The use of predictive modeling for real-time clinical decision making is increasingly recognized as a way to achieve the Triple Aim of improving outcomes, enhancing patients' experiences, and reducing health care costs. The development and validation of predictive models for clinical practice is only the initial step in the journey toward mainstream implementation of real-time point-of-care predictions. Integrating electronic health care predictive analytics (e-HPA) into the clinical work flow, testing e-HPA in a patient population, and subsequently disseminating e-HPA across US health care systems on a broad scale require thoughtful planning. Input is needed from policy makers, health care executives, researchers, and practitioners as the field evolves. This article describes some of the considerations and challenges of implementing e-HPA, including the need to ensure patients' privacy, establish a health system monitoring team to oversee implementation, incorporate predictive analytics into medical education, and make sure that electronic systems do not replace or crowd out decision making by physicians and patients.

BackgroundThere is increasing interest in using prediction models to identify patients at risk of readmission or death after hospital discharge, but existing models have significant limitations. Electronic medical record (EMR) based models that can be used to predict risk on multiple disease conditions among a wide range of patient demographics early in the hospitalization are needed. The objective of this study was to evaluate the degree to which EMR-based risk models for 30-day readmission or mortality accurately identify high risk patients and to compare these models with published claims-based models.MethodsData were analyzed from all consecutive adult patients admitted to internal medicine services at 7 large hospitals belonging to 3 health systems in Dallas/Fort Worth between November 2009 and October 2010 and split randomly into derivation and validation cohorts. Performance of the model was evaluated against the Canadian LACE mortality or readmission model and the Centers for Medicare and Medicaid Services (CMS) Hospital Wide Readmission model.ResultsAmong the 39,604 adults hospitalized for a broad range of medical reasons, 2.8 % of patients died, 12.7 % were readmitted, and 14.7 % were readmitted or died within 30 days after discharge. The electronic multicondition models for the composite outcome of 30-day mortality or readmission had good discrimination using data available within 24 h of admission (C statistic 0.69; 95 % CI, 0.68-0.70), or at discharge (0.71; 95 % CI, 0.70-0.72), and were significantly better than the LACE model (0.65; 95 % CI, 0.64-0.66; P =0.02) with significant NRI (0.16) and IDI (0.039, 95 % CI, 0.035-0.044). The electronic multicondition model for 30-day readmission alone had good discrimination using data available within 24 h of admission (C statistic 0.66; 95 % CI, 0.65-0.67) or at discharge (0.68; 95 % CI, 0.67-0.69), and performed significantly better than the CMS model (0.61; 95 % CI, 0.59-0.62; P < 0.01) with significant NRI (0.20) and IDI (0.037, 95 % CI, 0.033-0.041).ConclusionsA new electronic multicondition model based on information derived from the EMR predicted mortality and readmission at 30 days, and was superior to previously published claims-based models.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0162-6) contains supplementary material, which is available to authorized users.

While the independent impacts of particular firm resources and deployment capabilities on firm performance are unambiguous cornerstones of the strategy field, it is commonly assumed that their joint impacts are synergistic. This article seeks to understand whether this common misconception of resource‐based theory can be refuted empirically. Using data from hospitals conducting specialist surgery, I find hospital performance improves independently through better surgical resource quality and from more use of a streamlined form of resource management in which overall patient team leadership and operating team leadership are held by the same physician. Generally the interaction of these two firm activities had no impact on performance. These results contribute to the strategy field's understanding of whether and when internal fit affects performance, clarifying an incorrect inference commonly made about resource‐based theory. Copyright © 2013 John Wiley & Sons, Ltd.

. Study Design. Observational study measuring the responses of targeted users of Twitter, Facebook, and Google Search exposed to our sponsored messages soliciting them to start an engagement process by clicking through to a study website containing information on maternity care quality information for the Los Angeles market. Principal Findings. Campaigns reached a little more than 140,000 consumers each day across the three platforms, with a little more than 400 engagements each day. Facebook and Google search had broader reach, better engagement rates, and lower costs than Twitter. Costs to reach 1,000 targeted users were approximately in the same range as less well-targeted radio and TV advertisements, while initial engagements-a user clicking through an advertisement-cost less than $1 each. Conclusions. Our results suggest that commercially available online advertising platforms in wide use by other industries could play a role in targeted public health interventions.

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ObjectivesPublicly available hospital quality reports seek to inform consumers of important healthcare quality and affordability attributes, and may inform consumer decision-making. To understand how much consumers search for such information online on one Internet search engine, whether they mention such information in social media and how positively they view this information.Setting and designA leading Internet search engine (Google) was the main focus of the study. Google Trends and Google Adwords keyword analyses were performed for national and Californian searches between 1 August 2012 and 31 July 2013 for keywords related to ‘top hospital’, best hospital’, and ‘hospital quality’, as well as for six specific hospital quality reports. Separately, a proprietary social media monitoring tool was used to investigate blog, forum, social media and traditional media mentions of, and sentiment towards, major public reports of hospital quality in California in 2012.Primary outcome measures(1) Counts of searches for keywords performed on Google; (2) counts of and (3) sentiment of mentions of public reports on social media.ResultsNational Google search volume for 75 hospital quality-related terms averaged 610 700 searches per month with strong variation by keyword and by state. A commercial report (Healthgrades) was more commonly searched for nationally on Google than the federal government's Hospital Compare, which otherwise dominated quality-related search terms. Social media references in California to quality reports were generally few, and commercially produced hospital quality reports were more widely mentioned than state (Office of Statewide Healthcare Planning and Development (OSHPD)), or non-profit (CalHospitalCompare) reports.ConclusionsConsumers are somewhat aware of hospital quality based on Internet search activity and social media disclosures. Public stakeholders may be able to broaden their quality dissemination initiatives by advertising on Google or Twitter and using social media interactively with consumers looking for relevant information.

To address the higher likelihood of elective cesarean delivery, attention needs to be given to currently unmeasured patient-level health factors, to the quality of provider-physician interactions, as well as to patient preferences.

Objective. To examine impacts of operating surgeon scale and cumulative experience on postoperative outcomes for patients treated with coronary artery bypass grafts (CABG) by ''new'' surgeons. Pooled linear, fixed effects panel, and instrumented regressions were estimated. Data Sources. The administrative data included comorbidities, procedures, and outcomes for 19,978 adult CABG patients in Florida in 1998-2006, and public data on 57 cardiac surgeons who completed residencies after 1997. Study Design. Analysis was at the patient level. Controls for risk, hospital scale and scope, and operating surgeon characteristics were made. Patient choice model instruments were constructed. Experience was estimated allowing for ''forgetting'' effects. Principal Findings. Panel regressions with surgeon fixed effects showed neither surgeon scale nor cumulative volumes significantly impacted mortality nor consistently impacted morbidity. Estimation of ''forgetting'' suggests that almost all prior experience is depreciated from one quarter to the next. Instruments were strong, but exogeneity of volume was not rejected. Conclusions. In postresidency surgeons, no persuasive evidence is found for learning by doing, scale, or selection effects. More research is needed to support the cautious view that, for these ''new'' cardiac surgeons, patient volume could be redistributed based on realized outcomes without disruption.