FEATURED SOLUTION
SAS delivers a comprehensive solution for detecting and prevent health care fraud, waste and abuse at every stage of the claims process, and stopping improper payments before claims are paid.
What Analysts Are Saying
"SAS’ legacy and strength in data analytics and aggregation come into play at a time when health plans can benefit from robust analytics capabilities to uncover irregular claim submissions."
Recommended Resources for Modernizing Medicaid Management
Health care payers are on a quest to make informed decisions as they strive to optimize claims processing and payment operations. Improving the quality of claims data and more easily substantiating claims is also part of that quest.
What is secondary use?
Secondary use refers to the act of using data generated as a by-product of clinical care for a second purpose such as business process improvement, evaluation of compliance to clinical guidelines, or estimation of patient safety or care quality metrics. In the HIPAA world these investigations fall under the realm of ‘quality improvement’ meaning that the activity is exempt from notifying the patient that their data is being used for these purposes not related to their direct care. Big Data has challenged a lot of the traditional frameworks for quality improvement particularly when you consider the amount of data external to the healthcare environment that now can be combined with clinical data.
What is informed consent in healthcare?
Informed consent is the process for getting permission before conducting a healthcare intervention on a person , and analyzing one’s private healthcare data can be construed as an intervention. Challenges with Big Data and informed consent tend to come about when researchers want to combine huge repositories of data in novel ways as to ask questions of data that only very large populations can answer. Comparative effectiveness research, for example, is a field that has the possibility of making great headway in understanding the differential cost-benefit of drugs and treatments over time. The speed of these discoveries, however, are constrained by the nature of the informed consent process where getting permission from every individual would be prohibitively expensive and time-consuming. Some health organizations, such as Vanderbilt University Medical Center (VUMC), take an opt-out approach where you are automatically opt-ed into big data investigations unless you sign specific paperwork mandating that your data may not be used.
What is ethical data use?
But this whole concept of ethics goes beyond just being compliant with patient privacy rules and HIPAA policy. Ethical data use means that all investigations are conducted while complying with the federal regulations for patient privacy. It requires daily vigilance into how data passes between stages in your analytic workflow. Even a simple oversight, such as using a non-HIPAA compliant storage site such as DropBox to share data with a coworker, puts your organization at risk.
What is an EHR?
An Electronic Health Record (or EHR) is an electronic version of a patient’s medical history, that is maintained by the provider over time, and may include all the key administrative clinical data relevant to that person’s care under a provider, including demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data. and radiology reports. Depending on the breadth of an EHR, it can also include practice management and enterprise resource planning functions as to schedule patients and direct the flow of supplies.
Why is analytics important in healthcare?
The healthcare industry realizes in principle the importance of analytics in enhancing care quality and economic viability, but the execution of data and analytics strategies tends to fall short for many organizations. The core problem here is not technology nor a lack of analysts able to use Big Data tools. The real issue is that the ability of analytic professionals to make an impact on furthering a health organization’s mission is dependent upon the unique confluence of analytic readiness (i.e. culture) and analytic maturity at that organization. Without the foundational underpinnings of data governance, change management, and systems thinking, analytic projects at best can attain a pilot state. It may be worth noting that there exist challenges related to both data governance and change management that really need to be addressed. For example, see the recent news about IBM Watson’s success (or lack thereof) at the University of Texas Health System. (The University of Texas System Administration Special Review of Procurement Procedures Related to the M.D. Anderson Cancer Center Oncology Expert Advisor Project, 2016) http://www.utsystem.edu/sites/utsfiles/documents/system-audit/ut-system-administration-special-review-procurement-procedures-related-utmdacc-oncology-expert-advis/ut-system-administration-special-review-procurement-procedures-related-utmdacc-oncology-expert-advis.pdf