Page 39 - GHES-3-2

Global Health Economics and
Sustainability
Random price generators in health policy

Figure 2. Examples of results on disease econometric studies using a cumulative logit model (Huttin, 2017)
Source: Huttin, 2017. Reprinted with permission of IOS Press-SAGE group, Copyright C, 2017 Technology and Health Care Journal, IosPress.

Table 1 presents an example of the result of the and acquirers. Such modifications are especially affected
specification test comparing two and three choices of drug by additional market characteristics, such as constraints
sets and the coefficients for the two random price variables: imposed by technical wireless capacities or other
prices for Medicare enrollees and prices for all population infrastructures (e.g., 3G, 5G, or 6G).
enrollees. As shown, prices for these diabetes drugs were
similar for Medicare patients under the choice of two At this point, the random price parameters were used in the
(alternatives 2 and 3) versus three alternatives (alternatives baseline physicians’ choice model. The disease econometric
1, 2, and 3), contrary to the coefficients associated with the study (presented in Figure 2) included the computerization
other price parameters for the sampled population. of medical records in the design of the analytical dataset. It
uses partitioning and clustering algorithms for the impact
4. Design of health policy-aiding tools in of ICT diffusion on electronic billing variables and variables
the context of digitalization and mobile on types of information computerized in electronic medical
economics records (documentation such as open notes, nurses’ notes,
and reports for public health).
The pricing of services in digital economics, such as
platforms and mobile economics, differs significantly Digitalization also transforms how various layers of
from conventional pricing theories. For example, Huttin’s information are processed for reminder systems and
(2012) first paper on mobile pricing highlighted how decision aids. For instance, layers of information from
health/biological data pricing could be modified along the the cognitive architecture of the decision-making ruling
value chain between significant data providers, such as system help adjust mechanisms of influence or persuasion;
big pharma or some big tech companies, and data users however, in this user case, the healthcare organizational

Volume 3 Issue 2 (2025) 31 https://doi.org/10.36922/ghes.3579