From Meaningful Use to Meaningful Analytics

I’ve written about the three data waves we are facing in healthcare: data capture, data sharing, and data analytics. Primarily due to the “meaningful use” program the United States has made huge strides in electronic health record (EHR) adoption and is beginning to make progress in health information exchange (HIE). These are the principal drivers of the increased capability and use of clinical analytics, since it is the patient data captured, shared, and aggregated by these applications that is the primary source of the data that healthcare organizations analyze using these clinical analytics tools. This allows us to turn those data into information - actionable information that actually provides the ability to improve quality and lower the cost of care. It is the meaningful use of EHR technology that will ultimately enable meaningful analytics.

Two key factors for using clinical analytics to translate data into information are: achieving high quality of care and improving patient safety, as well as increasing awareness about the costs associated with providing care. One way in which organizations are framing these quality of care issues is within the context of meaningful use. Because of incentives when meaningful use criteria are met, and the impending penalties when they are not, many healthcare organizations and providers are evaluating how they are capturing and sharing data. Since organizations are required to report on multiple measures to achieve meaningful use, they often attempt to find ways to capture and report successfully on all measures rather than focusing on only a handful of measures. Clinical data analytics do not only leverage meaningful use rules, but also can help satisfy compliance with them.

Meaningful Use Priorities

Since meaningful use is requiring greater interoperability and data sharing, there is now much greater opportunity to aggregate data at a community level and have an even broader data set than just the EHR to mine for clinical intelligence. One benefit from HIE, besides improved care coordination, is the ability to perform queries and apply analytical tools to those data that were not previously available. The five health outcomes policy priorities included in meaningful use are:

• Improve quality, safety, efficiency and reduce health disparities
• Engage patients and families
• Improve care coordination
• Improve population and public health
• Ensure adequate privacy and security protections for personal health information

Meaningful Use Analytics

Obviously the reporting requirements for meaningful use can make good use of clinical analytics tools, but some of this reporting capability is also useful when participating in new payment models such as accountable care organizations (ACOs). Although not directly called out in meaningful use, lowering costs is a high priority and part of the over-arching Triple Aim. I can not imagine succeeding in a truly transformed healthcare system without having the clinical and business intelligence tools that will allow for targeted interventions and not only a retrospective look via claims data, but the real-time capabilities of an Enterprise Data Warehouse with robust analytic and reporting functionality. .

In addition to a focus on meaningful use measures and ACOs, the industry’s shift to the use of ICD-10 (International Statistical Classification of Diseases and Related Health Problems-10th revision), mandated for the coding of all inpatient and outpatient claims beginning in October 2014, will also impact the use of clinical analytics. Conversion to the ICD-10 coding will dramatically increase the specificity and granularity, and therefore the value, of diagnostic datasets. For example, this change will increase the number of codes available for identifying diagnoses and procedures from 17,000 to 155,000. This will improve the classification of patient interactions by expanding the information that is relevant to ambulatory and managed care encounters, offer expanded injury codes and enable the combination of diagnosis and symptom codes to reduce the number of codes needed to fully describe a condition. This increased granularity, combined with the continued increased in digital capture of clinical data will yield new data sets which healthcare organizations will have the opportunity to translate into meaningful information that can be used to improve the delivery of healthcare.

Increasing Value of Clinical Analytics

As the healthcare system continues to harness bigger and better data sets, including claims data, genomic data, imaging and other important data sets, the value of clinical analytics will increase. Just because data is housed in a data warehouse still doesn't mean that access to information is easy or timely. Clinical analytics continues to be used primarily for retrospective analyses, rather than real-time clinical decision support. In my previous post on “Realizing the Value of Health IT” http://www.ahier.net/2013/09/analytics-realizing-value-of-health-it.html I wrote about some companies that are making good headway in this area, including Health Catalyst. The technical architecture used for a clinical data repository is a key consideration. As we continue to aggregate digital health data of all different stripes, analytics will provide real value to clinicians and healthcare organizations from both a quality and a financial perspective. I’m hopeful that we will continue to see development of the Healthcare Analytics Adoption Model and broader implementation of this critical technology.

This post originally appeared on HealthCatalyst.com