Data Quarantine in the Time of the COVID-19 Pandemic
Year: 2020
Type: article
Abstract: Our healthcare information is trapped.It is trapped in the proprietary data models of the electronic medical record (EMR) and in our healthcare systems' data warehouses.This reality has become strikingly clear as the coronavirus disease 2019 (COVID-19) pandemic has swept across the globe, killing >80 000 people in the United States alone.We need answers but struggle to address even the simplest questions.How many individuals are infected?Who is at the highest risk for developing severe infection?What therapies are being used to treat hospitalized patients?This crisis is testing the limits of our public health and healthcare systems in many ways, including a quarantined health information system.This perspective reviews several deficiencies in healthcare information technology that currently limit our ability to deal with the pandemic and suggests current solutions moving forward.In an ideal world, healthcare systems would speak the same language, communicate with public health agencies, and engage directly with the community.This type of system would allow us to track, learn, and innovate during the current crisis.The COVID-19 pandemic lays bare just how far we are from this vision, and, sadly, the deficiency will have dire consequences.For example, pooling data across multiple institutions is critical for scientific discovery and community surveillance.No single healthcare system reflects the status of a community or region, and individually each lacks the sample size and diversity for robust, generalizable results.Yet bringing data together for pooled analyses is currently too difficult.Different healthcare systems essentially speak different languages.Even if 2 systems use the same EMR software, each build is individualized such that the same concept may be hidden in different places in the data.For example, hydroxychloroquine has emerged as a potential treatment option for COVID-19 but also has known QTprolonging effects and can cause ventricular arrhythmias.As these are rare complications, healthcare systems and researchers must pool data to identify these events among treated patients.When a clinician researcher says, "Find me the patients treated with hydroxychloroquine," a data scientist hears, "Find me the patients with one of the drug codes that represents hydroxychloroquine."Yet one healthcare system may use the National Drug Code directory to represent medications and another system might use Medi-Span to represent medications.The systems speak different languages-a barrier to pooling data for rapid analyses.Common data models (CDMs) address the interoperability issue to some degree, but this is an imperfect solution.Using our example above, the 2 different hydroxychloroquine representations could be mapped to a single data format, to facilitate pooling.But systems must still map their data accurately to the CDM, and the upfront cost is generally steep for this labor-intensive process.The CDM is essentially a middle man, and, currently, the transformation process is not automated nor does it occur in real time.After data are mapped to the CDM, the tables in
Authors: Rashmee U. Shah, Lesley H. Curtis
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3021826256