title: Early technology assessment of new medical devices.
abstract: OBJECTIVE
  In the United States, medical devices represent an eighty-billion dollar a year market. The U.S. Food and Drug Administration rejects a significant number of applications of devices that reach the investigational stage. The prospects of improving patient condition, as well as firms' profits, are thus substantial, but fraught with uncertainties at the time when investments and design decisions are made. This study presents a quantitative model focused on the risk aspects of early technology assessment, designed to support the decisions of medical device firms in the investment and development stages.
  
  
  METHODS
  The model is based on the engineering risk analysis method involving systems analysis and probability. It assumes use of all evidence available (both direct and indirect) and integrates the information through a linear formula of aggregation of probability distributions. The model is illustrated by a schematic version of the case of the AtrialShaper, a device for the reduction of stroke risk that is currently in the preprototype stage.
  
  
  RESULTS
  The results of the modeling provide a more complete description of the evidence base available to support early-stage decisions, thus allowing comparison of alternative designs and management alternatives.
  
  
  CONCLUSIONS
  The model presented here provides early-stage decision-support to industry, but also benefits regulators and payers in their later assessment of new devices and associated procedures.
authors.affiliation: Department of Management Science and Engineering, Stanford University, Stanford, CA 94305-4026, USA. pietzsch@stanford.edu
authors.forename: Jan B
authors.initials: JB
authors.lastname: Pietzsch
authors.forename: M Elisabeth
authors.initials: ME
authors.lastname: Paté-Cornell
mesh_terms: D003132:Commerce; D003659:Decision Making, Organizational; D004864:Equipment and Supplies; D017146:Models, Organizational; D013673:Technology Assessment, Biomedical / Q000379:methods*; D014481:United States