May Webex PDM – Covid-19 Data Quality Case Study

Covid-19 Data Quality Case Study

Managing a public health crisis requires reliable metrics on a local and aggregate level.  Unlike many industrial processes, raw data about a pandemic is captured through a variety of distributed locations, events, and jurisdictions.  Data quality challenges abound.  Definitions of events, metrics, and timing of aggregates create ambiguities and uncertainties.

We will examine critical elements of the current pandemic including explaining how a virus works, definitions of antigens, antibodies, co-morbidity, false negatives, and long-haul symptoms resulting from Covid-19 infection.  We introduce a graphic model of the chain of events in a patient’s illness, and how we could benefit from more metrics along that chain with greater precision.  We will introduce the concept of the event-to-information supply chain and issues of timing and ambiguity of high-level metrics.  We show examples of political bias in expression and manipulation of information.  We will finally look at the quality challenges of vaccine testing and distribution.  If time, we will also discuss how Covid-19 places new demands upon medical records and public health metrics.

About Michael:

Michael Scofield, M.B.A. is an Assistant Clinical Professor at Loma Linda University.  He has spent most of his career in systems development, data management, and information quality.  He has lectured to over 340 professional audiences around the U.S., Canada, and in the U.K. and Australia.  This includes 47 ASQ section presentations.  He has over 27 published articles in data management, particularly in data quality assessment.  Recently, his more popular topics include a “Covid-19 Data Quality Case Study”, sources of organizational complexity, semantic data integration, and data visualization.  He also lectures widely to general audiences on topics of travel, history, interpreting satellite imagery, and civics.