Health Analytics

With the rising popularity of evidence-based medical practices and the minimization of intuitive medicine, health analytics is rapidly assuming the “go-to” role for healthcare industry professionals, especially doctors, medical facility managers, pharmaceutical companies and big health insurance agencies. Broadly, health analytics can be described as the systematic review of available clinical data in order to implement the best treatment decisions possible. By combining individual bundles of data into algorithms augmented with big data, health analytics can not only provide the kind of robust results healthcare professionals need to achieve optimum outcomes but it has also been proven to significantly reduce the exorbitant costs of medical treatments.

Applications of Healthcare Analytics–Where It Should Be Used

To help narrow down the practical meaning of healthcare analytics, the following categories represent areas where analysis of data sets are most useful for developing clinical and financial strategies aimed at improving medical practices and facility operations:

  • Clinical Analytics–population health management, compliance and reporting, clinical decision support
  • Financial Analytics–elimination of waste, abuse and fraud; claims processing, revenue cycle management
  • Administrative and operational analytics–strategic, supply chain and workforce analytics

In the Kaufmann Report, “Valuing Health Care: Improving Productivity and Quality”, the authors suggest that a combination of inefficiencies, lack of incentives for controlling costs and inadequate information resulted in over $2 trillion being spent on healthcare in the U.S. in 2010, with $700 billion of that amount spent unnecessarily. Alternately, statisticians estimate that integrating big data into health analytics may produce $300 billion in savings, especially in areas of research and development (R&D) and clinical operations.

The Power of Health Analytics to Improve the U.S. Healthcare Industry–A Wide Overview

  • As the foundation for clinical decision systems, health analytics has the potential to enhance the quality and efficiency of emergency room operations by providing real-time data to medical professionals responsible for triage, diagnoses and treatment.
  • Health analytics also supports predictive analytics, a subfield of statistics that employs big data to identify decisive patterns for the purpose of quantitatively predicting future outcomes. Predictive analytics is especially useful in the healthcare industry as a way to circumvent preventable situations, lower costs and reduce patient readmittance.
  • Health analytics provides algorithms that have been shown to enhance patient recruitment for clinical trial designs by matching individual patients to treatments more precisely, thus facilitating the introduction of new treatments to the market and decreasing trial failures.
  • Transforming big data into actionable data benefits lower income populations by identifying their needs and predicting crises so that preventive services can be initiated.

Healthcare Analytics Adoption Model

Developed by several healthcare industry professionals, the Healthcare Analytics Adoption Model classifies areas where health analytics could be applied as a “sustainable analytics strategy”. According to the creators of the HAAM, their model will help those working in the healthcare field to understand and implement the benefits of health analytics to improve patient care and reduce costs. There are eight levels of the Healthcare Analytics Adoption Model, including Automated Internal and External Reporting, Waste and Care Variability Reduction and Personalized Medicine & Prescriptive Analytics.

The Future of Health Analytics

As the ability to amass big data progresses for the purpose of clarifying clinical research, improving operational processes and providing the highest quality of patient care possible, health analytics and the technology associated with sustaining its evolution is expected to expand dramatically over the next decade. Although in its early stages of fruition, health analytics is already minimizing costs, improving treatment protocols and streamlining facility operations aided by various algorithms such as predictive and prescriptive analytics, a type of quantitative and qualitative branch of analytic statistics that suggests actions beneficial to a particular outcome. Fueled by big data, prescriptive analytics also highlights weaknesses, strengths, probabilities and opportunities in treatment methods while mitigating costs and elevating the quality of patient care.

Big data is here and health analytics is taking full advantage of it.

To read about the Kauffman report: