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Abstract

Background

The COVID-19 pandemic has affected healthcare systems globally. Various health care technologies have been used to mitigate the risk of disease transmission. Telemedicine is one such technology, and remote consulting and prescribing comprise one of its key aspects. In Saudi Arabia, telephone health services have been widely used through the free Medical Consultation Call Center (937). This platform facilitates medical consultations for all citizens, residents, and visitors. After consultations, healthcare providers are able to issue authenticated e-prescriptions using the Anat platform.

Objectives

To explore the utilization of the Anat remote prescription system in Saudi Arabia during the COVID-19 pandemic and to identify the factors associated with antibiotic prescription and primary medication adherence.

Methods

This retrospective analysis included data from the Anat e‑prescription system using a stratified random sample of 25000 prescriptions issued in Saudi Arabia in 2020. Predictive factors related to the patients, practitioners, and prescriptions were identified through bivariate and multivariate logistic regression analyses.

Results

Out of 25,000 e-prescriptions, 8885 were dispensed, resulting in a 35.5% primary medication adherence rate. The significant predictors of primary adherence were children, respiratory diseases, and antibacterial drugs. In addition, antibiotics made up 32.1% of the e-prescriptions. The prescription of antibiotics was significantly associated with male sex, children, genitourinary system diseases, and being treated by radiologists.

Conclusions

Almost two thirds 62.2% of e-prescriptions were undispensed, with antibiotic eprescriptions at 32.1%. Findings emphasize the need to enhance primary medication adherence and antibiotic prescription interventions. These findings could aid decision-makers in improving patient-centered e-prescribing practices.

Keywords

Electronic prescribing e-prescriptions primary adherence antibiotic prescribing COVID-19 Saudi Arabia

Introduction

The 2019 Coronavirus pandemic outbreak has increased the burden on healthcare systems around the world.^1,2 Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a novel Coronavirus (SARS-CoV-2).^3,4 Currently, COVID-19 is considered the leading cause of death worldwide, according to the WHO.⁵ In 2021, the Kingdom of Saudi Arabia (KSA) ranked 42nd out of 221 countries in the number of COVID-19 cases, with a prevalence of ∼392,000 confirmed cases and 6690 deaths.⁵ The first wave of COVID-19 started in Saudi Arabia in March 2020.⁶ In response, the government has taken various measures to contain the spread of the virus, including closing borders, suspending international flights, and implementing strict protocols for public places. The country also scaled up its healthcare infrastructure to ensure adequate treatment and care for COVID-19 patients.^4,7,8 Due to the high number of Coronavirus cases and deaths, healthcare delivery methods have changed, as have the ways patients communicate with their caregivers^.^9,10

A variety of health technologies have been used to mitigate the risk of pandemic transmission, and telemedicine is one such technology.¹¹ Telemedicine provides medical services remotely using information technologies. Originally, the term “telemedicine” was used to refer to the process of virtual interaction between the healthcare provider and the patient.^12,13 It has then transformed medical service delivery by introducing various healthcare applications and services to patients.¹⁴ Remote prescribing is one example, where a physician orders medications online in the absence of in-person visits at the clinic.¹⁵ In response to the COVID-19 crisis, most in-person visits have been restricted to reduce the risk of viral transmission.^15,16 Consequently, remote prescription has become standard practice in some countries, such as the United Kingdom,¹⁵ to reduce hospital visits and clinic wait times.^15,17 This telehealth solution is usually provided by telemedicine services, which include patient consultations via telephone, video, or online.¹⁵

Despite the increase in remote prescribing, patients still have concerns about its safety and effectiveness.^18,19 Primary non-adherence to prescribed medications is a challenge in pharmacological treatment.²⁰ Primary adherence occurs when a patient successfully fills the first medication prescription within a specified number of days after the prescription is issued.²¹ In contrast, secondary adherence occurs when the patient successfully refills their prescription within a specified number of days after the dispensing of the first prescription.²¹ The timely initiation of medication is a critical step for long-term success of therapeutic outcomes and disease control.²² As the patient is examined virtually, remote prescribing may lead to unintended consequences, such as overprescribing, especially antibiotics.^19,23,24 An additional limitation is the inability to accurately diagnose the patient's condition due to restricted access to the patient's medical information, particularly during the initial consultation.¹⁵

In Saudi Arabia, the Saudi Ministry of Health (MOH) has introduced telemedicine services to improve the quality of healthcare. These include telephone health services through the Medical Consultation Call Center (937), online medical consultation (Seha Mobile Application), and electronic prescribing through the Anat platform.^25–30 The e- prescription system in Saudi Arabia started to grow significantly due to the government's initiatives to promote digital healthcare services. The country's healthcare system is adopting advanced technologies, including e-prescriptions such as Anat e-prescribing system, to improve patient care and reduce medication errors.^25–31 Anat system is an online platform managed by the Saudi MOH and the Saudi Commission for Health Specialties (SCFHS). It is developed by Lean Business Services, a government-owned company operating under the Saudi MOH, which offers e-services to healthcare providers, including electronic prescriptions.^28–30

Anat system allows physicians to electronically initiate prescriptions for patients after a telephone consultation through 937 call center or an online medical consultation Seha Mobile Application. These e-prescriptions are then sent directly to private pharmacies, where patients can collect them in person for a fee.^28–30 The system was designed to streamline dispensing medications, reduce waste, and ensure medication availability to all patients throughout the Kingdom, particularly during the COVID-19 pandemic.^28–30

Objective

The study aims to explore the utilization of the Anat remote prescription system in Saudi Arabia during the COVID-19 pandemic and to identify the factors associated with antibiotic prescription and primary medication adherence.

Methods

Study design and setting

We conducted a retrospective analysis of remote e-prescriptions issued in Saudi Arabia during the period of January 2020 to December 2020, which coincided with the first wave of the COVID-19 pandemic in the country.⁶ During this period, the only virus in circulation was the novel Coronavirus (SARS-CoV-2). The remote e-prescriptions were obtained through the medical consultation call center (937), which provides free medical consultations to Saudi citizens, residents, and visitors, and dispensed by the electronic prescription system (Anat) platform. Following the consultation, the provider can issue an authenticated e-prescription via the Anat platform, allowing the patient to purchase these medications in person from a private pharmacy where the patient can receive the prescription for a fee.

Ethical approvals were granted by the King Abdullah International Medical Research Center (IRB number H-01-R-005) and The General Department of Research and Studies of the MOH in Saudi Arabia (IRB number H-01-R-009).

Data source

Data were requested from the Saudi MOH and Lean Business Services for e-prescriptions issued in 2020 through the 937 consultation service, which generated a total of 156,417 e-prescriptions. The data analyzed in this study included a stratified random sample of 25,000 e-prescriptions. Stratified random sampling is a method that divides a population into subgroups based on shared characteristics called strata and then selects a random sample from each stratum.³² The sample was stratified by the research office at Lean Business Services, and stratification was done using Alteryx software. Alteryx Analytics Automation Platform is a propriety product to automate data preparation and modeling processes.³³ For this study, stratification, randomization, and sampling tools within Alteryx were used. To ensure the representativeness of the sample, e-prescriptions were first stratified by months. Thus, a sample from each month was extracted based on e-prescriptions distribution from the total e-prescriptions. Second, each stratification was further stratified by patient age group and gender.

Data cleaning

The patient demographic characteristics were categorized as age, gender, nationality, and diagnosis. The patient diagnosis was coded according to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). The chronic diseases were categorized by the researchers as chronic disease and non-chronic disease according to the Centers for Disease Control and Prevention (CDC). Data on the practitioners’ medical specialties were extracted and categorized according to the SCFHS.³⁴

The prescription issue dates were categorized into three stages in line with the precautionary measures taken by the Kingdom of Saudi Arabia surrounding the COVID-19 pandemic. These included the pre-crisis stage (January 1 to March 1), the curfew stage (March 2 to June 20), and the post-curfew stage (June 21 to December 31),.⁶

Each prescription's status was categorized into one of four categories according to the Anat system classification. An e-prescription was considered “dispensed” if the patient obtained the prescription from the pharmacy within 3 days, “expired” if the patient did not obtain the prescription within 3 days of issuing, “updated” if the prescriber changed any of the prescription medication information before dispensing, and “voided” if the provider canceled the prescription before the medication was dispensed.

“Primary adherence” refers to a patient successfully receiving a medication within a specified number of days after the prescription is issued.²¹ In this study, primary medication adherence was defined as when the patient obtaining and filling their first medication prescription from the pharmacy within 3 days of the issue date, as any e-prescribed medication that is not dispensed within 3 days is considered expired by Anat system.

The 460 drugs corresponding to the 13 therapeutic areas were categorized according to the Anatomical Therapeutic Chemical (ATC) classification system.³⁵

Drugs were also categorized by legal status as over-the-counter (OTC) or prescription-only medicines (PoM) following the Saudi Food and Drug Authority (SFDA) classification.³⁶ Additionally, medications were also classified into antibiotics and non-antibiotics using the antibiotic medication ATC code (J01).

Statistical analysis

After the data were cleaned in Microsoft Excel, version 2018 for Windows, the data were analyzed using Statistical Package for the Social Sciences Statistics (SPSS) for Windows, version 28 software.

A descriptive analysis was used to summarize the data. The categorical variables were presented as numbers and percentages to present the characteristics of patients, practitioners, and e-prescriptions. In addition, we used the chi-square test to determine the relationship between primary medication adherence and the antibiotics prescribing and their association with the independent variables.

The logistic regression analysis was performed with two levels: primary adherence and antibiotics prescribing rate, to test the associations between all the variables. Results are reported as adjusted odds ratio (AOR) and 95% confidence interval (CI). A p value < 0.05 was considered statistically significant.

Results

Patient characteristics

A stratified random sample of 25,000 e-prescriptions showed a higher percentage of e-prescribing to female patients than to their male counterparts 58.1% (n = 14,515) and 41.1% (n = 10,304), respectively. The majority (69.9%, n = 17,474) of the patients was Saudis. Only 3.5% (n = 863) e-prescriptions were issued for babies (0–2 years), with 33.5% (n = 8380) middle-aged adults receiving the most e-prescriptions.

The highest proportion of e-prescriptions (19%, n = 4759) was issued for respiratory diseases (ICD-10 code J); the fewest e-prescriptions (0.9%, n = 222) were issued for pregnancy and childbirth (ICD-10 code O). Overall, the most common reasons for e-prescriptions were related to non-chronic diseases (77.4%, n = 19,355) (Table 1).

Table 1.

Characteristics of the sample: Patients.

	Remote Prescriptions
Variable	n	%
Gender
Male	10,304	41.1
Female	14,515	58.1
Missing	181	0.7
Nationality
Saudi	17,474	69.9
Non-Saudi	1576	6.3
Missing	5950	23.8
Age
Babies (0–2)	863	3.5
Children (3–16)	3886	15.5
Young adults (17–30)	7539	30.2
Middle-aged adults (31–45)	8380	33.5
Old adults (+45)	4329	17.3
Missing	3	0.0
Diagnosis
Respiratory system	4759	19
Skin and subcutaneous tissue	3818	15.3
Digestive system	2612	10.4
Genitourinary system	2236	8.9
Infectious and parasitic diseases	2173	8.7
Mental disorders	1992	8.0
Eye diseases	1990	8.0
Injury and poisoning	932	3.7
Ear and mastoid process	889	3.6
Endocrine and metabolic diseases	726	2.9
Musculoskeletal system	701	2.8
Circulatory system	447	1.8
Nervous system	309	1.2
Pregnancy and childbirth	222	0.9
Other^a	1194	4.8
Chronic diseases
Chronic	5645	22.6
Non-chronic	19,355	77.4

Other: Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified, neoplasms, blood and blood-forming organs, perinatal period, congenital malformations, deformations and chromosomal abnormalities, external causes of morbidity, health status and contact with health services, codes for special purposes. Any diagnosis (<100) or un-clear diagnosis was categorized in Other.

Practitioners’ characteristics

The most common prescriber specialties were associated with primary care (family medicine and general medicine). Of the 25,000 e-prescriptions, the highest proportion was prescribed by family medicine practitioners (56%, n = 14,039), followed by general medicine practitioners (19%, n = 4757); the lowest numbers were from cardiology and radiology specialists 0.4% (n = 89) and 0.1% (n = 30), respectively (Table 2).

Table 2.

Characteristics of the sample: Practitioners.

	Remote Prescriptions
Variable	n	%
Specialty
Family medicine	14,039	56
General medicine	4757	19
Psychiatry	1460	5.8
Internal medicine	798	3.2
Surgery	770	3.1
Dermatology	730	2.9
Pediatrics	491	1.9
Ophthalmology	257	1
Obstetrics and gynecology	183	0.7
Dentistry	182	0.7
Neurology	124	0.5
Emergency	115	0.5
Cardiovascular medicine	89	0.4
Radiology	30	0.1
Other^a	263	1.1
MISSING	712	2.8

Other: endocrinology, gastroenterology, geriatrics, hematology, immunology, intensive care medicine, nephrology, oncology, pulmonology, rehabilitation, social service, pharmacy, preventive medicine, nuclear medicine, nursing, medical jurisprudence, medical microbiology, epidemiology, clinical pathology, anatomical pathology, anesthesiology. Any specialty (<100) was categorized in Other.

Among the 25,000 practitioners included in the study, a total of 8421 practitioners (33%) were found to have prescribed anti-infective drugs. The most prescribed anti-infective drug was amoxicillin (ATC code J01CA04), which was included in prescriptions by 4285 practitioners (50%). Notably, 86% (n = 3696) of those prescribing amoxicillin were primary care practitioners. A detailed breakdown of each practitioner’s specialty-specific prescriptions can be found in Supplementary Material 1.

Electronic prescriptions characteristics

The largest proportion of prescriptions (32.4%, n = 8106) comprised anti-infective drugs (ATC group J). The least commonly prescribed drug category (0.3%, n = 68) was antineoplastic and immunomodulating drugs (ATC group L; Table 3).

Table 3.

Characteristics of the sample: E-prescriptions.

	Remote Prescriptions
Variable	n	%
Therapeutic area
Anti-infective	8106	32.4
Sensory organs	3929	15.7
Nervous system	3618	14.5
Dermatological	3156	12.6
Alimentary tract and metabolism	1811	7.2
Respiratory system	1536	6.1
Musculoskeletal system	1104	4.4
Cardiovascular system	596	2.4
Genitourinary system and sex hormones	428	1.7
Blood and blood-forming organs	260	1
Antiparasitic products	196	0.8
Systemic hormonal preparations	192	0.8
Antineoplastic and immunomodulating	68	0.3
Drug class
Antibacterial	8034	32.1
Ophthalmological	3916	15.7
Psycho-analeptics	1524	6.1
Analgesics	1304	5.2
Antifungals	1117	4.5
Anti-inflammatory	1000	4.0
Corticosteroid	947	3.8
Antihistamine	851	3.4
Anti-acne	606	2.4
Gastrointestinal	460	1.8
Acid drugs	457	1.8
Psycholeptic	432	1.7
Obstructive airway	382	1.5
Diabetes drugs	327	1.3
Vitamins	307	1.2
Gynecological drugs	298	1.2
Nasal preparation	252	1.0
Anti-epileptic	244	1.0
Antiviral	238	1.0
Dermatological	209	0.8
Antimycotics	150	0.6
Renin-angiotensin	141	0.6
Anti-anemic drugs	136	0.5
Beta-blockers	136	0.5
Lipid-modifying agents	136	0.5
Stomatological preparations	102	0.4
Other*	1294	5.2
Drug classification
OTC	5300	21.2
PoM	19,700	78.8
Antibiotics group
Antibiotics medication	8034	32.1
Non-antibiotics medications	16,966	67.9
Prescription status
Dispensed	8885	35.5
Expired	15,547	62.2
Voided	353	1.4
Updated	215	0.9

OTC: over-the-counter; PoM: prescription-only medicines.

*Any drug class (<100 drugs) was categorized in Other.

With respect to drug classes, the most commonly prescribed class was antibacterial medications (32%, n = 8034). The least commonly prescribed class (0.408%, n = 102) was stomatological preparations (dental treatments). With respect to drug classifications, nearly 78.8% (n = 19,700) of the 25,000 prescribed medications were for PoMs, whereas 21% (n = 5300) were for OTC prescriptions. In terms of prescription status, expired e-prescriptions made up the largest proportion, at 62.2% (n = 15,547), followed by dispensed e-prescriptions (35.5%, n = 8885) (Table 3).

The monthly rate of e-prescriptions

During the pre-COVID-19 stage (from January 1 to March 1), 2.3% (n = 573/25,000) e-prescriptions were issued in 2020. With the beginning of the curfew stage, the total rate of e-prescriptions was 65.5% (n = 16,377).

Compared to the curfew stage, the rate of e-prescriptions decreased dramatically during the post-curfew stage (32.2%, n = 8050/25,000) (Figure 1).

Figure 1.

Monthly rate of prescriptions in 2020.

During the curfew stage (March 2 to June 20), most of the e-prescriptions (74.3%, n = 12,166/16,377) were issued during the weekdays (Sunday to Thursday), and fewer (25.7%, n = 4211/16,377) were issued during the weekends (Friday and Saturday; Figure 2). In addition, the rate of dispensed medication was higher during the weekdays than on the weekends 59.2% (n = 5260/8886) and 21.4% (n = 1906/8886), respectively.

Figure 2.

Total e-prescriptions over time, by time of week.

The association of primary adherence and antibiotics prescription with the sample characteristics

Chi-squared tests for independent variables were utilized to examine the associations with primary adherence. There were statistically significant associations between primary adherence and the following variables: age, diagnosis, drug class, and time stage (p < 0.001). There was no statistically significant association between primary adherence and gender (24,255; χ21 = 0.772; p > 0.001) (Supplementary Material 2).

Among drug classes, antibiotics were the most frequently prescribed. The prescription of antibiotics was significantly associated with patient age, diagnosis, and practitioner specialty (p < 0.001). In contrast, there was no statistically significant relationship between gender and antibiotic prescription (24,819; χ21 = 2.0; p = 0.152). In addition, time stages were not significantly associated with the prescription of antibiotics (25,000; χ21 = 3.3; p = 0.188) (Supplementary Material 3).

Bivariate analysis

The logistic regression analysis was performed in two stages. First, in the bivariate analysis of primary adherence, all independent variables were significantly associated with primary adherence, with the exception of gender (odds ratio (OR) 1.024; 95% CI 0.971–1.080; p = 0.379; Table 4).

Table 4.

Primary adherence: bivariate and multivariate logistic regression analysis.

	Primary adherence
	Bivariate			Multivariate
Variable	OR	95% CI	p value	AOR	95% CI	p value
Gender
Male	Ref			Ref
Female	0.977	(0.926–1.030)	0.379	0.896	(0.941–1.054)	0.896
Age
Babies (0–2)	1.082	(0.936–1.251)	0.287	1.043	(0.896–1.215)	0.585
Children (3–16)	1.377	(1.274–1.489)	<0.001	1.150	(1.058–1.215)	0.001
Young adults (17–30)	0.857	(0.802–0.916)	<0.001	0.901	(0.840–0.965)	0.003
Middle-aged adults (31–45)	Ref			Ref
Old adults (+45)	0.882	(0.815–0.954)	0.002	1.064	(0.978–1.158)	0.148
Diagnosis
Respiratory system	Ref			Ref
Skin and subcutaneous tissue	0.594	(0.544–0.650)	<0.001	0.754	(0.676–0.841)	<0.001
Digestive system	1.005	(0.912–1.107)	0.926	0.994	(0.893–1.107)	0.914
Genitourinary system	0.753	(0.679–0.835)	<0.001	0.803	(0.715–0.902)	<0.001
Infectious and parasitic diseases	0.685	(0.616–0.761)	<0.001	0.858	(0.759–0.971)	0.015
Mental disorders	0.353	(0.312–0.399)	<0.001	0.713	(0.515–0.986)	0.041
Eye diseases	0.798	(0.717–0.888)	<0.001	0.889	(0.770–1.028)	0.112
Injury and poisoning	0.710	(0.613–0.822)	<0.001	0.759	(0.649–0.887)	<0.001
Ear and mastoid process	1.006	(0.870–1.163)	0.936	0.951	(0.817–1.106)	0.513
Endocrine and metabolic diseases	0.294	(0.241–0.359)	<0.001	0.479	(0.377–0.607)	<0.001
Musculoskeletal system	0.629	(0.532–0.745)	<0.001	0.690	(0.574–0.829)	<0.001
Circulatory system	0.321	(0.251–0.410)	<0.001	0.494	(0.373–0.654)	<0.001
Nervous system	0.293	(0.218–0.394)	<0.001	0.432	(0.307–0.607)	<0.001
Pregnancy and childbirth	0.576	(0.430–0.772)	<0.001	0.727	(0.535–0.988)	0.042
Other	0.614	(0.535–0.704)	<0.001	0.695	(0.601–0.805)	<0.001
Drug class
Antibacterial	Ref			Ref
Ophthalmological	0.783	(0.724–0.848)	<0.001	0.838	(0.752–9.33)	0.001
Psycho-analeptics	0.361	(0.317–0.412)	<0.001	0.566	(0.408–0.786)	<0.001
Analgesics	1.037	(0.920–1.169)	0.551	0.951	(0.839–1.077)	0.427
Antifungals	0.524	(0.456–0.602)	<0.001	0.577	(0.496–0.670)	<0.001
Anti-inflammatory	1.113	(0.975–1.272)	0.114	1.063	(0.923–1.225)	0.396
Corticosteroid	0.621	(0.537–0.718)	<0.001	0.701	(0.599–819)	<0.001
Antihistamine	1.007	(0.872–1.163)	0.922	0.965	(0.831–1.121)	0.639
Anti-acne	0.312	(0.253–0.384)	<0.001	0.387	(0.308–0.485)	<0.001
Gastrointestinal	0.799	(0.658–0.971)	0.024	0.770	(0.629–0.944)	0.012
Acid drugs	0.686	(0.562–0.839)	<0.001	0.639	(0.518–0.788)	<0.001
Psycholeptic	0.395	(0.313–0.499)	<0.001	0.570	(0.389–0.835)	0.004
Obstructive airway	0.575	(0.460–0.720)	<0.001	0.515	(0.408–0.651)	<0.001
Diabetes drugs	0.258	(0.191–0.350)	<0.001	0.390	(0.277–0.550)	<0.001
Vitamins	0.619	(0.480–0.798)	<0.001	0.862	(0.656–1.132)	0.286
Gynecological drugs	0.868	(0.686–1.099)	0.241	0.983	(0.766–1.260)	0.890
Nasal preparation	0.955	(0.740–1.231)	0.721	0.834	(0.641–1.086)	0.177
Anti-epileptic	0.321	(0.232–0.445)	<0.001	0.541	(0.371–0.790)	0.001
Antiviral	0.574	(0.431–0.764)	<0.001	0.662	(0.488–0.898)	0.008
Dermatological	0.594	(0.441–0.799)	<0.001	0.719	(0.527–0.982)	0.038
Antimycotics	0.602	(0.425–0.853)	0.004	0.698	(0.487–0.999)	0.050
Renin-angiotensin	0.339	(0.222–0.519)	<0.001	0.486	(0.306–0.773)	0.002
Anti-anemic drugs	0.453	(0.306–0.671)	<0.001	0.566	(0.377–0.850)	0.006
Beta-blockers	0.307	(0.197–0.480)	<0.001	0.493	(0.304–0.800)	0.004
Lipid-modifying agents	0.173	(0.101–0.298)	<0.001	0.236	(0.134–0.415)	<0.001
Stomatological preparations	1.256	(0.850–1.856)	0.253	1.043	(0.700–1.553)	0.836
Other	0.441	(0.385–0.504)	<0.001	0.500	(0.432–0.579)	<0.001
Time stages
Pre-COVID-19 (Jan 1–Mar 1)	0.275	(0.219–0.344)	<0.001	0.257	(0.205–0.323)	<0.001
Curfew (Mar 2–Jun 20)	Ref			Ref
Post-curfew (Jun 21–Dec 31)	0.439	(0.413–0.466)	<0.001	0.451	(0.424–0.480)	<0.001

OR: odds ratio; CI: confidence interval; AOR: adjusted odds ratio.

In the bivariate analysis of antibiotic prescriptions, all independent variables were found to have statistically significant relationship, with the exceptions of gender (OR 1.040; 95% CI 0.787–1.128; p = 152) and time stages (curfew stage: OR 0.942; 95% CI 0.986–1.098; p = 0.518; and post-curfew stage: OR 0.992; 95% CI 0.827–1.191; p = 0.935; Table 5).

Table 5.

Antibiotic prescribing: Bivariate and multivariate logistic regression analysis.

	Antibiotic Prescribing
	Bivariate			Multivariate
Variable	OR	95% CI	p value	AOR	95% CI	p value
Gender
Male	Ref			Ref
Female	0.961	(0.911–1.015)	0.152	0.877	(0.823–0.935)	<0.001
Age
Babies (0–2)	0.708	(0.604–0.829)	<0.001	0.940	(0.787–1.123)	0.494
Children (3–16)	1.533	(1.418–1.657)		1.465	(1.338–1.603)	<0.001
Young adults (17–30)	0.859	(0.803–0.918)		0.934	(0.865–1.008)	0.079
Middle-aged adults (31–45)	Ref			Ref
Old adults (+45)	0.614	(0.565–0.668)		0.876	(0.794–0.966)	0.008
Diagnosis
Respiratory system	Ref			Ref
Skin and subcutaneous tissue	0.269	(0.245–0.295)	<0.001	1.070	(0.868–1.319)	0.526
Digestive system	0.931	(0.846–1.024)	0.141	0.831	(0.521–1.326)	0.437
Genitourinary system	1.258	(1.136–1.392)	<0.001	2.731	(1.173–6.361)	0.020
Infectious and parasitic diseases	0.263	(0.234–0.295)	<0.001	0.155	(0.046–0.520)	0.003
Mental disorders	0.003	(0.001–0.006)	<0.001	0.756	(0.620–0.920)	0.005
Eye diseases	0.024	(0.018–0.031)	<0.001	1.025	(0.949–1.106)	0.531
Injury and poisoning	0.416	(0.358–0.482)	<0.001	0.798	(0.664–0.960)	0.017
Ear and mastoid process	0.751	(0.651–0.867)	<0.001	0.704	(0.330–1.502)	0.364
Endocrine and metabolic	0.037	(0.025–0.053)	<0.001	1.651	(1.205–2.263)	0.002
Musculoskeletal system	0.160	(0.130–0.197)	<0.001	0.280	(0.121–0.647)	0.003
Circulatory system	0.034	(0.021–0.055)	<0.001	0.647	(0.455–0.920)	0.015
Nervous system	0.023	(0.011–0.046)	<0.001	1.258	(0.813–1.947)	0.303
Pregnancy and childbirth	0.636	(0.485–0.835)	0.001	0.839	(0.710–0.991)	0.039
Other	0.270	(0.234–0.312)	<0.001	0.961	(0.706–1.308)	0.802
Specialty
Family medicine	Ref			Ref
General medicine	1.015	(0.947–1.087)	0.677	1.025	(0.949–1.106)	0.531
Psychiatry	0.010	(0.005–0.020)	<0.001	0.280	(0.121–0.467)	0.003
Internal medicine	0.625	(0.531–0.735)	<0.001	0.798	(0.664–0.960)	0.017
Surgery	1.011	(0.869–1.176)	0.888	0.839	(0.710–0.991)	0.039
Dermatology	0.451	(0.375–0.543)	<0.001	0.756	(0.620–0.920)	0.005
Pediatrics	1.091	(0.906–1.314)	0.361	1.070	(0.868–1.319)	0.526
Ophthalmology	0.205	(0.136–0.308)	<0.001	0.831	(0.521–1.326)	0.437
Obstetrics and gynecology	0.762	(0.553–1.049)	0.095	0.647	(0.455–0.920)	0.015
Dentistry	2.320	(1.728–3.115)	<0.001	1.651	(1.205–2.263)	0.002
Neurology	0.142	(0.072–0.281)	<0.001	0.704	(0.330–1.502)	0.364
Emergency	1.008	(0.687–1.479)	0.967	1.258	(0.813–1.947)	0.303
Cardiovascular	0.063	(0.020–0.201)	<0.001	0.155	(0.046–0.520)	0.003
Radiology	1.592	(0.776–3.265)	<0.204	2.731	(1.173–6.361)	0.020
Other	0.686	(0.522–0.901)	0.007	0.961	(0.706–1.308)	0.802
Time stages
Pre-COVID-19 (Jan 1–Mar 1)	0.942	(0.787–1.128)	0.518	0.967	(0.791–1.182)	0.741
Curfew (Mar 2–Jun 20)	Ref			Ref
Post-curfew (Jun 21–Dec 31)	0.950	(0.897–1.006)	0.077	1.280	(1.196–1.369)	<0.001

Multivariate analysis

In the second stage of the logistic regression analysis, we further analyzed the associations of all the variables using multivariate logistic regression. Variability in primary adherence was found across ages, diagnoses, drug classes, and time stages. Gender was found to have no effect on primary adherence (AOR 0.896; 95% CI 0.941–1.054; p = 0.896).

Overall, the multivariate logistic regression model was statistically significant (χ² (47) = 1766.618, p < 0.05); the model explained 9.6% (Nagelkerke R²) of the variance in the adherence rate and correctly classified 64.8% of cases.

Primary adherence

Primary adherence was found to be correlated with age, with the highest rate of adherence observed in children between 3 and 16 years (AOR 1.150; 95% CI 1.058–1.215) and the lowest rate observed in young adults (17–30 years; AOR 0.901; 95% CI 0.840–0.965; Table 4).

Among the diagnosed diseases, patients diagnosed with respiratory system diseases showed a significantly higher rate of adherence than those diagnosed with other diseases. The lowest rate of adherence was observed in patients with nervous system diseases (ICD-10 code N; AOR 0.432; 95% CI 0.307–0.607; Table 4).

With respect to drug classes, the highest rate of primary adherence was observed for antibacterial drugs as compared with other variables, followed by ophthalmological medications (AOR 0.838; 95% CI 0.752–09.33) The lowest rates of adherence were observed for lipid-modifying agents (AOR 0.236; 95% CI 0.134–0.415; Table 4).

Finally, when primary adherence was assessed by time stage, e-prescriptions that were issued during the curfew stage were associated with a significantly higher rate of adherence than those issued during the other stages (Table 4).

Antibiotic prescribing

A multivariate logistic regression was also performed with only antibiotic prescriptions to ascertain the effects of gender, age, diagnosis, specialty, and time stage.

The multivariate logistic regression model was statistically significant (χ²(45) = 5942.890; p < 0.05). The model explained 30.5% (Nagelkerke R²) of the variance in the rate of antibiotics and correctly classified 73% of cases.

In the multivariate analysis, gender was found to have a significant effect on the prescription of antibiotics; female patients showed lower odds than male patients of being prescribed antibiotics (AOR 0.877; 95% CI 0.823–0.935).

The prescription of antibiotics was found to be associated with age, with the highest rate of antibiotics prescribed for children between the ages of 3 and 16 years (AOR 1.465; 95% CI 1.338–1.603) and the lowest rate prescribed for older adults (+45 years) (AOR 0.876; 95% CI 0.794–0.966).

With respect to the diagnosed diseases, antibiotics were prescribed at the highest rate for patients diagnosed with genitourinary system diseases (AOR 2.731; 95% CI 1.173–6.361). The lowest rates of antibiotic prescriptions were observed in patients diagnosed with infectious diseases (a disease caused by a pathogenic organism, such as bacteria, viruses, or fungi³⁷) (AOR 0.155; 95% CI 0.046–0.520).

With respect to prescriber specialties, radiologists (AOR 2.731; 95% CI 1.173–6.361) and dentists (AOR 1.651; 95% CI 1.205–2.263) prescribed antibiotics at significantly higher rates than prescribers of other specialties.

Finally, when antibiotic prescriptions were assessed by time stage, prescriptions that were issued during the curfew stage showed higher odds of being for antibiotics than those issued during other stages (Table 5).

Discussion

This research explored the utilization of the remote prescription system in Saudi Arabia during the COVID-19 pandemic and the factors associated with primary medication adherence and antibiotic prescribing rates.

This study resulted in three key findings. First, most of the remote e-prescriptions (58%, n = 14,515) were issued for female patients; 33.5% (n = 8380) were for middle-aged adults; 70% (n = 17,474) were for Saudi citizens. The most commonly diagnosed diseases were respiratory diseases (19%, n = 4759). Most of the prescribed drugs were by primary care specialists (75%, n = 18,796), and 74% (n = 18,484) of the prescriptions were issued on weekdays.

Second, the total primary adherence rate was 35.5% (n = 8885); i.e. nearly two out of five e-prescriptions were actually dispensed. The predictors of primary adherence were as follows: children, patients with respiratory diseases, and antibiotic medications.

Third, antibiotics were the most commonly prescribed type of drug, accounting for 32% of all prescriptions (n = 8034). Antibiotic use was significantly associated with male gender, children, genitourinary system diseases, and being treated by radiologists.

Comparison with other studies and explanation

Primary adherence

This study showed that the rate of primary adherence to remote e-prescriptions was suboptimal, as only (35.5%) of the prescriptions were ultimately dispensed. This figure is somewhat lower than those reported in other studies. A previous study from Poland on 119,880 e-prescriptions found the rate of medication adherence to be nearly (79%).³⁸ Another study from Spain on 2.9 million prescriptions reported that the adherence rate for all prescriptions was (53.6%).³⁹ The discrepancy between the results of the present study and those of prior studies indicates that primary adherence is a significant issue. One possible explanation is that primary adherence is a multifaceted challenge; it is patient- and medication-dependent throughout the process, from consultation to dispensation.⁴⁰

Primary adherence in both remote e-prescriptions and on-site prescriptions refers to the concept of patients filling their prescriptions after receiving them from their healthcare provider.^41,42 Previous studies that have compared primary adherence between e-prescriptions and on-site prescriptions showed that remote e-prescriptions have the potential to improve primary adherence.^43,44 A study conducted in the United State analyzed 423,616 prescriptions and found that e-prescriptions were associated with a higher likelihood of primary medication adherence (53%) compared to paper prescriptions (43%).⁴⁵ Similarly, another study conducted in the United States which analyzed 4318 prescriptions found that e-prescriptions had higher rates of medication adherence (80.2%) compared to paper prescriptions (62.8%).⁴⁶ These findings support the notion that remote e-prescriptions can be effectively enhance primary adherence, likely due to the convenience and automation they offer.^44,47,48 However, it is important to note that the rate of primary medication adherence observed in our study (35.5%) was still suboptimal compared to previous research findings.

In this study, patient age was significantly associated with primary adherence. The highest level of adherence was associated with patients between 3 and 16 years of age. Similarly, in a Massachusetts study, the highest adherence rate of all prescribed drugs (87%) was observed in children under the age of 18 years.²² Additionally, a study from Sweden found an (82%) adherence rate for antidepressant prescriptions in individuals under 19 years of age.⁴⁹ A possible explanation for the higher adherence rate in children could be attributed to the parents’ eagerness to receive the medications and follow up on their children's medical conditions.⁵⁰

In this study, certain types of diseases and drugs were also associated with primary adherence. With respect to these factors, the highest adherence rates were observed in patients with respiratory diseases (45%) and those taking antibacterial drugs (43%). Potential explanations for the higher rate of adherence in patients with respiratory diseases include that these types of conditions, especially asthma, require the continued use of medications to achieve successful disease control and better therapeutic outcomes.^51,52 The antibiotic medication adherence finding aligns with results from other studies. In a prior study in Poland, the lowest non-adherence rate (14.3%) was observed in patients who were prescribed antibiotics.³⁸ Additionally, a previous study in Spain found that 76.88% of filled prescriptions were for penicillin.³⁹

In terms of time stages, the adherence rate was significantly higher during the curfew stage, from March 2 to June 20. A previous study from Germany showed that primary adherence during the pandemic (March 2020) was significantly higher than that prior to the pandemic (March 2019).⁵³ In the United States, during the beginning of the COVID-19 pandemic (from January to March 2020), it was found that patients with respiratory illnesses showed increased medication adherence during the last week of March 2020 compared to the first 7 days of January 2020.⁵⁴ The comparison of studies regarding primary adherence is summarized in Supplementary material 4.

Improving primary medication adherence is a multifaceted issue that required system improvements as well as the establishment of strong patient–provider relationships.⁴⁰ It is important to address the root causes of primary non-adherence and develop tailored solutions that address individual patient needs. In Saudi Arabia, the Anat system specifies that remote e-prescriptions can be dispensed up to 3 days after the date of prescribing.³⁰ This short period may limit patients’ access to their medications, especially during the COVID-19 pandemic, when it can be difficult to fill prescriptions in a timely manner. This may contribute to the low adherence rate found in the present study. Therefore, efforts should be made to revise the current policy and increase the prescription timeframe to improve access to medication and decrease non-adherence rates. This revision would align with approaches used in other countries such as the United States, United Kingdom, and Australia, where prescriptions are valid for 6 to 12 months, depending on the medication type (controlled or non-controlled) and pharmacist's clinical judgment.^55–57 Implementing other strategies such as offering medication home delivery service and prescription pick-up reminders could also be effective in facilitating medication access and improving adherence rates and ultimately lead to better health outcomes for patients.^58–60 Furthermore, affordability and out-of-pocket medication costs may have played a role in the present study's low medication adherence rate, as insurance companies do not cover expenses for medications that are prescribed through the Anat platform.³⁰ Exploring options for reducing the cost of medications such as working with insurance companies to cover prescriptions issued from Anat system and utilizing generic medication name when possible can help address financial barriers that may contribute to primary non-adherence.⁶¹ Additionally, healthcare providers can also play a crucial role in addressing medication adherence issues.^41,62 They should prioritize open discussions with patients regarding medication adherence, provide education on how to obtain medications, and implement effective follow-up measures. By establishing a strong patient–provider relationship and actively involving patients in their treatment plans, healthcare providers can improve patient outcomes and reduce the burden of medication non-adherence on the healthcare system.^41,60,62,63

Antibiotic prescribing

The present study showed that male gender was associated with higher antibiotic e-prescription rates. A study from Eritrea found that the largest proportion of prescriptions (53%) comprised antibiotic prescriptions. The study also reported that males were more likely to receive antibiotic prescriptions than females.⁶⁴ This finding may reflect differences in the types of consultations and the infectious diseases between genders. Some infectious diseases affect males and females differently; in particular, urinary tract infections are more common in female patients, while respiratory diseases are more common in male patients.⁶⁵ These studies provide evidence that there may be gender disparities in antibiotic prescribing, with males being more likely to receive prescriptions than females. However, it's important to note that prescribing practices can vary depending on a variety of factors and more research is needed to fully understand the reasons behind these disparities in antibiotic e-prescriptions in Saudi Arabia.

Additional findings include children and genitourinary system diseases. Children from 3 to 16 years of age were more likely to receive antibiotics than any other age group. One possible explanation involves the roles of parents, as increased parental demand for antibiotics may lead to overprescribing.^66,67 Furthermore, genitourinary system diseases have been linked to antibiotic use in children. A previous study from Saudi Arabia reported that urinary tract infections were the most common type of infection in children <5 years old.⁶⁸ The prevalence rates of urinary tract infections in Saudi Arabia were 25% among all age groups and 26% among children between 4.5 and 5 years of age.^69,70

The present study also found that certain specialties were associated with the prescribing of antibiotics. Interestingly, radiologists were more likely to prescribe antibiotics than other specialties. Several studies have reported the roles of radiologists in prescribing antibiotics; most interventional radiologists prescribe antibiotics as preventive treatments (prophylaxes) in procedures.^71–73 However, their role in prescribing medication in outpatient's clinic setting is not very clear. More research is required to elucidate the reasons for this increase, and further training on remote e-prescribing and reviewing the prescribing privileges for controlled medications is recommended. The comparison of studies regarding antibiotic prescribing is summarized in Supplementary material 5.

In the study, Antibiotics were the most frequently prescribed type of drug. Antibiotic overuse and misuse are major contributors to antibiotic resistance, which is a growing global health threat.^19,24,74The Saudi MOH has developed guidelines for the appropriate use of antibiotics in healthcare facilities to combat antibiotic resistance and promote the rational use of antibiotics.^75–77 Additionally, the SFDA regulates the import and sale of antibiotics in the country to ensure their safety and efficacy.^78–80 Unfortunately, there is no specific information available regarding Saudi Arabia's antibiotics guideline for remote e-prescriptions. However, it is reasonable to assume that the guidelines for appropriate antibiotic use in healthcare facilities would also be applicable to remote e-prescriptions.⁷⁷ It is essential to recognize that remote e-prescriptions have the potential to improve the accuracy and efficiency of prescribing antibiotics. Nevertheless, it is crucial to consider that the process in which remote e-prescriptions are utilized, such as prescribing over the phone or through videoconferencing without direct patient assessment, may require tailored guidelines. While remote e-prescriptions offer many benefits, the appropriate use and monitoring of antibiotics remain vital in preventing antibiotic resistance. It is important for healthcare providers to exercise caution and adhere to best practices when prescribing antibiotics via remote e-prescriptions, ensuring that patient assessments and clinical considerations are appropriately addressed, striking a balance between the benefits and potential limitations associated with the remote e-prescribing process.

Limitations and strengths of the study

The present study employed a relatively large, real-world population and analyzed both prescription and dispensing data. The interpretation of the findings may contribute to a greater understanding of primary adherence and antibiotic e-prescribing issues in the Saudi population.

While the study used an electronic prescription database to determine antibiotic prescribing rates, no information about the practitioners’ gender or experience was provided. Further, it was not possible to compare primary adherence between 2019 and 2020 due to the lack of data.

Future research should prioritize investigating practitioner medical specialties in relation to remote e-prescriptions and on-site prescriptions in Saudi Arabia. This research is crucial to understand the unique challenges and advantages that different therapeutic areas present in remote e-prescribing. Identifying medical specialties that predominantly utilize remote e-prescriptions will provide valuable insights into prescribing practices across healthcare specialties. Studying the impact of remote e-prescriptions and on-site prescriptions in different therapeutic areas can pinpoint domains where remote e-prescriptions offer significant benefits and cost-effectiveness. This comprehensive understanding will inform the implementation and adoption of remote e-prescribing practices, assisting healthcare providers, policymakers, and stakeholders in making informed decisions and developing tailored strategies to optimize remote e-prescription use in Saudi Arabia. Furthermore, exploring the impact of antibiotic remote e-prescriptions on patient outcomes and revising the current antibiotic guidelines to effectively accommodate the unique aspects of remote e-prescriptions is essential. By comprehensively understanding the potential benefits and challenges associated with remote e-prescriptions in different contexts, healthcare providers and policymakers can make well-informed decisions, leading to improved patient care and reduced healthcare costs.

Conclusion

In conclusion, the rate of primary adherence in Saudi Arabia was 35% when all medications were considered. The adherence rate varied according to patient age, disease, medication, and time stage. The overall prevalence of antibiotic prescriptions in the study was 32%. Patient gender, age, medical disease, and provider specialty were significant predictors for the prescribing of antibiotics. The findings of this study highlight the need for interventions directed toward improving primary medication adherence and antibiotics prescription. Furthermore, this study's findings may help decision-makers in improving e-prescribing practices to ensure more patient-centered care.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076231194925 - Supplemental material for Utilization of remote e-prescription (Anat) in Saudi Arabia during COVID-19: Factors associated with primary adherence and antibiotic prescription

Supplemental material, sj-docx-1-dhj-10.1177_20552076231194925 for Utilization of remote e-prescription (Anat) in Saudi Arabia during COVID-19: Factors associated with primary adherence and antibiotic prescription by Roaa Khaled Alhassoun and Sharifah Abdullah AlDossary in DIGITAL HEALTH

Supplemental Material

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Supplemental material, sj-docx-2-dhj-10.1177_20552076231194925 for Utilization of remote e-prescription (Anat) in Saudi Arabia during COVID-19: Factors associated with primary adherence and antibiotic prescription by Roaa Khaled Alhassoun and Sharifah Abdullah AlDossary in DIGITAL HEALTH

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Supplemental material, sj-docx-4-dhj-10.1177_20552076231194925 for Utilization of remote e-prescription (Anat) in Saudi Arabia during COVID-19: Factors associated with primary adherence and antibiotic prescription by Roaa Khaled Alhassoun and Sharifah Abdullah AlDossary in DIGITAL HEALTH

Supplemental Material

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Supplemental material, sj-docx-5-dhj-10.1177_20552076231194925 for Utilization of remote e-prescription (Anat) in Saudi Arabia during COVID-19: Factors associated with primary adherence and antibiotic prescription by Roaa Khaled Alhassoun and Sharifah Abdullah AlDossary in DIGITAL HEALTH

Footnotes

Acknowledgements

The authors gratefully acknowledge the support provided by the Research Office at King Saud bin Abdulaziz University for Health Sciences (KSAU-HS). We would also like to acknowledge the cooperation of the Saudi Ministry of Health and Lean Business Services company in providing us with the data.

Contributorship

This study was conducted by two authors (R.K.A. and S.A.A.). R.K.A. contributed to the study conception, design, analysis, and discussion. S.A.A. contributed to the study conception, design, critical revising, and giving the final approval.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

The ethical clearance was obtained from the institutional review board of King Abdullah International Medical Research Center (SP21R/342/06–11 July 2021).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully acknowledge the funding for the publication of this paper by King Abdullah International Medical Research Center.

Guarantor

RKA.

Informed Consent

No informed consent was required for this study. All data used in this study were obtained from a database and data were deidentified prior to analysis.

ORCID iDs

Roaa Khaled Alhassoun

Sharifah Abdullah AlDossary

Supplemental material

Supplemental material for this article is available online.

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