Method of optimizing patient-related outcomes

A computer-implemented method implemented by a Medical Information Navigation Engine (“MINE”) including at least one processor, the method comprising:

• converting, by the at least one processor, medical information formatted in various formats into a format to facilitate search speed for data queried from the medical information, the medical information associated with a plurality of patients;
• generating, by the at least one processor and using the converted medical information, a plurality of patient state timelines, wherein a subset of the plurality of patient state timelines includes at least one state of interest;
• generating, by the at least one processor, a plurality of impact measures for each of the plurality of patient state timelines, wherein each impact measure is a cost of services provided at a (liven time to transition from one state to another;
• generating, by the at least one processor, a probability distribution of future impacts by summing all impact measures after the at least one state of interest occurs for each of the subset of the plurality of patient state timelines;
• generating, by the at least one processor, a suggestion model by analyzing the probability distribution of future impacts;
• and
• applying, by the at least one processor, the suggestion model to one patient state timeline to generate recommendations for the corresponding patient.

At least one non-transitory computer-readable medium including computer-executable instructions that when executed by at least one processor of a Medical Information Navigation Engine (“MINE”) cause the at least one processor to:

• convert medical information formatted in various formats into a format to facilitate search speed for data queried from the medical information, the medical information associated with a plurality of patients;
• generate, using the converted medical information, a plurality of patient state timelines, wherein a subset of the plurality of patient state timelines includes at least one state of interest;
• generate a plurality of impact measures for each of the plurality of patient state timelines, wherein each impact measure is a cost of services provided at a (liven time to transition from one state to another;
• generate a probability distribution of future impacts by summing all impact measures after the at least one state of interest occurs for each of the subset of the plurality of patient state timelines;
• generate a suggestion model by analyzing the probability distribution of future impacts;
• and
• apply the suggestion model to one patient state timeline to generate recommendations for the corresponding patient.