  We propose and experimentally motivate a new methodology to support
decision-making processes in healthcare with artificial intelligence based on
personal rankings of care decision making steps that can be identified with our
methodology, questionnaire data and its statistical patterns. Our longitudinal
quantitative cross-sectional three-stage study gathered self-ratings for 437
expression statements concerning healthcare situations on Likert scales in
respect to "the need for help", "the advancement of health", "the hopefulness",
"the indication of compassion" and "the health condition", and 45 answers about
the person's demographics, health and wellbeing, also the duration of giving
answers. Online respondents between 1 June 2020 and 29 June 2021 were recruited
from Finnish patient and disabled people's organizations, other health-related
organizations and professionals, and educational institutions (n=1075). With
Kruskal-Wallis test, Wilcoxon rank-sum test (i.e., Mann-Whitney U test),
Wilcoxon rank-sum pairwise test, Welch's t test and one-way analysis of
variance (ANOVA) between groups test we identified statistically significant
differences of ratings and their durations for each expression statement in
respect to respondent groupings based on the answer values of each background
question. Frequencies of the later reordering of rating rankings showed
dependencies with ratings given earlier in respect to various interpretation
task entities, interpretation dimensions and respondent groupings. Our
methodology, questionnaire data and its statistical patterns enable analyzing
with self-rated expression statements the representations of decision making
steps in healthcare situations and their chaining, agglomeration and branching
in knowledge entities of personalized care paths. Our results support building
artificial intelligence solutions to address the patient's needs concerning
care.
