I began my medical career on yellow paper. At the time, the patient’s record consisted of a page in a large 3-ring binder. Good times perhaps, but such a system felt more like one for the storage of ancient texts—difficult to decipher and almost inaccessible by all but a few. As a result, doctors’ orders were often overlooked, leading to delay in care. Chart review, whether to better understand a bad patient outcome or for research, meant hours of work.
How things have changed. In part due to the advent of computers but also because federal regulations require it, healthcare seems to have a data obsession. Almost everything we do is now recorded in an electronic chart. This is certainly a vast improvement over the yellow paper. However, somewhere along the way we seem to have lost sight of the idea that people take care of people. These days, though the EMR and computers may serve as the co-pilot, it’s how we interact with all the data that ultimately leads to better care.
4 Things to Remember about Data
- Data needs to be interpreted. At a simple level, say a lab value, a machine may alert us to an abnormal value. But abnormal for one person may be reasonable for another. Often interpretation depends on the experience and knowledge of the practitioner.
- Data, like fish, has an expiration data. An abnormal finding needs to be reported to the right person as soon as possible. Delays cost money and occasionally contribute to bad outcomes. In this respect, the best EMRs haven’t overcome the limitations of a fax, phone, and pager. I know of few other industries still making critical decisions based on such tools.
- Data needs to inform us not only about a particular patient with a particular outcome, but also about patterns to patient outcomes over time. As I’ve noted before, doctors tend learn from their last worst result. But one patient responding poorly to a drug may not mean a drug is bad for everyone, nor does such a case-by-case report provide a larger picture of a particular doctor’s outcomes over multiple similar cases and relative to other doctors in her field. Challenges to changing the way doctors report and use data are partly technical partly cultural. The medical community is after all somewhat reluctant to identify bad doctors. Still, it is only when data successfully transfers to information to knowledge to action in American healthcare that it becomes useful.
- Finally, I’m a little worried about the changing relationship between doctors and information. Whereas in my student days I would go to the library to research a topic, looking at many different sources, my medical-student daughter and her peers are quick to Google. They obtain an answer in seconds but don’t necessarily experience the learning process, something that may be key to long-term knowledge.
Data collection is necessary but not sufficient to transform healthcare. It’s in the interaction between people and machines where we’ll ultimately get the most value. For me personally, this leads to focusing on better ways to enable collaboration—to facilitate care teams and provide greater transparency around conversations in healthcare. So the next time your hospital administrator talks about her new EMR, data warehouse, or analytics tool, ask her politely how this will help people care for people.