For one of my classes, we’ve been reading several reports from the Institute of Medicine. The latest one we read was Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions (2009). This report provides insight into the use of information technology in healthcare giving an overview of its current state and providing a vision for how IT can most effectively be used.
One of the main points of the report is in the fact that the way IT is used in our healthcare system today is far removed from the ideal. IT as implemented now focuses primarily on automating existing tasks when there would be so much more to gain if it were used more effectively to enhance the cognitive models used by clinicians – provide more systemic and holistic views of patients’ health status rather than focus so much on raw data (though, the raw data should always remain accessible when needed). There are a series of vignettes in the report that highlight how this could be implemented and I found them quite interesting to consider. I share a couple of them here — the full report is available online (pages 20-24 have the vignettes).
Vignette 1: A clinician needs to know what medications an elderly, memory challenged patient is taking. Recognizing the important difference between medications prescribed and medications taken, the clinician asks the patient to bring all of his pill containers, both prescription and over-the-counter, to the appointment. She asks the patient to place all of the containers on a surface table computer, which automatically identifies the medications in each of the containers and counts the number of pills remaining in each container. The pill containers also carry RFID [radio-frequency identification] tags, on which the intial fill-up quantities of the containers are stored. The table can read these tags, and thereby make an inference about what pills were actually taken and provide information about likely compliance with a particular medication regime. Farther in the future, recognizing the differences in how individuals absorb or clear medications from their bodies, a blood sample of the patient in question is analyzed with a mass spectrometer or other similar device, and the resulting spectrum identified the actual level of all drugs in the patient’s body. Combined with information from the smart table, a profile of the patient’s compliance and pharmacokinetics for each drug is generated. The clinical significance of the smart medications table and the mass spectrometer is that together they help to reduce uncertainty by synthesizing different views into the patient’s medication history.
Vignette 5: A pediatrician in Los Angeles finds herself working with an ever growing set of young patients with severe asthma. A group of them have added her to their Facebook page where they run a special widget that shows her when and where they did moderate or high physical activity outdoors. The application does not rely on self-reporting. Rather, the young people run an application on their mobile phones that uploads an SMS message containing their current location every 30 seconds to a private account where an application processes and summarizes location-activity data generated from accelerometers on their phones. The doctor has recently introduced a new feature whereby her patients use special Bluetooth-equipped inhalers that report via the mobile phone each time the inhaler is used. The website then displays when and where they used their Bluetooth-enabled inhalers. In addition to viewing trends over time, and patterns based on time of year and day of the week, she runs an application that relates her patients’ activity to real-time pollution exposure models made available by the city. She uses the data to make a case to the city about other possible activity locations (e.g., different outdoor parks) and is soon going to enable her patients to sign up for automated customized alerts when they are overexerting themselves under hazardous environmental conditions. The clinical significance of an automated activity reporting and processing system is that it provides reliable data on what patients actually do (rather than what they say they do) in a form that is easy to understand, as well as additional detail to link to other data sources to clarify patterns, and delivery that is timely enough to support real-time feedback in time to change behavior.
Wouldn’t it be cool if our healthcare systems operated in this fashion? Think about the possibilities for information professionals in environments such as this. In fact, this IOM report was launched at the request of the National Library of Medicine who also highlights some of these concerns in their 2006-2016 Long Range Plan. The future is ours.
