Culture of Health Blog

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.

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. 

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.