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Overcome Data Disparity to Improve Care Quality

Overcome Data Disparity to Improve Care Quality.

Healthcare data should contribute significantly to the patient’s care continuum; adding value to the patient experience and general quality of care, however, most organizations struggle with multiple data source systems instead of employing that data to improve care quality and reduce care costs!  To employ data as a functioning tool for quality improvement and cost reduction, organizations must first understand their driving data challenges.  

One of the biggest challenges organizations face with data is when it’s housed in disparate systems that lack interoperability. When patient data is split between multiple systems, multiple competing patient identities can bring care quality down and care cost up.

With disparate data, there is no single source of truth for providers to use to achieve optimal patient outcomes. Disparate data inhibits the clinician’s ability to track and manage the care needs for their patient population. When data is dispersed under various identities in separate systems, understanding the true status of a patient’s care becomes a significant challenge. Clinicians are already hindered by the limited amount of time allowed for providing care in a typically wellness check. Consider how little care management would be achieved if the clinician spent the entire visit searching through multiple systems for lab results or screening histories. What other patient care elements would be neglected because of disparate data and impossible care gap management?

Disparate data can also lead to costly and traumatic health emergencies. One patient described as having a personal and family history of abdominal aortic aneurysms went in for a wellness exam where the provider failed to do an abdominal exam. The lack of critical healthcare data could very easily result in another health emergency for the patient. Not only is the patient’s care not being managed properly because of incomplete data but it’s also unlikely that proper coding took place to account for the abdominal aneurysms. A wellness check without proper care, coding, and documentation is the perfect recipe for a high cost and traumatic health emergency.

Perhaps last on the minds of clinicians is how they and their practice are performing on pay-for-performance reporting programs. Most organizations and providers feel a significant burden brought upon them through regulatory reporting requirements. Clinicians cannot and should not be told how to practice medicine. With that being said, the original intent (however lost in the complexities of each of these programs) is to push clinicians and practices to better manage outcomes to improve quality and reduce cost. When data points are dispersed in various systems that fail to share critical and pertinent health information, care gaps abound. Gaps in care result in imperfect performance, which reduces overall scoring and can often lead to large financial penalties. 

Once the challenges of data consumption and use are understood, organizations can begin to utilize available resources to meet those challenges with standards, processes, and protocols that allow data to work for, instead of against, organizations, clinicians, and patients. Those organizations that choose to dedicate resources to employing data as another functioning tool in their toolbox will, no doubt, benefit from all the opportunities data presents for elevating quality and lowering costs. The complete data picture means fewer care gaps and ensures accurate patient complexity coding and HCC scoring. With comprehensive data points, organizations can more easily identify high rate ED users and create programs or initiatives to target those users for more frequent preventive wellness checks. Having the whole data story also means having the whole data toolbox to create more opportunities for improved quality, reduced cost, and better population health.

ReportingMD enables high quality, complete, and up-to-date population health data assets based on normalized clinical EMR data, adjudicated claims-based data, SDoH, and other sources.

Large, disparate datasets are processed in near real-time thanks to the ReportingMD Data Connector. Our 18-years of EHR and Claims data experience make this connection seamless and precise.

  • Vendor-agnostic interface
  • Near universal compatibility with all EMR and PM systems
  • Also available delivered to CUBE, for use in 3rd party data visualization tools
  • Suggested for use with ReportingMD Total Outcomes Management (TOM™) Population health analytic solution

By effectively unifying claims and clinical data onto a single platform, ReportingMD helps organizations improve the quality of care.

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