Creating an EHR Solution for Pediatric Dose Range Checking
To enhance safety during the drug ordering process, Vinay Vaidya, M.D., CMIO, Kelly Basfield, director of clinical applications, and the clinical applications team at Phoenix Children's Hospital undertook a detailed and targeted project to build a robust pediatric dose range checking solution within their electronic health record (EHR).
To enhance safety during the drug ordering process, Vinay Vaidya, M.D., CMIO, Kelly Basfield, director of clinical applications, and the clinical applications team at Phoenix Children's Hospital undertook a detailed and targeted project to build a robust pediatric dose range checking solution within their electronic health record (EHR). Because of the strategy, goals, breadth, and impact of the “Strive for Zero Prescription Errors” initiative, it has been recognized as the second-place winner of the IT Innovation Advocate Award, jointly sponsored by Healthcare Informatics and the Association of Medical Directors of Information Systems (AMDIS) and awarded at the HCI Executive Summit in May. (Read more about the first place winner, Providence Alaska Medical Center's story)Because there wasn’t a plug and play solution that could be easily incorporated into the Phoenix Children's EHR (their vendor is the Chicago-based Allscripts), the clinical applications team built its own solution. In pediatrics, patients can weigh as little 10 ounces or as much as 200 pounds for adolescents, so because of this, dosing can also run the gamut. “Safety was the overriding factor for us, and because of the variability of children’s weights,” says Basfield, “that increases the likelihood that mistakes can be made, and the smaller the weight you don’t have much room for error.”Kelly BasfieldPhoenix Children's went live on its EHR in 2002, and gradually rolled out computerized physician order entry (CPOE) hospital-wide in 2009. With about 1,100 drugs in its formulary, Vaidya says that less than 10 percent of the drugs had dose range checking built into them. For the project’s phase one of implementation that began in October 2010, the team analyzed the frequency of drugs ordered since 2002, which yielded close to 750,000 orders. The next step was targeting the 100 most ordered drugs, as well as the most high-risk drugs. Basfield notes that this pre-analysis of ordering habits, rather than taking a different formulary approach (i.e. alphabetically), really enhanced the breadth of coverage in phase one.Creating No-‘Nuisance’ Drug AlertsTo create drug alerts within the CPOE system, Vaidya separated the configuration from the programming logic by creating a separate reference table. “That was the critical defining moment, we completely took [out] drug by drug configuration, and for our 1,100 drugs there is one single custom code that looks up all the drugs,” says Vaidya. Vaidya says his team identified a custom code that would identify the drug ordered; evaluate the order to see if it fell within the dosing ranges in the reference table. If the order did not fit within the ranges, the system would fire a soft stop alert; or if the dose was so high, it would initiate a hard stop.Vaidya also realized that the alerting configuration had to be sensitive and strike the right balance so that alerts wouldn’t be seen as nuisances. “When you’re about to alert a physician in real-time, you need to be very careful about at what level do you alert them,” Basfield says. “Part of what Dr. Vaidya came up with was a alarmingly high dose strategy that allowed us to figure out at what point do we want to alert a physician, which is very different than alerts at the normal dosing range.” Vaidya notes that since his team had access to dosing patterns, it could set appropriate limits to correspond with these patterns.“You often hear in discussions and literature that hard stops are not good; find a workaround,” says Vaidya. “Death and taxes are the only two hard stops in CPOE. We challenged that notion. We feel that if misused hard stops can bring system down, but if used judiciously with multidisciplinary buy-in, with data to support, they can be extremely powerful.”