U.S. Department of Health and Human Services
Agency for Healthcare Research and Quality: Advancing Excellence in Health Care
Sign up for a Free Account
Schiff GD. BMJ Qual Saf. 2012;21:89-92.
Schiff GD.Finding and fixing diagnosis errors: can triggers help?. BMJ Qual Saf. 2012; 21: 89-92
This editorial discusses the potential of using electronic screening tools, known as triggers, to detect and prevent diagnostic errors.
The impact of a diagnostic decision support system on the consultation: perceptions of GPs and patients.
Porat T, Delaney B, Kostopoulou O. BMC Med Inform Decis Mak. 2017;17:79.
Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices.
Rao G, Epner P, Bauer V, Solomonides A, Newman-Toker DE. Diagnosis. 2017;4:67-72.
Diagnostic accuracy of GPs when using an early-intervention decision support system: a high-fidelity simulation.
Kostopoulou O, Porat T, Corrigan D, Mahmoud S, Delaney BC. Br J Gen Pract. 2017;67:e201-e208.
Can computers help doctors reduce diagnostic errors?
Shryock T. Med Econ. December 5, 2016.
Comparison of physician and computer diagnostic accuracy.
Semigran HL, Levine DM, Nundy S, Mehrotra A. JAMA Intern Med. 2016;176:1860-1861.
The effectiveness of electronic differential diagnoses (DDX) generators: a systematic review and meta-analysis.
Riches N, Panagioti M, Alam R, et al. PLoS One. 2016;11:e0148991.
Clinical decision support for early recognition of sepsis.
Amland RC, Hahn-Cover KE. Am J Med Qual. 2016;31:103-110.
Clinical criteria to screen for inpatient diagnostic errors: a scoping review.
Shenvi EC, El-Kareh R. Diagnosis. 2014;2:3-19.
Diagnostic Error in Medicine.
Singh H, ed. BMJ Qual Saf. 2013;22(suppl 2):ii1-ii72.
The robot will see you now.
Cohn J. The Atlantic. March 2013;311:59–67.
Electronic health record-based surveillance of diagnostic errors in primary care.
Singh H, Giardina TD, Forjuoh SN, et al. BMJ Qual Saf. 2012;22:93-100.
Automated identification of postoperative complications within an electronic medical record using natural language processing.
Murff HJ, FitzHenry F, Matheny ME, et al. JAMA. 2011;306:848-855.
A framework for evaluating the appropriateness of clinical decision support alerts and responses.
McCoy AB, Waitman LR, Lewis JB, et al. J Am Med Inform Assoc. 2012;19:346-352.
Potential safety gaps in order entry and automated drug alerts: a nationwide survey of VA physician self-reported practices with computerized order entry.
Spina JR, Glassman PA, Simon B, et al. Med Care. 2011;49:904-910.
Notification of abnormal lab test results in an electronic medical record: do any safety concerns remain?
Singh H, Thomas EJ, Sittig DF, et al. Am J Med. 2010;123:238-244.
An empirical model to estimate the potential impact of medication safety alerts on patient safety, health care utilization, and cost in ambulatory care.
Weingart SN, Simchowitz B, Padolsky H, et al. Arch Intern Med. 2009;169;1465-1473.
Computerized decision support to reduce potentially inappropriate prescribing to older emergency department patients: a randomized, controlled trial.
Terrell KM, Perkins AJ, Dexter PR, Hui SL, Callahan CM, Miller DK. J Am Geriatr Soc. 2009;57:1388-1394.
What evidence supports the use of computerized alerts and prompts to improve clinicians' prescribing behavior?
Schedlbauer A, Prasad V, Mulvaney C, et al. J Am Med Inform Assoc. 2009;16:531-538.
Overrides of medication alerts in ambulatory care.
Isaac T, Weissman JS, Davis RB, et al. Arch Intern Med. 2009;169:305-311.
Impact of non-interruptive medication laboratory monitoring alerts in ambulatory care.
Lo HG, Matheny ME, Seger DL, Bates DW, Gandhi TK. J Am Med Inform Assoc. 2009;16:66-71.
Evaluating clinical decision support systems: monitoring CPOE order check override rates in the Department of Veterans Affairs' computerized patient record system.
Lin C-P, Payne TH, Nichol WP, et al. J Am Med Inform Assoc. 2008;15:620-626.
Sophisticated digital aids could help determine what ails you.
Maron DF. Sci Am. July 21, 2017.
Clinical alerts to decrease high-risk medication use in older adults.
Lord-Adem W, Brandt NJ. J Gerontol Nurs. 2017;43:7-12.
Deep learning is a black box, but health care won't mind.
Brouillette M. MIT Technol Rev. April 27, 2017.
Identifying hospitalized patients at risk for harm: a comparison of nurse perceptions vs. electronic risk assessment tool scores.
Stafos A, Stark S, Barbay K, et al. Am J Nurs. 2017;117:26-31.
PSNET: Patient Safety Network
PSNet is produced for the Agency for Healthcare Research and Quality by a team of editors at the University of California, San Francisco with guidance from a prominent Technical Expert/Advisory Panel. The AHRQ PSNet site was designed and implemented by Silverchair.
Agency for Healthcare Research and Quality
5600 Fishers Lane
Rockville, MD 20857
Telephone: (301) 427-1364