Handwritten Prescriptions Research Articles

An online cursive handwritten medical words recognition system for busy doctors in developing countries for ensuring efficient healthcare service delivery

Doctors in developing countries are too busy to write digital prescriptions. Ninety-seven percent of Bangladeshi doctors write handwritten prescriptions, the majority of which lack legibility. Prescriptions are harder to read as they contain multiple languages. This paper proposes a machine learning approach to recognize doctors’ handwriting to create digital prescriptions. A ‘Handwritten Medical Term Corpus’ dataset is developed containing 17,431 samples of 480 medical terms. In order to improve the recognition efficiency, this paper introduces a data augmentation technique to widen the variety and increase the sample size. A sequence of line data is extracted from the augmented images of 1,591,100 samples and fed to a Bidirectional Long Short-Term Memory (LSTM) network. Data augmentation includes pattern Rotating, Shifting, and Stretching (RSS). Eight different combinations are applied to evaluate the strength of the proposed method. The result shows 93.0% average accuracy (max: 94.5%, min: 92.1%) using Bidirectional LSTM and RSS data augmentation. This accuracy is 19.6% higher than the recognition result with no data expansion. The proposed handwritten recognition technology can be installed in a smartpen for busy doctors which will recognize the writings and digitize them in real-time. It is expected that the smartpen will contribute to reduce medical errors, save medical costs and ensure healthy living in developing countries.

Electronic Health Record Monitoring System and Data Security Using Blockchain Technology

Bangladesh should have owned a decentralized medical record server. We face a lot of issues, such as doctor’s appointments, report organization in one spot, and report follow-ups. People now bring a large number of papers to the doctor’s chamber. They carry prescriptions, reports, and X-ray files, among other things. It complicates everyone’s life as a result. All of the reports must be reviewed by doctors on a regular basis. It is difficult to read old reports on a regular basis, and patients do not receive the correct medications or treatment. Doctors also find it extremely difficult to comprehend handwritten prescriptions. Data security, authenticity, time management, and other areas of data administration are dramatically improved when blockchain (smart contract) technology is linked with standard database management solutions. Blockchain is a groundbreaking, decentralized technology that protects data from unauthorized access. After smart contracts are implemented, the management will be satisfied with the patients. As a result, maintaining data privacy and accountability in the system is tough. It signifies that the information is only accessible to those who have been authenticated. This study focuses on limiting third-party engagement in medical health data and improving data security. Throughout the process, this will improve accessibility and time efficiency. People will feel safer during the payment procedure, which is the most significant benefit. A smart contract and a peer-to-peer encrypted technology were used. The hacker will not be able to gain access to this system since this document uses an immutable ledger. They will not be able to change any of the data if they gain access to the system. If the items are found to be defective, the transaction will be halted. Transaction security will be a viable option for recasting these problems using cryptographic methodologies. We developed a website where patients and doctors will both benefit because of the use of blockchain technology to ensure the security of medical data. We have different profiles for doctors and patients. In the patient profile, they can create their own account by using a unique address, name, and age. This unique address will be created from the genesis block. The unique address is completely private to the owner, who will remain fully secure in our network. After creating an account, the patient can view the doctors’ list and they can upload their medical reports such as prescriptions and X-rays. All the records uploaded by the patient will be stored on our local server (Ganache). The records are stored as hashed strings of the data. Those files will also have a unique address, and it will be shown in the patient profile. After granting access, the doctors will be able to view their records in the respective doctor’s profile. For accessing the options such as uploading, viewing, or editing the data, Ethereum currency (a fee) will have to be paid in order to complete the request. On the other hand, doctors can enter their profile using their name and unique address. After logging in, they can view their name, unique address, and the list of patients that have granted access to the doctor to view their files. On our website, the front end is handled by JavaScript, ReactJS, HTML, and CSS. The backend is handled by Solidity. Storage is handled by Ganache as the local host. Finally, this paper will show how to ensure that the procedure is as safe as feasible. We are also maintaining transparency and efficiency here.

Study of prescribing errors of two different prescription systems: pre-printed prescription from historical medication and hand-written prescription

Study of prescribing errors of two different prescription systems: pre-printed prescription from historical medication and hand-written prescription

Pharmacist’s perception of the impact of electronic prescribing on medication errors and productivity in community pharmacies

Paper-based prescriptions have been used for several decades by many healthcare practitioners. The literature suggests that several challenges are associated with handwritten prescriptions that might impact patients’ safety and medication errors. Electronic prescribing (e-prescribing) has been developed to phase out handwritten and computer-generated prescriptions that are printed on paper or faxed directly to a dispensing pharmacy. This research aimed to examine pharmacists’ thoughts about the e-prescribing impact on their practice. We also evaluated the adoption rate of e-prescribing by assessing the proportion of electronic prescriptions (e-Rx) received in community pharmacies across the Canadian provinces. This research was conducted as a secondary analysis of the 2016 National Survey of Community-Based Pharmacists: Use of Digital Health Technology in Practice by Nielson. The survey was conducted in collaboration between Canada Health Infoway and the Canadian Pharmacy Association. The target population of the survey was Canadian pharmacists who were in community practice. The provinces included in this research were Ontario, Quebec, Saskatchewan, Alberta, and British Columbia (n = 450). The findings of this study suggest that community pharmacists in Canada were willing to embrace e-prescribing to support their practice. Most of pharmacists thought that e-prescribing was a useful tool to reduce medication errors and improve efficiency in pharmacies. However, the largest proportion of prescriptions issued by prescribers continue to be in paper form, whether handwritten or computer-generated. Further research is needed to investigate the barriers to the adoption of e-prescribing systems among primary care practitioners in Canada.

E-prescription in Poland - a preliminary report

Aim. Aim of the paper: to introduce the subject of e-prescription in Poland. E-prescription is a digital version of the previously used standard paper prescription. In Poland,professional arrangements were made, thanks to which it became possible to promotee-prescriptions in pharmacies and doctors’ surgeries throughout the country.Methods. The authors analyse how e-prescription is introduced in Poland. We appliedcomparative analysis (of eprescining in the European Union countries) , literature review(what are legal and technical constraints) and case study (how it was introduced in Poland).We showed the implementation of the e-Health (P1) system in Poland and describedthe use of the free application of the Ministry of Health. We presented preparations for theimplementation of digital health services by the Center for Health Information Systems(CSIOZ).Results and conclusion. For years in Poland, handwritten prescriptions used to be thepreferred method of communication for doctors when making decisions about therapywith medications and for pharmacists to distribute them. Nonetheless, over the last decade,interest in the subject of e-prescription, alongside other e-health solutions for processinghealth-related data, has increased. E-prescription is fi lled on the basis of a four-digit code,which we receive by text message sent to a given phone number or by email to an indicatedaddress. Alternatively, there is a possibility to obtain an information printout, dependingon the confi guration of our Patient Account. An important change introduced is that wedo not have to physically carry the printed prescription with us anymore. E-prescriptionprovides benefi ts for doctors, patients and pharmacists, such as: convenience, time saving,greater safety of the therapy, less risk of error, less bureaucracy. The main objectives of thee-prescribing system involve facilitation of the process of prescriptions delivery, reductionof errors, time optimisation for doctors and pharmacists and eliminating the problem ofillegible and fake prescriptions, which have so far been a common occurrence.Cognitive value. This article shows the process of introducing E-prescription in Poland.

VOICE BASED PRESCRIPTION USING MACHINE LEARNING

Medicine side effects are the major problem in the world, due to wrong prescriptions thousands of people die every year. Most of these mistakes are due to illegible handwriting which leads to taking the wrong medicine or dosage. To solve this issue, a voice-based prescription came into the picture where the prescription is taken as voice input, and a pdf file is generated which is then emailed to the patient. This method can save wealth and life throughout the world, particularly in developing countries where the prescriptions are generally paper-based. The system proposed in this paper is for those doctors and hospitals that are still using a paper-based handwritten prescription. Keywords: Healthcare, Voice-based, Python, Natural Language Processing (NLP), Electronic Prescription, Text Processing, Electronic Health Record (EHR).

Veterinary prescription errors in a community pharmacy setting: A retrospective review

BackgroundCommunity pharmacies are poised to see more veterinary prescriptions as a result of increased pet ownership especially during the coronavirus disease 19 pandemic. Concern has been raised about the lack of veterinary pharmacy training that community pharmacists receive, but no studies have evaluated the actual prevalence of errors in veterinary prescriptions including the prevalence of prescription writing errors. ObjectivesThis study identifies the prevalence of errors in veterinary prescriptions at independent community pharmacies. MethodsAn electronic form was used to ensure required information was pulled from the pharmacy software systems in a consistent manner. Information was pulled from the hard copy image and the prescription label corresponding to that fill. Prescribing trends, such as species and errors, were assessed using descriptive statistics for the overall sample. Error comparisons between written and verbal prescriptions and between weight-based and nonweight-based prescriptions were assessed using chi-square and Fisher exact tests. ResultsWeight, although not legally required but clinically necessary for evaluation of veterinary prescriptions, was omitted from 97.8% of prescriptions. When evaluating the prevalence of errors between handwritten and verbal prescriptions, it was more likely to see errors in prescriptions handwritten by the veterinarian (105 of 119; 88%) than verbal prescriptions (257 of 389; 66%). Conversely, handwritten prescriptions were less likely to omit the required Drug Enforcement Agency number on controlled substance prescriptions. ConclusionBased on the number of errors seen in both handwritten and verbal prescriptions, emphasis should be placed on training pharmacists to be competent in clinically evaluating veterinary prescriptions and training veterinarians on handwriting prescriptions to include both legally and clinically required information needed before dispensing.

Comparison of Dentist Preference towards Software Based Versus Handwritten Prescriptions in Pediatric Dental Patients- A Mixed Methods Study

Introduction: Prescriptions for children must be with accurate drug dosages and clear instructions. A digital tool to write, save and send prescriptions can potentially overcome the limitations of hand-written prescriptions. A preliminary mixed methods study comparing a novel ‘app-based’ and handwritten prescriptions in pediatric dental settings is reported.
 Methodology: Eighteen post-graduate students from departments of pediatric dentistry of two dental schools participated in this mix-methods crossover study. An android app prototype PREASY was indigenously developed to make digital prescriptions. The handwritten and app-based prescriptions were compared in terms of time taken (seconds), precision (accuracy, legibility and clarity) and satisfaction (Likert rating). The mean time taken was compared using t-test, accuracy was compared using ANOVA and satisfaction using a Chi squared test. Qualitative feedback was obtained regarding the PREASY app.
 Results: The mean time taken for handwritten prescriptions {154.28(+ 54.06)seconds} was almost three times higher than that for the app based prescriptions (54.22 (+ 25.66)seconds} [Student ‘t’ test P < .00001]. The mean differences for accuracy, legibility and clarity were 1.36 + 0.66, 1.39 + 0.44 1.11 + 0.50, respectively and were statistically non-significant (P=.247). A majority of participants were very satisfied or satisfied (94.45%); and the remaining (5.55%), were neutral, dissatisfied or very dissatisfied, [Chi square test, P <.00001]. Qualitative analysis identified important themes that could be segregated in four domains from feedback obtained through open-ended questions and semi-structured group discussion.
 Conclusion: App-based prescriptions were quicker and more accurate than the handwritten ones with the majority of participants satisfied. Valuable comments pertaining to limitations of the tool and improvement were identified.

Use of Primary Care Data in Research and Pharmacovigilance: Eight Scenarios Where Prescription Data are Absent.

The use of primary care databases has been integral in pharmacoepidemiological studies and pharmacovigilance. Primary care databases derive from electronic health records and offer a comprehensive description of aggregate patient data, from demography to medication history, and good sample sizes. Studies using these databases improve our understanding of prescribing characteristics and associated risk factors to facilitate better patient care, but there are limitations. We describe eight key scenarios where study data outcomes can be affected by absent prescriptions in UK primary care databases: (1) out-of-hours, urgent care and acute care prescriptions; (2) specialist-only prescriptions; (3) alternative community prescribing, such as pharmacy, family planning clinic or sexual health clinic medication prescriptions; (4) newly licensed medication prescriptions; (5) medications that do not require prescriptions; (6) hospital inpatient and outpatient prescriptions; (7) handwritten prescriptions; and (8) private pharmacy and private doctor prescriptions. The significance of each scenario is dependent on the type of medication under investigation, nature of the study and expected outcome measures. We recommend that all researchers using primary care databases be aware of the potential for missing prescribing data and be sensitive to how this can vary substantially between items, drug classes, patient groups and over time. Close liaison with practising primary care clinicians in the UK is often essential to ensure awareness of nuances in clinical practice.

Algorithms for rapid digitalization of prescriptions

Prescription data are invaluable for healthcare research and intelligence, yet, extraction of these data is challenging as this information is intertwined in the unstructured and non-grammatical text in prescription images. Moreover, text extraction from images in itself is hard, particularly for handwritten text. While piecemeal solutions exist, they are either limited to a small set of entities of interest or have very low accuracy and are not scalable. In this paper, we present two algorithms: the C-Cube algorithm for digitization of computer-printed prescriptions and the 3-Step Filtering algorithm for handwritten prescriptions. While a brute-force approach would match every word that is received from an optical character reader (OCR) with all possible entries in the database, this approach is inefficient and imprecise. The premise of our algorithms is an application of pattern intelligence to select a much smaller set of words (from the words returned by the OCR) as potential entities of interest. We rigorously tested the two algorithms on a corpus of more than 10,000 prescriptions’ images, taking the brute-force technique as the baseline methodology. Regarding latencies, we found that the C-Cube and the 3-Step Filtering algorithms were 588 and 231 times faster than the brute-force approach. In terms of accuracies, we found that the F-score of the C-cube algorithm was 90% higher than the F-score of the brute-force approach whereas the F-score for the 3-Step filtering algorithm was found to be 8,600% higher. The algorithms are decidedly faster and more accurate than the brute-force approach. These attributes make them suitable for implementation in real-time environments as well as for use in batch-mode for various applications. We expect the algorithms to play a significant role in the digitalization of healthcare information and briefly discuss a few applications.

Impact of Electronic Prescription on Prescribing Errors

Aims: There are series of medical errors that can be prevented by taking precautions. Therefore, the study evaluates the impact of the electronic prescribing system on prescription errors.
 Study Design: A pre-post study design was conducted.
 Place and Duration of Study: The study was conducted at outpatient pharmacy services of a teaching hospital in Jeddah city.
 Methodology: Prescriptions were evaluated for the presence of the essential prescription elements such as patient information, drug name, dose, frequency, strength, and other prescription completeness parameters.
 Results: In the pre-intervention study, 1182 handwritten prescriptions were evaluated, and 6627 errors were detected from these prescriptions. The length of the pre-and post-intervention period was two weeks each. The most prevalent prescribing errors were that of medications written without defined dosage forms were recorded 1653 (55.90%) time followed by prescriptions written by trade names 1493 (22.5%), without route of administration 1266 (19.1%), and without specified duration 1009 (15.2%). However, 1512 prescriptions were evaluated in the post-intervention study, among which 339 errors were detected. The errors included prescriptions written without diagnosis (5.09%), or without doctor’s name or stamp (1.52%), written by trade names (4.49%), without defined dosage forms (4.29%), and without specified duration (2.84%).
 Conclusion: The study concluded that E-prescribing eliminated prescription errors that resulted from handwritten prescriptions.

Was William Harvey's commitment to experimentation reflected in his clinical practice?

The physician and physiologist Dr William Harvey is known for having discovered that the heart pumps arterial blood round the whole body and receives venous blood from the periphery, which it forwards to the lungs for reoxygenation. Harvey's discovery was based on anatomical and physiological evidence and experiments using ligatures of varying tensions. As a clinician, however, Harvey does not appear to have appreciated the value of experiments in assessing treatment effects. Although he criticised Galenic views about the clinical value of experience and authority in the absence of accompanying empirical evidence, two handwritten prescriptions that he wrote for his friend and future biographer John Aubrey provide evidence that he conformed with Galenic theory when it came to drug therapy in clinical practice. This was consistent with his senior position in the College of Physicians, whose Pharmacopoeia Londinensis was based on Galenic principles, an appreciation of which was required for entry into the College. Harvey's prescriptions reflect this and open a window onto 17th-century therapeutic practice and the personal elements on which such practice was sometimes based.

Analysis of degree of errors in handwritten medication prescriptions in Rafha, Saudi Arabia

Purpose: To assess the prevalence of handwritten prescription errors in Rafha Central Hospital in Saudi Arabia, and to determine the most predominant type of prescription error.
 Methods: A cross-sectional study was carried out on randomly selected samples of hand-written prescriptions in out-patient and in-patient pharmacies of Rafha Central Hospital over a five-month period (October 2016 to February 2017). A data collection sheet specially designed for this purpose was used to collect relevant information. The collected prescriptions were analyzed for the presence of prescription errors based on prescription parameters defined by the World Health Organization (WHO) and current guidelines published in British National Formulary (BNF). Descriptive statistics and Microsoft Office were used for processing and analyzing the data collected.
 Results: Overall, 1019 prescription errors were identified. More than half of the total errors (610; 60 %) were associated with missing patient's information. Moreover, the parameters related to drug and prescriber information were absent in 204 (20 %) and 5 (0.4 %) prescriptions, respectively. In addition, 200 (19 %) miscellaneous errors related to date, legible handwriting and directions for patients were identified.
 Conclusion: This study discovered errors in hand-written prescriptions. A majority of the prescriptions did not adhere to accepted guidelines. The most common errors are absence of generic names of drugs, non-indication of duration of therapy or prescriber’s contact address, and absence of patient’s weight. Moreover, illegible handwriting was obvious in a substantial number of prescriptions.

Evaluation of the quality of 'do not use' medication abbreviation audits: a key enabler to successful implementation of audit and feedback.

Background Hospital pharmacy audit and feedback of 'do not use' medication abbreviations improves patient safety. For audit and feedback systems to be effective, the data captured must be of high quality such that end-users trust the information to guide practice change. The quality of data captured during monthly standardized pharmacy 'do not use' abbreviation audits is currently unknown. Objective We aimed to assess pharmacy 'do not use' abbreviation audit data quality. Method Primary audit data quality was assessed by examining a random sample of handwritten medication prescriptions for the presence and type of 'do not use' abbreviations. This data was compared with the pharmacy monthly audit data to determine data capture agreement and consistency over time. Results There were 1132 prescriptions from July, October, and December 2019 included. Data capture agreement between the pharmacy audit and the secondary assessment using Cohen's Kappa ranged from 0.53 to 0.63. The primary audit under-reported 'do not use' abbreviation rates, however this did not vary over time (χ2 = 1.215, p = 0.545). Conclusion Pharmacy staff audits under-reported 'do not use' abbreviation rates, however this was consistent over time. The quality of pharmacy audits should be assessed and disseminated to end-users prior to implementing feedback.

Audit of outpatient prescription at a tertiary care hospital in South India: An observational study

Background: Prescriptions are medico-legal documents and it is the duty of every physician to write a complete, legible, and valid prescription to ensure rational drug use and prevent the occurrence of medication errors. Periodic prescription audit is required for identification of common deficiencies in the prescriptions, to undertake corrective measures for improving the prescribing practices. Aim and Objective: The objective of the study was to assess the completeness of the outpatient hand-written prescriptions presented to the pharmacy of a multi-specialty tertiary care teaching hospital in South India. Materials and Methods: This was a prospective, descriptive cross-sectional study in which data from the outpatient prescriptions presented to the pharmacy for drug dispensing were recorded. The prescriptions were analyzed for completeness by assessing the presence or absence of the following essential components: (a) Date; (b) patient information (hospital registration number, name, age, gender, and address); (c) prescriber information (Name, Registration number, Signature); and (d) medication details (use of generic names, appropriateness of drugs, prescribing from national list of essential medicines [NLEM] 2015, capitalized drug names, dose, dosing schedule, route of administration, and instructions). Results: Of the total 600 prescriptions analyzed, all of them had date and complete patient details. Doctor’s name, registration number, and signature were mentioned in 70.8%, 38%, and 98.3% of prescriptions, respectively. About 30.3% and 26.8% of prescriptions had generic names and capitalized drug names, respectively. Only 2% of prescriptions were not legible. All the drugs prescribed were appropriate and from NLEM 2015. Dose and dosage schedule were clearly mentioned, each in 98% of prescriptions. Route of drug administration and instructions was present in 98.8% and 10% of prescriptions, respectively. Conclusion: This study showed that all the essential elements except doctors’ registration number, generic prescribing, drug names in capitals, and instructions to patient were present in majority of the prescriptions.

Speech Recognition Based Prescription Generator

Lots of cases have been recorded that are caused by misinterpretation of illegible handwritten prescriptions. In this paper, we propose a mobile device-based speech recognition approach to help doctors utter prescription and get the prescription in tabularized format rather than writing it manually. We start with data collection for the task at hand, which is a Natural Language Processing task. This is followed by its preprocessing and further, we use the Stack-Propagation approach to address the joint task of intent detection and slot filling. After refining our dataset and model parameters as well as the training parameters we finally achieve acceptable results. The scope of this paper is discussed, and future improvement plans are laid.

Text Formatting Based on Keyword Detection

Adverse drug effects are a major cause of death across the world each year because of prescription errors. Many of such errors involve the administration of the wrong drug or dosage by care givers to patients due to indecipherable handwritings, drug interactions, confusing drug names etc. The adoption of voice-based prescription project could eliminate some of these errors because they allow prescription information to be captured and heard through voice response rather than in the physician’s handwriting. Our project will generate an electronic prescription using a “Speech to Text converter” (Perceptual Linear Prediction (PLP)) and capture the data from the keywords spoken by doctor(s). There won’t be any need to carry paper prescriptions on revisiting doctors. A patient will be able to share his historic medical records to a new doctor. This project also provide facility to sign the prescription and send to the patient directly on his phone and email id. The System enables the patient to manage the privacy of their personal health record. This project is proposed to target those doctors and clinics that are still using paper-based handwritten prescriptions

Incidence and Variables Associated With Inconsistencies in Opioid Prescribing at Hospital Discharge and Its Associated Adverse Drug Outcomes

ObjectivesOpioid-related medication errors (MEs) can have a significant impact on patient health and contribute to opioid misuse. The objective of this study was to estimate the incidence of and variables associated with the receipt of an opioid prescription and opioid-related MEs (omissions, duplications, or dose changes) at hospital discharge. We also determined rates of adverse drug events and risks of emergency department visits, readmissions, or death 30 days and 90 days post discharge associated with MEs. MethodsA cohort of hospitalized patients discharged from the McGill University Health Centre between 2014 and 2016 was assembled. The impact of opioid-related MEs was assessed in a propensity score–adjusted logistic regression models. Multivariable logistic regression was used to determine characteristics associated with MEs and discharge opioid prescription. ResultsA total of 1530 (43.9%) of 3486 patients were prescribed opioids, of which 13.4% (n = 205) of patients had at least 1 opioid-related ME. Rates of MEs were higher in handwritten prescriptions compared to the electronic reconciliation discharge prescription group (20.6% vs 1.2%). Computer-based prescriptions were associated with a 69% lower risk of opioid-related MEs (adjusted odds ratio: 0.31, 95% confidence interval: 0.14-0.65) as well as 63% lower risk of receiving an opioid prescription. Opioid-related MEs were associated with a 2.3 times increased risk of healthcare utilization in the 30 days postdischarge period (adjusted odds ratio: 2.32, 95% confidence interval: 1.24-4.32). ConclusionsOpioid-related MEs are common in handwritten discharge prescriptions. Our findings highlight the need for computer-based prescribing platforms and careful review of medications during critical periods of care such as hospital transitions.

A randomised trial of pharmacist-led discharge prescribing in an Australian geriatric evaluation and management service.

Background Prescribing discharge medications is a potential "next step" for pharmacists in Australian hospitals, however, safety must be demonstrated via a randomised controlled study. Objective To determine if a collaborative, pharmacist led discharge prescribing model results in less patients with medication errors than conventional prescribing for both handwritten and digital prescriptions. Setting Geriatric Medical ward in a quaternary hospital, Australia Methods A prospective, single-blinded randomised controlled study of patients randomised to conventional (control) or a pharmacist-led prescribing (intervention) arms at discharge from hospital. This study had 2 phases; (1) handwritten prescribing and (2) digital prescribing. In addition, the two prescribing methods were compared. Main outcome measures The primary outcome was the percentage of patients with a medication error on their discharge prescription. Results In phase 1, 45 patients were recruited; 21 (control) and 24 (intervention). 95% of control patients and 29% in the intervention arm had at least one medication error, p < 0.0002, relative risk (RR) 0.31, confidence interval (CI) 0.16-0.58. The number of items with at least 1 error reduced from 69 to 4%; p < 0.0001, RR 0.06, CI 0.03-0.11 and fewer items had at least 1 clinically significant error (11% vs 2%, p = 0.0004, RR 0.15, CI 0.04-0.30). In phase 2, 39 patients were recruited; 18 (control) and 21 (intervention). 100% of control patients and 62% in the intervention arm had at least one medication error (p = 0.005, RR 0.62, CI 0.44-0.87). Items with at least 1 error decreased from 21 to 7% (p < 0.0001, RR 0.34, CI 0.44-0.56), there were fewer items with at least 1 clinically significant error (13% vs 5%, p < 0.003, RR 0.4, CI 0.22-0.72). There was no significant change in the primary outcome between handwritten and digital (60% vs 79%, p < 0.055). Conclusion In a geriatric setting, pharmacist-led partnered discharge prescribing results in significantly less patients with medication errors than the conventional method for both handwritten and digital methods.

English

Prescribing errors are common. Available data on these errors, particularly in family community centers in Saudi Arabia, presents a problem that must be addressed. The aim of this study is to investigate the rate and types of prescribing errors at an ambulatory care setting in Riyadh, Saudi Arabia. Data were collected to cover a 12-month period. A prospective review of hand-written prescriptions was made by a trained pharmacist to identify any potential errors therein. The main outcome measure was quantifiable rate and types of prescribing errors. A total of 177,406 prescriptions were scanned wherein 481 (0.27%) prescriptions consisted of at least one error for a total of 510 (0.09%) prescribing errors during the 12-month period of the study. The most common errors were: (1) inappropriate dosage and (2) inappropriate treatment. The uncommon errors consisted of: (1) incorrect drug, (2) incorrect strength, and (3) incorrect duration. All errors were corrected before the prescription reached the corresponding patient. Most errors were made during early morning hours. Lastly, the frequency of errors was highest with prescriptions relating to antidiabetics. Although the rate of prescribing errors in the subject family community center was not frequent, continuous education and monitoring is needed to limit such errors. Key words: Prescribing error, rate, family community, primary care, Riyadh &nbsp