The electronic health record: does it enhance or distract from patient safety?
Journal
Current opinion in anaesthesiology
ISSN: 1473-6500
Titre abrégé: Curr Opin Anaesthesiol
Pays: United States
ID NLM: 8813436
Informations de publication
Date de publication:
03 Sep 2024
03 Sep 2024
Historique:
medline:
9
9
2024
pubmed:
9
9
2024
entrez:
9
9
2024
Statut:
aheadofprint
Résumé
The electronic health record (EHR) is an invaluable tool that may be used to improve patient safety. With a variety of different features, such as clinical decision support and computerized physician order entry, it has enabled improvement of patient care throughout medicine. EHR allows for built-in reminders for such items as antibiotic dosing and venous thromboembolism prophylaxis. In anesthesiology, EHR often improves patient safety by eliminating the need for reliance on manual documentation, by facilitating information transfer and incorporating predictive models for such items as postoperative nausea and vomiting. The use of EHR has been shown to improve patient safety in specific metrics such as using checklists or information transfer amongst clinicians; however, limited data supports that it reduces morbidity and mortality. There are numerous potential pitfalls associated with EHR use to improve patient safety, as well as great potential for future improvement.
Identifiants
pubmed: 39248015
doi: 10.1097/ACO.0000000000001429
pii: 00001503-990000000-00229
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.
Références
Honavar SG. Electronic medical records – the good, the bad and the ugly. Indian J Ophthalmol 2020; 68:417–418.
Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff 2011; 30:464–471.
Lammers EJ, McLaughlin CG. Meaningful use of electronic health records and medicare expenditures: evidence from a panel data analysis of U.S Healthcare Markets, 2010–2013. Health Serv Res 2017; 52:1364–1386.
Lee J, Choi JY. Improved efficiency of coding systems with health information technology. Sci Rep 2021; 11:10294.
Ozonze O, Scott PJ, Hopgood AA. Automating electronic health record data quality assessment. J Med Syst 2023; 47:23.
Bowman S. Impact of electronic health record systems on information integrity: quality and safety implications. Perspect Health Inf Manag 2013; 10:1c.
Charlesworth M, van Zundert AAJ. Digital dystopias: will the electronic health record ever fulfil its potential? Anaesthesia 2019; 74:1361–1364.
Cutler DM, Feldman NE, Horwitz JR. US adoption of computerized physician order entry systems. Health Aff 2005; 24:1654–1663.
Adler-Milstein J. Electronic health record adoption in US hospitals: progress continues, but challenges persist. Health Aff 2015; 34:2174–2180.
Bani Issa W, Al Akour I, Ibrahim A, et al. Privacy, confidentiality, security and patient safety concerns about electronic health records. Int Nurs Rev 2020; 67:218–230.
Hydari MZ, Telang R, Marella WM. Saving patient ryan—can advanced electronic medical records make patient care safer? Manage Sci 2019; 65:2041–2059.
Frisse ME, Johnson KB, Nian H, et al. The financial impact of health information exchange on emergency department care. J Am Med Informatics Assoc 2011; 19:328–333.
Health information technology: an updated systematic review with a focus on meaningful use. Ann Intern Med. 2014;160:48–54. doi:10.7326/m13-1531%m 24573664.
Agha L. The effects of health information technology on the costs and quality of medical care. J Health Econ 2014; 34:19–30.
Bennett P, Hardiker NR. The use of computerized clinical decision support systems in emergency care: a substantive review of the literature. J Am Med Informatics Assoc 2017; 24:655–668.
Freedman S, Lin H, Prince J. Information technology and patient health: analyzing outcomes, populations, and mechanisms. Am J Health Econ 2018; 4:51–79.
Fritz D, Ceschi A, Curkovic I, et al. Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients. Eur J Clin Pharmacol 2012; 68:1209–1219.
Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 1998; 280:1311–1316.
McCullough JS, Casey M, Moscovice I, Prasad S. The effect of health information technology on quality in U.S. hospitals. Health Aff 2010; 29:647–654.
Donnelly C, Janssen A, Vinod S, et al. A systematic review of electronic medical record driven quality measurement and feedback systems. Int J Environ Res Public Health 2022; 20:200.
Prgomet M, Li L, Niazkhani Z, et al. Impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay, and mortality in intensive care units: a systematic review and meta-analysis. J Am Med Informatics Assoc 2016; 24:413–422.
Lammers EJ, McLaughlin CG, Barna M. Physician EHR adoption and potentially preventable hospital admissions among Medicare beneficiaries: panel data evidence, 2010–2013. Health Serv Res 2016; 51:2056–2075.
Campanella P, Lovato E, Marone C, et al. The impact of electronic health records on healthcare quality: a systematic review and meta-analysis. Eur J Public Health 2016; 26:60–64.
Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144:742–752.
Lin HL, Wu DC, Cheng SM, et al. Association between electronic medical records and healthcare quality. Medicine (Baltimore) 2020; 99:e21182.
DesRoches CM, Campbell EG, Rao SR, et al. Electronic health records in ambulatory care—a national survey of physicians. N Engl J Med 2008; 359:50–60.
Upadhyay S, Hu HF. A qualitative analysis of the impact of electronic health records (EHR) on healthcare quality and safety: clinicians’ lived experiences. Health Serv Insights 2022; 15:11786329211070722.
Tubaishat A. The effect of electronic health records on patient safety: a qualitative exploratory study. Inform Health Soc Care 2019; 44:79–91.
Naamneh R, Bodas M. The effect of electronic medical records on medication errors, workload, and medical information availability among qualified nurses in Israel- a cross sectional study. BMC Nurs 2024; 23:270.
Goldzweig C. Electronic patient portals: evidence on health outcomes, satisfaction, efficiency, and attitudes. Ann Intern Med 2013; 159:677–687.
Wright A, Poon EG, Wald J, et al. Randomized controlled trial of health maintenance reminders provided directly to patients through an electronic PHR. J Gen Intern Med 2012; 27:85–92.
Graber ML, Siegal D, Riah H, et al. Electronic health record-related events in medical malpractice claims. J Patient Saf 2019; 15:77–85.
Colletti AA, Wang E, Marquez JL, et al. A multifaceted quality improvement project improves intraoperative redosing of surgical antimicrobial prophylaxis during pediatric surgery. Paediatr Anaesth 2019; 29:705–711.
Jang J, Yu SH, Kim CB, et al. The effects of an electronic medical record on the completeness of documentation in the anesthesia record. Int J Med Inform 2013; 82:702–707.
Feinleib J, Foley L, Mark L. What we all should know about our patient's airway: difficult airway communications, database registries, and reporting systems registries. Anesthesiol Clin 2015; 33:397–413.
Halladay ML, Thompson JA, Vacchiano CA. Enhancing the quality of the anesthesia to postanesthesia care unit patient transfer through use of an electronic medical record-based handoff tool. J Perianesth Nurs 2019; 34:622–632.
Brovman EY, Preiss D, Urman RD, Gross WL. The challenges of implementing electronic health records for anesthesia use outside the operating room. Curr Opin Anaesthesiol 2016; 29:531–535.
Lundberg SM, Nair B, Vavilala MS, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2018; 2:749–760.
Kang AR, Lee J, Jung W, et al. Development of a prediction model for hypotension after induction of anesthesia using machine learning. PLoS One 2020; 15:e0231172.
Shim JG, Ryu KH, Cho EA, et al. Machine learning for prediction of postoperative nausea and vomiting in patients with intravenous patient-controlled analgesia. PLoS One 2022; 17:e0277957.
Muhiyaddin R, Elfadl A, Mohamed E, et al. Electronic health records and physician burnout: a scoping review. Stud Health Technol Inform 2022; 289:481–484.
Ligibel JA, Goularte N, Berliner JI, et al. Well being parameters and intention to leave current institution among academic physicians. JAMA Netw Open 2023; 6:e2347894.
Justinia T, Qattan W, Almenhali A, et al. Medication errors and patient safety: evaluation of physicians’ responses to medication-related alert overrides in clinical decision support systems. Acta Inform Med 2021; 29:248–252.
Obermeyer Z, Emanuel EJ. Predicting the future – big data, machine learning, and clinical medicine. N Engl J Med 2016; 375:1216–1219.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542:115–118.
Combes C, Meskens N, Rivat C, Vandamme JP. Using a KDD process to forecast the duration of surgery. Int J Prod Econ 2008; 112:279–293.
Devi SP, Rao KS, Sangeetha SS. Prediction of surgery times and scheduling of operation theaters in ophthalmology department. J Med Syst 2012; 36:415–430.
Houliston BR, Parry DT, Merry AF. TADAA: towards automated detection of anaesthetic activity. Methods Inf Med 2011; 50:464–471.
Feld SI, Hippe DS, Miljacic L, et al. A machine learning approach for predicting real-time risk of intraoperative hypotension in traumatic brain injury. J Neurosurg Anesthesiol 2023; 35:215–223.
Simpao AF, Nelson O, Ahumada LM. Automated anesthesia artifact analysis: can machines be trained to take out the garbage? J Clin Monit Comput 2021; 35:225–227.
Hashimoto DA, Witkowski E, Gao L, et al. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology 2020; 132:379–394.