Login
Introduction
Videos
Our Team
Blogs
Glossary
HCn3D System
Provider Logic
Patient Centered Network
Patient Journey
Value | Population
Patient Centered Medical Home
CMS AI Health Outcomes Challenge
TennCare
The Pyramid Metaphor
1st Dimension
2nd Dimension
3rd Dimension
Scenarios
Reinforcement
Contact
Introduction
Videos
Our Team
Blogs
Glossary
HCn3D System
Provider Logic
Patient Centered Network
Patient Journey
Value | Population
Patient Centered Medical Home
CMS AI Health Outcomes Challenge
TennCare
The Pyramid Metaphor
1st Dimension
2nd Dimension
3rd Dimension
Scenarios
Reinforcement
Contact
Glossary
Home
|
Introduction
|
Glossary
Healthcare in 3D Terms and Phrases
Barriers to Value
Organizational, semantical, and technological interoperabilities that inhibit the flow of information between
Network Entities
, making the value of Provider Actions difficult, if not impossible, to evaluate.
Future Value Outcomes
The ratio of predicted
outcomes
to cost, the 3rd dimension of HCn3d. Note: Value can be defined in ways other than monetary cost. For example longevity and pain management.
p-value
Is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. In a nutshell, the greater the difference between two observed values, the less likely it is that the difference is due to simple random chance, and this is reflected by a lower p-value.
— What a p-value tells you about statistical significance | Accessed December 14, 2021
. Typically a p-value of .05 is used as the cutoff for significance. If the p-value is less than .05 it is concluded that a significant difference does exist. Essentially a less than 5% chance a conclusion is wrong.
— What Can You Say When Your P-Value is Greater Than 0.05? | Accessed December 15, 2021
Video Primer:
p-values: What they are and how to interpret them
Q-value (Action value) (Machine Reinforcement Learning)
Is the prediction of the
Q Function
which formulates the highest probable
Reward
from the sequences of
State
/
Action
pairs, essentially a consideration of all
Action Spectrums
from the current
cube
through the last cube achieving the desired
Trajectory
. It is HCn3D best “guess” for the desired
future outcomes
at the current
Inflection Point
. Together with
Policy
the application of
Counterfactual
analysis.
Value
Ratio of
outcomes
to cost.
Value (Machine Reinforcement Learning)
Is reflected in the
Action Spectrum
at the end of a
cube
sequence, essentially a long-term return value as opposed to a short-term
reward
.
Page 1 of 1