Challenges exist to the process of prescribing medications, as physicians need to integrate a large amount of pharmacological information from different sources. As the body of scientific knowledge grows larger, there is an increasing market of new drugs that promise better results. With multiple classes of treatments to choose from that each may include dozens of individual drugs, prescribing decisions are getting harder to make (1). Taking into consideration individual patient needs and preferences further complicates the task. In this context, how does the pressure and complexity of modern-day prescribing affect patient care and outcomes?

Currently, most providers respond to the challenges of modern-day prescribing by using norms to determine treatments prescribed to broad classes of patients. These norms can help to reduce the costs of customization that make prescribing more cumbersome. They can reduce the time and effort it takes to communicate with patients, coordinate with other providers, filter through treatment options, and build expertise in multiple areas (1).

However, norm-based prescribing does not always work, as such decisions may not follow the best standards of practice. One study looked at primary care physicians treating depression and found that measures of symptom changes or patient responses to treatment had no significant effect on whether physicians made changes to antidepressant dosing. These results suggest that factors other than measurable clinical responses affected dosing and drug choice decisions, which goes against best practices for prescribing antidepressants. Especially for patients with psychiatric diagnoses, patients vary widely in their treatment responses and side effects, so customized dosing and drug choices are particularly crucial (2).

When physicians use norm-based prescribing, they may also lose the opportunity to maximize treatment benefits for each patient. In contrast to norm-based prescribing, personalized prescribing customizes the treatment to the individual patient and has been seen to improve patient adherence to medications in some populations (2). It is estimated that patients are adherent to their medications only 50% of the time (3). One study found that prescribing variation, which is used as a marker for treatment customization, was associated with greater duration of patient antidepressant usage. While causal mechanisms are only speculative, it was predicted that more customized prescribing could have led to reduced side effects or increased effectiveness, making patients more likely to adhere to their medications (3).

It is clear that there is a need to help physicians make prescribing decisions efficiently yet in the best interest of their patients. It is important to get a full clinical picture in order to make the right dosing and drug choices. One possible solution is implementing prescription decision support systems. For example, one observational study found that a web- and mobile-based application was able to identify off-label prescribing, overly high dosing, and polypharmacy (the use or two or more antipsychotics concurrently) in antipsychotic prescribing, behaviors that go against clinical guidelines (4). Using these support systems could thus limit errors commonly made in norm-based prescribing behavior.

What do you see in general as a common-sense problem in prescribing, and what kind of solutions would you like?


  1. Frank, R., & Zeckhauser, R. (2007). Custom Made Versus Ready to Wear Treatments; Behavioral Propensities in Physician’s Choices. Journal of Health Economics, 26(6), 1101-1127. doi:10.3386/w13445
  2. Tang, Y., Chang, C. H., Lave, J. R., Gellad, W. F., Huskamp, H. A., & Donohue, J. M. (2016). Patient, Physician and Organizational Influences on Variation in Antipsychotic Prescribing Behavior. The Journal of Mental Health Policy and Economics, 19(1), 45-59. Retrieved July 7, 2020, from
  3. Brown, M. T., Bussell, J., Dutta, S., Davis, K., Strong, S., & Mathew, S. (2016). Medication Adherence: Truth and Consequences. The American Journal of the Medical Sciences, 351(4), 387-399. doi:10.1016/j.amjms.2016.01.010
  4. Berrouiguet, S., Barrigón, M. L., Brandt, S. A., Nitzburg, G. C., Ovejero, S., Alvarez-Garcia, R., . . . Baca-García, E. (2017). Ecological Assessment of Clinicians’ Antipsychotic Prescription Habits in Psychiatric Inpatients: A Novel Web- and Mobile Phone–Based Prototype for a Dynamic Clinical Decision Support System. Journal of Medical Internet Research, 19(1), E25. doi:10.2196/jmir.5954