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A close examination of the COVID-19 patient journey will result in a better understanding of the impact of the virus on at-risk diabetes patients. Segmenting positive and negative COVID-19 patients provides a real world comparison of anonymized patient cohorts’ A1C, fasting blood sugars, and glucose levels. Linked with prescribed medications, this data will identify the impact of COVID-19 on the patients’ prescribed drug therapy. The...

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Patient-level data matching, a fundamental and critical success factor for the longitudinal alignment of anonymized health information, can be a daunting challenge given the complexity, sensitivity, volume, and heterogeneity of patient healthcare data, as well as the use of primitive technologies.   A comprehensive picture of the anonymized patient requires accurate matching of individual patients to their health records across settings, however, patient matching rates vary widely. Matching records to the correct anonymized...

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Patient privacy and data security are among the most pressing issues facing organizations trying to bring healthcare data together. Aligning data across the full patient journey is complicated by the fact that healthcare data in both clinical care and the “real world” is generated in many forms.   Committed to advancing healthcare while protecting Personal Health Information (PHI), MSA supports compliance with standards, guidelines, and legislation issued by regulatory bodies,...

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Aggregating Data

Specialty Pharmacy data analytics are changing due to the fact that combining clinical data with Specialty Pharmacy data gives a more complete picture of the patient’s story. Aggregated patient-centric data guides product development and product distribution, from the discovery process through patient outcomes measurement and ultimately determining the effectiveness of the drug. Specialty Pharmacy data analytics that aggregate Specialty Pharmacy data with data from other sources can measure both the...

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Quality Process

High quality healthcare data is a strategic asset. Having appropriate healthcare data quality processes in place directly correlates with an organization’s ability to make the right decisions. Rapidly growing volumes of differently structured healthcare data from multiple (internal and external) sources and stricter compliance requirements make it more important than ever to ensure that data quality standards are met.   MSA's Quality Process, a key differentiator, identifies and eliminates...

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