Abstract
This study provides data on the feasibility and impact of video-enabled telemedicine use among patients and providers and its impact on urgent and nonurgent healthcare delivery from one ...large health system (NYU Langone Health) at the epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States. Between March 2nd and April 14th 2020, telemedicine visits increased from 102.4 daily to 801.6 daily. (683% increase) in urgent care after the system-wide expansion of virtual urgent care staff in response to COVID-19. Of all virtual visits post expansion, 56.2% and 17.6% urgent and nonurgent visits, respectively, were COVID-19–related. Telemedicine usage was highest by patients 20 to 44 years of age, particularly for urgent care. The COVID-19 pandemic has driven rapid expansion of telemedicine use for urgent care and nonurgent care visits beyond baseline periods. This reflects an important change in telemedicine that other institutions facing the COVID-19 pandemic should anticipate.
Through the coronavirus disease 2019 (COVID-19) pandemic, telemedicine became a necessary entry point into the process of diagnosis, triage, and treatment. Racial and ethnic disparities in healthcare ...have been well documented in COVID-19 with respect to risk of infection and in-hospital outcomes once admitted, and here we assess disparities in those who access healthcare via telemedicine for COVID-19.
Electronic health record data of patients at New York University Langone Health between March 19th and April 30, 2020 were used to conduct descriptive and multilevel regression analyses with respect to visit type (telemedicine or in-person), suspected COVID diagnosis, and COVID test results.
Controlling for individual and community-level attributes, Black patients had 0.6 times the adjusted odds (95% CI: 0.58-0.63) of accessing care through telemedicine compared to white patients, though they are increasingly accessing telemedicine for urgent care, driven by a younger and female population. COVID diagnoses were significantly more likely for Black versus white telemedicine patients.
There are disparities for Black patients accessing telemedicine, however increased uptake by young, female Black patients. Mean income and decreased mean household size of a zip code were also significantly related to telemedicine use.
Telemedicine access disparities reflect those in in-person healthcare access. Roots of disparate use are complex and reflect individual, community, and structural factors, including their intersection-many of which are due to systemic racism. Evidence regarding disparities that manifest through telemedicine can be used to inform tool design and systemic efforts to promote digital health equity.
Background:
Nonadherence to statins limits the benefits of this common drug class. Individual studies assessing predictors of nonadherence haue produced inconsistent results.
Objective:
To identify ...reliable predictors of nonadherence to statins through systematic review and meta-analysis.
Methods:
Multiple databases, including MEDLINE, EMBASE, and PsycINFO, were searched (from inception through February 2009) to identify studies that evaluated predictors of nonadherence to statins. Studies were selected using a priori defined criteria, and each study was reviewed by 2 authors who abstracted data on study characteristics and outcomes. Relative risks were then pooled, using an inverse-variance weighted random-effects model.
Results:
Twenty-two cohort studies met inclusion criteria. Age had a U-shaped association with adherence; the oldest (≥70 years) and youngest (<50 years) subjects had lower adherence than the middle-aged (50-69 years) subjects. Women and patients with lower incomes were more likely to be nonadherent than were men (odds of nonadherence 1.07; 95% CI 1.04 to 1.11) and those with higher incomes (odds of nonadherence 1.18:95% CI 1.10 to 1.28), respectively. A history of cardiovascular disease predicted better adherence to statins (odds of nonadherence 0.68; 95% CI 0.66 to 0.78). Similarly, a diagnosis of hypertension or diabetes was associated with better adherence. Although there were too few studies for quantitative pooling, increased testing of lipid levels and lower out-of-pocket costs appeared to be associated with better adherence. There was substantial (l2 range 68.7-96.3%) heterogeneity between studies across factors.
Conclusions:
Several sociodemographic, medical, and health-care utilization characteristics are associated with statin nonadherence. These factors may be useful guides for targeting statin adherence interventions.
Despite the effectiveness of drug therapy in diabetes management high rates of poor adherence persist. The purpose of this study was to identify potentially modifiable patient disease and medication ...beliefs associated with poor medication adherence among people with diabetes. A cohort of patients with diabetes was recruited from an urban primary-care clinic in New York City. Patients were interviewed in English or Spanish about: disease beliefs, medication beliefs, regimen complexity, diabetes knowledge, depression, self-efficacy, and medication adherence (Morisky scale). Logistic regression was used to identify multivariate predictors of poor medication adherence (Morisky > 1). Patients (
n
= 151) had diabetes for an average of 13 years with a mean HgA1C of 7.6 (SD 1.7). One-in-four (28%) were poor adherers to their diabetes medicines. In multivariate analyses, predictors of poor medication adherence were: believing you have diabetes only when your sugar is high (OR = 7.4;2–27.2), saying there was no need to take medicine when the glucose was normal (OR = 3.5;0.9–13.7), worrying about side-effects of diabetes medicines (OR = 3.3;1.3–8.7), lack of self-confidence in controlling diabetes (OR = 2.8;1.1–7.1), and feeling medicines are hard to take (OR = 14.0;4.4–44.6). Disease and medication beliefs inconsistent with a chronic disease model of diabetes were significant predictors of poor medication adherence. These suboptimal beliefs are potentially modifiable and are logical targets for educational interventions to improve diabetes self-management.
Observational studies suggest that there are differences in adherence to antihypertensive medications in different classes. Our objective was to quantify the association between antihypertensive drug ...class and adherence in clinical settings.
Studies were identified through a systematic search of English-language articles published from the inception of computerized databases until February 1, 2009. Studies were included if they measured adherence to antihypertensives using medication refill data and contained sufficient data to calculate a measure of relative risk of adherence and its variance. An inverse-variance-weighted random-effects model was used to pool results. Hazard ratios (HRs) and odds ratios were pooled separately, and HRs were selected as the primary outcome. Seventeen studies met inclusion criteria. The pooled mean adherence by drug class ranged from 28% for β-blockers to 65% for angiotensin II receptor blockers. There was better adherence to angiotensin II receptor blockers compared with angiotensin-converting enzyme inhibitors (HR, 1.33; 95% confidence interval, 1.13 to 1.57), calcium channel blockers (HR, 1.57; 95% confidence interval, 1.38 to 1.79), diuretics (HR, 1.95; 95% confidence interval, 1.73 to 2.20), and β-blockers (HR, 2.09; 95% confidence interval, 1.14 to 3.85). Conversely, there was lower adherence to diuretics compared with the other drug classes. The same pattern was present when studies that used odds ratios were pooled. After publication bias was accounted for, there were no longer significant differences in adherence between angiotensin II receptor blockers and angiotensin-converting enzyme inhibitors or between diuretics and β-blockers.
In clinical settings, there are important differences in adherence to antihypertensives in separate classes, with lowest adherence to diuretics and β-blockers and highest adherence to angiotensin II receptor blockers and angiotensin-converting enzyme inhibitors. However, adherence was suboptimal regardless of drug class.
Effective communication of risks and benefits to patients is critical for shared decision making.
To review the comparative effectiveness of methods of communicating probabilistic information to ...patients that maximize their cognitive and behavioral outcomes.
PubMed (1966 to March 2014) and CINAHL, EMBASE, and the Cochrane Central Register of Controlled Trials (1966 to December 2011) using several keywords and structured terms.
Prospective or cross-sectional studies that recruited patients or healthy volunteers and compared any method of communicating probabilistic information with another method.
Two independent reviewers extracted study characteristics and assessed risk of bias.
Eighty-four articles, representing 91 unique studies, evaluated various methods of numerical and visual risk display across several risk scenarios and with diverse outcome measures. Studies showed that visual aids (icon arrays and bar graphs) improved patients' understanding and satisfaction. Presentations including absolute risk reductions were better than those including relative risk reductions for maximizing accuracy and seemed less likely than presentations with relative risk reductions to influence decisions to accept therapy. The presentation of numbers needed to treat reduced understanding. Comparative effects of presentations of frequencies (such as 1 in 5) versus event rates (percentages, such as 20%) were inconclusive.
Most studies were small and highly variable in terms of setting, context, and methods of administering interventions.
Visual aids and absolute risk formats can improve patients' understanding of probabilistic information, whereas numbers needed to treat can lessen their understanding. Due to study heterogeneity, the superiority of any single method for conveying probabilistic information is not established, but there are several good options to help clinicians communicate with patients.
None.
Background During the COVID-19 pandemic, acute respiratory infection (ARI) antibiotic prescribing in ambulatory care markedly decreased. It is unclear if antibiotic prescription rates will remain ...lowered. Methods We used trend analyses of antibiotics prescribed during and after the first wave of COVID-19 to determine whether ARI antibiotic prescribing rates in ambulatory care have remained suppressed compared to pre-COVID-19 levels. Retrospective data was used from patients with ARI or UTI diagnosis code(s) for their encounter from 298 primary care and 66 urgent care practices within four academic health systems in New York, Wisconsin, and Utah between January 2017 and June 2022. The primary measures included antibiotic prescriptions per 100 non-COVID ARI encounters, encounter volume, prescribing trends, and change from expected trend. Results At baseline, during and after the first wave, the overall ARI antibiotic prescribing rates were 54.7, 38.5, and 54.7 prescriptions per 100 encounters, respectively. ARI antibiotic prescription rates saw a statistically significant decline after COVID-19 onset (step change -15.2, 95% CI: -19.6 to -4.8). During the first wave, encounter volume decreased 29.4% and, after the first wave, remained decreased by 188%. After the first wave, ARI antibiotic prescription rates were no longer significantly suppressed from baseline (step change 0.01, 95% CI: -6.3 to 6.2). There was no significant difference between UTI antibiotic prescription rates at baseline versus the end of the observation period. Conclusions The decline in ARI antibiotic prescribing observed after the onset of COVID-19 was temporary, not mirrored in UTI antibiotic prescribing, and does not represent a long-term change in clinician prescribing behaviors. During a period of heightened awareness of a viral cause of ARI, a substantial and clinically meaningful decrease in clinician antibiotic prescribing was observed. Future efforts in antibiotic stewardship may benefit from continued study of factors leading to this reduction and rebound in prescribing rates.
New clinical practice recommendations include A1C as an alternative to fasting glucose as a diagnostic test for identifying pre-diabetes. The impact of these new recommendations on the diagnosis of ...pre-diabetes is unknown.
Data from the National Health and Nutrition Examination Survey 1999-2006 (n = 7,029) were analyzed to determine the percentage and number of U.S. adults without diabetes classified as having pre-diabetes by the elevated A1C (5.7-6.4%) and by the impaired fasting glucose (IFG) (fasting glucose 100-125 mg/dl) criterion separately. Test characteristics (sensitivity, specificity, and positive and negative predictive values) using IFG as the reference standard were calculated.
The prevalence of pre-diabetes among U.S. adults was 12.6% by the A1C criterion and 28.2% by the fasting glucose criterion. Only 7.7% of U.S. adults, reflecting 61 and 27% of those with pre-diabetes by A1C and fasting glucose, respectively, had pre-diabetes according to both definitions. A1C used alone would reclassify 37.6 million Americans with IFG to not having pre-diabetes and 8.9 million without IFG to having pre-diabetes (46.5 million reclassified). Using IFG as the reference standard, pre-diabetes by the A1C criterion has 27% sensitivity, 93% specificity, 61% positive predictive value, and 77% negative predictive value.
Using A1C as the pre-diabetes criterion would reclassify the pre-diabetes diagnosis of nearly 50 million Americans. It is imperative that clinicians and health systems understand the differences and similarities in using A1C or IFG in diagnosis of pre-diabetes.
To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD.
Combination ...symptom and demographic vignettes were developed for 27 most common symptom complaints. Standardized prompts, written from a patient perspective, with varying demographic permutations of age, sex, and race/ethnicity were entered into ChatGPT (GPT-4) between July and August 2023. In total, 3 runs of 540 ChatGPT prompts were compared to the corresponding WebMD Symptom Checker output using a mixed-methods approach. In addition to diagnostic correctness, the associated text generated by ChatGPT was analyzed for readability (using Flesch-Kincaid Grade Level) and qualitative aspects like disclaimers and demographic tailoring.
ChatGPT matched WebMD in 91% of diagnoses, with a 24% top diagnosis match rate. Diagnostic accuracy was not significantly different across demographic groups, including age, race/ethnicity, and sex. ChatGPT's urgent care recommendations and demographic tailoring were presented significantly more to 75-year-olds versus 25-year-olds (P < .01) but were not statistically different among race/ethnicity and sex groups. The GPT text was suitable for college students, with no significant demographic variability.
The use of non-health-tailored generative AI, like ChatGPT, for simple symptom-checking functions provides comparable diagnostic accuracy to commercially available symptom checkers and does not demonstrate significant demographic bias in this setting. The text accompanying differential diagnoses, however, suggests demographic tailoring that could potentially introduce bias.
These results highlight the need for continued rigorous evaluation of AI-driven medical platforms, focusing on demographic biases to ensure equitable care.
There is consensus that incorporating clinical decision support into electronic health records will improve quality of care, contain costs, and reduce overtreatment, but this potential has yet to be ...demonstrated in clinical trials.
To assess the influence of a customized evidence-based clinical decision support tool on the management of respiratory tract infections and on the effectiveness of integrating evidence at the point of care.
In a randomized clinical trial, we implemented 2 well-validated integrated clinical prediction rules, namely, the Walsh rule for streptococcal pharyngitis and the Heckerling rule for pneumonia. INTERVENTIONS AND MAIN OUTCOMES AND MEASURES: The intervention group had access to the integrated clinical prediction rule tool and chose whether to complete risk score calculators, order medications, and generate progress notes to assist with complex decision making at the point of care.
The intervention group completed the integrated clinical prediction rule tool in 57.5% of visits. Providers in the intervention group were significantly less likely to order antibiotics than the control group (age-adjusted relative risk, 0.74; 95% CI, 0.60-0.92). The absolute risk of the intervention was 9.2%, and the number needed to treat was 10.8. The intervention group was significantly less likely to order rapid streptococcal tests compared with the control group (relative risk, 0.75; 95% CI, 0.58-0.97; P= .03).
The integrated clinical prediction rule process for integrating complex evidence-based clinical decision report tools is of relevant importance for national initiatives, such as Meaningful Use. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01386047.
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