Understanding responses to a renal dosing decision support system in primary care.

Clinical Decision Support Tools

Clinical decision support software (CDSS) has been increasingly implemented to assist improved prescribing practice. Reviews and studies report generally positive results regarding prescribing changes and, to a lesser extent, patient outcomes. Little information is available, however, concerning the use of CDSS in community pharmacy practice. Given the apparent paucity of publications examining this topic, we conducted a review to determine whether CDSS in community pharmacy practice can improve medication use and patient outcomes. A literature search of articles on CDSS relevant to community pharmacy and published between 1 January 2005 and 21 October 2013 was undertaken. Articles were included if the healthcare setting was community pharmacy and the article indicated that pharmacy use of CDSS was part of the study intervention. Eight studies were found which assessed counselling, selected drug interactions, inappropriate prescribing and under-prescribing. One study was halted due to insufficient data collection. Six studies showed statistically significant improvements in the measured outcomes: increased patient counselling, 31% reduced frequency of drug-drug interactions (DDIs), reduced frequency of inappropriate medications in the elderly (2·2-1·8% patients) and in pregnant women (5·5-2·9% patients), and increased pharmacists' interventions for under-prescribed low-dose aspirin (1·74 vs. 0·91 per 100 patients with type 2 diabetes) and over-prescribed high-dose proton-pump inhibitors (PPIs) (1·67 vs. 0·17 interventions per 100 high-dose PPI prescriptions). Most studies showed improved prescribing practice, via direct communication between pharmacists and doctors or indirectly via patient education. Factors limiting the impact of improved prescribing included alert fatigue and clinical inertia. No study investigated patient outcomes and little investigation had been undertaken on how CDSS could be best implemented. Few studies have been undertaken in community pharmacy practice, and based on the positive findings reported, further research should be directed in this area, including investigation of patient outcomes.

Clinical decision support systems (CDSSs) provides vital information for managing patients by advising clinicians through an alert or reminders about adverse events and medication errors. Clinicians receive a high number of alerts, resulting in alert override and workflow disruptions. A systematic review was carried out to identify factors affecting CDSS alert appropriateness in supporting clinical workflows using a recently introduced framework. The review findings identified several influencing factors of CDSS alert appropriateness including: technology (usability, alert presentation, workload and data entry), human (training, knowledge and skills, attitude and behavior), organization (rules and regulation, privacy and security) and process (waste, delay, tuning and optimization). The findings can be used to guide the design of CDSS alert and minimise potential safety hazards associated with CDSS use.

A strange paradox haunts our attempts to prevent ischemic heart disease. Medicine has addressed the relation between serum lipids and atherosclerosis through a series of scientific triumphs. First, epidemiology demonstrated the association between abnormal cholesterol levels and coronary artery disease. Then, physiology and biochemistry elucidated the pivotal role of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase in the synthesis of lipoproteins. Next, pharmacology led to the development of new medications to target that enzyme without the often-unbearable side effects of earlier drugs. And finally, clinical researchers enrolled tens of thousands of patients at hundreds of centers worldwide to prove the efficacy of these treatments and help guide their use. Taken together, these steps represent one of the finest examples of the application of science to understand and ameliorate human disease. Article p 371 But shifting our focus from the successful laboratory or clinical trial site to the population reveals more disappointing data. We physicians fail to diagnose potentially dangerous lipid levels in up to a third of our patients with cardiovascular disease1,2; when the problem is identified, we often do not properly deploy the impressive arsenal of treatments that have been put in our hands. In the United States, fewer than half of those who would benefit from lipid-modifying treatment for coronary heart disease risk reduction are receiving it.3 And of those on therapy, just half of patients actually achieve their cholesterol goals.4 This results in part from poor persistence by physicians in monitoring lipid levels and adjusting therapy accordingly, as well as poor persistence by patients in staying with their prescribed regimens for both economic and noneconomic reasons.5 In summary, despite the wealth of tools available to diagnose and manage this common condition, only a fraction of people who have it are being adequately treated. The …

Primary care physician attitudes concerning follow-up of abnormal test results and ambulatory decision support systems

Cancer Multidisciplinary Meeting (MDM) is a widely endorsed mechanism for ensuring high quality evidence-based health care. However, there are shortcomings that could ultimately result in unintended patient harm. On the other hand, clinical guidelines and clinical decision support systems (DSS) have been shown to improve decision-making in various measures. Nevertheless, their clinical use requires seamlessly interoperation with the existing electronic health record (EHR) platform to avoid the detrimental effects that duplication of data and work has in the quality of care. The aim of this work is to propose a computational framework to provide a clinical guideline-based DSS for breast cancer MDM. We discuss a range of design and implementation issues related to knowledge representation and clinical service delivery of the system, and propose a service oriented architecture based on the HL7 EHR functional model. The main result is the DSS named MATE (Multidisciplinary Assistant and Treatment sElector), which demonstrates that decision support can be effectively deployed in a real clinical setting and suggest that the technology could be generalised to other cancer MDMs.KeywordsCancer Multidisciplinary MeetingDecision Support SystemClinical GuidelineHL7 Functional ModelElectronic Health Record

In kidney transplant patients, clinically relevant drug-drug interactions (DDIs) with immunosuppressants potentially lead to serious adverse drug events (ADEs). The aim of this study was (i) to show that five clinical decision support systems (CDSSs) differ in their ability to identify clinically relevant potential DDIs (pDDIs) of immunosuppressants in kidney transplant patients and (ii) to compare CDSSs in terms of their ability to identify clinically relevant pDDIs in this context. All pDDIs being possible between nine immunosuppressants and 234 comedication drugs were identified for 264 intensive care unit (ICU) kidney transplant patients from 1999 to 2010. For pDDI identification, five CDSSs were used: DRUG-REAX® , ID PHARMA CHECK® , Lexi-Interact, mediQ and Meona. PDDIs from high severity categories were defined as clinically relevant. Classification of pDDIs as clinically relevant/non-clinically relevant by a clinical pharmacist using Stockley's Drug Interactions was employed as benchmark. We analysed inter-rater agreement, sensitivity, specificity, positive predictive value and negative predictive value. Clinical decision support systems generated a total of 759 pDDI alerts. A total of 240 pDDI alerts were in high severity categories. A total of 391 different pDDIs were identified. Only 5% (n = 35) of different pDDIs were identified by all CDSSs. A total of 49 pDDIs were classified as clinically relevant by clinical pharmacists' rating using Stockley's Drug Interactions. Meona (0·72) has the highest inter-rater agreement with the benchmark for clinically relevant pDDIs. ID PHARMA CHECK® and mediQ show highest sensitivities (0·74, respectively). Meona has the highest specificity (0·99) and positive predictive value (0·89). Five CDSSs differ in their ability to identify clinically relevant pDDIs of immunosuppressants in kidney transplant patients. Data may assist in selecting CDSSs for kidney transplant patients in the ICU. Using CDSSs to identify clinically relevant pDDIs could prevent ADEs and contribute to the overall goal of avoiding patient harm and increasing patient safety.

Health information technology, such as electronic medical records, computerized provider order entry systems, and clinical decision support systems,

BackgroundSuicide is a global public health concern, but it is preventable. Increased contact with primary care before the suicide or attempted suicide raises opportunities for intervention and prevention. However, suicide assessment and management are areas that many general practitioners (GPs) find particularly challenging. Previous research has indicated significant variability in how GPs understand, operationalize, and assess suicide risk, which subsequently has an impact on clinical decision making. Clinical decision support systems (CDSS) have been widely implemented across different health care settings, including primary care to support practitioners in clinical decision making. A CDSS may reduce inconsistencies in the identification, assessment, and management of suicide risk by GPs by guiding them through the consultation and generating a risk assessment plan that can be shared with a service user or with specialized mental health services.ObjectiveOur aim is to co-develop and test with end users (eg, GPs, primary care attendees, mental health professionals) an electronic clinical decision support system (e-CDSS) to support GPs in the identification, assessment, and management of suicidality in primary care.MethodsOurs is an ongoing embedded mixed-methods study with four phases: (1) qualitative interviews with GPs to explore their views on the content, format, and use of the e-CDSS, as well as consultation with two service-user advisory groups (people aged ≤25 and people aged ≥25) to inform the content of the e-CDSS including phrasing of items and clarity; (2) participatory co-production workshops with GPs, service users, and clinical experts in suicidality to determine the content and format of the e-CDDS; gain consensus of the relevance of items; establish content validity and identify pathways to implementation, using the Consolidated Framework for Implementation Research; (3) building the e-CDSS so that it guides the GP through a consultation; and (4) usability testing of the e-CDSS with GPs and service users in one primary care practice involving a nonlive and a live stage.ResultsThe study was funded for four years, to take place between 2015 and 2019, and is currently completing phase 4 data collection. The first results are expected to be submitted for publication in June 2019. The findings will enable us to evaluate the feasibility, acceptability, and usability of a suicide-specific, electronic, guided decision support system in primary care.ConclusionsThis study will be the first to explore the feasibility, acceptability, and usability of an electronic, guided decision support system for use in primary care consultations for the improved assessment and management of suicidality.International Registered Report Identifier (IRRID)RR1-10.2196/11135

The effectiveness of an electronic clinical-decision support tool embedded into the patient medical record at the point of care to guide Mohs surgery utilization: A cohort study

The interoperability of Electronic Health Records (EHR) and Clinical Decision Support (CDS) systems is a major challenge in the medical informatics field. International initiatives propose the use of ontologies for bridging both types of systems. The next-generation of EHR and CDS systems are supposed to use ontologies, or at least ontologies should be fundamental for enabling their interoperability. This situation makes necessary to analyze if current ontologies are ready for playing such intended role. In this paper we describe and discuss some important issues that need to be solved in order to have optimal ontologies for such a purpose, such as the need for increasing reuse in ontologies, as well as getting axiomatically richer ontologies. We also describe how our recent research results in the areas of ontology enrichment and ontology evaluation may contribute to such a goal.

Decision tools such as clinical decision support systems must be built on a solid foundation of nursing knowledge. However, current methods to determine the best evidence do not include a broad range of knowledge sources. As clinical decision support systems will be designed to assist nurses when making critical decisions, methods need to be devised to glean the best possible knowledge. This paper presents a comprehensive knowledge development process to develop a nursing clinical decision support system. The Nurse Computer Decision Support Project (N-CODES) is developing a prototype for a prospective decision support system. The prototype is being constructed on rules and cases generated by the best available evidence. To accommodate the range of decisions made in practice, different types of evidence are necessary. The process incorporates procedures to uncover, evaluate, and assimilate information to develop the knowledge domain for a clinical decision support systems. Both formal and practice-based knowledge are included. The model contains several innovative approaches including the use of clinical experts and a network of practicing clinicians. These strategies will assist scientists and practitioners interested in determining the best evidence to support clinical decision support systems.

BackgroundPharmacogenetics can impact patient care and outcomes through personalizing the selection of medicines, resulting in improved efficacy and a reduction in harmful side effects. Despite the existence of compelling clinical evidence and international guidelines highlighting the benefits of pharmacogenetics in clinical practice, implementation within the National Health Service in the United Kingdom is limited. An important barrier to overcome is the development of IT solutions that support the integration of pharmacogenetic data into health care systems. This necessitates a better understanding of the role of electronic health records (EHRs) and the design of clinical decision support systems that are acceptable to clinicians, particularly those in primary care.ObjectiveExplore the needs and requirements of a pharmacogenetic service from the perspective of primary care clinicians with a view to co-design a prototype solution.MethodsWe used ethnographic and think-aloud observations, user research workshops, and prototyping. The participants for this study included general practitioners and pharmacists. In total, we undertook 5 sessions of ethnographic observation to understand current practices and workflows. This was followed by 3 user research workshops, each with its own topic guide starting with personas and early ideation, through to exploring the potential of clinical decision support systems and prototype design. We subsequently analyzed workshop data using affinity diagramming and refined the key requirements for the solution collaboratively as a multidisciplinary project team.ResultsUser research results identified that pharmacogenetic data must be incorporated within existing EHRs rather than through a stand-alone portal. The information presented through clinical decision support systems must be clear, accessible, and user-friendly as the service will be used by a range of end users. Critically, the information should be displayed within the prescribing workflow, rather than discrete results stored statically in the EHR. Finally, the prescribing recommendations should be authoritative to provide confidence in the validity of the results. Based on these findings we co-designed an interactive prototype, demonstrating pharmacogenetic clinical decision support integrated within the prescribing workflow of an EHR.ConclusionsThis study marks a significant step forward in the design of systems that support pharmacogenetic-guided prescribing in primary care settings. Clinical decision support systems have the potential to enhance the personalization of medicines, provided they are effectively implemented within EHRs and present pharmacogenetic data in a user-friendly, actionable, and standardized format. Achieving this requires the development of a decoupled, standards-based architecture that allows for the separation of data from application, facilitating integration across various EHRs through the use of application programming interfaces (APIs). More globally, this study demonstrates the role of health informatics and user-centered design in realizing the potential of personalized medicine at scale and ensuring that the benefits of genomic innovation reach patients and populations effectively.

We sought to develop a computerized clinical decision support for clinicians treating patients with type 2 diabetes mellitus (T2DM). We designed, developed, and tested a computer-assisted decision support (CADS) system using statistical analyses of self-monitoring of blood glucose data, laboratory data, medical and medication history, and individualized hemoglobin A1c goals. A rule-based expert system generated recommendations for changes in therapy and accompanying explanations. A clinical decision support system (CADS) was developed that considers 9 classes of medications and 69 regimens with combinations of up to 4 therapeutic agents. The preferred sequences of regimens can be customized. The program is integrated with a "comprehensive diabetes management system," electronic medical record systems, and a method for uploading data from memory glucose meters via telephone without use of a computer or the Internet. The software provides a report to the clinician regarding the overall quality of glycemic control and identifies problems, e.g., hypoglycemia, hyperglycemia, glycemic variability, and insufficient data. The program can recommend continuation of current therapy, adjustment of dosages of current medications, or change of regimen and can provide explanations for its recommendations. If the user rejects the recommendations, the program will recommend alternative approaches. The CADS also provides access to Food and Drug Administration-approved prescribing information, guidelines from professional organizations, and selections from the general medical literature. The system has been extensively tested with real and synthetic data and is ready for evaluation in multicenter clinical trials. A clinical decision support system to assist with the management of patients with T2DM was designed, developed, tested, and found to perform well.

Safdar and Aslam's narrative evaluation of primary care chronic kidney disease management provides a comprehensive overview of current evidence-based approaches [1]. While their review effectively summarizes contemporary CKD screening, diagnosis, and management protocols, we propose focusing on two specific, well-evidenced implementation strategies that could substantially enhance the translation of these guidelines into routine primary care practice. Electronic health record-integrated clinical decision support (CDS) systems represent a promising yet complex intervention for addressing CKD management gaps in primary care. The largest cluster randomized trial to date, conducted by Sperl-Hillen et al. [2], involved 32 primary care clinics and 6420 patients with CKD stages G3–G4. This rigorous study provides critical insights into CDS effectiveness for CKD management. The trial demonstrated modest improvements favoring CDS intervention, though none achieved statistical significance: CKD diagnosis documentation increased from 21.8% to 26.6% (RR 1.17; 95% CI 0.91–1.51, p = 0.21), and nephrology referrals improved from 36.1% to 38.7% (RR 1.02; 95% CI 0.79–1.32, p = 0.86). However, other outcomes showed minimal differences, with blood pressure control remaining essentially unchanged (20.2% vs. 20.4%). The study revealed critical implementation barriers that limited CDS effectiveness. COVID-19 pandemic disruptions substantially reduced intervention exposure, with CDS print rates recovering to only 67% of pre-pandemic levels [2]. Team-based CKD management demonstrates substantially stronger evidence for clinical effectiveness compared to CDS systems alone. The nationwide Japanese multicenter study by Masanori et al. [3] examined 2954 CKD patients and provided compelling evidence for multidisciplinary care superiority. Additionally, implementation of a primary care CKD registry has been shown to improve identification and management of at-risk patients, facilitating team-based care and tracking outcomes in high-need populations [4]. Multidisciplinary care significantly reduced the composite endpoint of renal replacement therapy initiation and all-cause mortality compared to conventional care (HR 0.71; 95% CI 0.60–0.85, p = 0.0001) [3]. Teams involving more healthcare disciplines showed superior outcomes, with inpatient-based multidisciplinary care (mean 4.5 professionals) outperforming outpatient-based care (mean 2.6 professionals). Effective teams require nephrologists, specialist nurses, registered dietitians, pharmacists, and social workers working collaboratively [3]. For healthcare systems serving diverse populations, systematic cultural competency interventions show measurable effectiveness. Kanagaratnam et al.'s systematic review [5] identified specific culturally adapted CKD interventions with demonstrated clinical benefits. Cultural Health Liaison Officers, implemented in six studies involving 106 patients, improved clinical outcomes including blood pressure reduction compared to routine care [5]. Community-based interventions designed with local input and delivered through trusted community partners achieved superior outcomes compared to hospital-based programs. While CDS systems show promise, implementation requires careful attention to workflow integration, competing clinical priorities, and sustained organizational support to achieve effectiveness. Shaher Yar conceived, wrote, and revised the manuscript. Fizza Asghar revised the manuscript. Zahin Shahriar worked on references and data collection. Muhammad Shahzad Asif supervised the project. The authors have nothing to report. The authors declare no conflicts of interest. This article is linked to Safdar and Aslam papers. To view these articles, visit https://doi.org/10.1002/jgf2.70054. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.