Reweighting to address nonparticipation and missing data bias in a longitudinal electronic health record study

When a Diagnosis Has No Name: Uncertainty and Opportunity

Diagnostic uncertainty, commonly encountered in rheumatology and other fields of medicine, is an opportunity: Stakeholders who understand uncertainty's causes and quantitate its effects can reduce uncertainty and can use uncertainty to improve medical practice, science, and administration. To articulate, bring attention to, and offer recommendations for diagnostic uncertainty, the Barbara Volcker Center at the Hospital for Special Surgery sponsored, in April 2021, a virtual international workshop, “When a Diagnosis Has No Name.” This paper summarizes the opinions of 72 stakeholders from the fields of medical research, industry, federal regulatory agencies, insurers, hospital management, medical philosophy, public media, health care law, clinical rheumatology, other specialty areas of medicine, and patients. Speakers addressed the effects of diagnostic uncertainty in their fields. The workshop addressed the following six questions: What is a diagnosis? What are the purposes of diagnoses? How do doctors assign diagnoses? What is uncertainty? What are its causes? How does understanding uncertainty offer opportunities to improve all fields of medicine? The workshop's conveners systematically reviewed video recordings of formal presentations, video recordings of open discussion periods, manuscripts, and slide files submitted by the speakers to develop consensus take‐home messages, which were as follows: Diagnostic uncertainty causes harm when patients lack access to laboratory test and treatments, do not participate in research studies, are not counted in administrative and public health documents, and suffer humiliation in their interactions with others. Uncertainty offers opportunities, such as quantifying uncertainty, using statistical technologies and automated intelligence to stratify patient groups by level of uncertainty, using a common vocabulary, and considering the effects of time.

A narrative review on the validity of electronic health record-based research in epidemiology

Electronic health records (EHRs) are widely used in epidemiological research, but the validity of the results is dependent upon the assumptions made about the healthcare system, the patient, and the provider. In this review, we identify four overarching challenges in using EHR-based data for epidemiological analysis, with a particular emphasis on threats to validity. These challenges include representativeness of the EHR to a target population, the availability and interpretability of clinical and non-clinical data, and missing data at both the variable and observation levels. Each challenge reveals layers of assumptions that the epidemiologist is required to make, from the point of patient entry into the healthcare system, to the provider documenting the results of the clinical exam and follow-up of the patient longitudinally; all with the potential to bias the results of analysis of these data. Understanding the extent of as well as remediating potential biases requires a variety of methodological approaches, from traditional sensitivity analyses and validation studies, to newer techniques such as natural language processing. Beyond methods to address these challenges, it will remain crucial for epidemiologists to engage with clinicians and informaticians at their institutions to ensure data quality and accessibility by forming multidisciplinary teams around specific research projects.