This is one of a collection of stories that are like “Final Destination” meets “The Monkey’s Paw” (W. W. Jacobs, 1902). As such, they are tragedies more than either mysteries or horror, and would appeal most to readers who enjoy the inexorable pull of a story arc that leads to doom. In each story, a protagonist makes a wish that comes true with fatal results for someone, often the person making the wish. Nothing supernatural, but just how things work out. (Or is it?) The technical details surrounding the fatal (or near-fatal) event are drawn from real cases in the US OSHA incident report database or similar sources and are therefore entirely realistic, even if seemingly outlandish. The plots draw lightly from cultural beliefs around actions such as pointing at someone with a stick or knife, wishing in front of a mirror, or stepping on a crack.
Harold was the harried COO of a large for-profit chain of hospitals. He kept his finger firmly on the performance of the chain and moved briskly in everything he did. For example, the average pedestrian in New York City walks at a speed of five feet per second, but Harold whizzed about the office at seven. It was also vaguely infectious—people around Harold began to move faster and were more nervous.
As an MBA graduate, Harold had studied, and been much impressed by, Frederick Winslow Taylor and his theories on “scientific management.” Wherever possible, Harold broke work down to its most essential components, and paid staff, wherever legal, by piece work. The idea of RVUs was just up his alley and he paid staff by the RVU. On the surface, this meant that staff could decide for themselves how much they wanted to earn and therefore how hard they wished to work. In practice, it generally meant a scramble for an undersupply of work by an oversupply of workers, often pitting clinicians against each other in competition for RVUs. Harold liked it that way because it kept costs down and revenue up. He was also a firm believer in what Douglas McGregor of the MIT Sloan School of Management had called “Theory X,” that workers were naturally lazy, lacking in motivation, and would goof off if you gave them half a chance. He believed strongly that you needed to keep your eye on them constantly and always keep a stick and a carrot at hand.
Nurse Jenny was jittery. She seemed calm and controlled, even confident on the outside, but she roiled inside. She was chronically aware of how easily the world could unravel, how things could spin out at random in usually harmful ways unless you kept your thumb on them. As a result, she was far more cautious, regulated, and mindful in her work, and it irritated other people no end. According to the stats that percolated up into the daily metrics on Harold’s desk, she was 21.6% slower than her peers, took 16% longer to initiate tasks, and her RVUs were 17.9% lower. If not for the balancing effect of her patient satisfaction rate (4.96), net promoter score (96.3%), and performance in quality and safety rankings (98th percentile), her name would have popped up on the cut list long ago. Harold had implemented a ranking system on staff performance; every quarter, he received a cut list of the lowest 10% of staff. They were usually fired.
Harold regarded automation as a key factor in improving efficiency and reducing costs, and he implemented it wherever he could. Sometimes this had the salutary effect of reducing paperwork and cutting the time to do charting, but sometimes it was just burdensome and intrusive to staff. Being able to log many patient care activities automatically by scanning barcodes was welcome, but having to scan in and out of the toilets felt weird and intrusive.
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It was late, and all the admin staff had left the executive floor, but Harold was poring over performance statistics. Harold liked the solitude and had been enjoying the tranquility of numbers fitting an orderly and proper pattern, but then alarms went off on his operational KPI dashboard. It was Jenny, again. In her extra cautious way, she had scanned a drug in a fifth-floor med/surgery ward, but when she examined the box contents, it was a different drug than what was displayed on screen. She painstakingly reviewed her tracks. She checked the patient: correct. Patient ID bracelet, prescription, and chart agreed. She checked the drug box, contents, and prescription: fail. The box didn’t match the prescription. Puzzled, she scanned the box again, and now the box and prescription matched, but the drug didn’t. She rotated the box, and scanned again, and now the box and prescription no longer matched. Peering at the two barcodes on opposite sides of the box, she noticed that they were slightly different. Dutifully, Jenny flagged the error, wrote a problem description, and drew the same drug from a different dispensing robot at the other end of the ward. This time, the drug, box, prescription, and patient all matched. Slightly behind schedule, Jenny continued to do her medication rounds.
Up in the admin suite, things were not as rosy, and there were two flashing warnings on Harold’s screen. First, it said that a nurse was severely behind on the medication round schedule (amber advisory), and it also said that a serious drug error had been reported (amber warning). It also said that a nurse had bypassed security protocols by drawing meds from two different robots for the same patient from two different locations (red warning). Harold was out of his chair and nearly sprinting down the corridor to investigate. He sidestepped a solitary floor cleaning robot and strode toward the elevators at bronze medalist speed. Rounding the corner before the elevators, he stepped on a wet patch left by the robot, who also struggled with cornering, and fell heavily. The bulk of the impact was absorbed by his shoulder, but the side of his head walloped the shiny clean floor surface like a bowling ball being dropped on a dance floor. The impact disrupted the temporal artery and an epidural hematoma began to grow like a little red balloon.
After a few minutes, the robot cleaner encountered him, paused to review this unexpected obstacle in its path, then neatly avoided him and continued on its mission. A human cleaner would probably have summoned help, but would have been less efficient or cost effective for floor cleaning. By the time a human discovered Harold lying still on the cold floor, rigor had fully set in, and removing him was highly inefficient.