Paging Dr. Luddite

Image credit: David P. Hall/Corbis

A premature baby can be as small as the hand that cradles the head of a full-term infant. In a neonatal intensive-care unit, babies are often so covered with sensors that doctors and nurses struggle to find enough skin to place them on. A squadron of machines stands vigil around their tiny beds, monitoring heart rate and half a dozen other vital signs, in intervals that can be measured in thousandths of a second. All of this watchfulness is very expensive; a stay in a neonatal intensive-care ward can last months and cost hundreds of thousands of dollars.

Given that expense, and the constant danger these babies face as their underdeveloped lungs and immune systems struggle to cope with the world, the use we make of all this information is surprisingly primitive. Periodically, a nurse stops by, eyeballs what has happened since the last check, and makes a note in a chart. A doctor reviews the chart, and may scroll back through the readouts. But he or she has no easy way to view them all in one place. The machines do not talk to each other, or to anyone else; each electronic guardian stands its own lonely watch.

Technology analysts refer to the elements of systems like this as “data silos”—each data set stored by itself, never touching the others. Over the past few decades, many industries have started breaking data out of their bunkers and using powerful computers to cross-index them, revealing previously unsuspected patterns. In health care, however, data isolation is still the norm.

IBM hopes to change this. Pioneering technology now enables the company’s systems to store and analyze streaming data in real time, a task that was previously too big for ordinary computers to handle. In a small field trial at Toronto’s Hospital for Sick Children, IBM is using that technology to test a theory already suggested by some studies: that tiny changes in heart rate may indicate infections at least 12 hours before they would otherwise become apparent. At the moment, the machines are simply watching, storing data and their predictions, so that IBM can test whether its prognostication works. But if all goes well, in the summer of 2011 the machines will start relaying heart-rate changes to clinicians, who will then be able to start antibiotics earlier, before an infection rages out of control.

The new system would be a significant advance. Preemies are already vulnerable to lifelong complications ranging from vision problems to permanent brain damage. Infections can play a big role in those problems, and early detection offers a chance to stop bacteria before they can compromise organs or kill their victims. That should mean shorter intensive-care stays, smaller medical bills, and most important, a chance at a longer, healthier life.

But early treatment of infections is just the start. Researchers also hope that bringing together these streams of data will allow them to “mine” records for other potential early warnings—perhaps enabling them to detect the seizures that so often inflict brain damage on neonates. This sort of monitoring could be expanded to the many adults who also need watching, in intensive-care units and trauma centers everywhere.

Eventually, such systems might transform not just diagnosis, but the whole medical system. If we could develop more-comprehensive medical records, and collect that data in some central location, data mining might detect patterns in disease and treatment that we now discover only through painful trial and error. More than that, it could finally allow us to reach the holy grail of health-care wonks: paying for wellness rather than for doctors’ visits and procedures.

Technology’s champions have promised that sort of radical transformation before. But their plans have foundered in part because building a comprehensive system that can interact with so many different providers, from acupuncturists to pharmacists to heart surgeons, is hard. They have also often met fierce resistance from all the groups that are part of the current system: patients concerned about privacy; doctors concerned about autonomy; people at every level of the system concerned about expense. Building better computers, it turns out, is the easy part. You also have to change human behavior—and human beings are often quite happy the way they are.

When you interview experts on health-care IT, they inevitably agree on its backward state. Health care now accounts for roughly one-sixth of GDP, yet its IT infrastructure is barely in the 20th century, much less the 21st. Although most hospitals now have electronic medical-record systems, many physicians still do not, and those that do have not necessarily succeeded in integrating their systems with those of other providers—or their own workflow. Physicians will often jot down notes to be entered into the computer later, rather than altering their patient interactions so that they can talk and type at the same time. This behavior not only extends the workday, but also limits how useful the tools are. Though these systems could theoretically harness computing power to enhance diagnosis and patient discussion, many doctors use them as a poor substitute for a pen and paper.

Kavita Patel, the director of the Health Policy Program at the New America Foundation and a veteran of the Obama administration’s health-care-reform efforts, says that physicians are cautiously watching what might be done with more-detailed records; they’re used to operating as autonomous professionals, not closely monitored employees. Patel has been a practicing physician, and she points out that some of the concerns that doctors have, such as how electronic records might be used in malpractice cases, are valid. Inadvertent data loss, poor tracking of changes to the records, data-entry errors, and privacy breaches all raise potential liability issues for physicians. Also understandable is their fear that standards will be set in a way that turns their white coats into straitjackets. Over the past decade or so, insurers have become more aggressive about holding physicians to standards of care, not always with great results.

Take a woman with chronic urinary-tract infections or recurrent sinus problems. Ten years ago, physicians might have prescribed a prophylactic dose of Cipro; thanks to a shift toward rules-based medicine and tighter cost controls at insurance companies, they now tend to ask patients to come in for tests and then try a cheaper antibiotic. But since these extraordinarily painful infections often hit on a night, a weekend, or a business trip, the result can be a patient who ends up in the emergency room after hours of unnecessary agony. This outcome is more expensive, and worse for the patient: a lose-lose proposition. More-advanced data mining might let us set more-complex standards that take into account things like emergency-room visits. On the other hand, if data mining is done badly, it might simply lead to more crude rules with more unintended side effects.