Public hospital quality report awareness: evidence from National and Californian Internet searches and social media mentions, 2012.

OBJECTIVES: Publicly 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 DESIGN: A 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. RESULTS: National 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. CONCLUSIONS: Consumers 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.

Industry in motion: using smart phones to explore the spatial network of the garment industry in New York City.

Industrial agglomerations have long been thought to offer economic and social benefits to firms and people that are only captured by location within their specified geographies. Using the case study of New York City's garment industry along with data acquired from cell phones and social media, this study set out to understand the discrete activities underpinning the economic dynamics of an industrial agglomeration. Over a two week period, data was collected by employing the geolocative capabilities of Foursquare, a social media application, to record every movement of fashion workers employed at fashion design firms located both inside and outside the geographical boundaries of New York City's Garment District. This unique method of studying worker activity exposed the day-to-day dynamics of an industrial district with a precision thus far undocumented in literature. Our work suggests that having access to the cluster provides almost the same agglomeration economies as residing within its borders.

Measurement and risk adjustment of prelabor cesarean rates in a large sample of California hospitals.

OBJECTIVE: Prelabor cesareans in women without a prior cesarean is an important quality measure, yet one that is seldom tracked. We estimated patient-level risks and calculated how sensitive hospital rankings on this proposed quality metric were to risk adjustment. STUDY DESIGN: This retrospective cohort study linked Californian patient data from the Agency for Healthcare Research and Quality with hospital-level operational and financial data. Using the outcome of primary prelabor cesarean, we estimated patient-level logistic regressions in progressively more detailed models. We assessed incremental fit and discrimination, and aggregated the predicted patient-level event probabilities to construct hospital-level rankings. RESULTS: Of 408,355 deliveries by women without prior cesareans at 254 hospitals, 11.0% were prelabor cesareans. Including age, ethnicity, race, insurance, weekend and unscheduled admission, and 12 well-known patient risk factors yielded a model c-statistic of 0.83. Further maternal comorbidities, and hospital and obstetric unit characteristics only marginally improved fit. Risk adjusting hospital rankings led to a median absolute change in rank of 44 places compared to rankings based on observed rates. Of the 48 (49) hospitals identified as in the best (worst) quintile on observed rates, only 23 (18) were so identified by the risk-adjusted model. CONCLUSION: Models predict primary prelabor cesareans with good discrimination. Systematic hospital-level variation in patient risk factors requires risk adjustment to avoid considerably different classification of hospitals by outcome performance. An opportunity exists to define this metric and report such risk-adjusted outcomes to stakeholders.