Citations

Patient portals have been widely used by patients to enable timely communications with their providers via secure messaging for various issues including transportation barriers. The large volume of portal messages offers an invaluable opportunity for studying transportation barriers reported by patients. In this work, we explored the feasibility of cutting-edge deep learning techniques for identifying transportation issues mentioned in patient portal messages with deep semantic embeddings. The successful creation of annotated corpus and identification of 7 transportation issues showed the feasibility of this strategy. The developed annotated corpus could aid in developing an artificial intelligence tool to automatically identify transportation issues from millions of patient portal messages. The identified specific transportation issues and the analysis of patient demographics could shed light on how to reduce transportation gaps for patients.

Background . Patients increasingly use asynchronous communication platforms to converse with care teams. Natural language processing (NLP) to classify content and automate triage of these messages has great potential to enhance clinical efficiency. We characterize the contents of a corpus of portal messages generated by patients using NLP methods. We aim to demonstrate descriptive analyses of patient text that can contribute to the development of future sophisticated NLP applications. Methods . We collected approximately 3,000 portal messages from the cardiology, dermatology, and gastroenterology departments at Mayo Clinic. After labeling these messages as either Active Symptom, Logistical, Prescription, or Update, we used NER (named entity recognition) to identify medical concepts based on the UMLS library. We hierarchically analyzed the distribution of these messages in terms of departments, message types, medical concepts, and keywords therewithin. Results . Active Symptom and Logistical content types comprised approximately 67% of the message cohort. The “Findings” medical concept had the largest number of keywords across all groupings of content types and departments. “Anatomical Sites” and “Disorders” keywords were more prevalent in Active Symptom messages, while “Drugs” keywords were most prevalent in Prescription messages. Logistical messages tended to have the lower proportions of “Anatomical Sites,”, “Disorders,”, “Drugs,”, and “Findings” keywords when compared to other message content types. Conclusions . This descriptive corpus analysis sheds light on the content and foci of portal messages. The insight into the content and differences among message themes can inform the development of more robust NLP models.

OBJECTIVEThe level of radiation awareness by surgeons and residents in spinal surgery does not match the ubiquity of fluoroscopy in operating rooms in the United States. The present method of monitoring radiation exposure may contribute to the current deficiency in radiation awareness. Current dosimeters involve a considerable lag from the time that the surgical team is exposed to radiation to the time that they are provided with that exposure data. The objective of the current study was to assess the feasibility of monitoring radiation exposure in operating room personnel during lateral transpsoas lumbar interbody fusion (LLIF) and minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) procedures by using a wearable personal device with real-time feedback.METHODSOperating room staff participating in minimally invasive surgical procedures under a single surgeon during a 6-month period were prospectively enrolled in this study. All radiation dose exposures were recorded for each member of the surgical team (surgeon, assistant surgeon, scrub nurse, and circulating nurse) using a personal dosimeter (DoseAware). Radiation doses were recorded in microsieverts (μSv). Comparisons between groups were made using ANOVA with the Tukey post hoc test and Student t-test.RESULTSThirty-nine patients underwent interbody fusions: 25 underwent LLIF procedures (14 LLIF alone, 11 LLIF with percutaneous screw placement [PSP]) and 14 underwent MI-TLIF. For each operative scenario per spinal level, the surgeon experienced significantly higher (p < 0.035) average radiation exposure (LLIF: 167.9 μSv, LLIF+PSP: 424.2 μSv, MI-TLIF: 397.9 μSv) than other members of the team, followed by the assistant surgeon (LLIF: 149.7 μSv, LLIF+PSP: 242.3 μSv, MI-TLIF: 274.9 μSv). The scrub nurse (LLIF: 15.4 μSv, LLIF+PSP: 125.7 μSv, MI-TLIF: 183.0 μSv) and circulating nurse (LLIF: 1.2 μSv, LLIF+PSP: 9.2 μSv, MI-TLIF: 102.3 μSv) experienced significantly lower exposures. Radiation exposure was not correlated with the patient’s body mass index (p ≥ 0.233); however, it was positively correlated with increasing patient age (p ≤ 0.004).CONCLUSIONSReal-time monitoring of radiation exposure is currently feasible and shortens the time between exposure and the availability of information regarding that exposure. A shortened feedback loop that offers more reliable and immediate data would conceivably raise the level of concern for radiation exposure in spinal surgeries and could alter patterns of behavior, leading to decreased exposures. Further studies are ongoing to determine the effect of real-time dosimetry in spinal surgery.

Certain medical schools have begun teaching courses in health systems science (HSS) to train medical students in skills aimed to improve health care in the United States. Although substantial research has been done on the potential benefit of HSS courses, reactions from students have not been reported. In this Invited Commentary, five medical students who have completed the first year of a longitudinal HSS course at the Arizona campus of the Mayo Clinic School of Medicine offer their observations of how early exposure to HSS affected their reactions to subsequent course work and current events in health care. The authors describe the HSS course and outline three benefits they have observed from their experience so far: (1) thinking more critically about health care delivery during all educational experiences, (2) gaining a better understanding of the complexity of the health care system, and (3) having a greater consideration for the many facets of health care delivery. The HSS course helped the authors identify health systems problems, develop solutions that incorporated diverse domains of health care delivery, and recognize the role and responsibility of the physician as an agent of change in a health care system.

AbstractSubclinical hypothyroidism (SCH), a mild form of hypothyroidism defined as elevated TSH with normal free thyroxine levels, is a common diagnosis among women of reproductive age. In some, but not all, studies, it has been associated with infertility, an increased risk of adverse pregnancy and neonatal outcomes, and possibly with an increased risk of neurocognitive deficits in offspring. Despite well-established recommendations on treatment of overt hypothyroid pregnant women, a consensus has not yet been reached on whether to treat women with SCH. This review focuses on examining the evidence informing the clinical strategy for using levothyroxine (LT4) in women with SCH during pregnancy and those who are planning conception. A crucial first step is to accurately diagnose SCH using the appropriate population-based reference range. For pregnant women, if this is unavailable, the recommended TSH upper normal limit cutoff is 4.0 mIU/L. There is evidence supporting a decreased risk for pregnancy loss and preterm delivery for pregnant women with TSH &gt; 4.0 mIU/L receiving LT4 therapy. LT4 treatment has been associated with better reproductive outcomes in women with SCH undergoing artificial reproductive techniques, but not in those who are attempting natural conception. Thyroid function tests need to be repeated throughout pregnancy to monitor LT4 therapy. In addition to potential harms, LT4 contributes to treatment burden. During a consultation, clinicians and patients should engage in a careful consideration of the current evidence in the context of the patients’ values and preferences to determine whether LT4 therapy initiation is the best next step.