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Evolutionary Pressures on the Electronic Health Record

Donna M. Zulman, MD, MS1,2; Nigam H. Shah, MBBS, PhD3; Abraham Verghese, MD4

Frances Peabody’s timeless lecture to Harvard Medical School students, published in JAMA almost 90 years ago,1 spoke of the complex and deeply human experience of illness, as epitomized by the powerful observation “for the secret of the care of the patient is in caring for the patient.”

Peabody emphasized how caring meant understanding for each patient how particular personal and emotional circumstances influenced his or her health. Today, clinicians encounter a level of complexity—co-occurring chronic and rare diseases, organ transplantation, artificial devices—that has completely altered the practice of medicine, while the personal experience of illness and the social context are as important as ever.

Escalating clinical complexity has increased the dependence on technology for diagnosis, illness monitoring, and treatment, and most physicians experience this dependence daily in interactions with the electronic health record (EHR). The EHR has many virtues: It supports arduous and time-intensive tasks such as order entry and medical history review, and most systems routinely alert clinicians if they prescribe medication combinations that might cause harm. These features and others have the potential to prevent medication errors and decrease duplicative tests, contributing to the safety and value of care.2

But the evolution of EHRs has not kept pace with technology widely used to track, synthesize, and visualize information in many other domains of modern life. While clinicians can calculate a patient’s likelihood of future myocardial infarction, risk of osteoporotic fracture, and odds of developing certain cancers, most systems do not integrate these tools in a way that supports tailored treatment decisions based on an individual’s unique characteristics. Similarly, some algorithms (many developed by insurers) can identify patients at high risk for hospitalization,3 but evidence lags when it comes to using predictive analytics to deliver preventive care and services to targeted individuals. Existing EHRs also have yet to seize one of the greatest opportunities of comprehensive record systems—learning from what happened to similar patients and summarizing that experience for the treating physician and the patient.4 For instance, when a 55-year-old woman of Asian heritage presents to her physician with asthma and new-onset moderate hypertension, it would be helpful for an EHR system to find a personalized cohort of patients (based on key similarities or by using population data weighted by specific patient characteristics) to suggest a course of action based on how those patients responded to certain antihypertensive medication classes, thus providing practice-based evidence when randomized trial evidence is lacking.

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