Computerized Physician Order Entry in Hospital Information System: Literature Review
Keywords:
Health Information System, Electronic Medical Record, CPOE, CDSS, Alert
Abstract
Background: “To err is human” stated by Institute of Medicine showing the abundance of medication prescription errors which compromises patient safety and exaggerates health care cost. These errors could be prevented by the introduction of health information technology such as Electronic Medical Records.
Method: The researcher conducted the literatures study to confirm the choice of the keywords, then do a search in 4 databases (PubMed, ScienceDirect, EBSCO, dan ProQuest) from that databases, we screened it based on its title, then its abstract and then the full paper. At the end, we checked if it’s Scopus-Indexed, we included it in the analysis.
Result: 40 journals that were stated to be included in this analysis, grouped by study site, whether one-center or multi-center. 26 journals are one-center. From 40 journals, only 2 types of CPOE that are shared by different hospitals and only 3 types of CDSS that are shared by different hospitals. Both CPOE and CDSS are mostly a commercial program.
Conclusion: There is no sufficient explanation of the existence of the most trustful institution which controls the production and the development of CPOE and CDSS.
References
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[2] McCoy AB, Waitman LR, Gadd CS, Danciu I, Smith JP, Lewis JB, et al. A Computerized Provider Order Entry Intervention for Medication Safety During Acute Kidney Injury: A Quality Improvement Report.
[3] Kim J, Chae YM, Kim S, Ho SH, Kim HH, Park CB. A Study on User Satisfaction regarding the Clinical Decision Support System (CDSS) for Medication. 2012;18(1):9.
[4] Cho I. Acceptability and feasibility of the Leapfrog computerized physician order entry evaluation tool for hospitals outside the United States. International Journal of Medical Informatics. 2015;8.
[5] Mille F, Schwartz C, Brion F, Fontan J-E, Bourdon O, Degoulet P, et al. Analysis of overridden alerts in a drug– drug interaction detection system. 2018;6.
[6] Jung M, Hoerbst A, Hackl WO, Kirrane F, Borbolla D, Jaspers MW, et al. Attitude of Physicians Towards Automatic Alerting in Computerized Physician Order Entry Systems. Methods Inf Med. 2013;10.
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[8] Balasuriya L, Vyles D, Bakerman P, Holton V, Vaidya V, Garcia-Filion P, et al. Computerized Dose Range Checking Using Hard and Soft Stop Alerts Reduces Prescribing Errors in a Pediatric Intensive Care Unit. 2014;00(00):5.
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[10] Ahn EK, Cho S-Y, Shin D, Jang C, Park RW. Differences of Reasons for Alert Overrides on Contraindicated Co-prescriptions by Admitting Department. 2014;20(4):8.
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[12] Mahoney CD, Berard-Collins CM, Coleman R, Amaral JF, Cotter CM. Effects of an integrated clinical information system on medication safety in a multi-hospital setting. 2007;64:9.
[13] Westbrook JI, Reckmann M, Li L, Runciman WB, Burke R, Baysari MT, et al. Effects of Two Commercial Electronic Prescribing Systems on Prescribing Error Rates in Hospital In- Patients: A Before and After Study. PLoS Medicine. 2012;9(1):11.
[14] Haueis P. Evaluation of Drug Interactions in a Large Sample of Psychiatric Inpatients: A Data Interface for Mass Analysis With Clinical Decision Support Software. Clinical pharmacology. 2011;90(4):9.
[15] Frölich T, Zorina O, Fontana AO, Kullak-Ublick GA, Vollenweider A, Russmann S. Evaluation of medication safety in the discharge medication of 509 surgical inpatients using electronic prescription support software and an extended operational interaction classification. Eur J Clin Pharmacol. 2011;10.
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[25] Yeh M-L. Physicians’ responses to computerized drug–drug interaction alerts for outpatients. computer methods and programs in biomedicine. :9.
[26] Cornu P. Pilot evaluation of an optimized context-specific drug–drug interaction alerting system: A controlled pre-post study. :13.
[27] Tora H, Bo H, Bodil L, Goran P, Birgit E. Potential drug related problems detected by electronic expert support system in patients with multi-dose drug dispensing. :10.
[28] Straichman YZ. Prescriber response to computerized drug alerts for electronic prescriptions among hospitalized patients. International Journal of Medical Informatics. 2017;6.
[29] Jayawardena S, Eisdorfer J, Indulkar S, Pal SA, Sooriabalan D, Cucco R. Prescription Errors and the Impact of Computerized Prescription Order Entry System in a Community-based Hospital. American Journal of Therapeutics. 2007;5.
[30] Sethuraman U, Kannikeswaran N, Murray KP, Zidan MA, Chamberlain JM. Prescription Errors Before and After Introduction of Electronic Medication Alert System in a Pediatric Emergency Department. ACADEMIC EMERGENCY MEDICINE. 2015;22(6):6.
[31] Xie M, Weinger MB, Gregg WM, Johnson KB. Presenting Multiple Drug Alerts in an Ambulatory Electronic Prescribing System. 2014;15.
[32] Abraham J, Kannampallil TG, Jarman A, Sharma S, Rash C, Schiff G, et al. Reasons for computerised provider order entry (CPOE)-based inpatient medication ordering errors: an observational study of voided orders. :10.
[33] Perlman SL, Fabrizio L, Shaha SH, Magid SK. Response to Medication Dosing Alerts for Pediatric Inpatients Using a Computerized Provider Order Entry System. 2011;12.
[34] Stultz JS, Porter K, Nahata MC. Sensitivity and specificity of dosing alerts for dosing errors among hospitalized pediatric patients. 2018;7.
[35] Ali J, Barrow L, Vuylsteke A. The impact of computerised physician order entry on prescribing practices in a cardiothoracic intensive care unit*. 2009;5.
[36] Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma’Luf N, et al. The Impact of Computerized Physician Order Entry on Medication Error Prevention. 1999;6(4):9.
[37] Diplomate RSF. Think time: A novel approach to analysis of clinicians’ behavior after reduction of drug-drug interaction alerts. International Journal of Medical Informatics. 2017;9.
[38] Beeler PE, Eschmann E, Rosen C. Use of an On-demand Drug–Drug Interaction Checker by Prescribers and Consultants: A Retrospective Analysis in a Swiss Teaching Hospital. :8.
[39] Luna DR. User-Centered Design Improves the Usability of Drug-Drug Interaction Alerts: Experimental Comparison of Interfaces. :26.
[40] Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. :8
[41] Schwartzberg D, Ivanovic S, Patel S, Burjonrappa SC. We thought we would be perfect: medication errors before and after the initiation of Computerized Physician Order Entry. J Surg Res. 2015 Sep;198(1):108–14.
[42] Yeh M-L, Chang Y-J, Wang P-Y, Li Y-CJ, Hsu C-Y. Physicians’ responses to computerized drug-drug interaction alerts for outpatients. Comput Methods Programs Biomed. 2013 Jul;111(1):17–25.
[43] Charles K, Cannon M, Hall R, Coustasse A. Can utilizing a computerized provider order entry (CPOE) system prevent hospital medical errors and adverse drug events? Perspectives in health information management. 2014;11(Fall).
[44] Cohen M. Choosing the Right Oncologist and the Value of a Second Opinion. In: Management of Cancer in the Older Patient [Internet]. Elsevier; 2012 [cited 2020 Feb 24]. p. 51–4. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9781437713985100050
[45] Research C for DE and. The FDA’s Drug Review Process: Ensuring Drugs Are Safe and Effective. FDA [Internet]. 2019 Feb 9 [cited 2020 Feb 24]; Available from: http://www.fda.gov/drugs/drug-information-consumers/fdas-drug-review-process-ensuring-drugs-are-safe-and-effective
[46] e-CFR: TITLE 21—Food and Drugs [Internet]. Electronic Code of Federal Regulations. Available from: https://www.ecfr.gov/cgi-bin/text-idx?SID=f3ece07b28492f7f62aca7ab913de455&mc=true&tpl=/ecfrbrowse/Title21/21cfrv8_02.tpl#0
[47] Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer? The American Journal of Medicine. 2018 Feb;131(2):129–33.
[48] Angus DC. Randomized Clinical Trials of Artificial Intelligence. JAMA [Internet]. 2020 Feb 17 [cited 2020 Feb 24]; Available from: https://jamanetwork.com/journals/jama/fullarticle/2761468
[49] Park SH, Do K-H, Kim S, Park JH, Lim Y-S. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof. 2019 Jul 3;16:18.
[50] Health C for D and R. Clinical Decision Support Software [Internet]. U.S. Food and Drug Administration. FDA; 2019 [cited 2020 Feb 27]. Available from: http://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software
[2] McCoy AB, Waitman LR, Gadd CS, Danciu I, Smith JP, Lewis JB, et al. A Computerized Provider Order Entry Intervention for Medication Safety During Acute Kidney Injury: A Quality Improvement Report.
[3] Kim J, Chae YM, Kim S, Ho SH, Kim HH, Park CB. A Study on User Satisfaction regarding the Clinical Decision Support System (CDSS) for Medication. 2012;18(1):9.
[4] Cho I. Acceptability and feasibility of the Leapfrog computerized physician order entry evaluation tool for hospitals outside the United States. International Journal of Medical Informatics. 2015;8.
[5] Mille F, Schwartz C, Brion F, Fontan J-E, Bourdon O, Degoulet P, et al. Analysis of overridden alerts in a drug– drug interaction detection system. 2018;6.
[6] Jung M, Hoerbst A, Hackl WO, Kirrane F, Borbolla D, Jaspers MW, et al. Attitude of Physicians Towards Automatic Alerting in Computerized Physician Order Entry Systems. Methods Inf Med. 2013;10.
[7] Fritz D, Ceschi A, Curkovic I, Huber M, Egbring M, Kullak-Ublick GA, et al. Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients. Eur J Clin Pharmacol. 2012;11.
[8] Balasuriya L, Vyles D, Bakerman P, Holton V, Vaidya V, Garcia-Filion P, et al. Computerized Dose Range Checking Using Hard and Soft Stop Alerts Reduces Prescribing Errors in a Pediatric Intensive Care Unit. 2014;00(00):5.
[9] Rommers MK, Zegers MH, Clercq PAD, Bouvy ML, Teepe-Twiss IM, Guchelaar H-J. Development of a computerised alert system, ADEAS, to identify patients at risk for an adverse drug event. Qual Saf Health Care. :7.
[10] Ahn EK, Cho S-Y, Shin D, Jang C, Park RW. Differences of Reasons for Alert Overrides on Contraindicated Co-prescriptions by Admitting Department. 2014;20(4):8.
[11] Bryant AD, Fletcher GS, Payne TH. Drug interaction alert override rates in the Meaningful Use era. 2014;12.
[12] Mahoney CD, Berard-Collins CM, Coleman R, Amaral JF, Cotter CM. Effects of an integrated clinical information system on medication safety in a multi-hospital setting. 2007;64:9.
[13] Westbrook JI, Reckmann M, Li L, Runciman WB, Burke R, Baysari MT, et al. Effects of Two Commercial Electronic Prescribing Systems on Prescribing Error Rates in Hospital In- Patients: A Before and After Study. PLoS Medicine. 2012;9(1):11.
[14] Haueis P. Evaluation of Drug Interactions in a Large Sample of Psychiatric Inpatients: A Data Interface for Mass Analysis With Clinical Decision Support Software. Clinical pharmacology. 2011;90(4):9.
[15] Frölich T, Zorina O, Fontana AO, Kullak-Ublick GA, Vollenweider A, Russmann S. Evaluation of medication safety in the discharge medication of 509 surgical inpatients using electronic prescription support software and an extended operational interaction classification. Eur J Clin Pharmacol. 2011;10.
[16] Wong A. Evaluation of medication-related clinical decision support alert overrides in the intensive care unit. Journal of Critical Care. 2017;6.
[17] Rommers MK. Evaluation of rule effectiveness and positive predictive value of clinical rules in a Dutch clinical decision support system in daily hospital pharmacy practice. Artificial Intelligence in Medicine. 2013;7.
[18] Zwart-van Rijkom JEF, Uijtendaal EV, ten Berg MJ, van Solinge WW, Egberts ACG. Frequency and nature of drug-drug interactions in a Dutch university hospital. British Journal of Clinical Pharmacology. 2009 Aug;68(2):187–93.
[19] Zaal RJ, Tijssen CC, Roukema JA. Identification of drug-related problems by a clinical pharmacist in addition to computerized alerts. :10.
[20] Roberts LL, Ward MM, Brokel JM, Wakefield DS, Crandall DK, Conlon P. Impact of health information technology on detection of potential adverse drug events at the ordering stage. 2010;67:10.
[21] Nanji KC, Seger DL, Slight SP, Amato MG, Her QL, Dalleur O, et al. Medication-related clinical decision support alert overrides in inpatients. Journal of the American Medical Informatics Association. 2018;0(0):6.
[22] Lau G, Ho J, Lin S, Yeoh K, Wan T, Hodgkinson M. Patient and clinician perspectives of an integrated electronic medication prescribing and dispensing system: A qualitative study at a multisite Australian hospital network. HIM J. 2019 Jan;48(1):12–23.
[23] Seidling HM, Schmitt SPW, Bruckner T, Kaltschmidt J, Pruszydlo MG, Senger C, et al. Patient-specific electronic decision support reduces prescription of excessive doses. Qual Saf Health Care. :8.
[24] Abramson EL. Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing. Internationaljournal of medical informatics. :10.
[25] Yeh M-L. Physicians’ responses to computerized drug–drug interaction alerts for outpatients. computer methods and programs in biomedicine. :9.
[26] Cornu P. Pilot evaluation of an optimized context-specific drug–drug interaction alerting system: A controlled pre-post study. :13.
[27] Tora H, Bo H, Bodil L, Goran P, Birgit E. Potential drug related problems detected by electronic expert support system in patients with multi-dose drug dispensing. :10.
[28] Straichman YZ. Prescriber response to computerized drug alerts for electronic prescriptions among hospitalized patients. International Journal of Medical Informatics. 2017;6.
[29] Jayawardena S, Eisdorfer J, Indulkar S, Pal SA, Sooriabalan D, Cucco R. Prescription Errors and the Impact of Computerized Prescription Order Entry System in a Community-based Hospital. American Journal of Therapeutics. 2007;5.
[30] Sethuraman U, Kannikeswaran N, Murray KP, Zidan MA, Chamberlain JM. Prescription Errors Before and After Introduction of Electronic Medication Alert System in a Pediatric Emergency Department. ACADEMIC EMERGENCY MEDICINE. 2015;22(6):6.
[31] Xie M, Weinger MB, Gregg WM, Johnson KB. Presenting Multiple Drug Alerts in an Ambulatory Electronic Prescribing System. 2014;15.
[32] Abraham J, Kannampallil TG, Jarman A, Sharma S, Rash C, Schiff G, et al. Reasons for computerised provider order entry (CPOE)-based inpatient medication ordering errors: an observational study of voided orders. :10.
[33] Perlman SL, Fabrizio L, Shaha SH, Magid SK. Response to Medication Dosing Alerts for Pediatric Inpatients Using a Computerized Provider Order Entry System. 2011;12.
[34] Stultz JS, Porter K, Nahata MC. Sensitivity and specificity of dosing alerts for dosing errors among hospitalized pediatric patients. 2018;7.
[35] Ali J, Barrow L, Vuylsteke A. The impact of computerised physician order entry on prescribing practices in a cardiothoracic intensive care unit*. 2009;5.
[36] Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma’Luf N, et al. The Impact of Computerized Physician Order Entry on Medication Error Prevention. 1999;6(4):9.
[37] Diplomate RSF. Think time: A novel approach to analysis of clinicians’ behavior after reduction of drug-drug interaction alerts. International Journal of Medical Informatics. 2017;9.
[38] Beeler PE, Eschmann E, Rosen C. Use of an On-demand Drug–Drug Interaction Checker by Prescribers and Consultants: A Retrospective Analysis in a Swiss Teaching Hospital. :8.
[39] Luna DR. User-Centered Design Improves the Usability of Drug-Drug Interaction Alerts: Experimental Comparison of Interfaces. :26.
[40] Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. :8
[41] Schwartzberg D, Ivanovic S, Patel S, Burjonrappa SC. We thought we would be perfect: medication errors before and after the initiation of Computerized Physician Order Entry. J Surg Res. 2015 Sep;198(1):108–14.
[42] Yeh M-L, Chang Y-J, Wang P-Y, Li Y-CJ, Hsu C-Y. Physicians’ responses to computerized drug-drug interaction alerts for outpatients. Comput Methods Programs Biomed. 2013 Jul;111(1):17–25.
[43] Charles K, Cannon M, Hall R, Coustasse A. Can utilizing a computerized provider order entry (CPOE) system prevent hospital medical errors and adverse drug events? Perspectives in health information management. 2014;11(Fall).
[44] Cohen M. Choosing the Right Oncologist and the Value of a Second Opinion. In: Management of Cancer in the Older Patient [Internet]. Elsevier; 2012 [cited 2020 Feb 24]. p. 51–4. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9781437713985100050
[45] Research C for DE and. The FDA’s Drug Review Process: Ensuring Drugs Are Safe and Effective. FDA [Internet]. 2019 Feb 9 [cited 2020 Feb 24]; Available from: http://www.fda.gov/drugs/drug-information-consumers/fdas-drug-review-process-ensuring-drugs-are-safe-and-effective
[46] e-CFR: TITLE 21—Food and Drugs [Internet]. Electronic Code of Federal Regulations. Available from: https://www.ecfr.gov/cgi-bin/text-idx?SID=f3ece07b28492f7f62aca7ab913de455&mc=true&tpl=/ecfrbrowse/Title21/21cfrv8_02.tpl#0
[47] Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer? The American Journal of Medicine. 2018 Feb;131(2):129–33.
[48] Angus DC. Randomized Clinical Trials of Artificial Intelligence. JAMA [Internet]. 2020 Feb 17 [cited 2020 Feb 24]; Available from: https://jamanetwork.com/journals/jama/fullarticle/2761468
[49] Park SH, Do K-H, Kim S, Park JH, Lim Y-S. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof. 2019 Jul 3;16:18.
[50] Health C for D and R. Clinical Decision Support Software [Internet]. U.S. Food and Drug Administration. FDA; 2019 [cited 2020 Feb 27]. Available from: http://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software
Published
2020-08-15
Section
Articles
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