ImportanceSARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals.ObjectiveTo develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections.Design, Setting, and ParticipantsProspective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling.ExposureSARS-CoV-2 infection.Main Outcomes and MeasuresPASC and 44 participant-reported symptoms (with severity thresholds).ResultsA total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months.Conclusions and RelevanceA definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC.

BackgroundAdvance care planning allows patients to articulate their future care preferences should they no longer be able to make decisions on their own. Early advance care planning in outpatient settings provides benefits such as less aggressive care and fewer hospitalizations, yet it is underutilized due to barriers such as provider time constraints and communication complexity. Novel methods, such as patient portals, provide a unique opportunity to conduct advance care planning previsit planning for outpatient care. This follow-up to our pilot study aimed to conduct pragmatic testing of a novel electronic health record-tethered framework and its effects on advance care planning delivery in a real-world primary care setting.ObjectiveOur intervention tested a previsit advance care planning workflow centered around a framework sent via secure electronic health record-linked patient portal in a real-world clinical setting. The primary objective of this study was to determine its impact on frequency and quality of advance care planning documentation.MethodsWe conducted a pragmatic trial including 2 sister clinical sites, one site implementing the intervention and the other continuing standard care. A total of 419 patients aged between 50 and 93 years with active portal accounts received intervention (n=200) or standard care (n=219). Chart review analyzed the presence of advance care planning and its quality and was graded with previously established scoring criteria based on advance care planning best practice guidelines from multiple nations.ResultsA total of 19.5% (39/200) of patients who received previsit planning responded to the framework. We found that the intervention site had statistically significant improvement in new advance care planning documentation rates (P<.01) and quality (P<.01) among all eligible patients. Advance care planning documentation rates increased by 105% (19/39 to 39/39) and quality improved among all patients who engaged in the previsit planning framework (n=39). Among eligible patients aged between 50 and 60 years at the intervention site, advance care planning documentation rates increased by 37% (27/96 to 37/96). Advance care planning documentation rates increased 34% among high users (27/67 to 36/67).ConclusionsAdvance care planning previsit planning using a secure electronic health record-supported patient portal framework yielded improvement in the presence of advance care planning documentation, with highest improvement in active patient portal users and patients aged between 50 and 60 years. Targeted previsit patient portal advance care planning delivery in these populations can potentially improve the quality of care in these populations.

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Background Advance care planning is the process of discussing health care treatment preferences based on patients’ personal values, and it often involves the completion of advance directives. In the first months of 2020, a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began circulating widely in the American state of Colorado, leading to widespread diagnosis of coronavirus disease (COVID-19), hospitalizations, and deaths. In this context, the importance of technology-based, non–face-to-face methods to conduct advance care planning via patient portals has increased. Objective The aim of this study was to determine the rates of use of a web-based advance care planning tool through a health system–based electronic patient portal both before and in the early months of the COVID-19 pandemic. Methods In 2017, we implemented web-based tools through the patient portal of UCHealth’s electronic health record (EHR) for patients to learn about advance care planning and complete an electronically signed medical durable power of attorney (MDPOA) to legally appoint a medical decision maker. Patients accessing the portal can complete and submit a legally valid MDPOA, which becomes part of their medical record. We collected data on the patients’ date of MDPOA completion, use of advance care planning messaging, age, sex, and geographic location during the early phase of the COVID-19 pandemic (December 29, 2019, to May 30, 2020). Results Over a 5-month period that includes the early phase of the COVID-19 pandemic in Colorado, total monthly use of the advance care planning portal tool increased from 418 users in January to 1037 users in April and then decreased slightly to 815 users in May. The number of MDPOA forms submitted per week increased 2.4-fold after the stay-at-home order was issued in Colorado on March 26, 2020 (P<.001). The mean age of the advance care planning portal users was 47.7 years (SD 16.1), and 2206/3292 (67.0%) were female. Women were more likely than men to complete an MDPOA, particularly in younger age groups (P<.001). The primary use of the advance care planning portal tools was the completion of an MDPOA (3138/3292, 95.3%), compared to sending an electronic message (148/3292, 4.5%). Over 50% of patients who completed an MDPOA did not have a prior agent in the EHR. Conclusions Use of a web-based patient portal to complete an MDPOA increased substantially during the first months of the COVID-19 pandemic in Colorado. There was an increase in advance care planning that corresponded with state government shelter-in-place orders as well as public health reports of increased numbers of COVID-19 cases and deaths. Patient portals are an important tool for providing advance care planning resources and documenting medical decision makers during the pandemic to ensure that medical treatment aligns with patient goals and values.

IntroductionGestational diabetes mellitus (GDM) is the most common complication of pregnancy and is associated with an increased risk for type 2 diabetes. Racial/ethnic minority populations are at a higher risk than non-Hispanic white populations of developing type 2 diabetes after GDM. The aim of this study was to describe racial/ethnic differences in hyperglycemia and receipt of screening services in a nationally representative sample of women with a history of GDM.MethodsOur sample included 765 women from the US National Health and Nutrition Examination Survey (2007–2016) with a history of GDM. We used logistic, multinomial, linear, and proportional hazards regression to evaluate racial/ethnic differences in development of diabetes after GDM, hyperglycemia (measured by HbA1c), and receipt of diabetes screening services.ResultsNon-Hispanic black women had 63% higher risk and Hispanic women and “other” racial/ethnic women had more than double the risk for diabetes compared with non-Hispanic white women. Among women with a GDM history who did not receive a diagnosis of diabetes by the time of the study examination, both non-Hispanic black women and Hispanic women were more likely than non-Hispanic white women to be in the prediabetes or diabetes range (measured HbA1c ≥5.7%). However, non-Hispanic black women had 2.07 (95% confidence interval, 1.29–3.81) times the odds of being screened for diabetes compared with non-Hispanic white women (P = .02).ConclusionDelays in identification of hyperglycemia and diagnosis of diabetes in racial/ethnic minority women may reflect differential delivery of guideline-based care or poor follow-up of abnormal screening test results.

Background Artificial intelligence (AI) is rapidly expanding in medicine despite a lack of consensus on its application and evaluation. Objective We sought to identify current frameworks guiding the application and evaluation of AI for predictive analytics in medicine and to describe the content of these frameworks. We also assessed what stages along the AI translational spectrum (ie, AI development, reporting, evaluation, implementation, and surveillance) the content of each framework has been discussed. Methods We performed a literature review of frameworks regarding the oversight of AI in medicine. The search included key topics such as “artificial intelligence,” “machine learning,” “guidance as topic,” and “translational science,” and spanned the time period 2014-2022. Documents were included if they provided generalizable guidance regarding the use or evaluation of AI in medicine. Included frameworks are summarized descriptively and were subjected to content analysis. A novel evaluation matrix was developed and applied to appraise the frameworks’ coverage of content areas across translational stages. Results Fourteen frameworks are featured in the review, including six frameworks that provide descriptive guidance and eight that provide reporting checklists for medical applications of AI. Content analysis revealed five considerations related to the oversight of AI in medicine across frameworks: transparency, reproducibility, ethics, effectiveness, and engagement. All frameworks include discussions regarding transparency, reproducibility, ethics, and effectiveness, while only half of the frameworks discuss engagement. The evaluation matrix revealed that frameworks were most likely to report AI considerations for the translational stage of development and were least likely to report considerations for the translational stage of surveillance. Conclusions Existing frameworks for the application and evaluation of AI in medicine notably offer less input on the role of engagement in oversight and regarding the translational stage of surveillance. Identifying and optimizing strategies for engagement are essential to ensure that AI can meaningfully benefit patients and other end users.

Purpose: This study aimed to determine the impact of advanced care planning (ACP) on potentially avoidable hospital admissions at the end of life (EOL) among a sample of hospice-referred patients with cancer, in order to present actionable considerations for the practicing clinician. Methods: This study was designed as a retrospective cohort using electronic health record data that assessed likelihood of hospital admissions in the last 30 days of life for 1185 patients with a primary diagnosis of cancer, referred to hospice between January 1, 2014, and December 31, 2015, at a large academic medical center. Inverse probability treatment weighting based on calculated propensity scores balanced measured covariates between those with and without ACP at baseline. Odds ratios (ORs) were calculated from estimated potential outcome means for the impact of ACP on admissions in the last 30 days of life. Results: A verified do-not-resuscitate (DNR) order prior to the last 30 days of life was associated with reduced odds of admission compared to those without a DNR (OR = 0.30; P < .001). An ACP note in the problem list prior to the last 30 days of life was associated with reduced odds of admission compared to those without an ACP note (OR = 0.71, P = .042), and further reduced odds if done 6 months prior to death (OR = 0.35, P < .001). Conclusions: This study shows that dedicated ACP documentation is associated with fewer admissions in the last 30 days of life for patients with advanced cancer referred to hospice. Improving ACP processes prior to hospice referral holds promise for reducing EOL admissions.

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