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. 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.
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.
When talking about The Future of Health Analytics it is important to pay attention to the changes that are happening because it is a very dynamic subject area in a very dynamic industry. Healthcare data analytics has continued to evolve and we are lucky to have a good seat to watch as it transforms different areas of the industry for good. Below are different ways healthcare data analytics is used in the healthcare industry.
Uses of Healthcare Data Analytics
1. An Enterprise Data Warehouse: This foundational step for technology and data involves a warehouse with a minimum of HIMSS EMR Stage 3 data, Patient Experience, Financial, Costing, Revenue Cycle and Supply Chain. A searchable metadata repository and insurance claim records are available. Updates take place within a month or less. Data governance starts to take shape.
2. Standardized Patient Registries and Vocabulary: Core data is organized and standardized, with patient registries based upon ICD billing data. Registries and data management capacity evolve gradually.
3. Internal Automated Reporting: Consistent, efficient production is in evidence as analytics are focused on the production of reports in an efficient, consistent manner that supports the operation and management of the healthcare organization at a basic level.
Key performance indicators can be accessed from both front-line managers and executives.
4. External Automated Reporting: This stage shows consistent, efficient agility and production focused on consistent report production for key regulatory requirements. Basic keyword searches and centralized data governance are available.
5. The Variability Reduction of Waste and Care: This stage relates to the management and measurement of evidence-based care and is focused on minimizing waste, reducing variability and measuring adherence to best practices. Management teams focused on improving patient health are assisted by population-based analytics. Multi-discipline teams monitor how to improve quality and lower cost and risk in a much more precise manner. Data is standardized and evidence-based, a combination of patient registry cost and clinical data that includes all insurance claims. Data is updated within about a week.
6. Suggestive Analytics and Population Health Management: This step involves a financial commitment and preparing the workplace for more integrated analytics and best practices. Half or more of cases use bundled payments, and analytics from point of care to patient care quality, the economics of care, and population management are covered. Bedside devices, external pharmacy data, home monitoring data and more detailed information are also covered. Updates take place within about a day.
7. Predictive Analytics and Clinical Risk Intervention: Higher financial risk can be managed proactively, and analytics expands to cover fixed-fee per capita, diagnosis-based reimbursement models. Beyond just case management, there is also a collaboration with payer and clinician partners to manage care with forecasting, predictive modeling, and risk stratification. Home monitoring data, protocol-specific outcomes, and long-term care facility data are also reported. Updates take place in an hour or less.
8. Prescriptive Analytics and Personalized Medicine: This level involves managing and contracting for health, prescriptive analytics/personalized medicine, wellness management, physical and behavioral functional health, interventional decision support and mass customization of care. Prescriptive analytics at the point of care improve patient outcomes due to analyzing overall population outcomes. There is rapid updating of all data including familial data, biometrics, and genomic data.