Artificial intelligence (AI) technologies can improve the use of data in care delivery. This review recommends steps to enhance the use of AI in bedside care. The author highlights the need for clinicians to accept that AI tools will affect care processes and be trained to participate in AI integration on the front line.
Artificial intelligence can improve practice by making synthesized data available in real time to inform frontline decision-making. This commentary describes factors clinicians should consider as artificial intelligence becomes more prevalent in health care and discusses how this technology can enable clinicians to focus on helping patients navigate complex care choices.
Powers EM, Shiffman RN, Melnick ER, et al. J Am Med Inform Assoc. 2018;25:1556-1566.
Although hard-stop alerts can improve safety, they have been shown to result in unintended consequences such as delays in care. This systematic review suggests that while implementing hard stops can lead to improved health and process outcomes, end-user involvement is essential to inform design and appropriate workflow integration.
Wong A, Plasek JM, Montecalvo SP, et al. Pharmacotherapy. 2018;38:822-841.
Natural language processing (NLP) can efficiently analyze large narrative data sets to identify adverse events. Exploring the application of NLP to reduce medication errors, this AHRQ-funded review describes challenges associated with using NLP to extract information from clinical sources and highlights how engaging pharmacists in developing NLP systems can improve medication safety.
Recently, several mobile health care applications have been developed and marketed directly to nonclinician consumers. Researchers reviewed the literature regarding direct-to-consumer diagnostic applications. They found wide variation in the safety of these applications and suggest that further research is needed to thoroughly assess their effectiveness.
Varghese J, Kleine M, Gessner SI, et al. J Am Med Inform Assoc. 2018;25:593-602.
This systematic review of clinical decision support on inpatient outcomes identified mostly positive effects. Clinical decision support was found to be most effective for managing blood glucose and blood transfusions and for preventing venous thromboembolism, pressure ulcers, acute kidney injury, and incipient clinical deterioration. The authors advocate for prioritizing clinical decision support for these specific conditions.
Yeung S, Downing L, Fei-Fei L, et al. New Engl J Med. 2018;378:1271-1273.
Artificial intelligence technologies can support diagnostic decision-making. This commentary discusses application of deep learning tools to create visual cues to track deviations in activities to flag areas of improvement. Although early in its development, the authors outline the potential of this technology in clinical care and review early efforts employed to enhance hand hygiene.
Tolley CL, Slight SP, Husband AK, et al. Am J Health Syst Pharm. 2018;75:239-246.
This systematic review of clinical decision support for safe medication use found that such systems are incompletely implemented and lack standardization and integration of patient-specific factors. The authors suggest that reducing alert fatigue and employing human factors principles would enhance decision support effectiveness.
Wong A, Amato MG, Seger DL, et al. BMJ Qual Saf. 2018;27:718-724.
Clinical decision support systems in electronic health records (EHRs) aim to avert adverse events, especially medication errors. However, alerts are pervasive and often irrelevant, leading patient safety experts to question whether their modest improvement in safety outweighs the harms of alert fatigue. This study assessed provider overrides of a commercial EHR's medication alerts in intensive care units at one institution. Providers overrode most alerts, and the majority of those overrides were appropriate. Inappropriate overrides occasionally led to medication errors and did so more frequently than appropriate overrides. A recent WebM&M commentary recommends employing human factors engineering to make clinical decision support more effective.
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