BackgroundMany online physician-rating sites provide patients with information about physicians and allow patients to rate physicians. Understanding what information is available is important given that patients may use this information to choose a physician.ObjectivesThe goals of this study were to (1) determine the most frequently visited physician-rating websites with user-generated content, (2) evaluate the available information on these websites, and (3) analyze 4999 individual online ratings of physicians.MethodsOn October 1, 2010, using Google Trends we identified the 10 most frequently visited online physician-rating sites with user-generated content. We then studied each site to evaluate the available information (eg, board certification, years in practice), the types of rating scales (eg, 1–5, 1–4, 1–100), and dimensions of care (eg, recommend to a friend, waiting room time) used to rate physicians. We analyzed data from 4999 selected physician ratings without identifiers to assess how physicians are rated online.ResultsThe 10 most commonly visited websites with user-generated content were HealthGrades.com, Vitals.com, Yelp.com, YP.com, RevolutionHealth.com, RateMD.com, Angieslist.com, Checkbook.org, Kudzu.com, and ZocDoc.com. A total of 35 different dimensions of care were rated by patients in the websites, with a median of 4.5 (mean 4.9, SD 2.8, range 1–9) questions per site. Depending on the scale used for each physician-rating website, the average rating was 77 out of 100 for sites using a 100-point scale (SD 11, median 76, range 33–100), 3.84 out of 5 (77%) for sites using a 5-point scale (SD 0.98, median 4, range 1–5), and 3.1 out of 4 (78%) for sites using a 4-point scale (SD 0.72, median 3, range 1–4). The percentage of reviews rated ≥75 on a 100-point scale was 61.5% (246/400), ≥4 on a 5-point scale was 57.74% (2078/3599), and ≥3 on a 4-point scale was 74.0% (740/1000). The patient’s single overall rating of the physician correlated with the other dimensions of care that were rated by patients for the same physician (Pearson correlation, r = .73, P < .001).ConclusionsMost patients give physicians a favorable rating on online physician-rating sites. A single overall rating to evaluate physicians may be sufficient to assess a patient’s opinion of the physician. The optimal content and rating method that is useful to patients when visiting online physician-rating sites deserves further study. Conducting a qualitative analysis to compare the quantitative ratings would help validate the rating instruments used to evaluate physicians.

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; for the Multicenter Perioperative Outcomes Group (MPOG) IMPORTANCE Overlapping surgery, in which more than 1 procedure performed by the same primary surgeon is scheduled so the start time of one procedure overlaps with the end time of another, is of concern because of potential adverse outcomes. OBJECTIVE To determine the association between overlapping surgery and mortality, complications, and length of surgery. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of 66 430 operations in patients aged 18 to 90 years undergoing total knee or hip arthroplasty; spine surgery; coronary artery bypass graft (CABG) surgery; and craniotomy at 8 centers between January 1, 2010, and May 31, 2018. Patients were followed up until discharge. EXPOSURES Overlapping surgery (Ն2 operations performed by the same surgeon in which Ն1 hour of 1 case, or the entire case for those <1 hour, occurs when another procedure is being performed). MAIN OUTCOMES AND MEASURES Primary outcomes were in-hospital mortality or complications (major: thromboembolic event, pneumonia, sepsis, stroke, or myocardial infarction; minor: urinary tract or surgical site infection) and surgery duration. RESULTS The final sample consisted of 66 430 operations (mean patient age, 59 [SD, 15] years; 31 915 women [48%]), of which 8224 (12%) were overlapping. After adjusting for confounders, overlapping surgery was not associated with a significant difference in in-hospital mortality (1.9% overlapping vs 1.6% nonoverlapping; difference, 0.3% [95% CI, −0.2% to 0.7%]; P = .21) or risk of complications (12.8% overlapping vs 11.8% nonoverlapping; difference, 0.9% [95% CI, −0.1% to 1.9%]; P = .08). Overlapping surgery was associated with increased surgery length (204 vs 173 minutes; difference, 30 minutes [95% CI, 24 to 37 minutes]; P < .001). Overlapping surgery was significantly associated with increased mortality and increased complications among patients having a high preoperative predicted risk for mortality and complications, compared with low-risk patients (mortality: 5.8% vs 4.7%; difference, 1.2% [95% CI, 0.1% to 2.2%]; P = .03; complications: 29.2% vs 27.0%; difference, 2.3% [95% CI, 0.3% to 4.3%]; P = .03). CONCLUSIONS AND RELEVANCE Among adults undergoing common operations, overlapping surgery was not significantly associated with differences in in-hospital mortality or postoperative complication rates but was significantly associated with increased surgery length. Further research is needed to understand the association of overlapping surgery with these outcomes among specific patient subgroups.

Documenting a patient's anesthetic in the medical record is quite different from summarizing an office visit, writing a surgical procedure note, or recording other clinical encounters. Some of the biggest differences are the frequent sampling of physiologic data, volume of data, and diversity of data collected. The goal of the anesthesia record is to accurately and comprehensively capture a patient's anesthetic experience in a succinct format. Having ready access to physiologic trends is essential to allowing anesthesiologists to make proper diagnoses and treatment decisions. Although the value provided by anesthesia information management systems and their functions may be different than other electronic health records, the real benefits of an anesthesia information management system depend on having it fully integrated with the other health information technologies. An anesthesia information management system is built around the electronic anesthesia record and incorporates anesthesia-relevant data pulled from disparate systems such as laboratory, billing, imaging, communication, pharmacy, and scheduling. The ability of an anesthesia information management system to collect data automatically enables anesthesiologists to reliably create an accurate record at all times, regardless of other concurrent demands. These systems also have the potential to convert large volumes of data into actionable information for outcomes research and quality-improvement initiatives. Developing a system to validate the data is crucial in conducting outcomes research using large datasets. Technology innovations outside of healthcare, such as multitouch interfaces, near-instant software response times, powerful but simple search Published online in Wiley Online Library (wileyonlinelibrary.com).