Enhancing Pharmacy Practice: Role of Clinical Decision Support System in Mitigating Drug-related Problems

Drug-related Problems in Pharmacy Practice

DRPs, a major concern across medical disciplines, often arise from the complexity of pharmacotherapy, patient-related factors, and system issues. Pharmacists have a key role in optimizing medication by assessing the appropriateness, effectiveness, and safety of each regimen, as well as monitoring adherence and identifying unmet therapeutic needs.^27–30

Unwanted or duplicate therapy, underuse or overuse of medications, and adverse events are DRPs that result in compromising the patient’s quality of life and increasing the chances of hospitalization, morbidity, and healthcare expenses.^2,14,17–19

Incorrect dose, dosage form, duration, and frequency, prescribing errors, and ambiguous medication details are among the main factors that contribute to medication errors.^31–33 The multifactorial causality of DRPs necessitates multidisciplinary collaborations of HCPs, including pharmacists and physicians.^33,34 Open communication, knowledge sharing, and developing a plan to identify DRPs by a multidisciplinary team can minimize DRPs, improve outcomes, and patient safety.^30,33,34

Impact of Drug-related Problems on Patients and the Healthcare System

Pharmacotherapy is complex, and DRPs are unavoidable. Identifying DRPs in early stages, its systematic management approach can prevent the majority of DRPs. Strategies include pharmacokinetic and pharmacodynamic monitoring, clinical pharmacists’ involvement in multidisciplinary teams, and MTM services.^14,17,18

CDSSs integrated with EHRs have emerged as an important tool for pharmacists and clinicians to detect and resolve DRPs.^22,23,35 For instance, CDSSs have helped clinical pharmacists efficiently identify a high proportion of DRPs in hospitalized patients and prioritize those requiring intervention. ³⁵ Likewise, CDSS alerts for look-alike and sound-alike medications have reduced wrong-drug errors and prompted prescription modifications. ³⁶ CDSSs also partly mitigate workforce constraints by supporting DRP detection and monitoring in settings with limited access to clinical pharmacists, provided EHR data (including drug allergies) are accurately and consistently updated.^37,38

Integration of CDSS into Pharmacy Practice

Pharmacists provide medication management and pharmaceutical care services, mainly involving drug therapy decision-making. Services involve drug, patient, disease-related, population-specific, community, and pharmacy-related factors, and therapeutic goals.

The current adoption of drug therapy decisions predominantly supports checks for drug–drug interactions and other safety aspects in pharmacy dispensing applications. Such drug therapy decision supports the lack of personalized patient information, which could add excellent value to the decision-making process. Only EHRs can integrate personalized patient information with current population-specific knowledge.

EHRs play multiple roles in documentation and communication, clinical event recording, quality control process automation, CDSS, clinical process redesign, care coordination, payment bundling, and patient access to health records. Integrating the EHR with the CDSS changes the dispensing mode from a “push” to a “pull” model. In the push model, information received by individual staff members is shared with relevant team members, generally resulting in an excessive information load.

Benefits of CDSS Implementation

Implementing CDSS can optimally enable staff to participate in clinical management activities, despite certain patient groups being more susceptible to management-related DRPs. Pharmacy practice can become more proactive in clinical decision-making by implementing advanced HIT tools. CDSS can also enable evidence-based clinical advice, real-time alerts, and reminders, patient support services that improve outcomes and medication adherence, and administrative tools for workflow and resource management. Moreover, computerized surveillance and monitoring of prescribing and dispensing can detect pharmaceutical errors. Clinical dashboards displaying critical performance indicators empower data-driven decision-making. CDSS can enhance patient care by improving medication safety and medication management, avoiding adverse drug events and error prevention, in addition to DRP handling and interprofessional communication. Moreover, CDSS can lower healthcare costs and improve patient outcomes and satisfaction. By combining clinical expertise, patient-specific data, and computer algorithms, CDSS also provide clinicians with critical and accurate clinical information, evidence-based recommendations, and decision support alerts to aid patient care decision delivery. CDSS can also aid in evaluating treatment alternatives, implementing clinical recommendations into practice, and identifying potential drug interactions, drug allergies, and other patient variables that alter responses to medication. CDSS enhances communication between HCPs by exchanging patient data, treatment plans, and medication histories across healthcare settings. CDSS can enhance treatment continuity, lower medication errors, and improve patient outcomes.^23,39,40 CDSS is a strategic instrument that can potentially transform pharmacy practice by integrating clinical knowledge, patient-specific data, digital technologies, and evidence-based medicine. ²³

Recent incorporation of artificial intelligence (AI) with CDSS further improved DRP management by identifying complex drug-related patterns and facilitating individualized pharmacotherapy.^36,41,42 AI-driven CDSS prototypes utilizing machine-learning (ML) algorithms improved the detection of high-risk prescriptions with fewer non-actionable alarms.^35,36,42 The accuracy and efficiency of decision-making in pharmacy practice are possibly better with the application of advanced natural language and other AI models in conjunction with EHRs.^41,42 AI integration with CDSS has transformed healthcare. ⁴³ Deep learning-based CDSS performs better in early breast cancer detection. Large language models integrated with EHRs simplify and improve decision-making while lowering healthcare costs. ⁴⁴

Advanced CDSS can improve management of DRPs in particular patient populations, enabling improved drug safety procedures and proactive clinical decisions on medicine management. A legitimate hybrid decision support technology by Levivien et al. combined ML and a rule-based expert system, which outperformed a traditional rule-based CDSS, by reducing severe DRPs, and determining the possible need for medicinal intervention depending on event severity, ⁴⁵ enhancing patient diagnostics, ensuring correct medicine dispensing, and precise prescriptions. Additionally, an advanced CDSS can substantially improve the detection of possible drug interactions and may help to comply with laws and regulations pertaining to prescription orders.

Figures 1–3 highlight how the CDSS can enhance medication safety by mitigating key DRPs. The integration of CDSS reduces the frequency of medication errors and adverse drug events by real-time identification of high-risk circumstances and prompt medication safety alerts. CDSS also helps identify potential DRPs, such as drug–drug and drug allergy interactions, drug-disease contraindications, adverse drug reactions, and inappropriate therapy or duration, particularly in vulnerable populations.

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Figure 1.

Note: RoA: Route of administration, Rx: Prescription.

OPEN IN VIEWER Figure 2.

In Routine Pharmacy Practice, Drug–Drug and Drug-disease Interactions, Drug Allergy Contraindications, and dose Adjustments in Renal/Hepatic Impairment are the Major DRPs. CDSS Helps Pharmacists Manage these by Providing Timely Alerts.

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Figure 3.

Note: ADR: Adverse drug reaction.

Challenges and Barriers to CDSS Adoption

A critical challenge in CDSS implementation in healthcare settings is the requirement for integrating an advanced EHR system with CDSS. ⁴⁶ Undoubtedly, CDSSs are complex applications, and developing, adapting, and deploying them demands developer resources and expertise.

In some settings, critical patient-level data or information are stored in separate, non-interfaced databases or systems, hindering organizations’ ability to use them in CDSS. The most reported factors in implementation were compatibility of CDSS with workflows, practical use of CDSS output, CDSS technical dependencies, and design. Also, the credibility of users in the CDSS input data and evidence base, and the compatibility of the CDSS with the user’s role or clinical setting. ⁴⁶ Other issues include data connectivity, updating guidelines, precision of AI-driven recommendations, and the possibility of losing human input in decision-making. ⁴⁷ The main obstacles to adoption were simplified CDSS, limited clinical usefulness in multimorbidity, and inadequate workflow integration. ⁴⁸ System failure, overreliance on the system, and inability to trust the system are possible threats to implementation. The closeness of the system and the ease of use of the interface may have helped in selecting the most effective CDSS. ⁴⁹

Overall, while CDSSs can reduce selected DRPs, medication errors, and inappropriate prescribing, the magnitude and consistency of benefits differ across settings and systems. Many reports are single-center or pre–post designs with short follow-up, focusing on process indicators (for example, alert acceptance, guideline-concordant orders, or number of DRPs detected) rather than hard patient outcomes such as mortality or hospital readmissions.^{11,20,22,35,40,48,50} Differences in CDSS architecture, clinical context, user groups, and outcome definitions also make direct comparison difficult and limit the generalizability of results, especially to resource-constrained environments.^11,20,22,35

Research on implementation further shows that the real-world impact of CDSSs is strongly influenced by data quality, workflow integration, usability, and user attitudes.^{20,23,46,48,50–52} Incomplete or inconsistently coded clinical data, poor interface design, and high volumes of non- actionable alerts can undermine trust and contribute to alert fatigue, reducing the likelihood of following clinically important recommendations.^{20,23,46,48,49,52} Limitations highlight that CDSSs should complement, rather than replace, the clinical judgment of pharmacists and other HCPs in preventing and managing DRPs.^23,49,50

Implication for Pharmacy Professionals

Like other HITs, CDSS have important implications for delivering pharmacy practices in a well-organized and patient-centric manner.^23,40,50 Best practice advisory alert (BPA) in CDSS helps pharmacists notify and address the most frequent and significant DRPs such as drug–drug and drug-disease interactions, allergies, dose-related issues, and deviations from globally approved protocols and clinical guidelines by providing evidence-based suggestions and thereby ensuring safe medication practices that prioritize patient safety.^{12,22,23,35,50} However, the individual role of pharmacists in managing DRPs remains unavoidable because their clinical role in decision making is very rigid and important in interpreting the BPA suggestions on benefits risk ratio by considering each patient on a case-by-case basis.^23,30,45,50 This narrative review summarizes evidence guiding the appropriate implementation and use of CDSS in pharmacy practice, ensuring that these tools enhance rather than replace professional decision-making. CDSS can enhance the clinical standards of pharmacy practice, but it is not a substitute for pharmacy professionals in decision-making.