2nd Annual Health Care Predictive Analytics Summit

Use of data and analytics to better understand and reach consumers is fairly developed when looking outside of health care, but health care organizations tend to lag behind in adopting new processes. While some organizations are successful in implementing predictive analytics programs, others have struggled to take advantage of the data on consumers and use it to improve care and lower costs.

• Identify business needs, people pain points, and growth opportunities
• Establish a process that suits the employee and consumer
• Select the appropriate technology and align it with your processes

Looking at data as one group that encompasses every consumer is not the best way to use data to shape care plans and influence outcomes; yet viewing consumers individually is time consuming and inefficient. Rather than taking either extreme, health care organizations can significantly improve efficiency and success of predictive analytics initiatives by dividing the large population into segments based on similar characteristics (i.e. disease states, demographics, etc.) or financial risk.

• Discover various ways to group populations and the advantages and disadvantages of different strategies
• Understand the most effective ways to segment populations to make predictions based on population data
• Determine how to use segmented population data to intervene with individual patients to improve care and minimize financial risk

Health care organizations tend to focus on clinical data when providing health care services, but information beyond that collected at a health facility is useful in understanding consumers and their needs. Community data, such as social services available and environmental conditions – though a little more difficult to obtain – can help organizations better understand health issues within populations as well as suggest more appropriate treatment design.

• Discover what data is useful for health care providers when used in conjunction with clinical data
• Determine which organizations are willing to share community data and how to access it
• Explore ways to integrate non-clinical data with clinical data while avoiding data overload

While those familiar with predictive analytics see the significant benefits of utilizing this information to improve care and outcomes, administrators and providers are less enthusiastic about incorporating analytics into the workflow and the decision-making process.

• Understand providers’ hesitations for utilizing analytics in clinical decision making
• Determine concerns of administrators in implementing predictive analytics programs
• Communicate the significant benefits of employing predictive analytics and reduce administrator and physician concerns

While many organizations have embarked on programs to develop and deploy their analytics competencies, they often fall short of their potential to deliver value and transform the business. This session showcases ways in which capability building is accomplished.

• Marry business strategies to the predictive analytics landscape
• Develop capability roadmaps and investment approaches for predictive analytics
• Ensure that the data on which all analysis depends is ready for consumption

Readmissions are costing health care facilities more than ever before while also affecting eligibility for bonus payments. Predicting readmissions and taking additional steps to prevent them can significantly help a health care organization from a financial standpoint while also improving outcomes and quality of life for the individual.

• Utilize analytics to determine the risk of readmission for hospitalized patients
• Explore ways to improve care to prevent a likely readmission from occurring

Predictive analytics is a great tool, but it is most valuable when providers can access information while delivering care and can adjust plans based on the available data.

• Explore ways to utilize predictive analytics at the point of care and include data in decision making
• Identify accelerated illness progression – through use of disease progression models – and alter care plans to prevent rapid progression

Choose a topic, network with peers, and engage in discussion regarding the evolving predictive analytics industry.