Predictive and Preventive Care Through Evidence-Based Population Analytics.
Best of Breed Advanced Analytics, Predictive Risk Stratification, and Care Plan Hypothesis Testing
Using Advanced Analytics and Machine Learning, the Augmented Care solutions identify opportunities for improved care and reduced costs of care.
Through a combination of correlation, pattern, and health trajectory models, Lifemesh provides enhanced risk stratification, segmentation, and time to event projections, enabling the clinician to intervene.
Rapid hypothesis testing enables clinicians to create dynamic cohorts based on extensive patient history and to design test changes in care plans, or alternate therapies on a small subset of the cohort, and allow the system to automatically validate the results of such changes. Rapid testing allows for small incremental experiments that can provide continuous improvements in care, while validating the effectiveness of a proposed change, and the ROI associated.
Comprehensive visual dashboard and reporting tool, with enhanced features such as:
Lifemesh has used its Population Analytics capabilities to provide value during the COVID-19 Pandemic. We were involved in projects sponsored by the American Heart Association (AHA), Food and Drug Administration (FDA), and the Veteran’s Administration (VA), to apply our Predictive Analytic models to the challenges that unfolded in 2020.
The content below describes our approach and outcomes.
Risk stratification is a function of "predicting" the likelihood of an adverse event occurring to individuals within the population and ascertaining a risk value for the targeted event. Risk Scores can also be made available for building out Dynamic Cohorts, and targeted outreach.
Predictive analytics is derived first at the population level, however, can also be applied to an individual level based on the risk profiles derived by demographic, condition, symptoms, and other valuable data.
Evidence-based medicine is a clear focus on the healthcare community. However, most providers rely exclusively on data from external sources,. While clearly the current agreed-upon protocols, performing short-lived test can allow for continuous improvements as the results can be validated automatically, and the cost of the change can be extrapolated across the population to determine a firm Cost/Benefit Analysis.
Query, segment, and filter patients based on symptoms, conditions, claims, demographics, social determinants and more, to build out dynamic cohorts to study. Further define control and study groups to track the effectiveness of modified care plans to identify the statistical relevance of the change, and associated cost/benefit analysis.
Proactive care comes both in the form of predicting risk and likelihood of an adverse event, but also in the form of small adjustments to care and care plans. But making changes that are not evidence-based are hard to justify. But rapid hypothesis testing allows for continuous improvement but allowing for care plan changes to be applied to a small subset of patients and to track the relevance and benefits of positive hypotheses
Access to real-time visual dashboards and reports using best-of-breed visual tools and AI-based causality and comparative insights
Please contact Lifemesh to discuss pricing for our analytic solutions
Change the way you engage with your patients through Advanced Analytics!
Applying award-winning customer engagement strategies from the retail industry, we help patients feel recognized and appreciated at every touchpoint.
