Review
Abstract
Background: Medication-related harm, including adverse drug events (ADEs) and medication errors, represents a significant iatrogenic burden in clinical care. Digital health technology (DHT) interventions can significantly enhance medication safety outcomes. Although the clinical effectiveness of DHT for medication safety has been relatively well studied, much less is known about the cost-effectiveness of these interventions.
Objective: This study aimed to systematically review the economic impact of DHT interventions on medication safety and examine methodological challenges to inform future research directions.
Methods: A systematic search was conducted across 3 major electronic databases (ie, PubMed, Scopus, and EBSCOhost). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed for this systematic review. Two independent investigators conducted a full-text review after screening preliminary titles and abstracts. We adopted recommendations from the Panel on Cost-Effectiveness in Health and Medicine for data extraction. A narrative analysis was conducted to synthesize clinical and economic outcomes. The quality of reporting for the included studies was assessed using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) guidelines.
Results: We included 13 studies that assessed the cost-effectiveness (n=9, 69.2%), cost-benefit (n=3, 23.1%), and cost-utility (n=1, 7.7%) of DHT for medication safety. Of the included studies, more than half (n=7, 53.9%) evaluated a clinical decision support system (CDSS)/computerized provider order entry (CPOE), 4 (30.8%) examined automated medication-dispensing systems, and 2 (15.4%) focused on pharmacist-led outreach programs targeting health care professionals. In 12 (92.3% ) studies, DHT was either cost-effective or cost beneficial compared to standard care. On average, DHT interventions reduced ADEs by 37.12% (range 8.2%-66.5%) and medication errors by 54.38% (range 24%-83%). The key drivers of cost-effectiveness included reductions in outcomes, the proportion of errors resulting in ADEs, and implementation costs. Despite a significant upfront cost, DHT showed a return on investment within 3-4.25 years due to lower cost related with ADE treatment and improved workflow efficiency. In terms of reporting quality, the studies were classified as good (n=10, 76.9%) and moderate (n=3, 23.1%). Key methodological challenges included short follow-up periods, the absence of alert compliance tracking, the lack of ADE and error severity categorization, and omission of indirect costs.
Conclusions: DHT interventions are economically viable to improve medication safety, with a substantial reduction in ADEs and medication errors. Future studies should prioritize incorporating alert compliance tracking, ADE and error severity classification, and evaluation of indirect costs, thereby increasing clinical benefits and economic viability.
doi:10.2196/65546
Keywords
Introduction
Medication-related harm, including adverse drug events (ADEs) and medication errors, represents a significant burden in clinical care []. A previous systematic review showed that 3% of patients in various health care settings experience preventable medication-related harm worldwide, with a quarter classified as severe or potentially life threatening []. Unsafe medication practices can occur at various stages of the medication process, including prescribing errors, where nearly 50% of medication errors occur (eg, inappropriate drugs for age and condition, incorrect dosage, contraindication, and drug-drug interactions (DDIs) overlooked) [,]; dispensing errors; administration errors; and inadequate monitoring (eg, hypokalemia due to inadequate renal and electrolyte monitoring among diuretic users) [,]. Additional costs associated with medication errors have been estimated at US $42 billion annually, indicating a significant burden of unsafe medication practices for patients and the health system [].
Digital health technology (DHT) applies information and communication technology to enhance health care outcomes []. DHT interventions range from a clinical decision support system (CDSS)/computerized provider order entry (CPOE) or electronic prescribing, automated medication-dispensing systems, telemedicine, and mobile health (mHealth) apps to telephone or text message reminders [-]. DHT may assist in supporting clinical decisions and enhancing the monitoring of medication use [,]. Several studies have shown that DHT may confer benefits by reducing ADEs and medication errors in both hospital and community settings [-]. Previous systematic reviews have examined the clinical impact of DHT interventions (ie, CPOE) to improve medication safety [,], but none have examined the economic impact of such interventions.
Given the increasing application of DHT, there is an urgent need to determine the economic benefits of DHT to allow clinicians to decide whether to implement DHT strategies []. Although DHT interventions can potentially improve clinical outcomes, their additional expense for infrastructure, implementation, and maintenance may hinder their adoption compared to standard care [,]. This study aimed to systematically review the economic impact of DHT interventions on medication safety and examine the methodological challenges in these interventions. Such understanding can better inform resource allocation and policy decisions for strengthening health system capacity [].
Methods
Search Strategy
This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guideline. A systematic search was performed across 3 major electronic databases (PubMed, Scopus, and EBSCOhost) to identify studies investigating economic evaluations of DHT to improve medication safety outcomes (ie, reduction in ADEs and medication errors). The search strategies included 3 categories of terms related to:
- DHT interventions, defined as strategies that use information and communication technology to enhance health outcomes (ie, reduction in ADEs and medication errors). To ensure comprehensive coverage of all available evidence, we included a broad range of DHT interventions, including a CDSS/CPOE, electronic prescribing, telemedicine, telepharmacy, automated medication-dispensing systems, mHealth apps, and telephone or text message reminders. Comparators included paper-based prescribing, traditional floor stock storage, and standard pharmaceutical care.
- Medication safety outcomes (ie, changes in the ADE and medication error rate). An ADE is defined as “an injury resulting from the use of a drug,” which includes harm resulting from either errors or medication-inherent effects []. The term “medication error” refers to “any error in the process of prescribing, dispensing, or administering a drug, which may or may not result in harm” []. A previous study showed that although medication errors are common, only around 1% result in actual harm or ADEs, which might be because these errors have little potential for injury or they are intercepted before an adverse outcome occurs [,].
- Economic evaluations (ie, cost-benefit analysis [CBA], cost-effectiveness analysis [CEA], cost-utility analysis [CUA], and cost minimization analysis [CMA]).
Full details of the search strategy are provided in 
Table 1. An additional search of reference lists of relevant reviews and included studies was performed.
| Type of key terms | Search strategy |
| Terms related to DHT interventions | Telemedicine[MeSHb] OR telepharmacy[tiab] OR computerized provider order entry[tiab] OR computerized physician order entry[tiab] OR computer provider entry[tiab] OR clinical decision support[tiab] OR automated medication system[tiab] OR automated pharmacy system[tiab] OR bar coding[tiab] OR electronic medication order entry[tiab] OR electronic medication management system[tiab] OR electronic prescribing[tiab] OR ePrescribing OR electronic prescription[tiab] OR electronic medication administration records[tiab] OR electronic system*[tiab] OR automated dispensing[tiab] OR computerized reminder system[tiab] OR information technology[tiab] OR medication ordering entry[tiab] OR electronic medication ordering and administration system[tiab] OR remote consultation[MeSH] OR electronic consult*[tiab] OR digital technolog*[tiab] OR teleconsult*[tiab] OR mhealth[tiab] OR m-health[tiab] OR multimedia[tiab] OR virtual[tiab] OR mobile health[tiab] OR telemedicine[tiab] OR electronic health record[tiab] OR telehealth[tiab] OR telecare[tiab] OR telehealth care[tiab] OR mobile health intervention*[tiab] OR mobile applications[tiab] OR mobile telemedicine[tiab] OR mcare[tiab] OR m-care[tiab] OR mobile communication[tiab] OR mobile technolog*[tiab] OR multimedia technolog*[tiab] OR mobile devic*[tiab] OR app[tiab] OR apps[tiab] OR mobile app*[tiab] OR website*[tiab] OR internet consultation*[tiab] OR internet monitoring[tiab] OR video consultation*[tiab] OR video monitoring[tiab] OR telephone*[tiab] OR mobile phone*[tiab] OR smart phone*[tiab] OR smart-phone*[tiab] OR text messag*[tiab] OR text messaging[tiab] OR SMS[tiab] OR short messag*[tiab] OR multimedia messag*[tiab] OR multi-media messag*[tiab] OR website platform[tiab] OR web-based medication platform[tiab] OR web-based application[tiab] OR web-based tool[tiab] OR electronic health[tiab] OR ehealth[tiab] OR e-health[tiab] |
| Terms related to medication safety outcomes | Drug-related side effects and adverse reactions [MeSH] OR adverse drug reaction*[tiab] OR adverse drug event*[tiab] OR drug related problem*[tiab] OR medication related problem[tiab] OR drug therapy problem*[tiab] OR drug safety[tiab] OR medication safety[tiab] OR medication error*[tiab] OR prescribing error*[tiab] OR prescription error*[tiab] OR dispensing error*[tiab] OR administration error*[tiab] OR inappropriate prescribing[tiab] OR inappropriate medication*[tiab] OR drug complication*[tiab] |
| Terms related to economic evaluations | Cost and cost analysis[MeSH] OR cost-benefit analysis[MeSH] OR cost-effectiveness[tiab] OR cost utility[tiab] OR cost minimi*[tiab] OR economic evaluation[tiab] OR economic analysis[tiab] |
aDHT: digital health technology.
bMeSH: Medical Subject Headings.
Inclusion and Exclusion Criteria
We included studies that reported health economic evaluations of DHT to improve medication safety outcomes (ie, ADEs and medication errors) compared to standard care. Detailed information related to inclusion criteria is provided in Table 2. We excluded studies where the intervention did not include DHT, studies that did not report medication safety outcomes, studies without full economic evaluation, non-English studies, conference abstracts, and editorials.
| Framework item | Inclusion criteria |
| Population/problem | Patients receiving medication at any point of care, including in hospital and community settings |
| Intervention | Any DHTb intervention, including:
|
| Comparator | Standard care (eg, paper-based prescribing, traditional floor stock storage) |
| Outcome | Reduction in ADEsf and medication errors |
| Study type | A full economic evaluation of DHT to improve medication safety categorized as CBAg, CEAh, CUAi, and CMAj |
aPICO: population/problem, intervention, comparator, outcome.
bDHT: digital health technology.
cCDSS: clinical decision support system.
dCPOE: computerized provider order entry.
emHealth: mobile health.
fADE: adverse drug event.
gCBA: cost-benefit analysis.
hCEA: cost-effectiveness analysis.
iCUA: cost-utility analysis.
jCMA: cost minimization analysis.
Screening and Data Extraction
The database-screening results were exported to the Mendeley reference manager library and examined for duplicates. Two investigators (authors WNI and NZ) independently conducted a full-text review after screening the preliminary titles and abstracts. Any discrepancies between the 2 reviewers were resolved through discussion. We adopted recommendations from the Panel on Cost-Effectiveness in Health and Medicine for data extraction. Data extracted included (1) study characteristics; (2) clinical and economic outcomes, including the incremental cost-effectiveness ratio (ICER), the cost-benefit ratio (CBR), and the return on investment (ROI); (3) cost components; (4) methodological challenges; and (5) quality of reporting. All monetary values were converted to 2024 US dollar values using the Campbell and Cochrane Economics Methods Group–Evidence for Policy & Practice Information Centre Cost Converter [].
Quality of Reporting
Reporting quality was assessed using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) 2022 checklist. The checklist included 28 items, classified into 6 categories: (1) title and abstract, (2) introduction, (3) methods (including choice of model, health outcomes, and measurement of effectiveness), (4) results, (5) discussion, and (6) others. Based on the reporting quality, the included studies were categorized as excellent (score 100%), good (score 75%-99%), moderate (score 50%-74%), and low (score ≤49%) [].
Results
Study Selection
A total of 408 citations were retrieved from the electronic databases. After removing duplicates, 355 (87%) papers remained for evaluation based on titles and abstracts, yielding 49 (13.8%) records eligible for full-text assessment. A total of 13 (26.5%) economic evaluation studies [,,,,-] were finally included in this systematic review. Most of the studies included CEA (n=9, 69.2%) [,,,-], followed by CBA (n=3, 23.1%) [,,], and CUA (n=1, 7.7%) []. The included studies were predominantly conducted in hospital inpatient settings (n=10, 76.9%) [,,,-]. The study selection flowchart based on PRISMA guidelines is provided in 
Figure 1.
Overview of Key Characteristics
The most frequently implemented DHT intervention was the CDSS/CPOE or electronic medication record system. This intervention typically replaced traditional paper-based prescribing, with a safety alerts feature to assist clinicians in making informed decisions during the prescribing process [,,,,-]. A wide range of features were used in the included studies, ranging from basic DDI to the addition of medication administration tracking and various alerts, including pregnancy contraindications, allergy checks, dosage checks, therapeutic duplications, and potentially inappropriate medication (PIM) usage in vulnerable populations at increased risk of ADEs [,,,,-]. One study [] compared a structured pharmacist medication review supported by a CDSS with a review conducted without CDSS support for older hospitalized patients. The intervention included medication reconciliation and personalized pharmaceutical care (Table 3).
Of the 13 studies, 4 (30.8%) [,,,] showed that the implementation of automated medication-dispensing systems has been increasingly used to reduce dispensing and administration errors, enhance workflow efficiency, and improve stock inventory tracking. These systems typically incorporated automated individual unit-dose dispensing, which ensured precise medication packaging tailored to patient-specific prescriptions, unlike manual systems where nurses prepare doses manually [,,]. The systems were often integrated with barcode scanning technology, either at the hospital pharmacy or at the patient bedside level, enabling verification of both the medication and the patient’s identity to prevent administration errors [,]. Additional functionalities included structured drug storage and controlled access (eg, automated dispensing cabinets [ADCs] and medicine carousel systems with rotating shelves or bins to minimize the time spent searching for medications and medication selection errors) [,,]. One intervention also improved inventory management by providing automated real-time stock monitoring []. Furthermore, some systems were linked with electronic medication administration records and other CDSS tools, allowing better coordination between dispensing and prescribing processes [,,].
| Author | Country | DHTa type | Target population | Sample size | Type of study | Time horizon |
| Vermeulen et al [] | The Netherlands | CDSSb/CPOEc | Patients admitted to the internal medicine, gastroenterology, or geriatric ward | 1195 | CEAd | Admission to discharge |
| Westbrook et al [] | Australia | CDSS/CPOE | Patients admitted to the cardiology ward | 1202 | CEA | 15 years |
| Wu et al [] | Canada | CDSS/CPOE | Patients admitted to all wards | 74,351 | CEA | 10 years |
| Avery et al [] | United Kingdom | IT-based pharmacist outreach | Targeted patients based on conditions and medications in general practices | 480,942 | CEA | 6 months |
| Berdot et al [] | France | Automated medication dispending | Patients admitted to all wards | 70,421 | CBAe | 1 year |
| Forrester et al [] | United States | CDSS/CPOE | Patients in multidisciplinary outpatient clinics | 10,080 | CEA | 5 years |
| Elliot et al [] | United Kingdom | IT-based pharmacist outreach | Targeted patients based on conditions and medications in general practices | 480,942 | CUAf | 6 months |
| Gallagher et al [] | Ireland | CDSS and pharmacist review | Older hospitalized patients | 737 | CEA | Admission to discharge or 10-day follow-up |
| Li et al [] | China | CDSS/CPOE | Inpatients and outpatients | 620,000 | CBA | 6 years |
| Maviglia et al [] | United States | Barcode dispensing | Patients admitted to all wards | 175,000 | CBA | 5 years |
| Nuckols et al [] | United States | CPOE/CDSS | Patients admitted to all wards | 4891g | CEA | 10 years |
| Risør et al [] | Denmark | Automated medication dispensing | Patients admitted to the hematological ward | 1336 | CEA | 6 months |
| Risør et al [] | Denmark | Automated medication dispensing | Patients admitted to acute wards | 90,000h | CEA | 6 months |
aDHT: digital health technology.
bCDSS: clinical decision support system.
cCPOE: computerized provider order entry.
dCEA: cost-effectiveness analysis.
eCBA: cost-benefit analysis.
fCUA: cost-utility analysis.
gNumber of acute care hospitals in the United States.
hTotal number of doses.
A technology-based pharmacist outreach was implemented in 2 (15.4%) studies [,], involving pharmacists engaging directly with other health care professionals to target specific high-risk prescribing errors, such as prescribing nonsteroidal anti-inflammatory drugs (NSAIDs) without proton pump inhibitors (PPIs) for patients with a history of peptic ulcers, beta-blockers for patients with asthma, and angiotensin-converting enzyme (ACE) inhibitors or diuretics without proper monitoring of renal function and electrolytes. The interventions involved a CDSS-supported feedback system and pharmacist educational outreach for general practice staff. This intervention model extended beyond simple error reporting by providing pharmacist-intensive support and guidance to other health care professionals.
Clinical Effectiveness Estimates
The DHT interventions were effective in reducing ADEs, with an average reported effectiveness of 37.12% (range 8.2%-66.5%) across the included studies ( and Table 4) [,,,,,]. Medication errors reduced by an average of 54.38% (range 24%-83%) [,,,,,-,]. The variability in the effectiveness of DHT in reducing ADEs and medication errors can be influenced by differences in DHT features and the target population. A comprehensive CDSS/CPOE with both prescription entry and administration tracking resulted in greater reduction in ADEs compared to a CDSS/CPOE with only basic alerts [,]. Nevertheless, none of the studies provided data on alert compliance tracking by health care professionals. Higher effectiveness was also observed in DHT interventions targeting high-risk populations, such as older and pediatric hospitalized patients. These populations are more prone to ADEs and medication errors resulting from comorbidities, concomitant medications, and differences in physiological characteristics. Consequently, interventions targeting these populations showed greater absolute reductions in ADEs and medication errors compared to interventions targeting lower-risk populations, as the elevated baseline risk provides more room for improvement [,].
| Safety outcomes | Examples | Reduction (%), mean (range) |
| ADEs |
| 37.12 (8.20-66.50) |
| Medication errors |
| 54.38 (24.00-83.00) |
aADE: adverse drug event.
bNSAID: nonsteroidal anti-inflammatory drug.
cACE: angiotensin-converting enzyme.
dPPI: proton pump inhibitor.
Cost-Effectiveness Estimates
CDSS/CPOE Intervention
Implementation of a CDSS/CPOE to replace paper-based prescribing was associated with an average cost ranging from US $25.64 to $81.36 per patient, depending on the complexity and features of the system. The key cost components for a CDSS/CPOE included initial investments in hardware and software, licensing fees, staff training, ongoing system maintenance, and periodic updates to ensure compatibility with clinical workflows (Table 5) [,,,,-]. The implementation cost of a CDSS/CPOE depends on the hospital size, influenced by the need for more extensive hardware infrastructure and software to support a higher number of workstations, higher provider and patient volume, and more comprehensive clinical decision support rules to support various specialties []. Most studies evaluating a CDSS/CPOE reported the interventions as cost-effective from perspectives of societal, hospital, and health care systems, with 3 (23.1%) studies [,,] identifying cost-saving results. Only 1 (7.7%) study showed an exceptionally high ICER per ADE prevented without stating a willingness-to-pay (WTP) threshold, highlighting that more data on the effectiveness of CPOE in reducing ADEs are needed []. This study was conducted in 2006; therefore, the data on effectiveness may not reflect the most current findings, as presented in more recent studies [,,,,]. Although the development of an electronic medical record system with a CDSS requires an initial high investment, 1 (7.7%) study [] showed that it yields a positive cost-benefit ratio of 1.45, indicating that every US $1 spent generates US $1.45 in benefits, with the ROI achieved within 3 years, driven by a 40% reduction in ADEs as additional treatments and hospitalizations due to ADEs reduced and efficiency improved from transitioning to electronic systems [].
| Author | DHT type | Currency, year | Cost components | Discount rate (%) |
| Vermeulen et al [] | CDSSb/CPOEc | Euro, 2009 |
| Not reported (follow-up period did not exceed 1 year) |
| Westbrook et al [] | CDSS/CPOE | AU $, 2012-2013 |
| 5 |
| Wu et al [] | CDSS/CPOE | US $, 2007 |
| 5 |
| Avery et al [] | IT-based pharmacist outreach | UK pound sterling, 2012 |
| Not reported (follow-up period did not exceed 1 year) |
| Berdot et al [] | Automated medication-dispensing system | Euro, 2015 |
| Not reported |
| Forrester et al [] | CDSS/CPOE | US $, 2010 |
| 3 |
| Elliot et al [] | IT-based pharmacist outreach | UK pound sterling, 2012 |
| 3.5 |
| Gallagher et al [] | CDSS and pharmacist review | Euro, 2012 |
| Not reported (follow-up period did not exceed 1 year) |
| Li et al [] | CDSS/CPOE | US $, 2009 |
| 10 |
| Maviglia et al [] | Barcode-dispensing system | US $, 2009 |
| 3 |
| Nuckols et al [] | CDSS/CPOE | US $, 2012 |
| 3 |
| Risor et al [] | Automated medication-dispensing system | Euro, 2017 |
| Not reported (follow-up period did not exceed one year) |
| Risor et al [] | Automated medication-dispensing system | Euro, 2018 |
| Not reported (follow-up period did not exceed one year) |
aDHT: digital health technology.
bCDSS: clinical decision support system.
cCPOE: computerized provider order entry.
dADC: automated dispensing cabinet.
eAMS: automated medication system.
Automated Medication-Dispensing System
Implementation of automated medication-dispensing systems to replace traditional floor stock systems was cost-effective and beneficial, with the ICER ranging from US $0.33 to $62.00 per medication error avoided [,,,]. Key cost components for an automated medication-dispensing system included purchase of dispensing cabinets or medicine carousel systems with rotating shelves or bins, equipment for the repackaging center, software development, a barcode scanner, labor for restocking and packaging, training expenses, and system integration with the existing CPOE and pharmacy system. A reported net benefit of US $5,379,938.17 was achieved over 5 years, with the ROI attained within 4.25 years after initiation [,]. The key drivers of the cost-effectiveness included handling costs and differences in the rates and types of medication errors [,,]. Different types of dispensing errors have varying cost consequences; for example, the administration of an incorrect drug, dosage, or strength can have more severe consequences than procedural or administrative errors, which may potentially but not necessarily result in actual harm [].
IT-Based Pharmacist Outreach
The implementation of a pharmacist-led DHT intervention was cost-effective in reducing clinically significant prescribing and monitoring errors in primary care, with an ICER of US $129.8 per medication error prevented and US $7788 per quality-adjusted life year (QALY) per practice [,]. In addition, this intervention not only improved health outcomes (ie, additional QALYs) but also reduced overall costs compared to usual care, indicating its potential for cost savings []. Key cost components included pharmacist training, the time spent reviewing patient records and consulting with general practitioners (GPs), report generation from the CDSS, facilitation of practice meetings, patient follow-up, and system integration with the existing IT infrastructure. The number of patients per general practice was a key driver of cost-effectiveness in this intervention. In larger practices, intervention costs (eg, pharmacist training and CDSS report generation) are distributed across a broader patient base. In addition, the targeted nature of this intervention, which addressed specific prescribing errors (ie, NSAIDs without PPIs, beta-blockers for asthma, and monitoring errors) increased the efficiency and impact of pharmacists’ and GPs’ time in practices with higher patient volumes due to economies of scale [,,].
Methodological Characteristics and Challenges in the Included Studies
Of the 13 studies 6 (46.2%) [,,,,,] used a quasi-experimental design (ie, before-after and interrupted-time-series approaches, with multiple data points tracking ADEs and medication errors before and after DHT initiation). A decision analytic model was used in 3 (23.1%) studies [,,], focusing on short-term, event-specific analysis, while a Markov model was used in 1 (7.7%) study []. Randomized controlled trial (RCT)–based evaluation was used in 2 (15.4%) studies [,]. Challenges related to methodological aspects of evaluating DHT interventions for medication safety are presented in Table 6. Methodological challenges included short follow-up times, the absence of CDSS alert compliance tracking, a lack of ADE severity classification, and the omission of indirect costs (eg, productivity loss and caregiver time).
| DHT type and methodological issues | Description | |
| CDSSb/CPOEc | ||
| Study design |
| |
| Cost data |
| |
| Clinical data |
| |
| Automated medication-dispensing system | ||
| Study design |
| |
| Cost data |
| |
| Clinical data |
| |
| Pharmacist-led IT intervention | ||
| Study design |
| |
aDHT: digital health technology.
bCDSS: clinical decision support system.
cCPOE: computerized provider order entry.
dADE: adverse drug event.
eQALY: quality-adjusted life year.
Quality of Reporting
Based on the quality assessment using the CHEERS checklist, most of the included studies (n=10, 76.92%) [,,,-,,] were rated as good, and the remaining 3 (23.1%) studies [,,] were rated as moderate. Adherence to the checklist items varied across the section. Most studies did not report any approach to engagement with patients in the study design and results, except for the RCT-based study []. The rationale for selecting the model was inadequately reported in over a third of the studies, indicating issues in validating the selected methodology [,,,,]. All studies reported how to measure ADEs and medication errors; however, the valuation of these safety outcomes was not reported in some studies [,,]. The components of the outcomes included costs related to additional treatments and hospital stays due to the occurrence of ADEs and medication errors. Although most studies conducted sensitivity analysis to assess the robustness of the findings, only 1 (7.7%) study [] assessed the heterogeneity of the outcomes based on different types of automated medication-dispensing systems. Most studies only focused on aggregated data without exploring subgroup differences, such as variations within patient populations, hospital wards, or intervention types.
Discussion
Principal Findings
This is the first systematic review that assessed the economic evaluations of DHT interventions to improve medication safety outcomes. More than half of the studies (n=7, 53.9%) focused on a CDSS/CPOE, less than a third (n=4, 30.8%) on automated medication-dispensing systems, and the remaining (n=2, 15.4%) on pharmacist-led outreach programs targeting health care professionals. On average, DHT interventions reduced ADEs by 37.12% and medication errors by 54.38%. In 92.3% (12/13) of the included studies, the DHT was either cost-effective or cost beneficial compared to standard care. Despite a significant upfront cost, DHT showed an ROI within 3-4.25 years. Key methodological challenges included short follow-up periods, a lack of ADE severity categorization, the absence of alert compliance tracking, and the omission of indirect costs.
A CDSS/CPOE has been increasingly used to support clinicians in making informed decisions by providing recommendations based on patient data and clinical guidelines []. Nevertheless, the effectiveness of a CDSS is often compromised by alert fatigue, which occurs when clinicians override a large number of potentially irrelevant drug safety alerts (eg, drug interactions that are not clinically significant, flagging dosages outside the standard guideline when the prescribed dosage is appropriate for the patient condition) [-]. A previous study showed that alert fatigue might be reduced by using a CDSS/CPOE with interactive features, such as incorporating tiered safety alerts with varying priority levels, offering action plans for clinicians (eg, dose reduction), and requiring clinicians to justify overriding an alert []. In our review, none of the studies tracked CDSS/CPOE alert compliance, which may influence their effectiveness. A previous study showed drug allergy alerts had the highest compliance []. Further DHT evaluation studies should investigate how alert compliance affects the outcomes to ensure optimal utility of DHT.
In addition, tailoring clinical roles within a CDSS can also enhance alert acceptance [-]. A pharmacist-mediated CDSS has improved prescriber acceptance by filtering irrelevant advice and providing actionable drug safety recommendations []. This approach leverages pharmacists’ expertise related to medication to support the decision-making process and reduce alert fatigue among prescribers [-]. Several included studies combined a CDSS with pharmacist-led interventions, such as structured medication review and targeted training for health care professionals for error correction, highlighting the central role of pharmacists in medication safety [,,].
Advancements in CDSSs/CPOE have significantly reduced prescribing errors, but administration errors, such as administering the wrong dose, using the incorrect route of administration, and failing to administer a scheduled dose, still present substantial room for improvement []. This review found that a CDSS/CPOE with integrated prescription entry and administration tracking that address prescribing errors, while also monitoring and mitigating administration errors, achieves a greater reduction in ADEs compared to systems with basic features [,]. The development and implementation of more comprehensive systems that target all stages of medication management, including administration, are essential to reduce medication errors and ADEs.
Different types of automated medication-dispensing systems were used in the included studies. Several factors need to be considered when selecting an automated medication-dispensing system, including the volume of prescriptions and types of medication the device can handle (eg, oral tablets, liquid medications, injectables), integration with the existing information technology system, and the presence of an error-checking mechanism [,]. Almaki et al [] and Tsao et al [] showed that integration with existing digital infrastructure is key to supporting a seamless workflow process to ensure all components of medication management (ie, prescribing, dispensing, and inventory control) are interconnected, enhancing efficiency and medication error prevention.
All the included studies focused on hospital-based automated medication-dispensing systems, with limited investigation in outpatient settings [,,,]. Williams et al [] demonstrated that automated medication-dispensing systems for chronic medication regimens, such as antiretroviral therapy, enable efficient one-time password (OTP)–based medication collection and reduced waiting times, benefiting high-volume, resource-limited outpatient clinics. Further studies may adopt this system for less complex medication regimens to improve patient satisfaction and reduce the staff burden, enabling health care providers to focus on other critical tasks, such as optimization of drug therapy [,].
Targeting medication errors with a substantial clinical impact was a key strategy in the technology-based pharmacist-led outreach [,]. As around 1% of medication errors lead to actual harm, prioritizing interventions is essential []. The National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) developed an index to standardize the classification of medication errors based on severity (ie, no error, error-no harm, error-harm, and error-death), which has been adopted in the hospital setting [,]. An initiative to standardize the interception of medication errors using this severity classification index has proven effective to prevent clinically significant patient harm and provide substantial cost savings to the health system [].
Various economic models were used in the included studies (eg, quasi-experimental design, RCT, decision analytic model), indicating the complexity and multifaceted nature of DHT for medication safety. Sculpher et al [] emphasized the importance of placing an RCT within a broader framework of evidence synthesis and decision analysis in economic evaluation studies to balance internal validity and broader applicability. Quasi-experimental design can be practical for assessing real-world effectiveness and scalability. However, the assessment of confounders that might affect the observed trend and the lack of guidance in conducting economic evaluations alongside quasi-experimental trials necessitate careful interpretation of the findings [].
Strengths and Limitations
This study is the first systematic review investigating economic evaluations of DHT interventions to improve medication safety outcomes. We conducted a rigorous literature search with a comprehensive strategy encompassing a wide range of DHT intervention types to provide a thorough review of different strategies to improve medication safety. We also included evaluations on methodological challenges in DHT intervention assessment to inform future research direction.
Nevertheless, our review has several potential limitations. First, the generalizability of findings may be restricted to high-income settings, as most included studies were conducted in such contexts. Second, heterogeneity in health care settings, study designs, and economic evaluation methods may hinder direct comparisons across studies. Despite this, a narrative synthesis was developed to integrate and interpret the findings. Third, the exclusion of non-English studies may have limited the comprehensiveness of the evidence base. Finally, there was a lack of medication error and ADE severity classification among the included studies. Since different medication errors and ADEs have varying cost implications, this variability makes the interpretation of overall cost-effectiveness less straightforward.
Conclusion
DHT interventions are economically viable to improve medication safety, with substantial reduction in ADEs and medication errors. On average, DHT interventions reduced ADEs by 37.12% and medication errors by 54.38%. In 92.3% of the included studies, DHT was either cost-effective or cost beneficial compared to standard care. Despite a significant upfront cost, DHT showed an ROI within 3-4.25 years. The key drivers of cost-effectiveness include reductions in outcomes, the proportion of errors resulting in ADEs, and implementation costs. Key methodological challenges included short follow-up periods, the absence of compliance tracking, the lack of ADE severity categorization, and the omission of indirect costs. Future studies should prioritize incorporating alert compliance tracking, ADE and medication error severity classification, and the evaluation of indirect costs, thereby increasing clinical benefits and economic viability.
Acknowledgments
This study was supported by an Academic Leadership Grant (ALG) from Padjadjaran University in 2024 through AAS.
Conflicts of Interest
None declared.
Multimedia Appendix 1
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) checklist.
PDF File (Adobe PDF File), 66 KBReferences
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Abbreviations
| ACE: angiotensin-converting enzyme |
| ADC: automated dispensing cabinet |
| ADE: adverse drug event |
| AMS: automated medication system |
| CBA: cost-benefit analysis |
| CBR: cost-benefit ratio |
| CDSS: clinical decision support system |
| CEA: cost-effectiveness analysis |
| CHEERS: Consolidated Health Economic Evaluation Reporting Standards |
| CMA: cost minimization analysis |
| CPOE: computerized provider order entry |
| CUA: cost-utility analysis |
| DDI: drug-drug interaction |
| DHT: digital health technology |
| GP: general practitioner |
| ICER: incremental cost-effectiveness ratio |
| MeSH: Medical Subject Headings |
| mHealth: mobile health |
| NSAID: nonsteroidal anti-inflammatory drug |
| PPI: proton pump inhibitor |
| PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
| QALY: quality-adjusted life year |
| RCT: randomized controlled trial |
| ROI: return on investment |
Edited by A Schwartz; submitted 20.08.24; peer-reviewed by J Aarts, J Waterson; comments to author 25.09.24; revised version received 03.12.24; accepted 08.01.25; published 05.02.25.
Copyright©Widya Norma Insani, Neily Zakiyah, Irma Melyani Puspitasari, Muhammad Yorga Permana, Kankan Parmikanti, Endang Rusyaman, Auliya Abdurrohim Suwantika. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.02.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.