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McDonald EG, Wu PE, Rashidi B, et al. JAMA Intern Med. 2022;182:265-273.
Deprescribing is one intervention to reduce the risk of adverse drug events, particularly in older adults and people taking five or more medications. In this cluster randomized trial, older adults (≥65 years) taking at least five medications at hospital admission were randomly assigned to intervention (personalized reports of deprescribing opportunities) or control. Despite an increase in deprescribing in both groups, there was no difference in adverse drug events or adverse drug withdrawal events.

A 31-year-old woman presented to the ED with worsening shortness of breath and was unexpectedly found to have a moderate-sized left pneumothorax, which was treated via a thoracostomy tube. After additional work-up and computed tomography (CT) imaging, she was told that she had some blebs and mild emphysema, but was discharged without any specific follow-up instructions except to see her primary care physician.

This commentary presents two cases highlighting common medication errors in retail pharmacy settings and discusses the importance of mandatory counseling for new medications, use of standardized error reporting processes, and the role of clinical decision support systems (CDSS) in medical decision-making and ensuring medication safety.

A 24-year-old woman with type 1 diabetes presented to the emergency department with worsening abdominal pain, nausea, and vomiting. Her last dose of insulin was one day prior to presentation. She stopped taking insulin because she was not tolerating any oral intake. The admitting team managed her diabetes with subcutaneous insulin but thought the patient did not meet criteria for diabetic ketoacidosis (DKA), but after three inpatient days with persistent hyperglycemia, blurred vision, and altered mental status, a consulting endocrinologist diagnosed DKA.

Kostopoulou O, Tracey C, Delaney BC. J Am Med Inform Assoc. 2021;28:1461-1467.
In addition to being used for patient-specific clinical purposes, data within the electronic health record (EHR) may be used for other purposes including epidemiological research. Researchers in the UK developed and tested a clinical decision support system (CDSS) to evaluate changes in the types and number of observations that primary care physicians entered into the EHR during simulated patient encounters. Physicians documented more clinical observations using the CDSS compared to the standard electronic health record. The increase in documented clinical observations has the potential to improve validity of research developed from EHR data.
Friebe MP, LeGrand JR, Shepherd BE, et al. Appl Clin Inform. 2020;11:865-872.
The prescribing of potentially inappropriate medications, particularly among older adults, is an ongoing quality and safety concern. Among adults 65 years and older, this study found that clinical decision support integrated with a new electronic health record system significantly reduced potentially inappropriate medications.   
A 55-year old woman became unarousable with low oxygen saturation as a result of multiple intravenous benzodiazepine doses given overnight. The benzodiazepine was ordered following a seizure in the intensive care unit (ICU) and was not revised or discontinued upon transfer to the floor; several doses were given for different indications - anxiety and insomnia.

Jena AB, Olenski AR. New York Times. February 20, 2020.

Unconscious biases affecting health care decisions elevate the potential for harm. This news story discusses how experience and implicit biases can impact physician decision-making. The use of decision support is one strategy highlighted to redirect heuristics and other cognitive biases to minimize their impact on treatment.   
Clinical decision support systems provide information or recommendations to help clinicians make safe and evidence-based decisions. The use and sophistication of these systems have grown markedly over the past decade, due to widespread implementation of electronic health records and advances in clinical informatics.
Whitaker P. New Statesman. August 2, 2019;148:38-43.
Artificial intelligence (AI) and advanced computing technologies can enhance clinical decision-making. Exploring the strengths and weaknesses of artificial intelligence, this news article cautions against the wide deployment of AI until robust evaluation and implementation strategies are in place to enhance system reliability. A recent PSNet perspective discussed emerging safety issues in the use of artificial intelligence.
McDonald EG, Wu PE, Rashidi B, et al. J Am Geriatr Soc. 2019;67:1843-1850.
This pre–post study compared patients who received medication reconciliation that was usual care at the time of hospital discharge to patients in the intervention arm who had decision support for deprescribing. Although the intervention did lead to more discontinuation of potentially inappropriate medications, there was no difference in adverse drug events between groups. The authors suggest larger studies to elucidate the potential to address medication safety using deprescribing decision support.
Reynolds TL, DeLucia PR, Esquibel KA, et al. JAMIA Open. 2019;2:49-61.
This pre–post mixed-methods implementation study examined a handheld decision support tool for nurses performing bedside administration of intravenous medications in intensive care units. Investigators found that though nurses desire decision support, the usability of the tool and fit with the critical care environment were suboptimal, leading to limited use. The authors suggest integrating mobile technology tools into existing infrastructure and developing user-informed implementation strategies.
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. J Natl Cancer Inst. 2019;111:916-922.
Artificial intelligence (AI) may have the capacity to improve diagnosis. Researchers found that an AI system was able to detect breast cancer using mammography with accuracy similar to that of the average of the 101 radiologists whose interpretations were included in the study.
van Balveren JA, van de Venne WPHGV-, Erdem-Eraslan L, et al. Diagnosis (Berl). 2019;6:69-71.
Failure to recognize potential drug–laboratory test interactions can lead to misinterpretation of results, misdiagnosis, and unneeded tests or therapies. This review examines the evidence on this issue and suggests that decision support technologies can help avoid these errors.
A woman with multiple myeloma required placement of a central venous catheter for apheresis. The outpatient oncologist intended to order a nontunneled catheter via computerized provider order entry but accidentally ordered a tunneled catheter. The interventional radiologist thought the order was unusual but didn't contact the oncologist. A tunneled catheter was placed without complications. When the patient presented for apheresis, providers recognized the wrong catheter had been placed, and the patient underwent an additional procedure.
Fontil V, Radcliffe K, Lyson HC, et al. JAMIA Open. 2019;2:40-48.
The use of collective intelligence platforms may have the potential to improve diagnostic accuracy in primary care, but little is known about the attitudes of primary care providers toward such platforms. This qualitative study found that primary care providers might be willing to use such platforms as long as they are easy to use, perceived as helpful and accurate, and that the collective opinions generated can be trusted.
Lowenstein EJ, Sidlow R. J Dermatol. 2018;179:1263-1276.
Cognitive shortcuts, or heuristics, are often used by experts to make decisions. This two-part review examines how heuristics affect diagnosis in dermatology. The first article discusses the strengths and weaknesses in visual diagnosis behaviors. The second recommends techniques for improving decision making and self-awareness of thought processes to avoid diagnostic error in dermatology practice.
Parikh R. MIT Technol Rev. October 23, 2018.
Computerized decision support and artificial intelligence (AI) are being utilized to enhance decision-making in health care. This magazine article explains how artificial intelligence presents clinicians with an opportunity to improve practice by reducing cognitive load when determining appropriate diagnoses and treatment decisions.