Clinical Outcome Tracking Module for Patient Stratification
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Solution Overview
Problem
Current clinical outcome tracking and analysis systems are ineffective in managing the high costs associated with aging populations and diseases like cancer, as they fail to provide quality alternatives for cost control and decision support for medical professionals, leading to inefficiencies in healthcare spending and treatment outcomes.
Innovation Solution
A clinical outcome tracking and analysis module that accounts for biological variance by grouping patients based on nodal addresses, enabling the selection of medical professionals and treatment options optimized by geography, clinical outcome, and cost, while reducing processing time and increasing the value of care through real-time monitoring and prediction of treatment needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional clinical pathways and disease management techniques are used to control costs, then healthcare spending can be managed, but treatment outcomes and quality of care deteriorate
Solution Approach 1:
The system changes the parameters for patient classification by incorporating biological variance factors (genetic markers, molecular characteristics) alongside traditional clinical parameters. This allows for more precise patient stratification that accounts for both cost control and treatment effectiveness, resolving the contradiction between spending management and outcome quality
Solution Approach 2:
The system segments the patient population into distinct subgroups based on multiple parameters including biological variance, clinical characteristics, and treatment response patterns. This segmentation enables tailored treatment protocols for each subgroup, improving outcomes while managing costs by avoiding unnecessary interventions for patients with similar profiles
2Reliability
If medical professionals try to keep up with rapid advancements in science and medicine, then treatment quality improves, but the complexity of practice increases
Solution Approach 1:
The system provides continuous feedback to medical professionals by tracking treatment outcomes across patient populations and comparing them against current scientific advancements and best practices. This automated feedback loop helps professionals stay updated with new evidence without manually monitoring every advancement, reducing the complexity burden
Solution Approach 2:
The system acts as an intermediary between rapidly evolving medical science and practicing professionals. It processes complex scientific data, clinical guidelines, and patient information to generate actionable recommendations, thereby translating complex advancements into practical, manageable guidance for daily practice
3Measurement precision
If patients are grouped by biological variance to remove it as a factor in care value, then treatment outcome analysis improves, but the complexity of data processing increases
Solution Approach 1:
The system segments patient data by biological variance factors (such as genetic markers, molecular characteristics) to create homogeneous subgroups. This segmentation allows for cleaner outcome analysis within each group by minimizing biological variability, thereby improving measurement precision while managing processing complexity through structured classification
Solution Approach 2:
The system performs preliminary classification and grouping of patients by biological characteristics before conducting outcome analysis. By pre-processing and organizing data into meaningful subgroups in advance, the system reduces the complexity of subsequent analysis while maintaining high measurement precision through biologically-informed stratification
Data Source
AI summary
The described invention provides a method, system and non-transitory computer readable medium storing computer program instructions for enabling a patient with a condition to optimize treatment options based on geography, clinical outcome, cost and other patient-set criteria. Computer program instructions when executed on a processor comprising a first clinical outcome tracking and analysis module causes the first clinical outcome and tracking module to account for biological variance up front by grouping patients in the patient population, thereby effectively removing biological variance as a factor in value of care, and leaving treatment variance as a predominant factor in treatment outcome by receiving, sorting, and classifying personal health information the latter by generating and assigning a plurality of nodal addresses, each nodal address representing a discrete punctuated string of digits comprising a prefix, a middle and a suffix that each represent a set of preselected variables that partition the sorted and classified information into a clinically relevant set of information. The described invention provides for communication between the processor comprising the first clinical outcome tracking and analysis module and a client device comprising a second clinical outcome tracking and analysis module that are communicatively linked so that a nodal address is assigned and communicated to the patient along with a geographically organized list of medical professionals treating patients within the assigned nodal address. Once the patient selects a medical professional that meets one or more of geographical, cost and clinical outcome needs of the patient, the first clinical outcome and tracking analysis module is communicatively linked to a computing device at the selected medical professional's office to facilitate scheduling of an appointment. The assigned nodal address can be associated with one or more bundles of predetermined patient care services for treatment of the condition, which can provide a predetermined course of treatment, cost certainty, or both.