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We propose and experimentally motivate a new methodology to supportdecision-making processes in healthcare with artificial intelligence based onpersonal rankings of care decision making steps that can be identified with ourmethodology, questionnaire data and its statistical patterns. Our longitudinalquantitative cross-sectional three-stage study gathered self-ratings for 437expression statements concerning healthcare situations on Likert scales inrespect to "the need for help", "the advancement of health", "the hopefulness","the indication of compassion" and "the health condition", and 45 answers aboutthe person's demographics, health and wellbeing, also the duration of givinganswers. Online respondents between 1 June 2020 and 29 June 2021 were recruitedfrom Finnish patient and disabled people's organizations, other health-relatedorganizations and professionals, and educational institutions (n=1075). WithKruskal-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 ofvariance (ANOVA) between groups test we identified statistically significantdifferences of ratings and their durations for each expression statement inrespect to respondent groupings based on the answer values of each backgroundquestion. Frequencies of the later reordering of rating rankings showeddependencies with ratings given earlier in respect to various interpretationtask entities, interpretation dimensions and respondent groupings. Ourmethodology, questionnaire data and its statistical patterns enable analyzingwith self-rated expression statements the representations of decision makingsteps in healthcare situations and their chaining, agglomeration and branchingin knowledge entities of personalized care paths. Our results support buildingartificial intelligence solutions to address the patient's needs concerningcare.

Developing patient-driven artificial intelligence based on personal rankings of care decision making steps