Data Liquidity: Why It Matters More Than Ever
“Many EHR systems don’t communicate well with others.” A frequently heard issue. However, the growing trend toward better data liquidity could change that.
The liquidity of patient data – that is, its’ ability to be exported to other systems as needed, then aggregated and analyzed – has grown exponentially in strategic importance. Without a full, easily-modeled picture of the patient population and their care history, senior executives are limited in their ability to analyze different possible reimbursement strategies, and their access to complete reporting for patient care improvement initiatives is severely curtailed.
Most of today’s value-based incentives — and penalties — rely on quality measures. These new value based models require providers to prove that they’re meeting quality standards and benefitting patients while cutting costs. Providers need sophisticated analytics to help them measure financial and quality performance for each population of patients. They don’t want to learn that their reimbursement is going to be poor after it’s too late to do anything about it. Rather, they want to know in the first quarter so they can improve their performance before the end of the year rolls around. To do this, they need to be able to measure performance on a continuous basis, and that means that a complete set of patient data must be available, regardless of whether the system has just acquired a practice or provider.
Health Data Management says, “Emerging technologies enable providers to collect and analyze data from virtually all ends of the care system, improve clinical outcomes and engage patients as never before—key drivers of accountable care organizations and population-based healthcare delivery.” They provide the following characteristics of a truly integrated data system:
- Your data management program is reliable and flexible. Harmonize, standardize and make data interoperable across systems within your organization and with partners. Update your program as circumstances change and as the industry moves towards combining claims and clinical data.
- Patient data is complete and comprehensible to those that use it. Your system must be able to interpret and include data from all sources that touch your patients inside and outside your organization. Population health management in particular requires accurate, longitudinal information that supports predictive analysis.
- Data is consistently gathered and provides meaningful context. Metadata, information that is not overtly visible to users, makes information relevant and fluid. Set up correctly, metadata distinguishes data points on the back end that improve the collection process and increase data certainty.
- Patients engage in clinical self-reporting and status review. Capturing data generated by patients through Internet-based logs, portals and mobile tracking applications gives clinicians more complete and timely input to care decisions. Access to an integrated medical record gives patients similar advantages.
Summed up via HealthStandards.com:
““The common thread to satisfying consumers and enabling advanced data analytics and seamless care coordination is interoperability. Interoperability is the ability of devices and systems to exchange and use electronic information from other devices and systems without special effort on the part of the user. In health care, this speaks to the capability of our technical underpinnings to support data liquidity – when patient information moves freely and securely from the point of care — be that a hospital bed, doctor’s office or someone’s home– to wherever it is needed, from a clinical decision-making app or electronic health record to an analytics engine, clinical trial repository or public health registry. Interoperability of the technologies used in patient care enables the liquidity of data, without which it is more difficult to meet our goals of providing individualized care and managing the health of populations.” – HCA CEO Milton Johnson
Until quite recently, clinical data conversions were often looked as a step that was required by law (in many states) and necessary in the short term when a practice was acquired or an independent physician joined an organization. These projects were not seen as a critical aspect of the population health strategy for a healthcare organization. It was considered “adequate” for data to be converted from the legacy EHR in a non-discrete format, often to .PDF chart summaries which were manually referenced as patients came in. Under this model, patient charts are slowly built up again in the new EHR, with only limited access to historical data. However, with the advent of Meaningful Use and Accountable Care, the approach to EHR conversions is shifting rapidly.
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