Author Archives: George Hobor

What Can We Do About Biases Baked Into Data?

Oct 31, 2022, 1:00 PM, Posted by George Hobor, Nicole Marwell

We believe that local data can help uncover inequities and inform decisions that support healthier communities. But what happens when the data we rely on fail to capture the social reality we imagine they do? Or when the data are flawed, incomplete—or worse, riddled with bias?

Robot Illustration

While data are critical in guiding policy and allocation decisions, it’s important to understand what data are and what data are not. Data are too often seen as objective, neutral, and accurate representations of reality. But the data points guiding our decisions are produced through human decision-making—and the bias and error that inherently comes with those decisions.

For example, while patient ratings and reviews of physicians can provide important insights, they are not an exact science and are subject to human bias on what to report and what to leave out. In fact, studies have found that patient reviews tend to be biased against physicians of color. This awareness of limitations and biases should inform decisions about how we use patient ratings as data. If we are not clear-eyed about these limitations and the conclusions that we can draw from these kinds of data, there will ultimately be consequences for healthcare organizations’ government reimbursement rates, decisions about salary and raises for individual physicians, diversity of the medical workforce, and ultimately health equity.

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How Can We Use Local Data to Address the Impacts of Structural Racism on Community Health?

Apr 4, 2022, 10:45 AM, Posted by George Hobor, Oktawia Wojcik

Thirty-five local non-profits will be awarded up to $40K to work with local data to dismantle structural racism via a new funding opportunity.

Editor's note: This funding opportunity is now closed. 

Columbus, Ohio redlining map. A redlining map from 1936. In communities throughout the nation, there is strong correlation between redlined places and worse health outcomes.

The COVID-19 pandemic reinforced how place matters for health. Some communities have the conditions needed to help their residents thrive—like safe streets and parks, safe and affordable housing, and access to healthy foods. But too many communities—particularly places where people of color and those with low incomes live—have lacked these resources. This lack of resources is a result of the legacies of structural racism, such as redlining that shaped the socio-economic trajectories of communities, and modern-day racist practices, like systemic disinvestment from communities of color.

To inform efforts to improve community conditions shaped by structural racism, the Robert Wood Johnson Foundation (RWJF) has released a call for proposals (CFP). In order to best represent the voices of those most impacted by societal injustices, we aim to meaningfully engage community organizations. Up to 35 local non-profit organizations will be awarded up to $40,000 over nine months.

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A Successful Model That Predicts and Prevents Violence

Jun 18, 2018, 11:00 AM, Posted by George Hobor, Laura Leviton

A surgeon in Cardiff, Wales, who regularly treated victims of violence, discovered that many cases went unreported. He devised a model for collecting data and collaborating with both law enforcement and community to predict and prevent violence. This approach is now taking root here in the United States.

Cardiff image

Weekend after weekend, the wave of emergency department (ED) patients would arrive. Oral and maxillofacial surgeon Jonathan Shepard would treat shattered jaws, knife wounds and other facial injuries at the hospital in Cardiff, Wales. These injuries stemmed from brawls in bars and nightclubs where broken glasses and bottles were wielded as weapons. Strangely, Dr. Shepard found that only 23 percent of these assaults treated in the hospital were reported to law enforcement.

Harnessing the Power of Data for Violence Prevention

Determined to find a way to stem the violence, Dr. Shepard mobilized health care providers, law enforcement heads, city officials and other local leaders in working together to address what was happening within their community.

Local hospitals agreed to gather basic anonymized information from each assault victim admitted to the emergency department, including the specific location of the violent incident, time of day, and weapon involved. They removed patient identifiers and shared the anonymous data with local law enforcement officials, who combined those data with their own records.

With these data, police were able to map when and where violence might happen, and concentrate resources on hotspot locations such as specific streets, businesses, schools, or transit stations, and during particular times of the week, to help prevent incidents.

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