References
- Sutton RT, Pincock D, Baumgart DC, et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17.
- I S, P G, Ra G, et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc. 2001;8:527–534.
- Harada T, Miyagami T, Kunitomo K, et al. Clinical decision support systems for diagnosis in primary care: a scoping review. IJERPH. 2021;18(16):8435.
- Jacob V, Thota AB, Chattopadhyay SK, et al. Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: a community guide systematic review. J Am Med Informatics Assoc. 2017;24(3):669–676.
- Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med. 2012;157(1):29–43.
- Ancker JS, Edwards A, Nosal S, et al. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak. 2017;17(1):36.,
- Van Der Sijs H, Aarts J, Vulto A, et al. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006;13(2):138–147.
- Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489–495.
- Bates DW, Kuperman GJ, Wang S, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003;10(6):523–530.
- O’Mahony D. STOPP/START criteria for potentially inappropriate medications/potential prescribing omissions in older people: origin and progress. Expert Rev Clin Pharmacol. 2020;13(1):15–22.
- Lapi F, Bianchini E, Michieli R, et al. Erratum to: Assessing risk of osteoporotic fractures in primary care: development and validation of the FRA-HS algorithm. Calcif Tissue Int. 2017;100(6):550–550.
- Dentali F, Fontanella A, Cohen AT, et al. Derivation and validation of a prediction model for venous thromboembolism in primary care. Thromb Haemost. 2020;120(4):692–701.
- Lapi F, Domnich A, Marconi E, et al. Predicting the risk of severe COVID-19 outcomes in primary care: development and validation of a vulnerability index for equitable allocation of effective vaccines. Expert Rev Vaccines. 2022;21(3):377–384.
- Wagner AK, Soumerai SB, Zhang F, et al. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299–309.
- Zhang F, Wagner AK, Ross-Degnan D. Simulation-based power calculation for designing interrupted time series analyses of health policy interventions. J Clin Epidemiol. 2011;64(11):1252–1261.
- Knol MJ, Janssen KJM, Donders ART, et al. Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example. J Clin Epidemiol. 2010;63(7):728–736.
- Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. 2006;15(5):291–303.