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 Table of Contents 
Year : 2008  |  Volume : 15  |  Issue : 2  |  Page : 51-56  

Prescription non-conformities in primary care settings: How useful are guidelines

Department of Family & Community Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia

Date of Web Publication16-Jun-2012

Correspondence Address:
Fahad A Al-Hussein
King Abdulaziz Medical City, National Guard Health Affairs, P.O. Box 22490, Riyadh 11426
Saudi Arabia
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Source of Support: None, Conflict of Interest: None

PMID: 23012167

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Background: Apart from having a negative impact on work flow in practice, prescription errors may pose a threat to patient safety. Such errors have been reported in the pharmaceutical services in spite of the clear guidelines issued by the parent organization.
Objective: This study was to explore the degree of conformity to the prescribing guidelines at Primary Care level in the Saudi National Guard Health Affairs in Riyadh.
Methods: Prescriptions were collected during audits done fortnightly through a simple random selection from a sampling frame of all prescriptions given within the period. Information about each prescription was entered in a database by the pharmacists and each prescription was classified according to its conformity to the guidelines. Information was presented on 330 prescriptions for eleven audits carried out from September 2004 to February 2005.
Results: 87% of the prescriptions did not conform to the given guidelines. Less than 1% of the inconsistencies were potentially harmful to the patient, 77.8% had possible negative effect on the pharmacist's work, while 21.3% were unimportant. Patient information was deficient in 16.9% of cases, drug information in 49.6% and archiving/record information related non-conformities constituted 33.5%.
Conclusions: Conformity to prescribing guidelines is quite low in spite of the significant input of resources by the parent organization. This burden on work flow, utilization of time and service delivery needs to be studied and addressed by ensuring that there are periodic audits in the work routines of primary health care, and a feedback given to the care providers.

Keywords: Prescription, prescribing, general practitioner, primary health care, pharmacy, non-conformity, audits.

How to cite this article:
Al-Hussein FA. Prescription non-conformities in primary care settings: How useful are guidelines. J Fam Community Med 2008;15:51-6

How to cite this URL:
Al-Hussein FA. Prescription non-conformities in primary care settings: How useful are guidelines. J Fam Community Med [serial online] 2008 [cited 2020 Sep 25];15:51-6. Available from:

   Introduction Top

Primary Health Care (PHC), as an entry point of the health system is the cornerstone of the health services. With the huge workload and the volume of patient flow at primary care facilities, the chances of errors and inconsistencies of writing and keeping records are more likely to occur in the work of the care providers than their colleagues in other clinical settings. Such errors can be particularly deleterious when these occur in prescription writing.

Prescribing is a major interventional activity performed by physicians in most healthcare facilities and especially general practitioners (GPs) in primary health care settings. Most consultations in primary care results in a prescription, [1],[2] as the prescription of a drug hastens the termination of a primary care consultation. This pattern of preferential prescribing with two or more drugs prescribed for each patient per consultation [1],[2],[3],[4],[5],[6],[7] is universal. [1],[2],[3]

During the long hours of work, GPs see a large number of new patients who, more often than not, present with a wide range of symptoms of mostly undifferentiated and complex medical conditions. [8] The diagnostic task is even more challenging for GPs, since most primary care facilities lack the full range of laboratory and other investigational facilities, which could aid the process of a correct early diagnosis.

High patient flow, short consultation times, and the required diversity of knowledge and diagnostic skills render the making of diagnosis and prescribing rather difficult with the result that the probability of errors with potentially serious consequences is increased.

Prescription errors consist of a whole spectrum of issues ranging from polypharmacy and the use of non-generic drug names to failures to record patient, provider, drug, or dispensing information. These errors are universally reported across various domains, disciplines, and drugs, [9],[10],[11],[12],[13],[14],[15],[16] and may range from the trivial to what is potentially dangerous to the patient. [17] They also affect work flow in the practice, especially in the pharmacy, causing unnecessary delays in dispensing, and affecting the quality of communication with patients.

Studies in Saudi Arabia have shown that a high proportion of prescriptions may be inappropriately written [1],[3],[18] with errors ranging from the trivial to the potentially dangerous, [1] highlighting concerns about prescribing habits and patterns. [2],[19] Failures to record patient weight, drug dosage, duration of treatment, generic drug names, and illegibility of hand writing have been among the problems reported.

The provision of clear prescribing guidelines is a definite step towards eliminating these errors. Such guidelines have been provided by the pharmaceutical services of National Guard Health Affairs, although the degree of compliance with these guidelines has not been systematically assessed.

The current study is aimed at reducing these errors by estimating the level of compliance to the provided guidelines in order to develop and incorporate a systematic assessment process in prescription errors in PHC and provide the care providers with a feedback.

   Methods Top

Simple random samples of 30 prescriptions were chosen every fortnight from a sampling frame of all prescriptions collected during the period. The current study is based on the analysis of a set of 330 prescriptions from 11 audits, carried out from September 2004 to February 2005. This sample size provides more than 90% power to detect a difference of 10% from the assumed proportion of non-conforming prescriptions at 50%.

Every prescription was checked for compliance to the 14 components provided in the guidelines; file number, age, weight, diagnosis, generic drug name, dose specification, frequency specification, duration specification, use of standard abbreviations, absence of contraindications (including interactions), pharmacist's name record, doctor's name record, date record and legible hand writing. Information from each selected prescription was entered in a specially prepared database using Epidata. [20] Based on yes-no answers to the status of compliance to the 14 indicators, an automated decision was generated by a computer system on the conformity of the prescription with the guidelines. Personal information on the patients or the prescribers was held back for reasons of confidentiality.

For the feedback to care providers, non-conformities were classified according to the component of prescribing process involved… patient, provider, prescribing, drug / dispensing or others [Table 1]. A second classification, used by Neville et al, [17] based on potential harm to the patient, was also used [Table 2].
Table 1 : Prescription non-conformities according to the component involved

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Table 2 : Prescription non-conformities according to potential harm to patient

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According to the later classification, type A errors are 'potentially serious to the patient', meaning that the prescription would be dangerous if dispensed. Type B errors are 'major nuisance', meaning that the pharmacist may have to contact the physician before being able to correctly dispense the prescription; type C errors are a 'Minor Nuisance' if the pharmacist has to make a professional decision before dispensing the prescribed medication, and type D termed 'trivial' errors, have no serious consequence though the prescription does not conform to the guidelines.

For all class A, B and C errors handled through the current system of error management in pharmaceutical services, the concerned pharmacist would contact the physician by telephone and send a feedback to him on a standard form highlighting the error. The physician, after making the necessary correction on the prescription, would send it back to the pharmacy. Class D errors, as well as other errors are reported to the clinicians, without the names of the concerned employees, during continuing education sessions in the center.

   Data Analysis Top

Proportions are presented as percentages in the format #.#%(± standard deviation). Exact binomial confidence intervals are used to estimate parameters for binary variables. Analyses were carried out with Stata Version 8.2(21), Epi-Info 6.04d(22), and R version 2.0.1(23).

   Results Top

Out of 330 selected prescriptions 288(87.3%) did not conform to the given guidelines (95% CI: 83.2 - 90.7), while only 42(12.7%) did (95% CI: 9.3 - 16.8). The mean number of those that did not conform was 2.3 (± 1.8), with a range of 0 - 8 per prescription. Forty two out of 330 (12.7%; 95% CI: 3.3-8.5) prescriptions fully conformed to the given guidelines [Table 3]. The same number of prescriptions did not conform in five areas.
Table 3 : Number of non-conformities per prescription

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A total of 750 instances of non-conformity were noted in the 330 prescriptions [Table 1], [Table 2] & [Table 4]. The most frequent non-conformity was that of a failure to write generic drug name (28.9% of the non-conformity; 95% CI: 25.7-32.3) Failure to record pharmacist's name, patient weight and duration of therapy constituted 18.4 % (15.7-21.4), 14.9% (12.5-17.7) and 10.9% (8.8-13.4) of the non-conformity [Table 4].
Table 4 : Distribution of prescription non-conformities

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Only 2.1% of the prescriptions (95% CI: 0.8 - 4.3) contained a type A error accounting for 0.9% of all non-conformity (95% CI: 0.4 - 1.9). Type B errors, accounting for 40.2% (95% CI: 36.6 - 43.7) of non-conformity, occurred in 52.7% (95% CI: 47.2 - 58.2) of prescriptions, while Type C errors, accounting for 37.6% of non-conformity (95% CI: 34.1 - 41.2), occurred in 70.3% (95% CI: 65.0 - 75.2) of prescriptions. Type D errors, 21.3% of non-conformity (95% CI: 18.5 - 24.4), were detected in 43.64% (95% CI: 38.2 - 49.2) of prescriptions [Table 2]. Almost half (49.6%; 95%CI: 46.0 - 53.2 ) of all instances of non-conformity dealt with drug information and dispensing issues while the prescribing process (12.1%; 95%CI: 9.9 - 14.7) was the category with least number of instances of non-conformity [Table 1].

   Discussion Top

Compared with certain other studies, [3],[24],[25] our rates of non-conformity were much lower, especially with regard to the generic names (28.9% vs. 84.9%), patient weight (14.9% vs. 100%), dose (3.6% vs. 44.4%), illegible handwriting (3.5% vs. 64.3%), and missing diagnosis (0.8% vs. 44%), according to the rates reported by Irshad et al. [24] These findings are still rather surprising to most doctors in the practice since the pharmaceutical services in National Guard Health Affairs are very well managed and have in-built multiple levels of error control. In addition to providing clear guidelines to the clinicians, this control is achieved through a two-tiered system. Structurally, two copies of special prescription sheets are available in each file; a copy is meant for the file and the other for the patient. This practice has proved effective in reducing prescription errors. [26],[27],[28] A second level of control is through individual feedback by the pharmacists to the clinicians when the need arises. Systematic, targeted group feedback, and face to face interviews, [29] have however, not yet been implemented. These measures, of course, are in addition to an intensive focus on continuing education for the different categories of care providers.

In view of all these organizational checks, such a high level of non-conformity to given guidelines may, in addition to knowledge, attitude and skill issues, be an indicator of excessive work load on the primary care providers. Root causes of this phenomenon should be explored through appropriately designed studies, and addressed accordingly. Although in the absence of such studies, the reasons of this non-conformity would remain a matter conjecture, different facets of the care provider's cognitive framework certainly need to be looked into. Knowledge of pharmacology and therapeutics, attitude towards complying with guidelines, time and work load management skills, and communication with patients and colleagues are all potentially important areas to target, through individually tailored continuing medical education, management and leadership initiatives. Such measures on the local and organizational level requires consistent collaborative effort of the local physicians and pharmacists.

Alternative prescription paradigms have recently been suggested as a means of eliminating prescribing errors. [30],[31],[32],[33],[34],[35],[36],[37] These solutions, however, may not themselves be free of problems [38],[39] and may require considerable investment of resources. While such measures want to be adapted and introduced, solutions must be found for the single most important resource of all, the human resource, through simple effective and easily implemented modalities relating to the work place.

Non-discovery of prescription errors has been seen as a barrier to learning from experience, important for practice improvements, [40] and the need for increased awareness, documentation, and the reporting of these errors has been identified as a priority. [41] We therefore, suggest routine audits to detect and deal with prescription errors and non-conformity to guidelines in primary health care services.

Although education and on the job training are important for performance improvement in the process of prescribing, [41] only such audits will ensure a process of collaborative learning in the team of care providers. Pharmacists can be trained to carry out these audits. Data automation can be used to deal with time constraints and maximize data validity, integrity, and confidentiality.

In addition to shaping the direction and focus of any efforts aimed at service improvement, such audits will provide an ongoing source of learning from the real situations in busy practice for all those involved in care. This will lead eventually, to positive changes in the culture of the workplace.

As improvements in the work environment, efficiency of relationships of different components of care, [42] and proper feedback by pharmacists [43] have all been shown to be effective tools for improving prescribing practices. It is hoped that by introducing such systems of ongoing audits, an inexpensive change can be effected.

   Conclusions Top

With the prevalence of prescription errors in the National Guard Health Affairs primary care facilities, questions on the effectiveness of current control systems are raised. It is our view that regular audits of the implementation of guidelines should be incorporated in work routines, and feedback of results given to the care providers. Studies should be conducted to find out the underlying causes of the problem and assess the effectiveness of any interventional initiatives.

   Acknowledgment Top

The staff of King Abdul Aziz Housing Family and Community Medicine center, National Guard Health Affairs, Riyadh deserve thanks for their resolute commitment to the improvement of the process of care delivery. Very special thanks are due to the pharmacist, Mr. Abdul Aziz Al-Kharji for his continued interest and support.

   References Top

1.Khoja T, Al-Shammari S, Farag M, Al-Mazroua Y. Quality of prescribing at Primary Health Care Centers in Saudi Arabia. J Pharmacy Tech 1996;2:284-8.  Back to cited text no. 1
2.Felimban F. The prescribing practice of Primary Health Care in Riyadh City. Saudi Medical Journal 1993;14(4):353-8.  Back to cited text no. 2
3.Al-Nasser AN. Prescribing patterns in primary healthcare in Saudi Arabia. Dicp 1991;25(1):90-3.  Back to cited text no. 3
4.Bawazir S. Prescribing patterns of Ambulatory Care Physicians in Saudi Arabia. Annals of Saudi Medicine 1993;13:172-7.  Back to cited text no. 4
5.Dizwani AG, Stein CM, Todd WT, Parirenyatwa D, Chakonda J. Morbidity patterns and prescribing habits in Harare primary care clinics. Family Practice 1985;2(2):82-5.  Back to cited text no. 5
6.Hogerzeil H, Walker G, Sallami A. Impact of an essential drug program on availability and rational use of drugs. Lancet 1989;1:141-2.  Back to cited text no. 6
7.Krishnaswamy K, Dinesh K, Radhaiah R. A drug survey-percepts and practices. Eur J Clin Pharmacol 1985;29:363-70.  Back to cited text no. 7
8.Gravelle H, Hole AR. The work hours of GPs: survey of English GPs. Br J Gen Pract 2007;57(535):96-100.  Back to cited text no. 8
9.Haavik S, Horn AM, Mellbye KS, Kjonniksen I, Granas AG. Prescription errors-dimension and measures. Tidsskrift for den Norske Laegeforening 2006;126(3):296-8.  Back to cited text no. 9
10.Stubbs J, Haw C, Taylor D. Prescription errors in psychiatry - a multi-centre study. Journal of Psychopharmacology (Oxford, England) 2006;20(4):553-61.  Back to cited text no. 10
11.Campino VA, Lopez HMC, Garcia FM, Lopez de Heredia GI, Valls ISA. Medication prescription and transcription errors in a neonatal unit. An Pediatr (Barc) 2006;64(4):330-5.  Back to cited text no. 11
12.Taylor BL, Selbst SM, Shah AE. Prescription writing errors in the pediatric emergency department. Pediatric Emergency Care 2005;21(12):822-7.  Back to cited text no. 12
13.Patel V, Vaidya R, Naik D, Borker P. Irrational drug use in India: a prescription survey from Goa. Journal of Postgraduate Medicine 2005;51(1):9-12.  Back to cited text no. 13
14.Passarelli MC, Jacob-Filho W, Figueras A. Adverse drug reactions in an elderly hospitalised population: inappropriate prescription is a leading cause. Drugs & Aging 2005;22(9):767-77.  Back to cited text no. 14
15.McGavock H. Prescription-related illness-a scandalous pandemic. Journal of Evaluation in Clinical Practice 2004;10(4):491-7.  Back to cited text no. 15
16.Francois P, Chirpaz E, Bontemps H, Labarere J, Bosson JL, Calop J. Evaluation of prescription-writing quality in a French university hospital. Clinical Performance and Quality Health Care 1997;5(3):111-5.  Back to cited text no. 16
17.Neville RG, Robertson F, Livingstone S, Crombie IK. A classification of prescription errors. The Journal of the Royal College of General Practitioners 1989;39(320):110-2.  Back to cited text no. 17
18.Mahfouz AA, Shehata AI, Mandil AM, Al-Erian RA, Al-Khuzayem AA, Kisha A. Prescribing patterns at primary health care level in the Asir region, Saudi Arabia: an epidemiologic study. Pharmacoepidemiol Drug Saf 1997;6(3):197-201.  Back to cited text no. 18
19.Schwartz R, Soumerai S, Avron J. Physician motivations for nonscientific drug prescribing. Social Science & Medicine 1982;28:577-82   Back to cited text no. 19
20.Lauritsen J, Bruus M. EpiData (version 3). A comprehensive tool for validated entry and documentation of data. The EpiData Association, Odense Denmark; 2003-2004.  Back to cited text no. 20
21.StataCorp. Stata Statistical Software:. 8.2 ed. College Station. TX: Stata Corporation; 2005.  Back to cited text no. 21
22.Dean A, Dean J, Coulombier D, Brendel K, Smith D, Burton A, et al. Epi Info, Version 6: a word processing, database, and statistics program for epidemiology on IBM microcomputers. Center for Disease Control and Prevention, Atlanta, Georgia, USA; 1995.  Back to cited text no. 22
23.Ihaka R, Gentleman R. R: A language for data analysis and graphics. J Cmp Gr St 1996;5:299-314.  Back to cited text no. 23
24.Irshaid YM, Al Homrany M, Hamdi AA, Adjepon-Yamoah KK, Mahfouz AA. Compliance with good practice in prescription writing at outpatient clinics in Saudi Arabia. Eastern Mediterranean Health Journal 2005;11(5-6):922-8.  Back to cited text no. 24
25.Irshaid YM, Al-Homrany MA, Hamdi AA, Adjepon-Yamoah KK, Mahfouz AA. A pharmacoepidemiological study of prescription pattern in outpatient clinics in Southwestern Saudi Arabia. Saudi Medical Journal 2004;25(12):1864-70.  Back to cited text no. 25
26.Kozer E, Scolnik D, MacPherson A, Rauchwerger D, Koren G. Using a preprinted order sheet to reduce prescription errors in a pediatric emergency department: a randomized, controlled trial. Pediatrics 2005;116(6):1299-302.  Back to cited text no. 26
27.Ollivier V, Thelcide C, Simon C, Favier M. Standardized order form for investigational drugs: effect on completeness of the prescription. Pharm World Sci 2004;26(3):178-9.  Back to cited text no. 27
28.Kennedy AG, Littenberg B. A modified outpatient prescription form to reduce prescription errors. Joint Commission Journal on Quality and Safety 2004;30(9):480-7.  Back to cited text no. 28
29.Perez Rodriguez MT, Crusat Sabate D, Ibanez Pardos JL, Jimenez Villa J. Impact of an informative feedback process on drug prescription. Atencion primaria / Sociedad Espanola de Medicina de Familia y Comunitaria 1996;18(7):386-9.  Back to cited text no. 29
30.Voeffray M, Pannatier A, Stupp R, Fucina N, Leyvraz S, Wasserfallen JB. Effect of computerisation on the quality and safety of chemotherapy prescription. Qual Saf Health Care 2006;15(6):418-21.  Back to cited text no. 30
31.Vuelta Arce M, Calabuig Munoz M, Jornet Montana S, Canadell Vilarrasa L, Riera Sendra G, Chumillas Chevalier E, et al. Assessing quality in the process of using hazardous drugs-prescription and preparation. Farm Hosp 2005;29(2):119-225.  Back to cited text no. 31
32.Hureau J, Simard M, Cabrit R, Bernard PF. An experience of the prescription process. Bulletin de l'Academie Nationale de Medicine 2004;188(1):125-37.  Back to cited text no. 32
33.Ridinger MH. The electronic prescription conundrum: why "e-Rx" isn't so "e-Z". Clinical Pharmacology and Therapeutics 2007;81(1):13-5.  Back to cited text no. 33
34.Crane J, Crane FG. Preventing medication errors in hospitals through a systems approach and technological innovation: a prescription for 2010. Hospital Topics 2006;84(4):3-8.  Back to cited text no. 34
35.Colpaert K, Claus B, Somers A, Vandewoude K, Robays H, Decruyenaere J. Impact of computerized physician order entry on medication prescription errors in the intensive care unit: a controlled cross-sectional trial. Critical care (London, England) 2006;10(1):R21.  Back to cited text no. 35
36.Oliven A, Michalake I, Zalman D, Dorman E, Yeshurun D, Odeh M. Prevention of prescription errors by computerized, on-line surveillance of drug order entry. International Journal of Medical Informatics 2005;74(5):377-86.  Back to cited text no. 36
37.Mirco A, Campos L, Falcao F, Nunes JS, Aleixo A. Medication errors in an internal medicine department. Evaluation of a computerized prescription system. Pharm World Sci 2005;27(4):351-2.  Back to cited text no. 37
38.Kushniruk AW, Triola MM, Borycki EM, Stein B, Kannry JL. Technology induced error and usability: the relationship between usability problems and prescription errors when using a handheld application. International Journal of Medical Informatics 2005;74(7-8):519-26.  Back to cited text no. 38
39.Ballentine AJ, Kinnaird D, Wilson JP. Prescription errors occur despite computerized prescriber order entry. Am J Health Syst Pharm 2003;60(7):708-9.  Back to cited text no. 39
40.Dean B. Learning from prescribing errors. Qual Saf Health Care 2002;11:258-60.  Back to cited text no. 40
41.Hritz R, Everly J, Care S. Mediation error identification is a key to prevention: a performance improvement approach. J Health Qual 2002;24(2):10-7.  Back to cited text no. 41
42.Kralewski JE, Dowd BE, Heaton A, Kaissi A. The influence of the structure and culture of medical group practices on prescription drug errors. Medical Care 2005;43(8):817-25.  Back to cited text no. 42
43.Westein MP, Herings RM, Leufkens HG. Determinants of pharmacists' interventions linked to prescription processing. Pharm World Sci 2001;23(3):98-101.  Back to cited text no. 43


  [Table 1], [Table 2], [Table 3], [Table 4]


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