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Commentary |

Using Information to Optimize Medical Outcomes

James R. Duncan, MD, PhD; Ronald G. Evens, MD
JAMA. 2009;301(22):2383-2385. doi:10.1001/jama.2009.827.
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An important health care–related decision of the Obama administration is to reduce waste and harm by modernizing health care information technology systems. This decision would be met with more enthusiasm if the design of the final system were driven by the goal of improving the efficacy and efficiency of medical processes through a strategy of collecting meaningful data. Before spending billions creating and implementing such systems, careful consideration must be given to how this information will be integrated into medical decision making. Some of the current health care information technology systems seem to have been planned using a “ready, fire, aim” approach, with little or no concern for how the data will be used. Ideally, health care information technology systems should collect and use information to improve the probability that patients will receive optimal care. Optimized decision making is the essence of evidence-based medicine. However, collecting, organizing, and storing information is only the first step in this process. The systems must also be designed to facilitate data analysis.

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Figure. Benefits vs Risks of Information Gathering During Fluoroscopic Procedures
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The performance model (top left) recognizes that the probability of the desired result increases as the amount of information increases. More information can be gathered by obtaining more fluoroscopic images or increasing the dose per image. The sigmoidal curve indicates that at a certain radiation dose, collecting additional information does not provide a benefit. The risk model (bottom left) indicates that risk increases linearly with radiation dose.  Combining the performance model and risk curves creates a risk-benefit curve (top right) that indicates there is an optimal target value (x, target radiation dose) for this procedure. The curves demonstrating the frequency distribution of achieving the desired result (bottom right) illustrate that experts (green curve) have optimized their performance such that they most frequently achieve the desired result at the target radiation dose, and variation is minimal (green brackets in the top 2 graphs). In contrast, novices (orange curve) follow the natural tendency to collect more information, typically exceeding the target radiation dose (right shift of the distribution curve) and increasing variation (orange brackets in the top 2 graphs).



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