The per capita healthcare expenditures in the U.S. are more than two times that of other developed countries and estimated to increase by over 65% over the next decade. Further, the implementation of the Affordable Care Act (ACA) has led to a change in reimbursement models from fee-for-service to outcome-based models. This requires healthcare providers to be adaptive to innovations that help improve service delivery, operational efficiency and patient engagement.

The variety of data points available and collected by healthcare providers is accelerating at a brisk pace, e.g.,

• Patient demographics and their socio-economic behaviors
• Patients’ medical conditions including co-morbidities
• Patients’ lifestyle and wellness management
• Care given prior to and post operations and procedures
• Patients’ adherence to regular diagnostics and investigative procedures for preventative maintenance

With this large volume of data generated everyday by healthcare providers it is imperative for administrators to adopt a scientific, fact-based and data-derived decision making process to drive business strategies. Past practices of making business decisions based on simple reporting and retrospective analysis through Excels are a thing of the past.

Providers need to deploy state-of-the-art open source big data technologies and adopt predictive analytics to answer questions such as:

• How can I predict bad debts from specific payer categories and target such patients with either easier payment options or help them enroll for coverage?
• Which patients are at risk of developing hospital acquired conditions (HACs) so that preemptive actions can be taken?
• Can we predict in-patients who are at risk for readmissions within 30 days of discharge and target such patients with appropriate care proactively?
• Can we identify high costs high risk patients for e.g. co-morbidity patients who are likely to have sharply increasing costs associated with their care?
• How can I streamline accounts payable (AP) processes using analytics to help avoid vendor fraud?

In fact, the business use cases are expanding rapidly as more and more administrators adopt decision science approaches to solve different business problems. For most administrators there are formidable constraints in implementing a large scale analytics project. Some of these challenges are highlighted below:

• Identifying, collecting, cleaning, and synthesizing the relevant data
• Getting the critical buy-ins needed to make analytics more than just an academic exercise
• Identifying the David vs. Goliath opportunities
• Structuring the analytics journey such that benefits percolate across the organization
• Figuring out and actually implementing the operational and tactical executions required based on the insights revealed

But the biggest hurdle faced by the administrators by far has been with acquiring the right talent to actually implement various analytics projects. Since analytics is a synthesis of knowledge derived from industry expertise, business domain, mathematics, and statistics finding talented analytics data scientists and statisticians who will want to build their careers within a hospital or a hospital chain can be extremely challenging. There are also additional investments to consider when setting up an analytics infrastructure such as investment in the right set of tools and technologies. With a focus on cost optimization several healthcare providers have limited budget dollars to allocate across departments and projects.

This is where companies like TransOrg Analytics play a big role in partnering with healthcare providers in implementing analytics. With experience in helping healthcare providers in deploying different kinds of analytics projects depending upon the organization’s level of analytics maturity; TransOrg takes the guess work out of analytics and frees up administrators’ valuable time and resources to do what matters most to them i.e. effectively optimize their operations, continuously find opportunities to improve care delivery, and improve patient satisfaction.