News and Articles on Health. Medical research topics.

Comparison of health care outcomes is of interest to both the clinical community and public (Halm and Chassin 2001; Fonarow and Peterson 2009; VanLare, Conway, and Sox 2010). New funding for comparative research from the American Recovery and Reinvestment Act of 2009 (U.S. Congress), coupled with the health care reform, has generated renewed interest as well as concern about methods of comparative effectiveness research and performance reporting (Fonarow and Peterson 2009; Gibbons et al. 2009). When comparing health care outcomes in large populations, clinically credible risk adjustment methodology that can be implemented on a large scale at low cost is important. Although clinical trials are the standard method of assessing health care effectiveness, they have high data collection costs, tend to be conducted on relatively small and homogeneous patient populations, and are not practical for all types of research. As a complement, observational studies enable large-scale investigations of outcomes, which may be more applicable to real world settings (VanLare, Conway, and Sox 2010). The observational studies have been further advanced by the development and proliferating use of technology that enables electronic capture of clinical data. A 2008 survey on representative U.S. hospitals found that 77 percent had fully implemented and an additional 14 percent had been partially or were in the process of implementing electronic laboratory reports (Jha et al. 2009).
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Recent publications demonstrated that automated laboratory data offer clinical credibility, objectivity, parsimony, and cost-effectiveness for risk adjustment (Jordan et al. 2007; Tabak, Johannes, and Silber 2007; Escobar et at. 2008; Render et al. 2008). Laboratory data were found to contribute most in predicting mortality among demographics, comorbidities, and other groups of variables (Tabak, Johannes, and Silber 2007; Escobar et al. 2008; Render et at. 2008). However, existing studies either did not assess contribution of additional clinical data, such as vital signs, in predicting mortality (Escobar et al. 2008; Render et al. 2008) or limited patient population to primarily male and ICU patients (Render et at. 2008). Tabak, Johannes, and Silber (2007) developed and validated disease-specific mortality predictive models, evaluating both cumulative and relative contributions of laboratory data in relation to demographics, administrative, and other manually collected clinical data. Their analysis, nevertheless, was limited to only six common clinical conditions. In a large patient population using disease-specific modeling, we sought to extend the previous work to a broad array of clinical conditions by addressing whether promising laboratory results observed in a few common clinical conditions are reproducible for other less frequently studied conditions. We further evaluated the value of manually extracted vital signs and mental status data in model predictive ability in relation to electronically captured laboratory results, demographics, and diagnosis-based administrative data. Because health care data are complex, prioritizing the electronic capture and utilization of the most standardized data elements for population-based research seems prudent. In addition to numerical laboratory results, vital signs are also objective and quantitative. Hence, determining the value of vital signs in risk adjustment may inform policy makers regarding the relative importance and priority of electronic data capture, storage, and transmission, given the federal government’s commitment to invest billions of dollars in the coming years to encourage the widespread adoption of health information technology in the United States (Blumenthal 2010).