OBJECTIVE: To conduct a systematic review of the evidence available in support of automated notification methods and call centers and to acknowledge other considerations in making evidence-based recommendations for best practices in improving the timeliness and accuracy of critical value reporting. DESIGN AND METHODS: This review followed the Laboratory Medicine Best Practices (LMBP) review methods (Christenson, et al. 2011). A broad literature search and call for unpublished submissions returned 196 bibliographic records which were screened for eligibility. 41 studies were retrieved. Of these, 4 contained credible evidence for the timeliness and accuracy of automatic notification systems and 5 provided credible evidence for call centers for communicating critical value information in in-patient care settings. RESULTS: Studies reporting improvement from implementing automated notification findings report mean differences and were standardized using the standard difference in means (d=0.42; 95% CI=0.2-0.62) while studies reporting improvement from implementing call centers generally reported criterion referenced findings and were standardized using odds ratios (OR=22.1; 95% CI=17.1-28.6). CONCLUSIONS: The evidence, although suggestive, is not sufficient to make an LMBP recommendation for or against using automated notification systems as a best practice to improve the timeliness of critical value reporting in an in-patient care setting. Call centers, however, are effective in improving the timeliness of critical value reporting in an in-patient care setting, and meet LMBP criteria to be recommended as an "evidence-based best practice."
OBJECTIVE: To conduct a systematic review of the evidence available in support of automated notification methods and call centers and to acknowledge other considerations in making evidence-based recommendations for best practices in improving the timeliness and accuracy of critical value reporting. DESIGN AND METHODS: This review followed the Laboratory Medicine Best Practices (LMBP) review methods (Christenson, et al. 2011). A broad literature search and call for unpublished submissions returned 196 bibliographic records which were screened for eligibility. 41 studies were retrieved. Of these, 4 contained credible evidence for the timeliness and accuracy of automatic notification systems and 5 provided credible evidence for call centers for communicating critical value information in in-patient care settings. RESULTS: Studies reporting improvement from implementing automated notification findings report mean differences and were standardized using the standard difference in means (d=0.42; 95% CI=0.2-0.62) while studies reporting improvement from implementing call centers generally reported criterion referenced findings and were standardized using odds ratios (OR=22.1; 95% CI=17.1-28.6). CONCLUSIONS: The evidence, although suggestive, is not sufficient to make an LMBP recommendation for or against using automated notification systems as a best practice to improve the timeliness of critical value reporting in an in-patient care setting. Call centers, however, are effective in improving the timeliness of critical value reporting in an in-patient care setting, and meet LMBP criteria to be recommended as an "evidence-based best practice."
Authors: G J Kuperman; J M Teich; M J Tanasijevic; N Ma'Luf; E Rittenberg; A Jha; J Fiskio; J Winkelman; D W Bates Journal: J Am Med Inform Assoc Date: 1999 Nov-Dec Impact factor: 4.497
Authors: Giuseppe Lippi; Davide Giavarina; Martina Montagnana; Gian Luca Salvagno; Piero Cappelletti; Mario Plebani; Gian Cesare Guidi Journal: Clin Chem Lab Med Date: 2007 Impact factor: 3.694
Authors: Benjamin H Slovis; William J K Vervilles; David K Vawdrey; Jordan L Swartz; Catherine Winans; John C Kairys; Jeffrey M Riggio Journal: Appl Clin Inform Date: 2022-07-13 Impact factor: 2.762
Authors: Benjamin H Slovis; Thomas A Nahass; Hojjat Salmasian; Gilad Kuperman; David K Vawdrey Journal: J Am Med Inform Assoc Date: 2017-11-01 Impact factor: 4.497
Authors: Nedra S Whitehead; Laurina Williams; Sreelatha Meleth; Sara Kennedy; Nneka Ubaka-Blackmoore; Michael Kanter; Kevin J O'Leary; David Classen; Brian Jackson; Daniel R Murphy; James Nichols; David Stockwell; Thomas Lorey; Paul Epner; Jennifer Taylor; Mark L Graber Journal: J Appl Lab Med Date: 2019-03-11