Patient-specific Recommendations Research Articles

Over 40% of cancer patients use complementary and alternative medications while receiving anticancer treatment

BackgroundThe use of complementary and alternative medications (CAM) is popular among cancer patients. CAM includes vitamins, minerals, phytotherapy, homeopathy, nutritional supplements and probiotics. CAM use may lead to unwanted risks...

Comparing safety, performance and user perceptions of a patient-specific indication-based prescribing tool with current practice: a mixed methods randomised user testing study

BackgroundMedication errors are the leading cause of preventable harm in healthcare. Despite proliferation of medication-related clinical decision support systems (CDSS), current systems have limitations. We therefore developed an indication-based prescribing...

Use of artificial intelligence to predict outcomes in mild aortic valve stenosis

Abstract Aims Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS. Methods and results A comprehensive database including 9611 patients with serial transthoracic echocardiograms was collected from a single institution across three clinical sites. The data set included parameters from echocardiograms, electrocardiograms, laboratory values, and diagnosis codes. Data from a single clinical site were preserved as an independent test group. Machine learning models were trained to identify progression to severe stenosis and all-cause mortality and tested in their performance for endpoints at 2 and 5 years. In the independent test group, the AS progression model differentiated those with progression to severe AS within 2 and 5 years with an area under the curve (AUC) of 0.86 for both. The feature of greatest importance was aortic valve mean gradient, followed by other valve haemodynamic measurements including valve area and dimensionless index. The mortality model identified those with mortality within 2 and 5 years with an AUC of 0.84 and 0.87, respectively. Smaller reduced-input validation models had similarly robust findings. Conclusion Machine learning models can be used in patients with mild AS to identify those at high risk of disease progression and mortality. Implementation of such models may facilitate real-time, patient-specific follow-up recommendations.

Leveraging Knowledge Graphs for Enhanced Medical Reasoning in Personalized Medicine for Rare Diseases

Leveraging knowledge graphs offers a promising approach to enhancing medical reasoning in the personalized treatment of rare diseases. Knowledge graphs provide an effective framework for organizing and interpreting complex biomedical data, facilitating a more comprehensive approach to diagnosis, prognosis, and treatment of rare diseases. By integrating diverse data sources—such as genomic profiles, clinical histories, and pharmacological information—into a cohesive graph structure, the project aims to improve clinical outcomes by enabling patient-specific insights and treatment recommendations. Rare diseases pose unique challenges due to limited data and complex symptoms, making traditional diagnostic methods inadequate. Knowledge graphs have the potential to bridge these gaps by linking disparate datasets, allowing clinicians to identify biomarkers, make more accurate diagnoses, and formulate personalized treatment pathways. This project involves constructing a multi-layered knowledge graph, applying machine learning and reasoning algorithms for pattern detection, and validating its accuracy in real-world settings. Expected outcomes include improved diagnostic accuracy, the discovery of new biomarkers, and the development of a scalable knowledge graph framework adaptable for a wide range of diseases. This work aims to contribute to the advancement of personalized medicine, offering a model for AI-driven reasoning that supports more informed and timely clinical decisions in the treatment of rare diseases. By organizing complex biomedical data into interconnected graphs, clinicians can gain comprehensive insights into rare disease mechanisms, facilitating more accurate diagnoses and personalized treatment strategies. Integrating diverse data sources, such as genomic, clinical, and pharmacological information, the knowledge graph structure enables the identification of relationships and patterns that are often missed in traditional analyses. Machine learning algorithms further support this process by identifying potential biomarkers and recommending treatment pathways based on patient-specific data. Through real-world validation with clinical partners, the knowledge graph approach is expected to improve diagnostic accuracy and support early intervention strategies. Ultimately, this framework not only addresses the unique challenges of treating rare diseases but also establishes a scalable model that can be adapted for broader applications in personalized medicine.

Gender, BMI, and Age-Related Variations in Lower Limb Alignment Parameters and CPAK Phenotypes in Chinese Patients with Knee Osteoarthritis.

Research on the distribution of and the variation in coronal plane alignment of the knee (CPAK) in the Chinese osteoarthritis population is limited. We aimed to establish the CPAK classification based on the characteristics of lower limb alignment in the Chinese osteoarthritis population. We also investigated variations in lower limb alignment parameters and CPAK phenotypes based on gender, body mass index (BMI), and age. A retrospective study was conducted on a total of 944 knees diagnosed with osteoarthritis in 479 patients from January 2017 to December 2023. A scatterplot was used to describe the distribution of the CPAK classification, and the differences in lower limb alignment parameters and the CPAK classification were compared across genders (male, female), ages (middle-aged/<65 years, elderly/≥65 years), and BMI categories (normal/<25 kg/m2, overweight and obese/≥25 kg/m2) using the chi-squared test or Fisher's exact test. The average arithmetic hip-knee-ankle angle and joint line obliquity (JLO) were -3.03° ± 5.69° and 174.45° ± 4.29°, respectively. There was a higher prevalence of constitutional varus alignment in males and the overweight or obese group, while constitutional valgus alignment was more common in females and the normal BMI group (p < 0.05). Additionally, females had a greater apex distal JLO than males (p < 0.05). There were no statistically significant differences in lower limb alignment parameters among different age groups (p > 0.05). Although there were variations in alignment parameters across different genders and BMI categories in the knee osteoarthritis population, the predominant CPAK classifications were type I (38.03%), followed by type II (20.02%) and type IV (17.06%). The most common CPAK types were I, II, and IV, and they were not influenced by gender, BMI, or age, indicating that the CPAK classification can reliably reflect constitutional alignment. A better understanding of native alignment variability can aid in providing patient-specific recommendations when considering orthopedic alignment strategies.

Implementation of a pharmacist-guided pharmacogenomics dosing service at a rural NCI-designated comprehensive cancer center.

The goal of pharmacogenetic testing is to identify genetic variants with significant implications on drug safety and efficacy. Several professional organizations and institutions have demonstrated the value of pharmacist involvement in the implementation of pharmacogenomic services. Therefore, we aimed to establish a pharmacist-guided model for interpretation of pharmacogenetic results for all oncology patients seen at the Dartmouth Cancer Center (DCC) in Lebanon, NH. A pilot of a pharmacist-guided pharmacogenomics dosing service was implemented at the DCC. Pharmacy services included review of results from a next generation sequencing panel for DPYD, TPMT, NUDT15, and UGT1A1 variants. The pharmacist wrote a note in the electronic health record (EHR) detailing actionable drug-gene interactions and drug-dosing guidance, which was then routed to the treating oncologist. Outcomes collected included highlighting actionable mutations and defining pharmacist interventions. In addition, time spent formulating and documenting patient-specific drug-dosing recommendations was collected. From February 2024 through May 2024, a total of 71 patients with pharmacogenetic results, provided by the clinical molecular laboratory at Dartmouth Health, were reviewed by the pharmacist. The majority of patients tested were diagnosed with a malignancy of gastrointestinal origin. Twenty-one patients were found to have actionable variants in at least one of the four genes evaluated, and five of the 21 identified patients had active treatment plans for which dose changes were then implemented. Implementation of a pharmacist-guided pharmacogenomics based dosing service aided in optimizing drug therapy and has positioned Dartmouth Health for further expansion of pharmacogenomics and personalized patient care.

The Role of Artificial Intelligence in Clinical Decision Support Systems

Clinical Decision Support Systems (CDSS) are integral tools in modern healthcare, designed to assist clinicians by providing patient-specific recommendations based on vast medical data and knowledge. The advent of Artificial Intelligence (AI) has significantly enhanced CDSS, enabling sophisticated predictive analytics, early detection of complications, and personalized interventions. AI techniques like machine learning, natural language processing, and deep learning play crucial roles in refining CDSS functionality. However, challenges such as data quality, AI transparency, and clinician trust hinder widespread adoption. Future trends focus on improving AI integration in CDSS through better data representation, automation, and ethical considerations. This paper investigates the fundamental aspects of CDSS, the applications of AI in healthcare, and the challenges and future directions for AI-driven CDSS. Keywords: Clinical Decision Support Systems (CDSS), Artificial Intelligence (AI), Machine Learning, Healthcare Technology, Predictive Analytics.

Mapping Medical Context: Workshop-Driven Clustering of Influencing Factors on Medical Decision-Making.

Clinical decision support systems (CDSS) can efficiently support doctors in coping with ever-increasing amounts of data by providing evidence-based recommendations for medical decisions. To integrate the systems into the medical workflow and provide patient-specific recommendations for action in the context of personalized medicine, it is essential to tailor the systems to the context of use. This study aims to present an overview of factors influencing medical decision-making that CDSS must consider. Our approach involves the systematic identification and categorization of contextual factors relevant to medical decision-making. Through extensive literature research and a structured card-sorting workshop, we systematized 774 context factors and mapped them into a model. This model includes six primary entities: the treating physician, the patient, the patient's family, disease treatment, the physician's institution, and professional colleagues, each with their relevant context categories. The developed model could serve as a foundation for communication between developers and physicians, supporting the creation of more context-sensitive CDSS in the future. Ultimately, this could enhance the utilization of CDSS and improve patient care.

OPTIMIS PRO – A Novel Algorithm to Improve Perioperative Antibiotic Administration

Objectives The selection and dosing of surgical antimicrobial prophylaxis (SAP) to prevent surgical site infections (SSIs) is often improvisational and inappropriate in clinical settings resulting in increased risk of SSI. We therefore developed and implemented a novel computer decision support tool, OPTIMIS PRO (OPTIMIzing PROphylaxis), to improve appropriate SAP selection specific to each patient’s procedure and clinical context. Methods: This quality improvement study was conducted at a tertiary hospital network over 2 years, divided into pre-intervention (June 2021-June 2022) and post-intervention (June 2022-June 2023) periods. The intervention was a computer decision support tool programmed within the hospital’s health information system to provide patient-specific SAP recommendations based on four variables: procedure name, patient’s beta-lactam allergy status, MRSA status, and weight. Approximately 3046 unique surgical procedures were identified and a specific best practice SAP recommendation for each surgery was identified based on international practice guidelines, up-to-date literature, and panel expertise input from 14 surgical divisions at our institution. Safety of cefazolin prophylaxis among patients with self-reported beta-lactam allergy was established in the pre-operative clinic using a validated simple two-item questionnaire (Figure 1). During each standard preoperative preparation, a best practice SAP recommendation alert was then provided to the responsible anesthesiologist based on the inputs from the four aforementioned variables (Figure 2). To assess the impact of the OPTIMIS PRO tool on antibiotic prescribing, we retrospectively audited SAP selection before and after implementation, also assessing appropriateness for each of the specific inputs using evidence-based criteria. Results: Over 30 000 OPTIMIS PRO recommendation alerts were logged in the 12-month post-intervention period. A random sample audit of 408 surgical encounters were selected from the pre- and post-intervention period for analysis. Overall, appropriate antibiotic administration rose from 77% (161/208) to 92.5% (185/200) (x2=18.0, p &lt; 0 .001) post-intervention. Usage of cefazolin among patients reporting a beta lactam allergy rose from 48% (16/33) to 100% (12/12). None of these 12 patients experienced adverse reactions as a result of beta lactam exposure. Appropriate antibiotic selection based on MRSA status was high pre- and post-implementation (98.4% vs 99.4%); but significant improvements were made for procedure-specific antibiotic selection (80.5 vs 94.5%; x2=19.3, p &lt; 0 .001) and weight-based dosing (92.5% vs 98.4%; x2=7.45, p=0.006). Conclusion: In this first-ever intervention designed to direct SAP prescribing based on patient specific variables, we significantly improved appropriate SAP selection across a comprehensive list of surgical procedures. Future analysis should include assessing potential reductions in SSIs as result of using the support

(045) A PHYSICIAN'S GUIDE TO LUBRICANT FOR SEX

Abstract Introduction Lubricant use during sexual activity has numerous benefits, minimal harms, and can play a role in managing common concerns in primary care. However, navigating evidence-based conversations about lubricant use can be challenging since there is a lack of easily accessible and reliable information. As a result, physicians are often hesitant to discuss the topic and can only offer anecdotal advice. Objective To review literature on lubricants used during sexual activity to (1) inform the development of an inclusive and practical evidence-based tool for physicians to help incorporate patient-specific recommendations for lubricants into clinical practice, and (2) create accessible materials for patients. Methods We conducted a scoping review of peer-reviewed literature using the Arksey and O’Malley framework. Using relevant keywords, we searched two electronic databases, one search engine, and reference lists for articles published in 2003 and onward. Articles generated were manually reviewed for relevance with further inclusion criteria: (1) identifies patient populations most likely to benefit from using lubricant during sexual activity, (2) addresses benefits and drawbacks of different lubricant classes, or (3) describes ingredients in or properties of lubricants that may cause harm. All sexually active individuals were deemed eligible for inclusion in the study, with no restrictions placed on location, age, gender, or sexual orientation. Following the completion of our scoping review, we used an evidence-based approach to develop a clinical practice tool and patient handout. During their development and to validate our findings, family physicians, nurses, psychologists specializing in sexual concerns, and sexual health educators provided iterative review. Results 27 articles were deemed eligible for inclusion in our scoping review. Results showed lubricant use to be especially beneficial for patients who (1) experience dryness, pain, or sexual dysfunction during any type of sex, (2) are at a higher risk of STI transmission (by reducing condom breakage), or (3) want to reduce the risk of having an unplanned pregnancy (by reducing condom breakage). There are three main types of lubricant available: oil, water, and silicone-based. Oil-based lubricant is not recommended since it is incompatible with latex condoms and is more likely to irritate genital epithelium and negatively impact genital microbiota. When comparing silicone and water-based lubricant, silicone-based lubricant lasts longer but is more difficult to wash off. Silicone-based lubricant is not compatible with silicone-based sex toys, whereas water-based lubricant is compatible with all types of sex toys. Water-based lubricant is also more likely than silicone-based lubricant to contain potentially irritating ingredients and properties, such as glycerol, propylene glycol, parabens, or a high osmolality. For patients who are struggling with infertility, most lubricants should be avoided as they can negatively affect sperm motility and viability. Conclusions Numerous patients can benefit from lubricant use during sexual activity. In particular, silicone and water-based lubricant without harmful additives or properties are recommended. Our guide on lubricants will assist physicians in identifying patients who may benefit from using lubricant and incorporating patient-specific recommendations into clinical practice. This, in addition to our patient handout, will help to improve discussions between patients and healthcare providers regarding sexual well-being and lubricant use. Disclosure No.

Outcomes of personalized therapy guided by Exacta encyclopedic tumor analysis for women with broadly refractory advanced breast cancer.

e13124 Background: Following the failure of three lines of systemic treatments in breast cancers, Standard of Care guidelines typically recommend palliative care or participation in clinical trials for such patients. Breast cancer remains a global health threat, impacting lives worldwide. For refractory patients, the outlook is grim, with limited treatment options post multiple failures. Dismal outcomes underscore the urgency for innovative approaches, as traditional interventions often fall short. Methods: The Resilient trial was a Phase-II, single-arm, open-label clinical trial. Its primary objective was to assess the effectiveness of Exacta ETA-guided treatments across a spectrum of solid organ cancers in advanced refractory cancer patients. Exacta Encyclopedic Tumor Analysis (ETA) comprising the interrogation of gene alterations, gene expression, immunohistochemistry (IHC), and in vitro chemoresistance profiling (CRP) of viable tumor cells was performed on freshly obtained tumor tissue from 21 breast cancer patients with broadly refractory advanced breast cancers who had failed multiple lines of treatment. Six (29%) patients had metastasis to the brain, 11 (52%) patients had metastasis to the liver, 9 (43%) patients had metastasis to the lungs, and 15 (71%) patients had skeletal metastasis. The findings obtained through Exacta ETA were integrated to formulate patient-specific therapy recommendations. Personalized treatment plans were devised, and organ- and label-agnostic treatment regimens were administered to each patient. Patients were monitored to ascertain Overall Survival (OS). Results: Median overall survival (OS) was not reached at the time of analysis. By considering the OS values censored at the interim (most recent follow-up), the median OS in the entire cohort (n = 21) was 11.5 months (95% CI 4.5 to 16.9). The median OS was 11.4 months (95% CI 4.0 to 15.1) among patients with brain metastases, 14.5 months (95% CI 2.6 to 22.8) among patients with liver metastases, 11.9 months (95% CI 3.7 to 22.3) among patients with pulmonary metastases and 11.4 months (95% CI 3.7 to 22.3) among patients with skeletal metastases, respectively. No grade IV adverse events or treatment-related deaths were reported in the cohort. Conclusions: Exacta Encyclopedic Tumor Analysis (ETA) guided treatments provide a viable and meaningful treatment option for patients with advanced refractory breast cancers with metastatic deposits. The observed high OS in this heavily pretreated population underscores the value of multi-analyte guided of Exacta ETA as a valuable approach to managing these challenging clinical cases. Clinical trial information: CTRI/2018/02/011808.

Radiofrequency for chronic lumbosacral and cervical pain: Results of a consensus study using the RAND/UCLA appropriateness method.

Despite the routine use of radiofrequency (RF) for the treatment of chronic pain in the lumbosacral and cervical region, there remains uncertainty on the most appropriate patient selection criteria. This study aimed to develop appropriateness criteria for RF in relation to relevant patient characteristics, considering RF ablation (RFA) for the treatment of chronic axial pain and pulsed RF (PRF) for the treatment of chronic radicular pain. The RAND/UCLA Appropriateness Method (RUAM) was used to explore the opinions of a multidisciplinary European panel on the appropriateness of RFA and PRF for a variety of clinical scenarios. Depending on the type of pain (axial or radicular), the expert panel rated the appropriateness of RFA and PRF for a total of 219 clinical scenarios. For axial pain in the lumbosacral or cervical region, appropriateness of RFA was determined by the dominant pain trigger and location of tenderness on palpation with higher appropriateness scores if these variables were suggestive of the diagnosis of facet or sacroiliac joint pain. Although the opinions on the appropriateness of PRF for lumbosacral and cervical radicular pain were fairly dispersed, there was agreement that PRF is an appropriate option for well-selected patients with radicular pain due to herniated disc or foraminal stenosis, particularly in the absence of motor deficits. The panel outcomes were embedded in an educational e-health tool that also covers the psychosocial aspects of chronic pain, providing integrated recommendations on the appropriate use of (P)RF interventions for the treatment of chronic axial and radicular pain in the lumbosacral and cervical region. A multidisciplinary European expert panel established patient-specific recommendations that may support the (pre)selection of patients with chronic axial and radicular pain in the lumbosacral and cervical region for either RFA or PRF (accessible via https://rftool.org). Future studies should validate these recommendations by determining their predictive value for the outcomes of (P)RF interventions.

Optimizing pharmacogenomic decision-making by data science

Healthcare systems have made rapid progress towards combining data science with precision medicine, particularly in pharmacogenomics. With the lack of predictability in medication effectiveness from patient to patient, acquiring the specifics of their genotype would be highly advantageous for patient treatment. Genotype-guided dosing adjustment improves clinical decision-making and helps optimize doses to deliver medications with greater efficacy and within safe margins. Current databases demand extensive effort to locate relevant genetic dosing information. To address this problem, Patient Optimization Pharmacogenomics (POPGx) was constructed. The objective of this paper is to describe the development of POPGx, a tool to simplify the approach for healthcare providers to determine pharmacogenomic dosing recommendations for patients taking multiple medications. Additionally, this tool educates patients on how their allele variations may impact gene function in case they need further healthcare consultations. POPGx was created on Konstanz Information Miner (KNIME). KNIME is a modular environment that allows users to conduct code-free data analysis. The POPGx workflow can access Clinical Pharmacogenomics Implementation Consortium (CPIC) guidelines and subsequently be able to present relevant dosing and counseling information. A KNIME representational state transfer (REST) application program interface (API) node was established to retrieve information from CPIC and drugs that are exclusively metabolized through CYP450, and these drugs were processed simultaneously to demonstrate competency of the workflow. The POPGx program provides a time-efficient method for users to retrieve relevant, patient-specific medication selection and dosing recommendations. Users input metabolizer gene, genetic allele data, and medication list to retrieve clear dosing information. The program is automated to display current guideline recommendations from CPIC. The integration of this program into healthcare systems has the potential to revolutionize patient care by giving healthcare practitioners an easy way to prescribe medications with greater efficacy and safety by utilizing the latest advancements in the field of pharmacogenomics.

Economic evidence of clinical decision support systems in mental health: A systematic literature review.

Mental health conditions are among the highest disease burden on society, affecting approximately 20% of children and adolescents at any point in time, with depression and anxiety being the leading causes of disability globally. To improve treatment outcomes, healthcare organizations turned to clinical decision support systems (CDSSs) that offer patient-specific diagnoses and recommendations. However, the economic impact of CDSS is limited, especially in child and adolescent mental health. This systematic literature review examined the economic impacts of CDSS implemented in mental health services. We planned to follow PRISMA reporting guidelines and found only one paper to describe health and economic outcomes. A randomized, controlled trial of 336 participants found that 60% of the intervention group and 32% of the control group achieved symptom reduction, i.e. a 50% decrease as per the Symptom Checklist-90-Revised (SCL-90-R), a method to evaluate psychological problems and identify symptoms. Analysis of the incremental cost-effectiveness ratio found that for every 1% of patients with a successful treatment result, it added €57 per year. There are not enough studies to draw conclusions about the cost-effectiveness in a mental health context. More studies on economic evaluations of the viability of CDSS within mental healthcare have the potential to contribute to patients and the larger society.

Testing interventions to reduce clinical inertia in the treatment of hypertension: rationale and design of a pragmatic randomized controlled trial

Clinical inertia, or failure to intensify treatment when indicated, leads to suboptimal blood pressure control. Interventions to overcome inertia and increase antihypertensive prescribing have been modestly successful in part because their effectiveness varies based on characteristics of the provider, the patient, or the provider-patient interaction. Understanding for whom each intervention is most effective could help target interventions and thus increase their impact. This three-arm, randomized trial tests the effectiveness of 2 interventions to reduce clinical inertia in hypertension prescribing compared to usual care. Forty five primary care providers (PCPs) caring for patients with hypertension in need of treatment intensification completed baseline surveys that assessed behavioral traits and were randomized to one of three arms: 1) Pharmacist e-consult, in which a clinical pharmacist provided patient-specific recommendations for hypertension medication management to PCPs in advance of upcoming visits, 2) Social norming dashboards that displayed PCP's hypertension control rates compared to those of their peers, or 3) Usual care (no intervention). The primary outcome was the rate of intensification of hypertension treatment. We will compare this outcome between study arms and then evaluate the association between characteristics of providers, patients, their clinical interactions, and intervention responsiveness. Forty-five primary care providers were enrolled and randomized: 16 providers and 173 patients in the social norming dashboards arm, 15 providers and 143 patients in the pharmacist e-consult arm, and 14 providers and 150 patients in the usual care arm. On average, the mean patient age was 64 years, 47% were female, and 73% were white. Baseline demographic and clinical characteristics of patients were similar across arms, with the exception of more Hispanic patients in the usual care arm and fewest in the pharmacist e-consult arm. This study can help identify interventions to reduce inertia in hypertension care and potentially identify the characteristics of patients, providers, or patient-provider interactions to understand for whom each intervention would be most beneficial. Clinicaltrials.gov (NCT, Registered: NCT04603560).

Clinical Decision Support to Reduce Opioid Prescriptions for Dental Extractions using SMART on FHIR: Implementation Report.

Clinical decision support (CDS) has the potential to improve clinical decision-making consistent with evidence-based care. CDS can be designed to save health care providers time and help them provide safe and personalized analgesic prescribing. The aim of this report is to describe the development of a CDS system designed to provide dentists with personalized pain management recommendations to reduce opioid prescribing following extractions. The use of CDS is also examined. This study was conducted in HealthPartners, which uses an electronic health record (EHR) system that integrates both medical and dental information upon which the CDS application was developed based on SMART (Substitutable Medical Applications and Reusable Technologies) on FHIR (Fast Healthcare Interoperability Resources). The various tools used to bring relevant medical conditions, medications, patient history, and other relevant data into the CDS interface are described. The CDS application runs a drug interaction algorithm developed by our organization and provides patient-specific recommendations. The CDS included access to the state Prescription Monitoring Program database. The pain management CDS was implemented as part of a study examining opioid prescribing among patients undergoing dental extraction procedures from February 17, 2020, to May 14, 2021. Provider-level use of CDS at extraction encounters ranged from 0% to 87.4% with 12.1% of providers opening the CDS for no encounters, 39.4% opening the CDS for 1%-20% of encounters, 36.4% opening it for 21%-50% of encounters, and 12.1% opening it for 51%-87% of encounters. The pain management CDS is an EHR-embedded, provider-facing tool to help dentists make personalized pain management recommendations following dental extractions. The SMART on FHIR-based pain management CDS adapted well to the point-of-care dental setting and led to the design of a scalable CDS tool that is EHR vendor agnostic. ClinicalTrials.gov NCT03584789; https://clinicaltrials.gov/study/NCT03584789.

Timely PAH Identification in Adults With Repaired Congenital Heart Disease? The ACHD-QuERI Registry Insights

BackgroundThe Quality Enhancement Research Initiative (QuERI) in adults with congenital heart disease (ACHD) was developed to improve detection of pulmonary arterial hypertension (PAH) after repair of systemic-to-pulmonary arterial shunt lesions. ObjectivesThis study sought to standardize use of accepted criteria for PAH diagnosis and evaluate utility in at-risk patients with ACHD. MethodsPatients ≥18 years of age with ACHD repaired ≥1 year before enrollment and with additional risk factors for developing PAH were eligible. History, physical examination, electrocardiogram, transthoracic echocardiogram, World Health Organization functional class, and 6-minute walk distance were evaluated at baseline and yearly for 3 years. Pop-up reminders of patient-specific evidence-based recommendations for PAH detection appeared during data entry. ResultsAmong 217 eligible patients, mean age (enrollment) was 44.0 ± 15.9 years, 72.3% were women, and 82.0% were World Health Organization functional class I. Electrocardiogram was performed in >80% and TTE in >70% of patients annually; capture of required transthoracic echocardiography (TTE) measures and alignment between study- and core-center interpretation improved over time, with more frequent assessment of pulmonary arterial flow acceleration time and documentation of right ventricular outflow tract Doppler notching. Approximately 40% of patients had ≥2 high-risk features for PAH on TTE, but only 7% (6/82) underwent right heart catheterization (RHC). Using current definitions, 2 patients were confirmed by RHC to have a diagnosis of PAH (maximum follow-up 3 years). ConclusionsA structured protocol may improve screening for patients with repaired ACHD at risk of developing PAH. RHC may be underutilized in patients with ACHD with TTE findings suggestive of PAH. (Adult Congenital Heart Disease Registry [QuERI]; NCT01659411)

Computer-interpretable guidelines: electronic tools to enhance the utility of thyroid nodule clinical practice guidelines and risk stratification tools.

Clinicians seeking guidance for evaluating and managing thyroid nodules currently have several resources. The principal ones are narrative clinical guidelines and clinical risk calculators. This paper will review the strengths and weaknesses of both. The paper will introduce a concept of computer interpretable guideline, a novel way of transforming narrative guidelines in to a clinical decision support tool that can provide patient specific recommendations at the point of care. The paper then describes an experience of developing an interactive web based computer interpretable guideline for thyroid nodule management, called Thyroid Nodule Management App (TNAPP). The advantages of this approach and the potential barriers for widespread adaptation are discussed.

Always ask your pharmacist: Using the media to enhance student drug information skills

Background and purposeSocial media is a source for information and misinformation, including healthcare information. Pharmacy students are trained to critically assess health information literature; however, this does not typically extend into assessing claims made on social media platforms. Students were asked to evaluate social media claims to provide a patient-specific recommendation and response. Educational activity and settingEach student was given one of five media claims and asked to assess the validity of the claim to create a response and a recommendation for a provider and a patient. Students were placed in small groups for a verbal presentation of all the claims and a facilitator-led discussion about the pharmacist's role in healthcare information. FindingsStudents responded to the media claims and were able to support or reject the validity of the statements. They also created a variety of patient-facing informational documents. SummaryThe inclusion of questions derived from media sources is a unique way to incorporate drug information skills that is relevant to real-world practice.

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow

BackgroundAccurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates.MethodAn iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design.ResultsAccording to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7).ConclusionThrough a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.