Successful patient-centered medical homes rely on good data

LH_low-resIn earlier posts, we discussed that good data is data that spurs action — on the part of both providers and patients. This is no truer than for the patient-centered medical home (PCMH) model, where primary care is organized in a way that emphasizes care coordination and communication. Patients get higher quality care and providers realize lower costs.

The PCMH model relies heavily on data analytics that shows patients how to make smart choices when seeking medical help. But the model presents unique challenges where patients may move and see different physicians for separate conditions. Structured data, which are typically numerical and organized by fields and categories, are relatively easy to track. It gets trickier for unstructured data, which are text based and more free-form.

Add to such complexity the fact that disparate data silos — across settings or even within the same organization — each contain patient data. Patient information may reside in databases at physician offices, hospitals, emergency departments or long-term care facilities.

Capturing unstructured data in a structured way requires a robust population analytics platform. The best systems apply population cohort analyses, risk stratification, predictive analytics and longitudinal outcomes to help care teams identify high-risk patients. This improves follow-up care and helps reduce the number of follow-up appointments for stable patients.

Getting a longitudinal view of all the data requires organizations to aggregate data at the patient level and ensure it is adequately normalized. Once aggregated and normalized, insights provided by analytics need to be accessible by all care sites. This is made possible via web-based care management platforms.

Good data drives strong patient relationships — a hallmark of PCMH success. Hospital case managers and nurses can share patient information and care plans with primary care doctors when a patient is discharged. The patient’s doctor can then continue treatments uninterrupted, which lowers hospital readmission rates and keeps patients healthier.

To read more about how PCMH models leverage good data, download the Optum white paper, “Getting from big data to good data: Creating a foundation for actionable analytics.”

In my next post, I’ll outline future uses for data and analytics.
About the Author:

Leslie Cozatt currently serves as Director of Marketing, Optum Provider – Thought Leadership and Content Strategy.

She directs the development of content that spotlights the role of data analytics in healthcare – specifically the transition to value-based care, risk management and population health management. She brings to her role more than 20 years of experience developing B2B and B2C integrated marketing campaigns for companies including ThreeWire, Eliance and 3M. Leslie attended the University of Minnesota and graduated from Wellington College with a BS in International Business & Communication.

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