Health Consensus: A Digital Adapted Delphi for Healthcare

Health Consensus: A Digital Adapted Delphi for Healthcare

J.M. Monguet, A. Trejo, T. Martí, J. Escarrabill
Copyright: © 2017 |Pages: 17
DOI: 10.4018/IJUDH.2017010103
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New tools are needed to facilitate the involvement of health professionals in healthcare participative processes, partially because a relevant segment of healthcare knowledge and decision-making is capillary distributed among them. A collaborative design strategy has been applied to the creation of an Internet tool to produce digitally adapted Delphi for healthcare purposes. During the period 2012-16 the prototype of the tool has been gradually improved through its application to 18 real cases. It is proposed the model Health Consensus as a digitally adapted Delphi supported by the various capabilities of Internet. The authors agree that Health Consensus is a useful and expandable tool for participative processes. The Internet provides several opportunities to overcome many of the limitations of conventional Delphi, as well as improving the final studies with new functionalities.
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Rand Corporation developed the Delphi method as a forecasting system based on the aggregation of expert opinions and roundtable discussions. 84 years passed between the publication of the Jury Theorem (Condorcet, 1875) and the first formal implementation of consensus among experts (Helmer and Rescher, 1959). Condorcet stated that if each member of a voting group is more likely than not to make a correct decision, the probability that the vote of the group is the correct decision increases with the number of members of the group. This simple principle was applied by RAND researchers to forecast the impact of technology on warfare. Commissioned by the US Air Force, RAND created during the 50’s the Delphi method. In the Delphi method, a group of experts anonymously reply to questionnaires and then receive feedback in the form of a statistical representation of participant’s response. The process may be repeated several times with the intention of reducing the range of responses and arrive at something closer to expert consensus.

Although some authors consider this methodology to be in a developmental stage (Day & Bobeva, 2004), nowadays Delphi is recognised as a useful tool to build consensus by using questionnaires to collect data from panels of selected subjects (Dalkey & Helmer, 1963) and is widely accepted as a “scientifically and practically proven” research technique (von der Grach, 2012). The method is often employed in the absence of precise analytical techniques for gathering subjective judgments through group consensus (Linstone, 1978). Researchers and practitioners have successfully applied Delphi to forecasting, planning and needs assessment both in the public and the private sector. In a nutshell, Delphi enables groups of people to share understanding and knowledge. A group of participating individuals will reach a more informed answer than a single person’s opinion to questions that have no alternative scientific answer.

Complexity rich domains like healthcare are ideally fitted for Delphi applications. In healthcare, there is an endless number of problems where solutions may be affordable, if the unevenly distributed knowledge and experience of healthcare professionals and patients, is tapped. Following the original use of Delphi in social sciences, the method has been widely used in healthcare research as an effective way to gain and measure group consensus (Holey, Feeley, & Dixon, 2007) and there is no doubt that it is an important method for achieving consensus on issues where is a lack of evidence (Keeney, Hasson, Mcckenna, & Kenna, 2006).

However, there are many situations where face-to-face meetings between all participants, that Delphi requires, is not possible due to time, space and cost constraints. Digital technologies may eventually overcome some of these limitations.

Since the first technology enabled Delphi back in the early seventies, digital methods have dramatically evolved. Delphi Conferencing was the first experience of using computers to assist in the method roll-out (Turoff, 1972). Along with the evolution and development of digital and communication technologies, several initiatives of what could be considered a digitally adapted Delphi have been reported in the literature, as networking, real time processing and social media boost innovation and new research opportunities to Delphi (Day & Bobeva, 2004) (Linstone and Turoff, 2002).

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