Describing the performance of U.S. hospitals by applying big data analytics

<div><p>Public reporting of measures of hospital performance is an important component of quality improvement efforts in many countries. However, it can be challenging to provide an overall characterization of hospital performance because there are many measures of quality. In the United States, the Centers for Medicare and Medicaid Services reports over 100 measures that describe various domains of hospital quality, such as outcomes, the patient experience and whether established processes of care are followed. Although individual quality measures provide important insight, it is challenging to understand hospital performance as characterized by multiple quality measures. Accordingly, we developed a novel approach for characterizing hospital performance that highlights the similarities and differences between hospitals and identifies common patterns of hospital performance. Specifically, we built a semi-supervised machine learning algorithm and applied it to the publicly-available quality measures for 1,614 U.S. hospitals to graphically and quantitatively characterize hospital performance. In the resulting visualization, the varying density of hospitals demonstrates that there are key clusters of hospitals that share specific performance profiles, while there are other performance profiles that are rare. Several popular hospital rating systems aggregate some of the quality measures included in our study to produce a composite score; however, hospitals that were top-ranked by such systems were scattered across our visualization, indicating that these top-ranked hospitals actually excel in many different ways. Our application of a novel graph analytics method to data describing U.S. hospitals revealed nuanced differences in performance that are obscured in existing hospital rating systems.</p></div

Summary characteristics of hospitals comprising each neighborhood, demographics of their Hospital Service Areas, and their U.S. News and World Report, Leapfrog, Consumer Reports, and Health Grades ratings.

<p>Summary characteristics of hospitals comprising each neighborhood, demographics of their Hospital Service Areas, and their U.S. News and World Report, Leapfrog, Consumer Reports, and Health Grades ratings.</p

Performance profiles of the 16 hospital performance profiles.

<p>The vertical bars represent the average normalized score for the central hospital in each neighborhood and its 10 nearest neighbors on the 84 quality measures. The vertical scale is standard deviations from the mean. The bars are grouped and shaded according to the domain of the quality measure (blue: process, orange: experience, red: value, purple: safety, navy: surgery, turquoise: readmission, green: mortality).</p

Diffusion maps showing all hospitals (A), hospitals labelled according to their performance profile (B) and top-rated hospitals (C).

<p>Diffusion maps showing all hospitals (A), hospitals labelled according to their performance profile (B) and top-rated hospitals (C).</p

Quality of Life and Risks Associated with Systemic Anti-inflammatory Therapy versus Fluocinolone Acetonide Intraocular Implant for Intermediate Uveitis, Posterior Uveitis, or Panuveitis Fifty-foure-Month Results of the Multicenter Uveitis Steroid Treatment Trial and Follow-up Study

Purpose: To evaluate the risks and quality-of-life (QoL) outcomes of fluocinolone acetonide implant versus systemic therapy with corticosteroid and immunosuppression when indicated for intermediate uveitis, posterior uveitis, and panuveitis. Design: Additional follow-up of a randomized trial cohort. Participants: Two hundred fifty-five patients with intermediate uveitis, posterior uveitis, or panuveitis, randomized to implant or systemic therapy. Methods: Randomized subjects with intermediate uveitis, posterior uveitis, or panuveitis (479 eyes) were followed up over 54 months, with 79.2% completing the 54-month visit. Main Outcome Measures: Local and systemic potential complications of the therapies and self-reported health utility and vision-related and generic health-related QoL were studied prospectively. Results: Among initially phakic eyes, cataract and cataract surgery occurred significantly more often in the implant group (hazard ratio [HR], 3.0; P = 0.0001; and HR, 3.8; P < 0.0001, respectively). In the implant group, most cataract surgery occurred within the first 2 years. Intraocular pressure elevation measures occurred more frequently in the implant group (HR range, 3.7-5.6; all P < 0.0001), and glaucoma (assessed annually) also occurred more frequently (26.3% vs. 10.2% by 48 months; HR, 3.0; P = 0.0002). In contrast, potential complications of systemic therapy, including measures of hypertension, hyperlipidemia, diabetes, bone disease, and hematologic and serum chemistry indicators of immunosuppression toxicity, did not differ between groups through 54 months. Indices of QoL initially favored implant therapy by a modest margin. However, all summary measures of health utility and vision-related or generic health-related QoL were minimally and nonsignificantly different by 54 months, with the exception of the 36-item Short-Form Health Survey physical component summary score, which favored implant by a small margin at 54 months (3.17 on a scale of 100; P = 0.01, not adjusted for multiple comparisons). Mean QoL results were favorable in both groups. Conclusions: These results suggest that fluocinolone acetonide implant therapy is associated with a clinically important increased risk of glaucoma and cataract with respect to systemic therapy, suggesting that careful monitoring and early intervention to prevent glaucoma is warranted with implant therapy. Systemic therapy subjects avoided a significant excess of toxicities of systemic corticosteroid and immunosuppressive therapies in the trial. Self-reported QoL measures initially favored implant therapy, but over time the measures converged, with generally favorable QoL in both groups. (C) 2015 by the American Academy of Ophthalmology