrast, Type 2 processes are slower, deliberate, analytic, and less prone to error.21 Although an individual’s Type 2 thought capacity is limited, a major goal of cognitive interventions is to encourage Type 2 over Type 1 thinking, an approach termed “de-biasing.”22-24 Unfortunately, cognitive interventions testing such approaches have suffered mixed results–perhaps because of lack of focus on collective wisdom or group thinking, which may be key to diagnosis from our findings.9,25 In this sense, morning rounds were a social gathering used to strategize and develop care plans, but with limited time to think about diagnosis.26 Introduction of defined periods for individuals to engage in diagnostic activities such as de-biasing (ie, asking “what else could this be)27 before or after rounds may provide an opportunity for reflection and improving diagnosis. In addition, embedding tools such as diagnosis expanders and checklists within these defined time slots28,29 may prove to be useful in reflecting on diagnosis and preventing diagnostic errors.
An unexpected yet important finding from this study were the challenges posed by distractions and the physical environment. Potentially maladaptive workarounds to these interruptions included use of headphones; more productive strategies included updating nurses with plans to avert pages and creating a list of activities to ensure that key tasks were not forgotten.30,31 Applying lessons from aviation, a focused effort to limit distractions during key portions of the day, might be worth considering for diagnostic safety.32 Similarly, improving the environment in which diagnosis occurs—including creating spaces that are quiet, orderly, and optimized for thinking—may be valuable.33Our study has limitations. First, our findings are limited to direct observations; we are thus unable to comment on how unobserved aspects of care (eg, cognitive processes) might have influenced our findings. Our observations of clinical care might also have introduced a Hawthorne effect. However, because we were closely integrated with teams and conducted focus groups to corroborate our assessments, we believe that this was not the case. Second, we did not identify diagnostic errors or link processes we observed to errors. Third, our approach is limited to 2 teaching centers, thereby limiting the generalizability of findings. Relatedly, we were only able to conduct observations during weekdays; differences in weekend and night resources might affect our insights.
The cognitive and system-based barriers faced by clinicians in teaching hospitals suggest that new methods to improve diagnosis are needed. Future interventions such as defined “time-outs” for diagnosis, strategies focused on limiting distractions, and methods to improve communication between team members are novel and have parallels in other industries. As challenges to quantify diagnostic errors abound,34 improving cognitive- and system-based factors via reflection through communication, concentration, and organization is necessary to improve medical decision making in academic medical centers.
Disclosures
None declared for all coauthors.
Funding
This project was supported by grant number P30HS024385 from the Agency for Healthcare Research and Quality. The funding source played no role in study design, data acquisition, analysis or decision to report these data. Dr. Chopra is supported by a career development award from the Agency of Healthcare Research and Quality (1-K08-HS022835-01). Dr. Krein is supported by a VA Health Services Research and Development Research Career Scientist Award (RCS 11-222). Dr. Singh is partially supported by Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality or the Department of Veterans Affairs.
