This post also appeared on KevinMD.
Software has opinions. No, I’m not talking about opinions on the next presidential election or opinions about flossing before or after brushing. Software has opinions about how data should be displayed, opinions about users’ comfort with the mouse, even, in some cases, opinions about what you should have for dinner (see your local on-demand food ordering service).
We tend to view software as a tool that is either good or bad. Good when it lets us do what we want with as little frustration as possible and bad when it doesn’t. Maybe we should be a little nicer to software.
I enjoy a good brainteaser, one that you really have to concentrate on and with enough revelations built in that make the end result a satisfying accomplishment. Here are some of my favorites. I made the answer text white so that you can’t see it unless you highlight (click and drag) over it.
Question: If you place 3 points randomly on the perimeter of a circle, what is the probability that all 3 lie on the same semi-circle?
In aggregate, community health centers account for the care of about 20 million people in the US. Over half of these patients represent racial or ethnic minorities and over a fifth (22%) prefer to speak Spanish rather than English.
Most CHC revenue comes from fee-for-service reimbursement paid by Medicaid (40%), private payers (7%), and Medicare (6%). This has led CHCs to pursue many of the strategies for maintaining solvency as other care centers across the US, including increasing patient visit volume and improving operational efficiency.
One problem all clinic sites face is the incidence of no-shows, patients for which an appointment is scheduled but that do not show up. It is estimated that no-shows account for 5-30% of appointments scheduled across the US and it is typically higher at CHCs. No-shows risk failing to deliver appropriate care to patients for whom they are scheduled in a timely or continuous manner, reduce access to scarce healthcare resources for those waiting for appointments, and represent up to 15% of lost revenue for the clinic.
Testing design assumptions with users is a critical ingredient in user-centered design. In Symcat’s early stages (ca 2012), we thought, for better or worse, that we would identify some eligible test users through Craigslist NYC. We were surprised by just how many people were willing to participate and collected some pretty interesting data in the process. I just stumbled upon it and I suspect much of it is still relevant, so I thought I would share. Get ready for some graphs.
I should begin by acknowledging the authors’ important contribution to elucidating the gap between what symptom checkers may hope to provide and the existing state of the art. Semigren et al adopt a pragmatic approach both by identifying which symptom checkers patients may reasonably find and assessing them in the most intuitive way imaginable: making them take the standardized patient tests we all take in medical school.
OK, so there are a lot of doctors: PhDs, JDs, DDS. For the sake of argument, I’m talking about MDs here. Let me start by explaining night float.
Night float is an interesting rotation during residency when most people who are working during the day leave their hospital and their patient’s care in your hands. It is alternately some of the quietest times during residency as patients drift off to sleep and some of the most hectic as in when a surge of patients finally arrive from their ambulance- or helicopter-assisted journey across the state. Night float, or “the night shift” arose out of a recognition that sleepy interns having worked 30-hours straight sometimes do not make the best decisions or confuse their lefts and their rights.
Just got back from Epic XGM 2015 presenting some of the work I have been doing at Atrius Health in predicting high risk patients.
Some of the session details (slides below):
Summary: Atrius Health expects a large proportion of commercially insured patients to shift into accountable care arrangements in the near future. The presenters will describe their work to develop new risk models for commercial patients, using both financial claims and Epic data, and compare these against other risk models.