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In our experiment entropy was used as an uncertainty modulator based on the relative frequency of decision outcomes.
We analyzed the frequency of decision attributes broken down by patient group.
The specific objectives were to identify, categorize and estimate the frequency of decision criteria reported in the literature.
The identification of DTs as a consistent and important category of health technology should encourage the sharing of lessons between DT developers and users and reduce the frequency of decision tool projects focusing only on technology.
The principal outcome measures included the frequency of decision by the specialist to offer a follow-up outpatient appointment, patient satisfaction (Ware Specific Questionnaire), wellbeing (SF12) and enablement (PEI), numbers of tests, investigations, procedures and treatments.
Fifty percent of Group 3 patients endorsed two decision attributesa (difficulty of frequent travel to VHA and urgency to treat) that did not appear in top attributes of the other two groups (Table 3).> We analyzed the frequency of decision attributes identified by male (n = 21) and female (n = 8) patients.
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ICU bed scarcity was associated with higher refusal rates of patients considered too sick to benefit or requiring palliative care [12, 16] and to higher frequency of decisions to forego life-sustaining treatment [16].
Additionally, an analysis of the frequency of decisions in which clinicians' input might substantially alter the decision making process would be helpful.
ICU bed scarcity was associated with higher refusal rates of patients considered too sick to benefit or requiring palliative care [ 12, 16] and to higher frequency of decisions to forego life-sustaining treatment [ 16].
All other variables held constant, cause of death, patient's age, doctor's age and specialty, and place of death, influenced the frequencies of decisions.
The other interventions generally had smaller effects, however, shifting the frequency of a decision by 2% to 3%.
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