Spending on unwarranted use of healthcare services, where no actual measurable benefit is obtained, has been estimated in the range of $250-$325 billion annually in the U.S., according to Thompson Reuters data from 2009. The unwarranted use of healthcare services is the largest single component to the $600-$850 billion surplus in healthcare spending that can be attributed to embedded inefficiencies; inefficiencies that ultimately increase healthcare cost and decrease the overall quality of public health.
The first step that agencies can take to reduce healthcare costs and improve quality is to identify the surplus of discretionary health care services. This is where analysis of patient and population data comes in.
Enter Big Data
Hospital organizations are sitting on large volume data sets, typically in the petabyte scale for each hospital. These are composed of individual patient electronic information that has the potential to root out these systemic inefficiencies in their healthcare services. Sorting through this data, however, presents a substantial challenge.
[Part 1: How to harness Big Data for improving public health.]
Even within a single patient record there are large varieties in the type and format of the data. Today, this data is becoming more complex with structured data (data that resides in fixed fields within a record) becoming comingled with unstructured data in the form of free text, images, audio, and video files.
During periods of critical patient care this data can have a high velocity requiring quick time-sensitive response. Hospitals are increasingly facing information overload and need to implement data strategies — ranging from data use to data retention — to uncover the information buried within these large volumes of population data. Collecting an overall dataset with the individual case details for each member of the entire population of patients can help identify inefficiencies in healthcare services.
Reducing Unwarranted Healthcare Expenditures
Specific areas of unwarranted health services include: overuse due to fee-for-service incentives; marginally valued direct care that has no measurable benefit or shows no improvement in patient outcome; unnecessary diagnostic or imaging tests that are performed to protect against malpractice exposure; and high cost diagnostics performed on patients at very low risk for the condition.
To systematically reduce these un-warranted expenditures, healthcare organizations are moving away from the current fee-for-service payment model and towards providing reimbursement for services based on health outcome. The new Accountable Care Organizations (ACOs) are working to provide pay-for-performance incentive models. An ACO is a payment and delivery reform model that ties provider healthcare reimbursements to quality measures and works to reduce the total cost of care for a population of patients. Predictive and prescriptive analytics is inherently embedded into this new model of care.
Predictive and prescriptive analytics looks at what might happen for a given health situation and prescriptive analytics tells either hospital or patients what they might want to do in the future to address a specific health situation. These analytic approaches are powerful tools for identifying un-warranted health services.
[Related: The HIT needs of ACOs: Analytic data.]
The analytic approach uses patterns found in historical data sets like medical records to identify risks, trends, and associations. One well-known example is credit scoring used throughout the financial services industry. Particular to healthcare, predictive analytics can be used to address un-warranted care by answering questions such as:
• What is a patient’s specific risk for readmission to a hospital over the next 30 days?
• What is the specific outcome a diagnostic test will likely have on the current treatment plan?
• What specific medical procedures, tests and prescribed drugs provide no measurable benefit in patient outcome?
• What specific tests are performed primarily for medical liability reasons?
