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versión impresa ISSN 0104-4230
Rev. Assoc. Med. Bras. vol.57 no.3 São Paulo mayo/jun. 2011
Eugenie Desiree Rabelo NériI; Paulo Gean Chaves GadêlhaII; Sâmia Graciele MaiaIII; Ana Graziela da Silva PereiraII; Paulo César de AlmeidaIV; Carlos Roberto Martins RodriguesV; Milena Pontes PortelaVI; Marta Maria de França FontelesVII
IM.Sc. in Pharmaceutical Sciences; Risk Manager, Universidade Federal do Ceará (UFC), Fortaleza, CE, Brazil
IISpecialist in Hospital Pharmacy; Resident in Hospital Pharmacy, UFC, Fortaleza, CE, Brazil
IIIM.Sc. in Pharmaceutical Sciences; Pharmacist at Walter Cantídio University Hospital, Fortaleza, CE, Brazil
IVPh.D. in Public Health; Professor, UFC, Fortaleza, CE, Brazil
VPh.D. in Cardiology; Professor, Medical School, UFC, Fortaleza, CE, Brazil
IVSpecialist in Hospital Pharmacy; M.Sc. Student in Pharmaceutical Sciences, UFC, Fortaleza, CE, Brazil
VIIPost-Doctorate in Clinical Pharmacy; Professor of the Pharmacy Graduation Course and the Post-Graduation Programme in Pharmaceutical Sciences, UFC, Fortaleza, CE, Brazil
OBJECTIVE: To identify the prevalence of clinically significant prescription errors in a Brazilian university hospital compared with their occurrence in 2003 and 2007.
METHODS: Variables and group of variables, such as readability, compliance with legal and institutional procedures of prescription, and prescription errors analysis were analyzed.
RESULTS: When the prevalence rates of clinically significant prescription errors were calculated, a statistically significant decrease was shown [year of 2003 (29.25%), year of 2007 (24.20%); (z = 2.99; p = 0.03)], reflecting on the safety rate [year of 2003 (70.75%), year of 2007 (75.80%); (z = 3.30; p = 0.0001)].
CONCLUSION: Despite significant, the increased safety rate reflected the quantitative reduction of errors, with no observed difference in severity between the studied periods. Our results suggest the institutional steps taken could reduce the number of errors, but they were ineffective in reducing the severity of the errors.
Keywords: Drug prescriptions; medication errors; medical education.
The use of new technologies in health care has promoted improvement in quality and increased life expectancy all over the world. However, these breakthroughs have made health care increasingly expensive, complex and permeated by risks1-3.
The negative results in patient care have received several names, such as medical errors, adverse events related to hospitalization, medicinal iatrogenics, and others. Generally, the term adverse event is used to designate unintentional damage resulting from medical treatment not related to the disease condition. Finally, the study of these events has been considered important for the quality of patient care and assurance of treatment benefit, a stimulus to the culture of health system safety and efficiency (structure, process and result)4. In hospital care, the most prevalent adverse events result from inappropriate use of drugs (preventable causes) or they are related to patient specificities (non-preventable causes), being divided into medication errors and adverse reactions to drugs1,2.
Injuries resulting from errors in health services are considered the eighth leading cause of death in the United States of America2. Forty-four thousand to 98,000 people are estimated to die yearly from damage resulting from errors and among these, about 7,000 deaths can be attributed to medication errors2. In Brazil, investigations of adverse events and medication errors are incipient.
Drugs are essential components in health care and are considered the cornerstone in palliative, symptomatic and curative treatment in many diseases. However, they are also the most common cause of significant adverse reactions, errors and sentinel events5. Errors involving drugs occur frequently in hospitals2,6, are multidisciplinary in nature2 and may occur in one or more steps in the therapeutic chain (prescription, dispensation, and administration), with a higher frequency upon prescribing7,8. Errors have varying rates among institutions6,9,10 and show potential to cause adverse outcomes to the patient3, being classified as preventable adverse events2. Considering all kinds of errors, each hospitalized patient is estimated to experience an average of more than one medication error per day9.
The prescription is essentially a communication tool among the physician, the pharmacist, the nurse, the caregiver and the patient. In order to be considered appropriate, in addition to being clear, the prescription must follow the World Health Organization (WHO) criteria for a rational prescription which is appropriate, safe, effective and economic11. These characteristics contribute to better success odds for the therapy applied and the patient's safety3,12.
Adopting tools for a continuous improvement of the care process focused on patient safety requires addressing the theme "error", which, in health care, is still under a heavy stigma. It is often associated with low competence, shame and punishment, making it difficult to discuss study conduction, error reporting and underlying cause3. The current study was conducted in an error systemic view focused on processes rather than people and had the purpose of identifying the prevalence of clinically significant prescription errors compared with their occurrence in the years of 2003 and 2007 in a Brazilian university hospital as a way of evaluating the impact of the actions taken to improve safety of the prescription process.
This was a descriptive, cross-sectional, comparative study with data collection from duplicates of prescriptions containing one or more drugs. Data collection was held in two periods: June 1 to 30, 2003 (first sampling) and May 7 to 13, 2007 (second sampling) in a federal university tertiary care public hospital with 243 beds designed to the Unified National Health System (SUS) in Fortaleza, Ceará, Brazil.
Within the first sampling period, 9,482 prescriptions were dispensed by the Pharmacy, with 474 being collected (3,460 drugs prescribed). Within the second sampling period, 10,500 prescriptions were dispensed, with 140 being collected (1,030 drugs prescribed). In both samplings, 10% of dispensed prescriptions each day were selected, randomized by conglomerate (services) every other day until the size of the calculated sample was achieved. The sample size was calculated for an alpha error of 5% and the sample calculation of the first sampling was based on the lower number of errors per 1,000 items prescribed (3.2 errors) identified in literature14. For the second sampling, the prevalence of errors (number of errors per 1,000 items prescribed) identified in the first sampling (292.5 errors) was used as a reference for the sample calculation.
The study followed these steps: design and validation of the data collection form; team training; first sampling, coding, quality control; entries into the data base; tabulation and analysis; intervention, second sampling, coding; quality control; entries into the data base; tabulation, analysis and data comparison. The intervention measures were implemented over 4 years (administrative managing) and designed on a base of error types identified in the first sampling, namely: 1) Introduction of the daily presence of a medical preceptor into each hospital clinic instructing residents and medical students about the prescription elaboration. 2) Elaboration of a manual of good prescription practices containing tips on how to avoid prescription errors (delivered yearly to prescriptors)12. 3) Training about the rational use of drugs (based on the WHO method11 and the elaborated manual12 with a semiannual periodicity). 4) Availability of medical journals via web to the prescriptors. 5) Clinical protocol elaboration. 6) Resumption of medical-surgical sessions (monthly periodicity).
The variables or variable groups studied were:
The prescription was considered unreadable when at least two investigators had found it difficult to read what was written, resorting to the prescriptor for clarification.
2. Compliance with legal procedures15-18:
a) Patient's name: the completeness (no abbreviations or omissions), the readability and the patient's correct identification.
b) Prescription date and prescriptor signature: for both, presence and readability were evaluated.
c) Practice number: presence and readability were evaluated, being considered present when handwritten or imprinted.
d) Type of designation used for the drug: whether the prescribed drug was evaluated by using the commercial generic name or the chemical formula.
3. Compliance with institutional procedures:
a) Number of the patient's record, bed and admission unit: the presence, readability and accuracy were evaluated.
b) Abbreviation use: an error was considered when the abbreviations were used for the drug name, the patient's name, U and IU (for units and international units, respectively) and when they could be confused with zero19,20.
4. Prescription error analysis:
a) Error existence: Each prescription contains one or more drug items and can contain one or more errors. Prescription errors were defined as drug prescriptions involving wrong patient, drug, dose, frequency, administration route and/or pharmaceutical formulation, inappropriate indication, double or redundant therapeutics, documented allergy to prescribed drugs, contraindicated therapy and absence of critical information (age, weight, serum creatinine, diagnosis, etc.) required for the drug dispensation and administration21. They still include inappropriate treatment combination13 and inadequate duration of treatment. Prescription drug manufacturer information (package leaflets), information available from Micromedex21 and tertiary source22.
b) Clinically significant errors (CSE): they were identified and quantified according to Meyer23, that is, the CSE would be that occurred as a result from a prescription decision or the elaboration process of the written prescription in an unintentional way, generating or contributing to a significantly reduced probability of a timely and effective therapy or increasing the damage risk compared with the current practice standards24,25. They were also used to identify the CSE existence, prescription drug manufacturer information (package inserts), information available from Micromedex21 and tertiary source22. The patients' records were consulted to identify clinical conditions that could influence analyses such as weight, body area, results of laboratory tests, related symptoms (constipation, nausea, vomiting), allergy record, disease history, habits (illegal drug use, alcoholism)26.
c) The CSE types in prescriptions were classified by Dean27 as decision or writing errors. Decision errors were associated with the prescriptor's understanding level about the patient's clinical picture and the choice of the therapy drug. Writing errors are essential information communication failures associated with the prescription elaboration process (e.g.: prescribing a drug, but omitting the administration route when it can be administered by more than one route; prescription going to the wrong patient; prescribing the wrong drug).
d) Error severity: The CSE were subdivided into actual (detected after their occurrence) and potential (prescription mistakes which are detected and completely corrected before the drug administration). The severity was thus classified as follows: 1) For actual errors, the medication error categorization index is based on the National Coordinating Council for Medication Error Reporting and Prevention - B: an error occurred, but the patient was not reached; C: an error occurred, the patient was reached, but no damage was caused; D: an error occurred, the patient was reached, monitoring to confirm no patient's damage was caused was required and/or an intervention to prevent damage was required; E: an error occurred and it may have contributed to or resulted in transient damage for the patient, requiring an intervention; F: an error occurred and may have contributed to or resulted in transient damage to the patient, causing a hospitalization extension28 was used. 2) For potential errors, the adapted Lesar20 scale was used, identifying them as: AA: potentially lethal; AB: potentially serious and AC: potentially significant (with a potential to produce an adverse effect).
The Lesar20 method adapted by Néri3 was used to determine the prevalence of clinically significant errors (CSE) in prescriptions. The prescription process safety rate (PPSR) was calculated through the following formula: PPSR = 100% - rate of prescription CSE. The study observation units are related to individuals (patients) and drugs.
At the quality control stage, the CSE identified were evaluated independently by an intensivist and a pharmacist who is a Ph.D. in pharmacology for analysis consensus. In the case of a disagreement, a specialist physician was consulted.
Values were expressed as mean and standard deviation (X ± MSD) and processed by Epi Info. For proportion analysis, the z test was used to compare the prescrition CSE prevalence rate values and PPSR. For proportion analysis in 2 x 2 tables, non-parametrical tests were used (X2, Fisher-Freeman-Halton). The statistical significance level considered was p < 0.05.
The study was approved by the Ethics Committee (protocols 193/02 and 356/05) according to the Resolution 196 of the National Health Council and the investigator was ethically obliged to intervene when an error was identified, either by preventing the error to reach the patient or by interrupting its course.
The number of collected prescriptions was 474 (3,460 prescribed drugs) over the period of June 1 to 30, 2003 (first sampling) and 140 prescriptions (1,030 prescribed drugs) in May 7 to 13, 2007 (second sampling). The mean number of items per prescription was 10.77 ± 6.20 (first sampling) and 10.50 ± 5.69 (second sampling) (p = 0.645). In turn, the mean number of drugs per prescription was 7.30 ± 4.70 and 7.36 ± 4.63 (p = 0.897), respectively, for the first and the second samplings.
Table 1 shows the occurrence of errors in complying with legal and institutional procedures. Incomplete and unreadable names [2003 (n = 168), 2007 (n = 38); p = 0.06] and prescriptor's signature present but unreadable [2003 (n = 464), 2007 (n = 120); p = 0.0001] were observed.
Abbreviation use was observed in about 98% of drugs prescribed in both periods (2003, n = 3,046; 2007, n = 1,017). In 2003, abbreviations were more frequent for administration route (n = 2,980), dosing (n = 2,279), pharmaceutical formula (n = 1,783) and drug name (n = 297). In 2007, they were more frequently used in dosing (n = 634), administration route (n = 265), drug name (n = 80) and pharmaceutical formula (n = 10). The abbreviation "U" use for unit was observed in both periods.
Generic denomination was used in 66.01% (n = 2284) of prescribed drugs in 2003 and in 69.61% (n = 717) in the year of 2007 (p = 0.034). Commercial denomination was adopted, in 2003, in 30.75% (n = 1064) drugs prescribed and, in 2007, for 28.30% (n = 291) (p = 0.135). The chemical formula was used in 3.24% (n = 114) of drugs prescribed in 2003 and 2.14% (n = 22) in 2007 (p = 0.086).
Allergy information was absent in prescriptions collected and the mention to the patient's questioning about allergies was not found in 40.5% (n = 192) and 53.57% (n = 75) of medical records in 2003 and 2007, respectively (p = 0.008). Information on weight was absent in 71.94% (2003; n = 341) and 75.71% (2007; n = 106) of medical records (p = 0.436).
The readability analysis of drugs prescribed revealed, in 2003, 99.54% of them (n = 3,444) were readable and, in 2007, 92.72% (n = 955) (p = 0.001). Omission of one or more items of information relevant for the dispensation safety and the prescribed drug administration was found, being identified that, in 2003, 75.35% of drugs (n = 2,607) had omission of relevant information, whereas this percentage was 79.22% (n = 816) (p = 0.012) in 2007. The omitted information was: infusion rate [2003 (78.22%, n = 2,706); 2007 (74.44%, n = 753)]; concentration [2003 (54.02%, n = 1869); 2007 (52.77%, n = 534) and pharmaceutical formula [2003 (53.12%, n = 1,838); 2007 (55.15%, n = 558)].
At the first sampling (2003), 8,271 prescription errors were identified in 474 prescriptions containing 3,460 drugs prescribed and, out of these errors, 12.24% (n = 1012) were CSE, with this number ranging from 1 to 10 CSE/prescription (mean = 2.60 ± 0.10). In 2007, all prescriptions had errors (n = 140), adding up to 2,608 errors for 1,030 drugs prescribed, from which 9.55% (n = 249) were CSE, ranging from 1 to 9 CSE/prescription (mean = 2.50 ± 1.80). By adding the two-period errors and dividing by the total prescriptions collected, a mean of 2 CSE/prescription was obtained, with 75.34% (n = 950) of errors concentrated in the writing process. The drugs most frequently involved in CSE, in both samplings, were dipyrone [2003 (11.74%); 2007 (10.03%)], regular insulin [2003 (7.28%); 2007 (8.70%)] and digoxin [2003 (3.53%); 2007 (10.03%)].
The CSE were categorized into writing and decision errors. The error percentage occurred in the writing process was, in 2003, 75.39% (n = 763) and, in 2007, 75.10% (n = 187) (p = 0.987) (Table 2).
As for errors in the decision process, in 2003, the occurrence of 249 CSE (24.6%) and, in 2007, 24.90% (n = 62) (p = 0.961) was observed. The types of decision errors and their frequencies are shown in Table 2.
In 2003, 98.4% (n = 112) of CSE were potential errors and, in 2007, 100% (n = 249). As for severity, CSE were distributed as shown in Table 3.
When prescription CSE prevalence rates were calculated, in 2003, 29.25% and, in 2007, 24.20% were identified (z = 2.99; p = 0.003), creating a prescription process safety rate, in 2003, of 70.75% and, in 2007, 75.80% (z = 3.30; p = 0.0001).
The results achieved bear out findings in Brazilian and international studies6,8,19,29-32 and showed prescription errors are common and should be faced by practitioners involved in health care, mainly in teaching hospitals, in which the safety culture, if incorporated over the practitioners' graduation, can result in health system changes.
The prescriptions analyzed in both phases had a mean number of items and drugs statistically similar, indicating the reproducibility of the method used, as well as the fact that patients are given a polypharmacy, favoring error occurrence3,30. The mean number of drugs per prescription was similar to that found by Cruciol-Souza33 in a teaching hospital in Paraná, stressing the need of a higher level of attention to prescriptions in this specific hospital group because of the confluence of factors associated with the higher error tendency3.
The prescriptor's name, his/her signature and practice number in the Council, when associated, give the prescription legal validity and, when this information is unreadable or missing, prescriptions should not be dispensed or fulfilled. This legal optics further contains discussions that are of technical and practical in nature, making the hospital routine more difficult. The results obtained in 2003 and 2007 regarding the prescriptor's identification, were better than Sebastião's34, who identified the practice number was missing in 83% of prescriptions, the prescriptor's signature in 19.2% and the practitioner's name in 45.2%. The prescriptor's signature readability had a statistically significant improvement between 2003 and 2007.
In addition to the items aforementioned, the date provides the prescription with validity which, in a hospital setting, usually lasts 24 hours. When the date is considered, the results achieved in the samplings are similar to Sebastião's33 (97.2%) and Miasso's29 (96%), but higher than Rosa's19 (90.6%).
The use of abbreviations and symbols in prescriptions is pointed as an error-related factor18,19, and at times these errors are fatal35. Several prescriptors see abbreviation use as a way of saving time, however they have no thoughts of the time spent by the other practitioners in clarifying the doubts35 and of the risks resulting from mistaken interpretations. Moreover, the abbreviation use is contrary to the Decree no. 20.931/3214, which determines the prescription must be made in full and must not be made in a secret mode. The practice of abbreviations was widely identified in both samplings, being similar to Miasso's29 results, with the drug name abbreviation and use of "U" for "unit" being highlighted, both facts described by Cohen36 as significant safety issues. The use of "U" is included in the abbreviation list prohibited by the Joint Commission on the Accreditation of Health Care Organizations35. In Brazil, in 2007, the National Health Surveillance Agency (Anvisa) discussed the standardization of names, concepts and abbreviations in pharmaceutical forms of drugs to assure a common understanding37.
The use of the generic denomination in prescription in both study years was twice the value found by Sebastião (30.2%)34. By comparing the years of 2003 and 2007, a significant increase in generic denomination adoption was shown, but the legal provision regulating the issue has not been wholly fulfilled17.
Information such as weight and allergy report is a basic tool for the safety of drug dispensation and administration3; however, this data is often missing in the medical record and in a similar level in both periods, in agreement with Devine's38 data. Concerning allergy, there was a significant increase in the percentage of medical records missing an answer the patient should have given to the question asked about it, which is worrisome, since the non-documentation exposes health care users to an unnecessary and preventable damage risk. According to Runciman39, over 75% of prior drug allergic reactions are not found in the medical record. This data stresses the need of further emphasis on good documentation practice adoption during the prescriptor's training3.
The low readability of prescriptions, mainly those handwritten19,29, has been indicated as a major cause for communication failure among practitioners involved in hospital care and a factor contributing to medication errors19,29,30,32,40,41. Despite showing a readability result over 90%, a significant reduction in prescription readability was found between 2003 and 2007, indicating a higher probability of patient damage. These findings can be minimized from the adoption of the electronic prescription42 and stress the need of a review in the prescriptor's training43.
Similarly, information missing is considered a major fault in the prescription process, negatively influencing the communication quality29,33. When this indicator was evaluated, a significant increase in the percentage of missing information relevant to the safety of drug dispensation and administration was observed. The concentration and pharmaceutical formulation figure among the information most frequently missing in both samplings, an issue also reported by Rosa19, Miasso29 and Sebastião34 in other Brazilian hospitals. The infusion rate missing also identified can lead to an Adverse Drug Reaction infusion rate-dependent, as in the case of the red man syndrome related to the quick vancomycin infusion3.
The analysis by segment revealed writing errors had a similar behavior and represented more than three quarts of the total in both periods. In this setting, the results were similar to Rosa's19, Devine's38 and Dean's44. The writing errors also had a similar behavior between 2003 and 2007. Decision errors are considered more complex to be prevented than the writing errors3.
Among the writing CSE, missing patient identification items (name, medical record number, bed, clinic or service where he/she was admitted) were predominant, making higher the chance of a patient receive drugs that were not prescribed for him(her) and suffer damage resulting from this exchange7. In this study, the patient's name on the prescription had a high inadequacy (incomplete, with an abbreviation, and unreadable) percentage, contributing to a prescription exchange between namesakes, a possibility already described in literature44 which can be prevented through the patient's appropriate identification.
The ambiguous/confused prescription had its percentage significantly reduced, comparing 2003 with 2007, but this result still represents twice the frequency identified by Ridley32 in a prescription study in an intensive care unit (ICU).
Regarding CSE in the decision process, the prevalence was observed as similar in both periods, but they had a lower frequency than writing errors, with a significantly reduced tendency for drug interaction being observed. The severity of the decision errors, according to Dean44, is greater than that with writing errors. The lack of knowledge and information about the patient was pointed by Louro30 as a factor related to prescription errors.
Drug interaction and drug prescription with no indication for the patient were predominant decision errors in the samplings, and occurrence percentage of the interaction, in relation to the total CSE identified, was about three to five times as high as those identified by Devine38 (2.8%) and Louro30 (3.4%), respectively. The percentage of clinically significant drug interactions identified in this study was similar to that obtained by Hammes45 in an ICU in Santa Catarina, Brazil. When the prescription of a contraindicated drug for the patient according to preexisting conditions reported in the medical record was analyzed, a significantly increased occurrence was found. Both errors are relevant and deserve to be considered in the reflection process aiming at the prescriptors' teaching improvement46.
The analysis of three drugs more frequently involved in CSE revealed that two of them are classified as potentially dangerous drugs associated with more serious errors19. In this study and in Devine's38 study, most errors were potential, with the minority of them being classified as potentially fatal. Regarding the actual errors, some of them reached the patient, resulting in a transient damage, with a prevalence 3.4 times as higher as that identified by Devine38 (0.2%).
The prescription CSE prevalence rate suffered a significant reduction between the periods and had a mean percentage similar to Devine's (27.4%)38. The reduced CSE prevalence rate, resulting from the quantitative reduction of clinically significant errors, provided a significant increase in this process safety rate; however, a reduced severity in the errors identified between the periods was not found.
Factors contributing mostly to elevate the PPSR were the increased percentage of a correct recording of the patient's hospitalization unit, signature readability on the prescription, increased generic denomination use and reduction of drugs prescribed in an ambiguous/confused way.
The findings suggest an improvement of the prescription process quality between the study periods, but there was no influence on the error severity. Ross46 reports there is little evidence in the present literature instructing medical schools on how to prepare students to prescribe and that the use of the WHO Guide for a Good Medical Prescription11 is the only model with evidence of prescription improvement. The WHO method was adopted in this study and may have contributed to reduced ECS. The reduced occurrence of errors following the education program adoption focused on prescriptors was also shown by other authors4,18,24.
The prescription process is complex and permeated by errors. Prescription errors are usually multifactorial and arise from active faults or conditions error-inducing, usually acting together to cause them. Face of this complexity, solutions involving only one cause, such as lack of knowledge, seem to have limited benefits.
The confrontation of the prescription error issue is a world challenge and must be an institutional goal. In this study, the statistical increase in the prescription process safety rate was identified, influenced by the quantitative reduction in clinically significant errors between 2003 and 2007, but the error severity was not changed, indicating the steps taken were ineffective in reducing the severity.
1. Velo GP, Minuz, P. Medication errors: prescribing faults and prescription errors. Br J Clin Pharmacol. 2009;67(6):624-8. [ Links ]
2. Kohn LT, Corrigan JM, Donaldson MS, editors. To err is human: building a safer health system Washington (DC): National Academy Press; 2000. [ Links ]
3. Néri EDR. Determinação do perfil dos erros de prescrição de medicamentos em um hospital universitário [dissertação]. Fortaleza: Universidade Federal do Ceará; 2004. [ Links ]
4. Montesi G, Lechi A. Prevention of medication errors: detection and audit. Br J Clin Pharmacol. 2009;67(6):651-5. [ Links ]
5. Fialová D, Onder G. Medication errors in elderly people: contributing factors and future perspectives. Br J Clin Pharmacol. 2009;67(6):641-5. [ Links ]
6. Lewis PJ, Dornan T, Taylor D, Tully MP, Wass V, Ashcroft DM. Prevalence, incidence and nature of prescribing errors in hospital inpatients: a systematic review. Drug Safety 2009;32(5):379-89. [ Links ]
7. Leape LL, Bates DW, Cullen DJ, Cooper J, Demonaco HJ, Gallivan T et al. Systems analysis of adverse drug events. JAMA 1995;274(1):35-43. [ Links ]
8. Lisby M, Nielsen LP, Mainz J. Errors in the medication process: frequency, type, and potential. Int J Qual Health Care. 2005;17(1):15-22. [ Links ]
9. Institute of Medicine of the National Academies. Preventing medication errors. 2006. [citado 2008 Sept. 9]. Disponível em: http://www.iom.edu/Object.File/Master/35/943/medication%20errors%20new.pdf [ Links ]
10. Aspden P, Wolcott JJ,. Bootman L, Cronenwett LR. Preventing medication errors: quality chasm series. The National Academies Press; 2007.[citado 2010 Jun. 10]. Disponível em: http://www.nap.edu/catalog/11623.html. [ Links ]
11. Vries TPGM, Henning RH, Hogerzeol HV, Fresle DAG. Guia para a boa prescrição médica. Porto Alegre: ArtMed; 1998. p. 67-71. [ Links ]
12. Néri EDR, Viana PR, Campos TA. Dicas para uma boa prescrição hospitalar. Fortaleza: Hospital Universitário Walter Cantídio; 2008. [acesso 2010 Jun. 10]. Disponível em: http://www.huwc.ufc.br/biblioteca_cientifica.php?acao=exibir&id=221. [ Links ]
13. Lesar TS. Medication prescribing error reporting and prevention program: a 14 year experience. [citado 2008 Oct. 17]. Medscape Pharmacists. 2000;1(2). Disponível em: http://www.medscape.com/viewarticle/408567. [ Links ]
14. Ross S, Bond C, Rothnie H, Thomas S, Macleod MJ. What is the scale of prescribing errors commited by junir doctors? A systematic review. Br J Clin Pharmacol. 2008;67(6):629-40. [ Links ]
15. Brasil. Decreto nº 20.931, de 11 de jan. 1932. Regula e fiscaliza o exercício da medicina, da odontologia e das profissões de farmacêutico, parteira e enfermeira. [citado 2008 Set. 9]. Disponível em: http://www.apo-pr.com.br/down/20931.pdf. [ Links ]
16. Brasil. Lei Nº 5.991 de 17/12/73. Dispõe sobre o controle sanitário do comércio de drogas, medicamentos, insumos farmacêuticos e correlatos e dá outras providências. [citado 2010 Maio 25]. Disponível em: http://www.planalto.gov.br/ccivil/leis/L5991.htm. [ Links ]
17. Brasil. Conselho Federal de Medicina. Resolução Nº 1246/88. [citado 2010 Maio 17]. Disponível em: http://www.portalmedico.org.br/resolucoes/cfm/1988/1246_1988.htm. [ Links ]
18. Brasil. Resolução nº 16, de 02 de março de 2007. Aprova regulamento técnico para medicamentos Genéricos, Anexo I. [citado 2008 Set. 9]. Disponível em: http://e-legis.anvisa.gov.br/leisref/public/showAct.php?id=25960&word=. [ Links ]
19. Abushaiqa ME, Zaran FK, Bach DS, Smolarek RT, Farber MS. Educational interventions to reduce use of unsafe abbreviations. Am J Health-Syst Pharm. 2007;64:1170-3. [ Links ]
20. Rosa MB, Perini E, Anacleto TA, Neiva HM, Bogutchi T. Erros de prescrição hospitalar de medicamentos potencialmente perigosos. Rev Saúde Pública 2009;43(3):490-8. [ Links ]
21. Lesar TS, Briceland LL, Delcoure K, Parmalee JC, Masta-Gornic V, Pohl H. Medication prescribing errors in a teaching hospital. JAMA.1990;263(17):2329-34. [ Links ]
22. Thomson Micromedex. [citado 2009 Dec. 2]. Disponível em: https://www.thomsonhc.com/hcs/librarian/PFDefaultActionld/pf.LoginAction/ss/true/ssl/true?ND_CPR=Login&login.username_index_0=capes77hcs&login.password_index_0=jlmzvazhipju. [ Links ]
23. American Society of Health-System Pharmacists-ASHP. Pharmacist's drug handbook. Pennsylvania: Springhouse; 2001. [ Links ]
24. Meyer TA. Improving the quality of the order-writing process for inpatient orders and outpatient prescriptions. Am J Health-Syst Pharm. 2000;57(Suppl 24):S18-22. [ Links ]
25. Brasil. Ministério da Saúde. Protocolos clínicos e diretrizes terapêuticas. 2001. [citado 2010 Maio 9]. Disponível em: http://dtr2001.saude.gov.br/sas/dsra/protocolos/index.htm. [ Links ]
26. Ramalho Júnior A, Katz A, Fernandes Junior CJ, Hilda JT, Hamerschlak N, Minatel VF. Protocolos de conduta - Hospital Israelita Albert Einstein. 2ª ed. São Paulo: Editora Atheneu; 2003. [ Links ]
27. Montesi G, Lechi A. Prevention of medication errors: detection and audit. Br J Clin Pharmacol. 2009;67(6):651-5. [ Links ]
28. Dean B, Barber N, Schachter M. What is a prescribing error? Qual Saf Health Care 2000;9(2):232-7. [ Links ]
29. National Coordinating Council for Medication Error Reporting and Prevention - NCC MERP. Index for categorizing medication errors. 2001 [citado 2009 Dec 2]. Disponível em: http://www.nccmerp.org/pdf/indexColor2001-06-12.pdf. [ Links ]
30. Miasso AI, Oliveira RC, Silva AEBC, Lyra Junior DPL, Gimenes FRE, Fakih FT et al. Prescription errors in brazilian hospitals: a multi-centre exploratory survey. Cad Saúde Pública 2009;25(2):313-20. [ Links ]
31. Louro E, Romano-Lieber NS, Ribeiro E. Eventos adversos a antibióticos em pacientes internados em um hospital universitário. Rev Saúde Pública 2007;41(6):1042-8. [ Links ]
32. Miasso AI, Grou CR, Cassiani SHB, Silva AEBC, Fakih FT. Erros de medicação: tipos, fatores causais e providências tomadas em quatro hospitais brasileiros. Rev Esc Enferm USP 2006;40(4):524-32. [ Links ]
33. Ridley SA, Booth SA, Thompson CM. Intensive care society's working group on adverse incidents. Prescription errors in UK critical care units. Anaesthesia 2004;59(10):1193-200. [ Links ]
34. Cruciol-Souza JM, Thomson JC, Catisti DG. Avaliação de prescrições medicamentosas de um hospital universitário brasileiro. Rev Bras Educ Med. 2008;32(2):188-96. [ Links ]
35. Sebastião ECO. Avaliação do cumprimento das exigências legais em ordens médicas em serviço de farmácia hospitalar de Ouro Preto e implicações na qualidade assistencial ao paciente. Rev Ciênc Farm. 2002;23(1):71-85. [ Links ]
36. Gaunt MJ, Cohen MR. Error-prone abbreviations and dose expressions. In: American Pharmacists Association. Medication errors. 2nd ed. Washington (DC): American Pharmacists Association; 2007. p. 153-71. [ Links ]
37. Cohen MR. Preventing prescribing errors. In: American Pharmacists Association. Medication errors. 2nd ed. Washington (DC): American Pharmacists Association; 2007. p. 175-203 [ Links ]
38. Brasil. Consulta Pública nº 50, de 28 de maio de 2007. Dispões sobre o vocabulário controlado de formas farmacêuticas. [citado 2009 Dez. 1]. Disponível em: http://www4.anvisa.gov.br/base/visadoc/CP/CP%5B18629-1-0%5D.PDF. [ Links ]
39. Devine EB, Wilson-Norton JL, Lawless NM, Hansen RN, Hazlet TK, Kelly K et al. Characterization of prescribing errors in an internal medicine clinic. Am J Health-Syst Pharm. 2007;64:1062-70. [ Links ]
40. Runciman WB, Roughead EE, Semple SJ, Adams RJ. Adverse drug events and medication errors in Australia. Int J Qual Health Care 2003;15(1):149-59. [ Links ]
41. National Coordinatig Council for Medication Error Reporting and Prevention - NCC MERP. Taxonomy of medication error. [citado 2008 Oct. 17]. Disponível em: http://www.nccmerp.org/pdf/taxo2001-07-31.pdf. [ Links ]
42. Aguiar G, Silva Júnior LA, Ferreira MAM. Ilegibilidade e ausência de informações nas prescrições médicas: fatores de risco relacionados a erros de medicação. Rev Bras Promoção Saúde 2006;19(2):84-91. [ Links ]
43. Velo, GP, Minuz P. Medication errors: prescribing faults and prescription errors. Br J Clin Pharmacol. 2009;67(6):624-8. [ Links ]
44. Schachter M. The epidemiology of medication errors: how many, how serious? Br J Clin Pharmacol. 2009;67(6):621-3. [ Links ]
45. Dean B, Schachter M, Vincent C, Barber N. prescribing errors in hospital inpatients: their incidence and clinical significance. Qual Saf Health Care 2002;11(4):340-4. [ Links ]
46. Hammes JA, Pfuetzenreiter F, Da Silveira F, Koening A, Westphal GA. Prevalência de potenciais interações medicamentosas droga-droga em unidades de terapia intensive. Rev Bras Terap Intens. 2008;20(4):349-54. [ Links ]
47. Ross S, Loke YK. Do educational interventions improve prescribing by medical students and junior doctors? A sistematic review. Br J Clin Pharmacol. 2009;67(6):662-70. [ Links ]
Correspondence to: Submitted on: 01/12/2011 Study conducted at Walter Cantídio University Hospital, Universidade Federal do Ceará, Fortaleza, CE, Brazil
Milena Pontes Portela
Rua Capitão Francisco Pedro, 1290, Rodolfo Teófilo
Fortaleza - CE CEP: 60430-372
Funding Support: Ceará Foundation of Support to Scientific and Technological Development of State of Ceará (FUNCAP) - funding for the first study data collection; Anvisa, second collection and data statistical analysis
Conflict of interest: None.
Submitted on: 01/12/2011
Study conducted at Walter Cantídio University Hospital, Universidade Federal do Ceará, Fortaleza, CE, Brazil