Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.

Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.

Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.

This review aims to assess the effectiveness of AI-driven CDSSs on patient outcomes and clinical practices. A comprehensive search was conducted across PubMed, MEDLINE, and Scopus. Studies published from January 2018 to November 2023 were eligible for inclusion. Following title and abstract screening, full-text articles were assessed for methodological quality and adherence to inclusion criteria. Data extraction focused on study design, AI technologies employed, reported outcomes, and evidence of AI-CDSS impact on patient and clinical outcomes. Thematic analysis was conducted to synthesise findings and identify key themes regarding the effectiveness of AI-CDSS. The screening of the articles resulted in the selection of 26 articles that satisfied the inclusion criteria. The content analysis revealed four themes: early detection and disease diagnosis, enhanced decision-making, medication errors, and clinicians' perspectives. AI-based CDSSs were found to improve clinical decision-making by providing patient-specific information and evidence-based recommendations. Using AI in CDSSs can potentially improve patient outcomes by enhancing diagnostic accuracy, optimising treatment selection, and reducing medical errors.

A Concept for Integrating AI-Based Support Systems into Clinical Practice.

The integration of artificial intelligence (AI) algorithms into clinical practice holds immense potential to improve patient care, but widespread adoption still faces significant challenges, including interoperability issues. We propose a concept for the agile development of an IT platform to integrate AI-based applications into clinical workflows for a use case in ophthalmology.

Evaluating EHR-Integrated Digital Technologies for Medication-Related Outcomes and Health Equity in Hospitalised Adults: A Scoping Review.

The purpose of this scoping review is to identify and evaluate studies that examine the effectiveness and implementation strategies of Electronic Health Record (EHR)-integrated digital technologies aimed at improving medication-related outcomes and promoting health equity among hospitalised adults. Using the Consolidated Framework for Implementation Research (CFIR), the implementation methods and outcomes of the studies were evaluated, as was the assessment of methodological quality and risk of bias. Searches through Medline, Embase, Web of Science, and CINAHL Plus yielded 23 relevant studies from 1,232 abstracts, spanning 11 countries and from 2008 to 2022, with varied research designs. Integrated digital tools such as alert systems, clinical decision support systems, predictive analytics, risk assessment, and real-time screening and surveillance within EHRs demonstrated potential in reducing medication errors, adverse events, and inappropriate medication use, particularly in older patients. Challenges include alert fatigue, clinician acceptance, workflow integration, cost, data integrity, interoperability, and the potential for algorithmic bias, with a call for long-term and ongoing monitoring of patient safety and health equity outcomes. This review, guided by the CFIR framework, highlights the importance of designing health technology based on evidence and user-centred practices. Quality assessments identified eligibility and representativeness issues that affected the reliability and generalisability of the findings. This review also highlights a critical research gap on whether EHR-integrated digital tools can address or worsen health inequities among hospitalised patients. Recognising the growing role of Artificial Intelligence (AI) and Machine Learning (ML), this review calls for further research on its influence on medication management and health equity through integration of EHR and digital technology.

An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR).

BACKGROUND AND OBJECTIVE: In everyday clinical practice, medical decision is currently based on clinical guidelines which are often static and rigid, and do not account for population variability, while individualized, patient-oriented decision and/or treatment are the paradigm change necessary to enter into the era of precision medicine. Most of the limitations of a guideline-based system could be overcome through the adoption of Clinical Decision Support Systems (CDSSs) based on Artificial Intelligence (AI) algorithms. However, the black-box nature of AI algorithms has hampered a large adoption of AI-based CDSSs in clinical practice. In this study, an innovative AI-based method to compress AI-based prediction models into explainable, model-agnostic, and reduced decision support systems (NEAR) with application to healthcare is presented and validated. METHODS: NEAR is based on the Shapley Additive Explanations framework and can be applied to complex input models to obtain the contributions of each input feature to the output. Technically, the simplified NEAR models approximate contributions from input features using a custom library and merge them to determine the final output. Finally, NEAR estimates the confidence error associated with the single input feature contributing to the final score, making the result more interpretable. Here, NEAR is evaluated on a clinical real-world use case, the mortality prediction in patients who experienced Acute Coronary Syndrome (ACS), applying three different Machine Learning/Deep Learning models as implementation examples. RESULTS: NEAR, when applied to the ACS use case, exhibits performances like the ones of the AI-based model from which it is derived, as in the case of the Adaptive Boosting classifier, whose Area Under the Curve is not statistically different from the NEAR one, even the model's simplification. Moreover, NEAR comes with intrinsic explainability and modularity, as it can be tested on the developed web application platform (https://neardashboard.pythonanywhere.com/). CONCLUSIONS: An explainable and reliable CDSS tailored to single-patient analysis has been developed. The proposed AI-based system has the potential to be used alongside the clinical guidelines currently employed in the medical setting making them more personalized and dynamic and assisting doctors in taking their everyday clinical decisions.

Exploring the role of professional identity in the implementation of clinical decision support systems-a narrative review.

BACKGROUND: Clinical decision support systems (CDSSs) have the potential to improve quality of care, patient safety, and efficiency because of their ability to perform medical tasks in a more data-driven, evidence-based, and semi-autonomous way. However, CDSSs may also affect the professional identity of health professionals. Some professionals might experience these systems as a threat to their professional identity, as CDSSs could partially substitute clinical competencies, autonomy, or control over the care process. Other professionals may experience an empowerment of the role in the medical system. The purpose of this study is to uncover the role of professional identity in CDSS implementation and to identify core human, technological, and organizational factors that may determine the effect of CDSSs on professional identity. METHODS: We conducted a systematic literature review and included peer-reviewed empirical studies from two electronic databases (PubMed, Web of Science) that reported on key factors to CDSS implementation and were published between 2010 and 2023. Our explorative, inductive thematic analysis assessed the antecedents of professional identity-related mechanisms from the perspective of different health care professionals (i.e., physicians, residents, nurse practitioners, pharmacists). RESULTS: One hundred thirty-one qualitative, quantitative, or mixed-method studies from over 60 journals were included in this review. The thematic analysis found three dimensions of professional identity-related mechanisms that influence CDSS implementation success: perceived threat or enhancement of professional control and autonomy, perceived threat or enhancement of professional skills and expertise, and perceived loss or gain of control over patient relationships. At the technological level, the most common issues were the system's ability to fit into existing clinical workflows and organizational structures, and its ability to meet user needs. At the organizational level, time pressure and tension, as well as internal communication and involvement of end users were most frequently reported. At the human level, individual attitudes and emotional responses, as well as familiarity with the system, most often influenced the CDSS implementation. Our results show that professional identity-related mechanisms are driven by these factors and influence CDSS implementation success. The perception of the change of professional identity is influenced by the user's professional status and expertise and is improved over the course of implementation. CONCLUSION: This review highlights the need for health care managers to evaluate perceived professional identity threats to health care professionals across all implementation phases when introducing a CDSS and to consider their varying manifestations among different health care professionals. Moreover, it highlights the importance of innovation and change management approaches, such as involving health professionals in the design and implementation process to mitigate threat perceptions. We provide future areas of research for the evaluation of the professional identity construct within health care.

Use of Computerized Physician Order Entry with Clinical Decision Support to Prevent Dose Errors in Pediatric Medication Orders: A Systematic Review.

BACKGROUND: Prescribing is a high-risk task within the pediatric medication-use process and requires defenses to prevent errors. Such system-centric defenses include electronic health record systems with computerized physician order entry (CPOE) and clinical decision support (CDS) tools that assist safe prescribing. The objective of this study was to examine the effects of CPOE systems with CDS functions in preventing dose errors in pediatric medication orders. MATERIAL AND METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 criteria and Synthesis Without Meta-Analysis (SWiM) items. The study protocol was registered in PROSPERO (CRD42021277413). The final literature search on MEDLINE (Ovid), Scopus, Web of Science, and EMB Reviews was conducted on 10 September 2023. Only peer-reviewed studies considering both CPOE and CDS systems in pediatric inpatient or outpatient settings were included. Study selection, data extraction, and evidence quality assessment (JBI critical appraisal tool assessment and GRADE approach) were carried out by two individual reviewers. Vote counting method was used to evaluate the effects of CPOE-CDS systems on dose errors rates. RESULTS: A total of 17 studies published in 2007-2021 met the inclusion criteria. The most used CDS tools were dose range check (n = 14), dose calculator (n = 8), and dosing frequency check (n = 8). Alerts were recorded in 15 studies. A statistically significant reduction in dose errors was found in eight studies, whereas an increase of dose errors was not reported. CONCLUSIONS: The CPOE-CDS systems have the potential to reduce pediatric dose errors. Most beneficial interventions seem to be system customization, implementing CDS alerts, and the use of dose range check. While human factors are still present within the medication use process, further studies and development activities are needed to optimize the usability of CPOE-CDS systems.

Precision Mental Health and Data-Informed Decision Support in Psychological Therapy: An Example.

Outcome measurement including data-informed decision support for therapists in psychological therapy has developed impressively over the past two decades. New technological developments such as computerized data assessment, and feedback tools have facilitated advanced implementation in several seetings. Recent developments try to improve the clinical decision-making process by connecting clinical practice better with empirical data. For example, psychometric data can be used by clinicians to personalize the selection of therapeutic programs, strategies or modules and to monitor a patient's response to therapy in real time. Furthermore, clinical support tools can be used to improve the treatment for patients at risk for a negative outcome. Therefore, measurement-based care can be seen as an important and integral part of clinical competence, practice, and training. This is comparable to many other areas in the healthcare system, where continuous monitoring of health indicators is common in day-to-day clinical practice (e.g., fever, blood pressure). In this paper, we present the basic concepts of a data-informed decision support system for tailoring individual psychological interventions to specific patient needs, and discuss the implications for implementing this form of precision mental health in clinical practice.

Validation of Pediatric Emergency Care Applied Research Network (PECARN) rule in children with minor head trauma.

The Pediatric Emergency Care Applied Research Network (PECARN) rule is commonly used for predicting the need for computed tomography (CT) scans in children with mild head trauma. The objective of this study was to validate the PECARN rule in Korean children presenting to the pediatric emergency department (PED) with head trauma. This study was a multicenter, retrospective, observational cohort study in two teaching PEDs in Korea between August 2015 and August 2016. In this observational study, 448 patients who visited PEDs were included in the final analysis. Risk stratification was performed with clinical decision support software based on the PECARN rule, and decisions to perform CT scans were subsequently made. Patients were followed-up by phone call between 7 days and 90 days after discharge from the PED. The sensitivity and specificity were analyzed. The sensitivity was 100% for all age groups, and no cases of clinically important traumatic brain injury (ciTBI) were identified in the very-low-risk group. CT scans were performed for 14.7% of patients in this study and for 33.8% in the original PECARN study. The PECARN rule successfully identified low-risk patients, and no cases of ciTBI were missed despite the reduced proportion of patients undergoing CT scans.

Web Services and Cloud Computing in Pediatric Care.

Electronic health record (EHR) systems do not uniformly implement pediatric-supportive functionalities. One method of adding these capabilities across EHR platforms is to integrate Web services and Web applications that may perform decision support and store data in the cloud when the EHR platform is able to integrate Web services. Specific examples of these services are described, such as immunization clinical decision support services, consumer health resources, and bilirubin nomograms. Health care providers, EHR vendors, and developers share responsibilities in the appropriate development, integration, and use of Web services and Web applications as they relate to best practices in the areas of data security and confidentiality, technical availability, audit trails, terminology and messaging standards, compliance with the Health Insurance Portability and Accountability Act, testing, usability, and other considerations. It is desirable for health care providers to have knowledge of Web services and Web applications that can improve pediatric capabilities in their own EHRs because this will naturally inform discussions concerning EHR features and facilitate implementation and subsequent use of these capabilities by clinicians caring for children.

User testing of a diagnostic decision support system with machine-assisted chart review to facilitate clinical genomic diagnosis.

OBJECTIVES: There is a need in clinical genomics for systems that assist in clinical diagnosis, analysis of genomic information and periodic reanalysis of results, and can use information from the electronic health record to do so. Such systems should be built using the concepts of human-centred design, fit within clinical workflows and provide solutions to priority problems. METHODS: We adapted a commercially available diagnostic decision support system (DDSS) to use extracted findings from a patient record and combine them with genomic variant information in the DDSS interface. Three representative patient cases were created in a simulated clinical environment for user testing. A semistructured interview guide was created to illuminate factors relevant to human factors in CDS design and organisational implementation. RESULTS: Six individuals completed the user testing process. Tester responses were positive and noted good fit with real-world clinical genetics workflow. Technical issues related to interface, interaction and design were minor and fixable. Testers suggested solving issues related to terminology and usability through training and infobuttons. Time savings was estimated at 30%-50% and additional uses such as in-house clinical variant analysis were suggested for increase fit with workflow and to further address priority problems. CONCLUSION: This study provides preliminary evidence for usability, workflow fit, acceptability and implementation potential of a modified DDSS that includes machine-assisted chart review. Continued development and testing using principles from human-centred design and implementation science are necessary to improve technical functionality and acceptability for multiple stakeholders and organisational implementation potential to improve the genomic diagnosis process.

An electronic decision support-based complex intervention to improve management of cardiovascular risk in primary health care: a cluster randomised trial (INTEGRATE).

OBJECTIVES: To determine whether a multifaceted primary health care intervention better controlled cardiovascular disease (CVD) risk factors in patients with high risk of CVD than usual care. DESIGN, SETTING: Parallel arm, cluster randomised trial in 71 Australian general practices, 5 December 2016 - 13 September 2019. PARTICIPANTS: General practices that predominantly used an electronic medical record system compatible with the HealthTracker electronic decision support tool, and willing to implement all components of the INTEGRATE intervention. INTERVENTION: Electronic point-of-care decision support for general practices; combination cardiovascular medications (polypills); and a pharmacy-based medication adherence program. MAIN OUTCOME MEASURES: Proportion of patients with high CVD risk not on an optimal preventive medication regimen at baseline who had achieved both blood pressure and low-density lipoprotein (LDL) cholesterol goals at study end. RESULTS: After a median 15 months' follow-up, primary outcome data were available for 4477 of 7165 patients in the primary outcome cohort (62%). The proportion of patients who achieved both treatment targets was similar in the intervention (423 of 2156; 19.6%) and control groups (466 of 2321; 20.1%; relative risk, 1.06; 95% CI, 0.85-1.32). Further, no statistically significant differences were found for a number of secondary outcomes, including risk factor screening, preventive medication prescribing, and risk factor levels. Use of intervention components was low; it was highest for HealthTracker, used at least once for 347 of 3236 undertreated patients with high CVD risk (10.7%). CONCLUSIONS: Despite evidence for the efficacy of its individual components, the INTEGRATE intervention was not broadly implemented and did not improve CVD risk management in participating Australian general practices. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12616000233426 (prospective).

Optimizing pharmacists' detection of prescribing errors: Comparison of on-ward and central pharmacy services.

WHAT IS KNOWN AND OBJECTIVE: Prescribing errors are the leading cause of adverse drug events in hospitalized patients. Pharmaceutical validation, defined as the review of drug orders by a pharmacist, associated with clinical decision support (CDS) systems, significantly reduces these errors and adverse drug events. In Belgium, because clinical pharmacy services have limited public financial support, most pharmaceutical validations are performed at the central pharmacy instead of on-ward, by hospital pharmacists doing dispensing activities. In that context, we aimed at evaluating whether the strategy of CDS-guided central validation was the most appropriate method to improve the quality and safety of medicines' use compared to an on-ward pharmaceutical validation. METHODS: Our retrospective observational study was conducted in a Belgian tertiary care hospital, in 2018-2019. Data were extracted from our validation software and pharmacists' charts. The outcomes of the study were the number of pharmaceutical interventions due to the detection of prescribing errors, reasons for interventions, their acceptance rate and their potential clinical impact (according to two blinded experts) in the central pharmacy and on-ward validation groups. RESULTS AND DISCUSSION: Despite the use of the same CDS, a pharmaceutical intervention following the detection of a prescribing error was made for 2.9% (20/698) of central group patients and 13.3% (93/701) of on-ward patients (χ2  = 49.97, p < 0.001). Interventions made at the central pharmacy (n = 20) mostly relied on CDS-alerts (i.e. drug-drug interaction [25%] or overdosing [20%]) while interventions made on-ward (n = 93) were also for pharmacotherapy optimization (i.e. no valid indication [25%] or inappropriate drug's choice [11%]). The on-ward validation group showed a higher acceptance rate compared to the central group (84% and 65%, respectively [Fisher's test, p = 0.053]). Proportions of interventions with significant or very significant clinical impact were similar between the two groups but as fewer interventions were made centrally, a significant proportion of errors were probably not detected by the central validation. WHAT IS NEW AND CONCLUSION: On-ward pharmaceutical validation leads to a higher rate of prescribing error detection. Pharmaceutical interventions made by on-ward pharmacists are also better accepted and more relevant, going further than CDS-alerts.

Clinical efficacy of a Decision Support Tool (Link-me) to guide intensity of mental health care in primary practice: a pragmatic stratified randomised controlled trial.

BACKGROUND: The volume and heterogeneity of mental health problems that primary care patients present with is a substantial challenge for health systems, and both undertreatment and overtreatment are common. We developed Link-me, a patient-completed Decision Support Tool, to predict severity of depression or anxiety, identify priorities, and recommend interventions. In this study, we aimed to examine if Link-me reduces psychological distress among individuals predicted to have minimal/mild or severe symptoms of anxiety or depression. METHODS: In this pragmatic stratified randomised controlled trial, adults aged 18-75 years reporting depressive or anxiety symptoms or use of mental health medication were recruited from 23 general practices in Australia. Participants completed the Decision Support Tool and were classified into three prognostic groups (minimal/mild, moderate, severe), and those in the minimal/mild and severe groups were eligible for inclusion. Participants were individually and randomly assigned (1:1) by a computer-generated allocation sequence to receive either prognosis-matched care (intervention group) or usual care plus attention control (control group). Participants were not blinded but intervention providers were only notified of those allocated to the intervention group. Outcome assessment was blinded. The primary outcome was the difference in the change in scores between the intervention and control group, and within prognostic groups, on the 10-item Kessler Psychological Distress Scale at 6 months post randomisation. The trial was registered on the Australian and New Zealand Clinical Trials Registry, ACTRN12617001333303. OUTCOMES: Between Nov 21, 2017, and Oct 31, 2018, 24 616 patients were invited to complete the eligibility screening survey. 1671 of these patients were included and randomly assigned to either the intervention group (n=834) or the control group (n=837). Prognosis-matched care was associated with greater reductions in psychological distress than usual care plus attention control at 6 months (p=0·03), with a standardised mean difference (SMD) of -0·09 (95% CI -0·17 to -0·01). This reduction was also seen in the severe prognostic group (p=0·003), with a SMD of -0·26 (-0·43 to -0·09), but not in the minimal/mild group (p=0·73), with a SMD of 0·04 (-0·17 to 0·24). In the complier average causal effect analysis in the severe prognostic group, differences were larger among those who received some or all aspects of the intervention (SMD range -0·58 to -1·15). No serious adverse effects were recorded. INTERPRETATION: Prognosis-based matching of interventions reduces psychological distress in patients with anxiety or depressive symptoms, particularly in those with severe symptoms, and is associated with better outcomes when patients access the recommended treatment. Optimisation of the Link-me approach and implementation into routine practice could help reduce the burden of disease associated with common mental health conditions such as anxiety and depression. FUNDING: Australian Government Department of Health.

Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals.

OBJECTIVE: To identify and summarize the current internal governance processes adopted by hospitals, as reported in the literature, for selecting, optimizing, and evaluating clinical decision support (CDS) alerts in order to identify effective approaches. MATERIALS AND METHODS: Databases (Medline, Embase, CINAHL, Scopus, Web of Science, IEEE Xplore Digital Library, CADTH, and WorldCat) were searched to identify relevant papers published from January 2010 to April 2020. All paper types published in English that reported governance processes for selecting and/or optimizing CDS alerts in hospitals were included. RESULTS: Eight papers were included in the review. Seven papers focused specifically on medication-related CDS alerts. All papers described the use of a multidisciplinary committee to optimize alerts. Other strategies included the use of clinician feedback, alert data, literature and drug references, and a visual dashboard. Six of the 8 papers reported evaluations of their CDS alert modifications following the adoption of optimization strategies, and of these, 5 reported a reduction in alert rate. CONCLUSIONS: A multidisciplinary committee, often in combination with other approaches, was the most frequent strategy reported by hospitals to optimize their CDS alerts. Due to the limited number of published processes, variation in system changes, and evaluation results, we were unable to compare the effectiveness of different strategies, although employing multiple strategies appears to be an effective approach for reducing CDS alert numbers. We recommend hospitals report on descriptions and evaluations of governance processes to enable identification of effective strategies for optimization of CDS alerts in hospitals.

Effects of a multifaceted intervention to promote the use of intravenous iron sucrose complex instead of ferric carboxymaltose in patients admitted for more than 24 h.

PURPOSE: Although more practical for use, the impact of ferric carboxymaltose (FCM) on the hospital budget is considerable, and intravenous iron sucrose complex (ISC) represents a cost-saving alternative for the management of iron deficiency anemia in patients during hospitalization. The Drug Committee decided to reserve FCM for day hospitalizations and contraindications to ISC, especially allergy. ISC was available for prescription for all other situations. METHODS: The impact of a multifaceted intervention promoting a switch from FCM to ISC was evaluated using an interrupted time series model with segmented regression analysis. The standardized rate of the dispensing of FCM, ISC, and oral iron by the hospital pharmacy, as well as the rate of the dispensing of packed red blood cells and the number of biological iron status measurements, was analyzed before and after the intervention. RESULTS: There was an immediate decrease in FCM consumption following the intervention, with a reduction of 88% (RR: 0.12 [CI95% 0.10 to 0.15]). Conversely, there was a large increase in ISC use (RR: 5.1 [CI95% 4.4 to 5.9]). We did not observe a prescription shift to packed red blood cells or oral iron after the intervention. The time series analysis showed the frequency of iron status testing to remain stable before and after. The direct savings for intravenous iron for 8 months were 187,417.54 €. CONCLUSION: Our intervention to lower the impact of intravenous iron therapy on the hospital budget was effective.

Transitioning Clinical Practice Guidelines into the Electronic Health Record through Clinical Pathways.

Clinical practice guidelines in oncology provide an evidence-based roadmap for most cancer care delivery but often lack directions for specific patient factors and disease conditions. Clinical pathways serve as a real-time clinical decision support system to translate guidelines to clinical practice. Pathways allow for the creation of a standardized, multidimensional roadmap for the continuum of care that can support clinical decision-making, maintain optimal outcomes, and limit unnecessary variation in cancer care. Here we describe the process to develop and implement clinical pathways in the electronic health record. This process includes building the appropriate foundation for a clinical pathways team with supports in the institutional ecosystem, creating visual representations of care paths, formalizing the pathway approval process, and translating clinical pathways into an electronic health record-integrated clinical decision support tool.

Development of a pharmacy 'patient prioritization tool' for use in a Tertiary Paediatric Hospital.

WHAT IS KNOWN AND OBJECTIVE: Pharmacists play an integral role in paediatric patient care by ensuring the safe and optimal use of medications. There are increasing demands on pharmacists' time and challenges to meet them within allocated resources, and therefore, it is important to ensure that resources are used efficiently. Patient prioritization tools for clinical pharmacists have been proposed via many studies, but are generally adult-based and/or have not been validated to confirm their effectiveness. The aim of this study was to create, pilot and validate a patient prioritization tool to be used by pharmacists providing clinical pharmacy services to paediatric patients. METHODS: A two-phase (retrospective and prospective) observational audit of pharmacists' interventions collected via notes made on their ward handover information sheets and patient case notes was conducted over a 2-year period in a tertiary paediatric hospital. A patient prioritization tool was created based on pharmacists' interventions in real time. This tool could be used at the start of the working day (without the need to review the patient or their case notes) to identify patients who would benefit most from a clinical pharmacist review. The tool was validated for effectiveness and selectivity. RESULTS AND DISCUSSION: The tool was easy to use and effective in identifying that 43% of paediatric inpatients did not require a routine clinical pharmacist review. It had 98% specificity in identifying patients who require a pharmacist intervention. It could be easily used at the start of the day to select patients for pharmacist review. WHAT IS NEW AND CONCLUSION: A new patient prioritization tool has been developed and validated for identifying paediatric inpatients requiring clinical pharmacist review.