Population Health is a broader application of analytics that looks to the broadest population available, to ascertain logical correlations and data clusters that point to a predictive measure for a specific problem. The data amassed from the population of all of the individuals in a set of data can be extended to all population features and extended data-points. This combination forms a “big data” analytics and AI platform whereby clinicians, researchers, municipalities, and others may identify trends and outliers within an aggregate population for insights and opportunities for improved health, modifications to protocols and mitigation strategies, and for possible changes in overall care plans.
The principle behind Population Health is the breadth of data used for analysis. In many cases a project data set can be enhanced and augmented by and through augmenting the project data with correlated information such as national census, CMS claims and mortality, and US Bureau of Labor Statistics data to identify broader national trends which can “influence” targeted population data where this data may be sparse. Additionally, enhancing the data captured within the target population is also key. For example survey data (assessments), remote patient monitoring device vitals, and even wearables can add to the breadth of data used for analysis. These latter sources consist of various Internet of Things (IoT) capabilities.
Population Health has many benefits on its own, and can form decisions at the macro level. However, the true value of Population Health comes in the form of using the data at the micro level… the interactions with the individual person. This is where the Personal Health solutions come in.
The combination of Personal Health and IoT, with big data population analytics, provides heretofore unseen benefits and synergies in solving the ongoing challenges of health and healthcare.
Population health leverages this aggregation of data from multiple sources, and in multiple formats, to provide the baseline data schema that provides insights, predictions, propensities, efficiencies, and more.
Better decisions, improved protocols/care plans, more efficient mitigation strategies, fewer adverse events, lower costs, and greater efficiencies may all be seen through population analytics.
Unique models have identified the mitigation strategies to prevent the escalation of prediabetes diabetes. Others have assessed and assigned Risk Profiles for all US counties for both infection of COVID-19 and mortality of the virus. Still further models have been designed to identify the overall health risk profile of the individual and the corresponding population (i.e. county) through the Lifemesh Care Index.
We encourage you to delve deeper into the benefits we can provide through efficient use of your data at the population level. We look forward to your thoughts, feedback, and questions.