Electronic medical records and what statistics can do

Groopman and Hartzband wrote an opinion piece arguing against electronic medical records. The issue is essentially analogous to a debate of the dangers of paper in comparison to the traditional clay tablets. Still, I can appreciate criticism that will make electronic records better. I will quote some of the criticism, and respond to it.

The impact of medication errors on malpractice costs is likely to be minimal, since the vast majority of lawsuits arise not from technical mistakes like incorrect prescriptions but from diagnostic errors, where the physician makes a misdiagnosis and the correct therapy is delayed or never delivered. There is no evidence that electronic medical records lower the chances of diagnostic error.

The electronic record can encourage a physician to consider all the relevant information. Now that features are known, automated prediction tools such as those developed at Memorial Sloan-Kettering Cancer Center can support a doctor in making a diagnosis. Such tools can reach considerably higher accuracy because they're based on considerably larger datasets than before. Related information and checklists can be provided. This way the joint knowledge of the whole medical community will complement an individual physicians schooling and experience.

All of us are conditioned to respect the printed word, particularly when it appears repeatedly on a hospital computer screen, and once a misdiagnosis enters into the electronic record, it is rapidly and virally propagated.[...]

But the propagation of mistakes is not restricted to misdiagnoses. Once data are keyed in, they are rarely rechecked with respect to accuracy. For example, entering a patient's weight incorrectly will result in a drug dose that is too low or too high, and the computer has no way to respond to such human error.

Most of what I see on my computer display are printed words. A computerized system based on a probabilistic view of diagnosis will make it easier to understand that a diagnosis is not a binary choice but a probabilistic one. By design, such a system will reveal other possible diagnoses. Just as a diagnosis is entered into the record, it will be possible to check it and re-check it. The design of the system should encourage multiple checks and individual responsibility of those confirming or checking.

Doctors in particular are burdened with checking off scores of boxes on the computer screen to satisfy insurance requirements, so called "pay for performance." But again, there are no compelling data to demonstrate that such voluminous documentation translates into better outcomes for their sick patients.

A statistical model can determine which boxes are more or less important, saving time that would otherwise be spent for checking off what does not matter. At the same time, a good user interface would allow doctors to enter a new box if they notice something salient.

Some have speculated that the patient data collected by the Obama administration in national electronic health records will be mined for research purposes to assess the cost effectiveness of different treatments.[...]
And Americans should decide whether they want to participate in such a national experiment only after learning about the nature of the analysis of their records and who will apply the results to their health care.

This is true, and one has to be careful here not to create mis-incentives: incorrect or biased data (biases emerge from self-selection too) that might lead to lower costs and better care for a patient, or higher costs for the doctor would dangerously pollute the models. At the same time, it is possible to detect such data fraud automatically.

In summary:

• It is important to collect the data correctly.


• Electronic medical records make it possible to deploy predictive models widely, improving health care. It is important to build user interfaces that make use of this.


• There will be opportunities for centers that specialize in predictive models for specific symptoms or diseases, combining the background knowledge aggregated in medical profession over many years with the modern data collection and analysis.
  

[Included some information from Bob Carpenter's comment]