There are 17 item(s) tagged with the keyword "Predictive Modeling".
Displaying: 1 - 10 of 17
- Feature Engineering in Healthcare Analytics
Jason Rudy, Data Scientist / Programmer and Matt Lewis, Programmer / Product Manager
In a previous blog post, we walked through the process of building a predictive model using healthcare data. One of the things we touched on briefly there, was how important proper feature engineering can be to creating a useful representation of the underlying data, and how the choices made around shaping data into features can change the nature and performance of the resulting model.
In this post, we’ll be going deeper into the process of feature engineering, and how to convert raw data inputs into a form that will make machine learning not only possible, but effective.
- Targeting Doctor Shopping, Prescription Abuse With Analytics: Population Health Lessons from the Front Lines
In recent years insurers and brokers have had to adapt to rising healthcare costs without much say in the matter. This is in part because brokers and employee benefit managers often have too much on their plates to sleuth out and address the costliest “blind spots” in health coverage. This has left many struggling to find a way to remain competitive in an increasingly technology-driven industry.
- Competitive Advantages That Every Health Insurance Broker Should Know
The webinar included Business Insurance, Advanced Plan for Health, Turner Industries and BancorpSouth Insurance Services, Inc. and offered insights on how to leverage advanced analytics throughout the year at various points in the annual sales and service cycle. It also outlined real-world case studies of health data analytics in practice, resulting ROI and the many benefits analytics has on health plan performance and the competitive advantages it can provide brokers looking to set themselves apart from the crowd.
- Three of the Costliest Health Conditions Ravaging Your Health Plan, and Steps for Prevention
High-cost claimants are only a part of the equation. There is a secondary subset of the insured population who are at-risk of entering the 5-7% of highest cost claimants that predictive modeling can uncover and help address. Not all predictive modeling engines have the capability to identify this subset of the employee population, but with Advanced Plan for Health’s Poindexter’s predictive modeling capabilities, determining the likelihood of an occurrence of coronary events, neurological events, orthopedic events and chronic kidney disease is made easy. Once interventions are made to improve the health of at-risk patients, the analytics system can retrospectively report actual cost versus the original predictive model to measure the positive health and financial effects the interventions created. Without the predictive-modelling enabled ability to identify those at-risk and intervene with targeted prevention strategies, health plan optimization will always be an uphill battle.
- 20 Daily Minutes of Stress-Free Recreation in the Workforce and What It Could Mean to Employers
In the above quote, TV powerhouse Oprah Winfrey, is discussing the positive effects of one type of wellness activity that has shown promise in the workplace: meditation and how it can promote a healthier, and compliant employee population.
- Wellness: Better Data, Better Buy-in, Better Savings
Population health has many definitions which are often pigeonholed to fit a specific market or agenda. Its end goal, however, is unequivocal: to manage the health of a given patient population to improve outcomes and keep costs in check. And without recent advancements in health technology, data aggregation and data analysis, case managers, data scientists and benefits managers alike would not have the tools and actionable intelligence necessary to make lasting improvements to the populations they manage. Wellness programs would not improve a health plan or reduce costs as effectively if case managers were missing crucial data sets of the population.
- Advanced Plan for Health Upgrades Next-Generation Predictive Analytics On Poindexter Population Health Management Platform
Poindexter’s newest release pushes the boundaries of predictive modeling by determining the likelihood of an occurrence of coronary events, neurological events, orthopedic events and chronic kidney disease. In addition, Poindexter now has the ability to predict hospital length of stay and general likelihood of an ER visit for any member of a patient population.
Third-party Administrators (TPAs) and brokers can leverage Poindexter’s new functionality to better identify at-risk individuals in their customers’ insured population. Employers with self-funded health plans can directly benefit, too.
- Data: Your Secret Weapon
So, where are your hidden health-related cost centers? The same places they’ve always been: In plain sight. But for the lack of data transparency and integration of data tools and services, they remain elusive to spot. To uncover these cost centers, consider partnering with an analytics firm that not only can access data sets from everywhere patients access healthcare, but also combine them to create nuanced action lists.
- Nurse Care Navigators' Value to the Self-Insured Health Plan
To nurse care navigators, care management is exactly that – managing people with care.
Mentoring clinicians and health plan professionals in learning how to guide and support people, wherever they are on the healthcare continuum, is my opportunity to pass on the secrets of population health management that so many wonderful professionals have shared with me.
Making a difference in people's health, while optimizing resources so that people and health plans get the desired results, is my idea of success.
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APH helps you manage health care costs, identify and predicts risk, and improve health outcomes. When you need real results, you need APH.
Displaying: 1 - 10 of 17