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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