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Tag: Digital Divide

Race, Ethnicity and Patient Engagement

flying cadeuciiA few years ago, I was upgraded to First Class on a flight from California back to Chicago. Not long after I settled in, a tall, muscular man easily four inches taller than me walked up to my aisle seat in the first row and prepared to sit by the window.

I envisioned him spending hours hemmed in by the bulkhead and offered to switch places. We began to talk, and soon he shared that his seatmates often hesitate to engage him in conversation. Women and even some men will turn or stiffen in their seats in order to send a clear body-language message.

That’s what happens when you’re a large, physically imposing black guy. People make assumptions. When it comes to patient engagement, we often make assumptions, too.

We minimize the influence of race, gender and ethnicity, or we confuse it with socio-economic status. We assume that “people like us” have communication preferences like us. We downplay the doctor-patient relationship and overemphasize technology.

Race and Ethnicity Matter

In truth, race and ethnicity matter as much in medicine as in the rest of the society. For example, whites, African-Americans and Latinos share the same expectations of their physicians, a study in Health Services Research found, but “patients from different racial and ethnic groups report differing experiences…when using well-validated measurement tools.” Translation: the perception reflects reality.

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Using Predictive Modeling to Make Better Decisions

In an article posted earlier this year on this blog I argued that hospitals have traditionally done a sub-par job of leveraging what has now been dubbed “big data.” Effectively mining and managing the ever rising oceans of data presents both a major challenge – and a significant opportunity – for hospitals.

By doing a better of job connecting the dots of their big data assets, hospital management teams can start to develop the crucial insights that enable them to make the right and timely decisions that are vital to success today. And, better, timelier decisions lead to improved results and a higher level of quality patient care.

That’s the good news. The less than positive story is that hospitals are still way behind in using the mountains of data that are being generated within their institutions every day. Nowhere is this more apparent than in the advanced data management practice of predictive modeling.

At its most basic, predictive modeling is the process by which data models are created and used to try to predict the probability of an outcome. The exciting promise of predictive modeling is that it literally gives hospitals the ability to see into (and predict) the future. Given the massive changes and continuing uncertainty that are buffeting all sectors of the healthcare industry (and especially healthcare providers), having a clearer future view represents an important strategic advantage for any hospital leader.

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