Dataset Information

Sex, Age, and Patient Experience in Cardiologist Reviews: A Large-Scale Artificial Intelligence-Enabled Analysis.

ABSTRACT:

Background

Patients are increasingly using online reviews to evaluate cardiologists. Online reviews can provide insights into factors driving patient satisfaction. Little is known about the effects of age and sex on the patient experience with cardiologists.

Objectives

The purpose of this study was to apply natural language processing techniques on online reviews to determine the factors underlying positive and negative patient experiences and the effects of age and sex on the patient experience with cardiologists.

Methods

Mixed effects logistic regression and sentiment analysis were applied to online cardiologist reviews from Healthgrades between 1998 and 2023. The results were then analyzed by sex and age to show trends with respect to rating statistics, sentiment analysis, and frequency of 2-word phrases.

Results

There were 100,334 online reviews of 9,461 cardiologists. Female cardiologists received lower average ratings compared to male cardiologists and were 34.5% less likely to receive a positive review (OR: 0.655; 95% CI: 0.481-0.893; P = 0.015). Older cardiologists received lower average ratings compared to younger cardiologists (4.145 ± 0.908 vs 4.348 ± 0.795; P < 0.01). Positive reviews were associated with time spent with patients (OR: 1.383; 95% CI: 1.251-1.528; P < 0.01), answering questions (OR: 2.622; 95% CI: 2.324-2.959; P < 0.01), and patients feeling they could trust their providers' decisions (OR: 2.285; 95% CI: 2.053-2.543; P < 0.01).

Conclusions

Positive reviews were associated with cardiologists being comprehensive and patients feeling a sense of trust in the relationship. There was a difference in ratings based on age and sex with female and older cardiologists receiving lower ratings.

SUBMITTER: Yang A

PROVIDER: S-EPMC11312787 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sex, Age, and Patient Experience in Cardiologist Reviews: A Large-Scale Artificial Intelligence-Enabled Analysis.

Yang Andrew A Rodriguez Fatima F Woo Jennifer P JP

JACC. Advances 20240703 7

<h4>Background</h4>Patients are increasingly using online reviews to evaluate cardiologists. Online reviews can provide insights into factors driving patient satisfaction. Little is known about the effects of age and sex on the patient experience with cardiologists.<h4>Objectives</h4>The purpose of this study was to apply natural language processing techniques on online reviews to determine the factors underlying positive and negative patient experiences and the effects of age and sex on the pat ...[more]

PMID: 39129993

Similar Datasets

Project description:Introduction A limitation to expanding laparoscopic simulation training programs is the scarcity of expert evaluators. In 2019, a new digital platform for remote and asynchronous laparoscopic simulation training was validated. Through this platform, 369 trainees have been trained in 14 institutions across Latin America, collecting 6729 videos of laparoscopic training exercises. The use of artificial intelligence (AI) has recently emerged in surgical simulation, showing usefulness in training assessment, virtual reality scenarios, and laparoscopic virtual reality simulation. An AI algorithm to assess basic laparoscopic simulation training exercises was developed. This study aimed to analyze the agreement between this AI algorithm and expert evaluators in assessing basic laparoscopic-simulated training exercises. Methods The AI algorithm was trained using 400-bean drop (BD) and 480-peg transfer (PT) videos and tested using 64-BD and 43-PT randomly selected videos, not previously used to train the algorithm. The agreement between AI and expert evaluators from the digital platform (EE) was then analyzed. The exercises being assessed involve using laparoscopic graspers to move objects across an acrylic board without dropping any objects in a determined time (BD < 24 s, PT < 55 s). The AI algorithm can detect object movement, identify if objects have fallen, track grasper clamps location, and measure exercise time. Cohen’s Kappa test was used to evaluate the agreement between AI assessments and those performed by EE, using a pass/fail nomenclature based on the time to complete the exercise. Results After the algorithm was trained, 79.69% and 93.02% agreement were observed in BD and PT, respectively. The Kappa coefficients test observed for BD and PT were 0.59 (moderate agreement) and 0.86 (almost perfect agreement), respectively. Conclusion This first approach of AI use in basic laparoscopic skills simulated training assessment shows promising results, providing a preliminary framework to expand the use of AI to other basic laparoscopic skills exercises. Supplementary Information The online version contains supplementary material available at 10.1007/s00464-022-09576-1.

Project description:BackgroundOnline physician reviews are an important source of information for prospective patients. In addition, they represent an untapped resource for studying the effects of gender on the doctor-patient relationship. Understanding gender differences in online reviews is important because it may impact the value of those reviews to patients. Documenting gender differences in patient experience may also help to improve the doctor-patient relationship. This is the first large-scale study of physician reviews to extensively investigate gender bias in online reviews or offer recommendations for improvements to online review systems to correct for gender bias and aid patients in selecting a physician.ObjectiveThis study examines 154,305 reviews from across the United States for all medical specialties. Our analysis includes a qualitative and quantitative examination of review content and physician rating with regard to doctor and reviewer gender.MethodsA total of 154,305 reviews were sampled from Google Place reviews. Reviewer and doctor gender were inferred from names. Reviews were coded for overall patient experience (negative or positive) by collapsing a 5-star scale and coded for general categories (process, positive/negative soft skills), which were further subdivided into themes. Computational text processing methods were employed to apply this codebook to the entire data set, rendering it tractable to quantitative methods. Specifically, we estimated binary regression models to examine relationships between physician rating, patient experience themes, physician gender, and reviewer gender).ResultsFemale reviewers wrote 60% more reviews than men. Male reviewers were more likely to give negative reviews (odds ratio [OR] 1.15, 95% CI 1.10-1.19; P<.001). Reviews of female physicians were considerably more negative than those of male physicians (OR 1.99, 95% CI 1.94-2.14; P<.001). Soft skills were more likely to be mentioned in the reviews written by female reviewers and about female physicians. Negative reviews of female doctors were more likely to mention candor (OR 1.61, 95% CI 1.42-1.82; P<.001) and amicability (OR 1.63, 95% CI 1.47-1.90; P<.001). Disrespect was associated with both female physicians (OR 1.42, 95% CI 1.35-1.51; P<.001) and female reviewers (OR 1.27, 95% CI 1.19-1.35; P<.001). Female patients were less likely to report disrespect from female doctors than expected from the base ORs (OR 1.19, 95% CI 1.04-1.32; P=.008), but this effect overrode only the effect for female reviewers.ConclusionsThis work reinforces findings in the extensive literature on gender differences and gender bias in patient-physician interaction. Its novel contribution lies in highlighting gender differences in online reviews. These reviews inform patients' choice of doctor and thus affect both patients and physicians. The evidence of gender bias documented here suggests review sites may be improved by providing information about gender differences, controlling for gender when presenting composite ratings for physicians, and helping users write less biased reviews.

Project description:PurposeTo develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.DesignRetrospective, cross-sectional, longitudinal cohort study.ParticipantsOf 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models.MethodWe entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma.Main outcome measuresThe severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma.ResultsAfter building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%.ConclusionsThe AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression.

Dataset Information

Sex, Age, and Patient Experience in Cardiologist Reviews: A Large-Scale Artificial Intelligence-Enabled Analysis.

Background

Objectives

Methods

Results

Conclusions

Publications

Sex, Age, and Patient Experience in Cardiologist Reviews: A Large-Scale Artificial Intelligence-Enabled Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets