Purpose: To evaluate the ability to merge disparate data sources in an enterprise-wide effort and use that data to evaluate criteria for calling, and efficacy of, rapid response teams (RRTs) in the inpatient hospital environment.
Method: We merged data from Q4, 2010, for all inpatients in the Mayo Clinic Rochester health care system. Data from automated collection systems (ChartPlus) as well as manual collection systems (Flowsheets) were merged with administrative data on admissions, in-hospital transfers, and discharges. In addition to the usual vital signs (heart rate, blood pressures, oxygen saturation, oxygen delivered, respiration rate), we also looked at medications and medical orders (e.g., oxygen delivered) data. Data were reviewed for suitability, consistency and validity. Simple rules for calling Rapid Response Teams (RRTs) were evaluated and compared, and a time-to-stabilization analysis conducted to investigate the efficacy of the RRT in patient care. Additionally, data mining techniques are used to discover new rules.
Result: We found that data across sources are often inconsistent. Administrative data sets are often unsuited for use in research that needs timing and level of care information.. Large false positive rates are to be expected if guidelines designed to be used by staff are simply converted to automated, rule based alarm systems. Time-to-stabilization analyses suggest that RRTs are effective in helping patients.
Conclusion: Large data sets can be constructed from multiple sources within the medical environment, and with reasonable cleanup those large data sets can be used to investigate, retrospectively, changes to systems and guidelines such as those built around patient care. Improved statistical techniques can be used to offset many of the biases to be expected in retrospective analyses, and a commitment to maintaining these large data sets promises to provide new opportunities for discovery.
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