The purpose of population health management is manifold: it helps improve the the health outcomes of people by improving the quality of care, increases preventive care, and provides access to better care. The golden promise is that healthcare providers will be able to deliver better care at a lower price. In order for this to happen, caregivers at all sides of a patient need to be interconnected with a digital framework.
As mentioned in another blog post, this past year has been a booming time for digital health companies that harness population health. There are a number of ways that digital health companies can approach population health. Below is a breakdown of the platforms that make up population health management as detailed by Miguel McInnis, the founder of McInnis & Associates, a healthcare management and consulting firm. As with any attempt at categorization, there is some overlap.
Population health intelligence platforms can provide plan administrators and care teams with cloud-stored access to extensive financial and clinical information, and access clinical data from a number of sources. They may also link to other population health platforms like risk stratification, hospital admission data, and referral data. Example: Conifer Health Intelligence
Medical Management systems combine people and information to effective and personalized services for acute care management, chronic care management, etc. These systems use accurately integrated data to identify at-risk patients, track results, analyze care and support wellness management. Example: NueMD
Risk Stratification tools identify different population needs across all levels of risk. WIth this info, providers can determine appropriate courses of action with which to approach the needs of a population. Of prime importance here are demographics, medical conditions, cre patterns and resource utilization. From here, patients can be stratified into five main categories: episode of care patients; high risk patients; chronically ill patients; healthy patients but with conditions; and healthy patients. Example: HExL
Patient Engagement services help help patients take part in their own healthcare. The goal here is to help create a supportive, long-term relationship with a patient using third-party data to figure out the needs of patients and facilitate more effective relationships with providers. Example: AthenaHealth
Predictive Analytics tools model medical conditions within a population to identify high people who may be risk. By identifying these patients ahead of the curve, predictive analytics can prevent these people from needing to shell out for expensive healthcare. Example: Evolent Health
Better Patient Access platforms help patients interact with providers. This is especially useful when patients have poor access to healthcare. One way of doing this is with telehealth which ties together some of the above platforms in order to provide not only better care to patients, but also better training to providers. Example: Doctor on Demand
For the full article, check out Forbes.com.