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Katy E Trinkley,1– 4 Michael G Kahn,5 Larry A Allen,2,4 Heather Haugen,6 Miranda E Kroehl,7 Chen-Tan Lin,2,3 Daniel C Malone,8 Daniel D Matlock2,4,9 1Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, USA; 2Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA; 3Clinical Informatics, University of Colorado Health, Aurora, CO, USA; 4Adult and Child Consortium for Outcomes Research and Delivery Science, Aurora, CO, USA; 5Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA; 6University of Colorado, Colorado Clinical and Translational Sciences Institute (CCTSI), Aurora, CO, USA; 7Charter Communications Corporation, Greenwood Village, CO, USA; 8Department of Pharmacotherapy, University of Utah Skaggs College of Pharmacy, Salt Lake City, UT, USA; 9VA Eastern Colorado Geriatric Research Education and Clinical Center, Aurora, CO, USACorrespondence: Katy E TrinkleyUniversity of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, 12850 E Montview Blvd., Mail Stop C238, Aurora, CO 80045, USATel +1-303-724-6563Fax +1-303-724-0979Email katy.trinkley@cuanschutz.eduIntroduction: Consideration of patient preferences for guideline-directed medical therapies (GDMT) for heart failure with reduced ejection fraction (HFrEF) may help improve major gaps in prescribing and adherence. This study aimed to identify the range and relative priority of factors influencing patients’ decisions to take HFrEF medications.Materials and Methods: This was a convergent mixed methods study of patients with HFrEF. Focus groups were conducted to identify a list of factors followed by individuals rating and ranking the influence of each factor on their decision to take a medication. Using thematic analysis, we summarized preferences into categories.Results: Two focus groups with 13 participants reported 22 factors. Of the factors, “keeping you alive” was most commonly ranked in the top three (seven participants), followed by “communication and understanding” (six participants). Factors were summarized into six categories (listed in order of patient-reported influence): 1) demonstrated improvements in quality of life and longevity, 2) decreased risk of hospitalization, 3) opportunity for shared decision-making and trust in provider, 4) absence of adverse events, 5) affordability, and 6) convenience of taking and absence of interference with daily life.Conclusion: Patients prioritize treatment benefits and being informed more than risks, cost and inconvenience of taking HFrEF medications.Keywords: heart failure, treatment preferences, medication preferences, patient preferences

Katy E Trinkley,1 S Michelle Nikels,2 Robert L Page II,1 Melanie S Joy11Skaggs School of Pharmacy and Pharmaceutical Sciences, 2School of Medicine, University of Colorado, Aurora, CO, USA Objective: The purpose of this paper is to serve as a review for primary care providers on the bedside methods for estimating glomerular filtration rate (GFR) for dosing and chronic kidney disease (CKD) staging and to discuss how automated health information technologies (HIT) can enhance clinical documentation of staging and reduce medication errors in patients with CKD.Methods: A nonsystematic search of PubMed (through March 2013) was conducted to determine the optimal approach to estimate GFR for dosing and CKD staging and to identify examples of how automated HITs can improve health outcomes in patients with CKD. Papers known to the authors were included, as were scientific statements. Articles were chosen based on the judgment of the authors.Results: Drug-dosing decisions should be based on the method used in the published studies and package labeling that have been determined to be safe, which is most often the Cockcroft–Gault formula unadjusted for body weight. Although Modification of Diet in Renal Disease is more commonly used in practice for staging, the CKD–Epidemiology Collaboration (CKD–EPI) equation is the most accurate formula for estimating the CKD staging, especially at higher GFR values. Automated HITs offer a solution to the complexity of determining which equation to use for a given clinical scenario. HITs can educate providers on which formula to use and how to apply the formula in a given clinical situation, ultimately improving appropriate medication and medical management in CKD patients.Conclusion: Appropriate estimation of GFR is key to optimal health outcomes. HITs assist clinicians in both choosing the most appropriate GFR estimation formula and in applying the results of the GFR estimation in practice. Key limitations of the recommendations in this paper are the available evidence. Further studies are needed to better understand the best method for estimating GFR.Keywords: laboratory automation, glomerular filtration rate, medications, dose adjustment

Importance: Poor medication adherence is common. Text messaging is increasingly used to change patient behavior but often not rigorously tested., Objective: To compare different types of text messaging strategies with usual care to improve medication refill adherence among patients nonadherent to cardiovascular medications., Design, Setting, and Participants: Patient-level randomized pragmatic trial between October 2019 to April 2022 at 3 US health care systems, with last follow-up date of April 11, 2023. Adult (18 to <90 years) patients were eligible based on diagnosis of 1 or more cardiovascular condition(s) and prescribed medication to treat the condition. Patients who did not opt out and had a 7-day refill gap were randomized to 1 of 4 study groups., Intervention(s): Generic text message refill reminders (generic reminder); behavioral nudge text refill reminders (behavioral nudge); behavioral nudge text refill reminders plus a fixed-message chatbot (behavioral nudge + chatbot); usual care., Main Outcomes and Measures: Primary outcome was refill adherence based on pharmacy data using proportion of days covered at 12 months. Secondary outcomes were clinical events of emergency department visits, hospitalizations, and mortality., Results: Among 9501 enrolled patients, baseline characteristics across the 4 groups were comparable (mean age, 60 years; 47% female [n = 4351]; 16% Black [n = 1517]; 49% Hispanic [n = 4564]). At 12 months, the mean proportion of days covered was 62.0% for generic reminder, 62.3% for behavioral nudge, 63.0% for behavioral nudge + chatbot, and 60.6% for usual care (P = .06). In adjusted analysis, when compared with usual care, mean proportion of days covered was 2.2 percentage points (95% CI, 0.3-4.2; P = .02) higher for generic reminder, 2.0 percentage points (95% CI, 0.1-3.9; P = .04) higher for behavioral nudge, and 2.3 percentage points (95%, 0.4-4.2; P = .02) higher for behavioral nudge + chatbot, none of which were statistically significant after multiple comparisons correction. There were no differences in clinical events between study groups., Conclusions and Relevance: Text message reminders targeting patients who delay refilling their cardiovascular medications did not improve medication adherence based on pharmacy refill data or reduce clinical events at 12 months. Poor medication adherence may be due to multiple factors. Future interventions may need to be designed to address the multiple factors influencing adherence., Trial Registration: ClinicalTrials.gov Identifier: NCT03973931.

Background: Coprescribing naloxone with opioid analgesics is a Centers for Disease Control and Prevention (CDC) best practice to mitigate the risk of fatal opioid overdose, yet coprescription by emergency medicine clinicians is rare, occurring less than 5% of the time it is indicated. Clinical decision support (CDS) has been associated with increased naloxone prescribing; however, key CDS design characteristics and pragmatic outcome measures necessary to understand replicability and effectiveness have not been reported., Objective: This study aimed to rigorously evaluate and quantify the impact of CDS designed to improve emergency department (ED) naloxone coprescribing. We hypothesized CDS would increase naloxone coprescribing and the number of naloxone prescriptions filled by patients discharged from EDs in a large health care system., Methods: Following user-centered design principles, we designed and implemented a fully automated, interruptive, electronic health record-based CDS to nudge clinicians to coprescribe naloxone with high-risk opioid prescriptions. "High-risk" opioid prescriptions were defined as any opioid analgesic prescription ≥90 total morphine milligram equivalents per day or for patients with a prior diagnosis of opioid use disorder or opioid overdose. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to evaluate pragmatic CDS outcomes of reach, effectiveness, adoption, implementation, and maintenance. Effectiveness was the primary outcome of interest and was assessed by (1) constructing a Bayesian structural time-series model of the number of ED visits with naloxone coprescriptions before and after CDS implementation and (2) calculating the percentage of naloxone prescriptions associated with CDS that were filled at an outpatient pharmacy. Mann-Kendall tests were used to evaluate longitudinal trends in CDS adoption. All outcomes were analyzed in R (version 4.2.2; R Core Team)., Unlabelled: Between November 2019 and July 2023, there were 1,994,994 ED visits. CDS reached clinicians in 0.83% (16,566/1,994,994) of all visits and 15.99% (16,566/103,606) of ED visits where an opioid was prescribed at discharge. Clinicians adopted CDS, coprescribing naloxone in 34.36% (6613/19,246) of alerts. CDS was effective, increasing naloxone coprescribing from baseline by 18.1 (95% CI 17.9-18.3) coprescriptions per week or 2,327% (95% CI 3390-3490). Patients filled 43.80% (1989/4541) of naloxone coprescriptions. The CDS was implemented simultaneously at every ED and no adaptations were made to CDS postimplementation. CDS was maintained beyond the study period and maintained its effect, with adoption increasing over time (τ=0.454; P<.001)., Conclusions: Our findings advance the evidence that electronic health record-based CDS increases the number of naloxone coprescriptions and improves the distribution of naloxone. Our time series analysis controls for secular trends and strongly suggests that minimally interruptive CDS significantly improves process outcomes., (© Stuart W Sommers, Heather J Tolle, Katy E Trinkley, Christine G Johnston, Caitlin L Dietsche, Stephanie V Eldred, Abraham T Wick, Jason A Hoppe. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Objective: Understand perceived barriers to and facilitators of using clinical informatics applications for pharmacogenomic (PGx) implementation in resource-limited settings., Materials and Methods: We conducted a qualitative research study using a semi-structured interview guide informed by the Consolidated Framework for Implementation Research (CFIR). Interview questions assessed CFIR contextual determinants related to: electronic health record (EHR) infrastructure; clinical informatics personnel and resources; EHR integration of PGx test results; PGx clinical decision support (CDS) tools; institutional resources; and partner receptivity. Transcripts were coded and analyzed to identify themes., Results: We interviewed 24 clinical informaticists and executive leaders working in rural or underserved health care settings in Montana ( n  = 15) and Colorado ( n  = 9) and identified three major themes: (1) EHR infrastructure limitations, (2) insufficient supporting resources, and (3) unique contextual considerations for resource-limited settings. EHR infrastructure limitations included limited agency related to EHR build and interoperability concerns. Theme 1 highlighted challenges associated with integrating structured data into the EHR and inadequate vendor support. Theme 2 included limited familiarity with PGx across the care team, cost concerns, and allocation of non-financial resources. Theme 3 highlighted perceptions about the clinical utility of PGx within rural and underrepresented populations. Potential facilitators, such as being able to act nimbly, were found to coexist among the reported barriers., Discussion and Conclusion: Our results provide insight into the clinical informatics infrastructure in resource-limited settings and identify unique considerations for clinical informatics-facilitated PGx implementation. Future efforts in these settings should consider innovative partnerships and strategies to leverage facilitators and minimize barriers associated with integrating PGx CDS applications., Competing Interests: The authors have no competing interests to declare., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving these goals, implementation science (IS) and its frameworks are increasingly being leveraged to ensure that LHSs are feasible, rapid, iterative, reliable, reproducible, equitable, and sustainable. However, 6 key challenges limit the application of IS to EHR-driven LHSs: barriers to team science, limited IS experience, data and technology limitations, time and resource constraints, the appropriateness of certain IS approaches, and equity considerations. Using 3 case studies from diverse health settings and 1 IS framework, we illustrate these challenges faced by LHSs and offer solutions to overcome the bottlenecks in applying IS and utilizing EHRs, which often stymie LHS progress. We discuss the lessons learned and provide recommendations for future research and practice, including the need for more guidance on the practical application of IS methods and a renewed emphasis on generating and accessing inclusive data., (©Katy E Trinkley, Anna M Maw, Cristina Huebner Torres, Amy G Huebschmann, Russell E Glasgow. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.10.2024.)

A potential contributor to the suboptimal rates of guideline directed medical therapy (GDMT) prescribing for heart failure with reduced ejection fraction (HFrEF) is the burden of multimorbidity in patients with HFrEF. We examined the effect of multimorbidity on GDMT prescription in the EPIC-HF trial, finding that multimorbidity was associated with decreased likelihood of GDMT intensification. Further study is needed to guide treatment in high-risk, multimorbid patients with HFrEF., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Larry Allen reports financial support was provided by 10.13039/100000968American Heart Association. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: The iPRISM webtool is an interactive tool designed to aid the process of applying the Practical, Robust Implementation and Sustainability Model (PRISM) for the assessment of and fit with context. A learning community (LC) is a multidisciplinary group of partners addressing a complex problem. Our LC coproduced the Physical TheraPy frEqueNcy Clinical decIsion support tooL (PT-PENCIL) to guide the use of physical therapist services in acute care hospitals., Objective: To describe our LC's activities to co-produce the PT-PENCIL, use of the iPRISM webtool to assess its preimplementation context and fit, and develop a multicomponent implementation strategy for the PT-PENCIL., Design: A descriptive research design., Setting: Three tertiary care hospitals., Participants: Thirteen LC partners: six clinical physical therapists, three rehabilitation managers, three researchers, and a bioinformaticist., Interventions: Not applicable., Outcome Measures: Using the iPRISM webtool, expected fit of the PT-PENCIL was rated 1 (not aligned) to 6 (well aligned) for each PRISM domain and expected reach, effectiveness, adoption, implementation, and maintenance were rated 1 (not likely at all) to 6 (very likely). Discrete implementation strategies were identified from the Expert Recommendations for Implementing Change., Results: The process spanned 18 meetings over 8 months. Ten LC partners completed the iPRISM webtool. PRISM domains with the lowest expected alignment were the "implementation and sustainability infrastructure" (mean = 4.7 out of 6; range = 3-6) and the "external environment" (mean = 4.9 of 6; range = 4-6). Adoption was the outcome with the lowest expected likelihood (mean = 4.5 out of 6; range = 1-6). Six discrete implementation strategies were identified and combined into a multicomponent strategy., Conclusions: Within a LC, we used existing implementation science resources to co-produce a novel clinical decision support tool for acute care physical therapists and develop a strategy for its implementation. Our methodology can be replicated for similar projects given the public availability of each resource used., (© 2024 The Author(s). PM&R published by Wiley Periodicals LLC on behalf of American Academy of Physical Medicine and Rehabilitation.)

Drug-induced QT prolongation (diLQTS), and subsequent risk of torsade de pointes, is a major concern with use of many medications, including for non-cardiac conditions. The possibility that genetic risk, in the form of polygenic risk scores (PGS), could be integrated into prediction of risk of diLQTS has great potential, although it is unknown how genetic risk is related to clinical risk factors as might be applied in clinical decision-making. In this study, we examined the PGS for QT interval in 2500 subjects exposed to a known QT-prolonging drug on prolongation of the QT interval over 500ms on subsequent ECG using electronic health record data. We found that the normalized QT PGS was higher in cases than controls (0.212±0.954 vs. -0.0270±1.003, P = 0.0002), with an unadjusted odds ratio of 1.34 (95%CI 1.17-1.53, P<0.001) for association with diLQTS. When included with age and clinical predictors of QT prolongation, we found that the PGS for QT interval provided independent risk prediction for diLQTS, in which the interaction for high-risk diagnosis or with certain high-risk medications (amiodarone, sotalol, and dofetilide) was not significant, indicating that genetic risk did not modify the effect of other risk factors on risk of diLQTS. We found that a high-risk cutoff (QT PGS ≥ 2 standard deviations above mean), but not a low-risk cutoff, was associated with risk of diLQTS after adjustment for clinical factors, and provided one method of integration based on the decision-tree framework. In conclusion, we found that PGS for QT interval is an independent predictor of diLQTS, but that in contrast to existing theories about repolarization reserve as a mechanism of increasing risk, the effect is independent of other clinical risk factors. More work is needed for external validation in clinical decision-making, as well as defining the mechanism through which genes that increase QT interval are associated with risk of diLQTS., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Simon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: To describe our experiences implementing and iterating CYP2C19 genotype-guided clopidogrel pharmacogenetic clinical decision support (CDS) tools over time in the setting of a large health system-wide, preemptive pharmacogenomics program., Summary: Clopidogrel-treated patients who are genetically predicted cytochrome P450 isozyme 2C19 (CYP2C19) intermediate or poor metabolizers have an increased risk of atherothrombotic events, some of which can be life-threatening. The Clinical Pharmacogenetics Implementation Consortium provides guidance for the use of clopidogrel based on CYP2C19 genotype in patients with cardiovascular and cerebrovascular diseases. Our multidisciplinary team implemented an automated, interruptive alert that fires when clopidogrel is ordered or refilled for biobank participants with structured CYP2C19 intermediate or poor metabolizer genomic indicators in the electronic health record. The implementation began with a narrow cardiovascular indication and setting and was then scaled in 4 primary dimensions: (1) clinical indication; (2) availability across health-system locations; (3) care venue (e.g., inpatient vs outpatient); and (4) provider groups (eg, cardiology and neurology). We iterated our approach over time based on evolving clinical evidence and proactive strategies to optimize CDS maintenance and sustainability. A key facilitator of expansion was socialization of the broader pharmacogenomics initiative among our academic medical center community, accompanied by clinician acceptance of pharmacogenetic alerts in practice., Conclusion: A multidisciplinary collaboration is recommended to facilitate the use of CYP2C19 genotype-guided antiplatelet therapy in patients with cardiovascular and cerebrovascular diseases. Evolving clopidogrel pharmacogenetic evidence necessitates thoughtful iteration of implementation efforts and strategies to optimize long-term maintenance and sustainability., (© American Society of Health-System Pharmacists 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Rationale: Little is known about the prescribing of medications with potential to cause QTc-prolongation in the ambulatory care settings. Understanding real-world prescribing of QTc-prolonging medications and actions taken to mitigate this risk will help guide strategies to optimize safety and appropriate prescribing among ambulatory patients., Objective: To evaluate the frequency of clinician action taken to monitor and mitigate modifiable risk factors for QTc-prolongation when indicated., Methods: This retrospective, cross-sectional study evaluated clinician action at the time of prescribing prespecified medications with potential to prolong QTc in adult patients in primary care. The index date was defined as the date the medication was ordered. Electronic health record (EHR) data were evaluated to assess patient, clinician and visit characteristics. Clinician action was determined if baseline or follow-up monitoring was ordered or if action was taken to mitigate modifiable risk factors (laboratory abnormalities or electrocardiogram [ECG] monitoring) within 48 h of prescribing a medication with QTc-prolonging risk. Descriptive statistics were used to describe current practice., Results: A total of 399 prescriptions were prescribed to 386 patients, with a mean age of 51 ± 18 years, during March 2021 from a single-centre, multisite health system. Of these, 17 (4%) patients had a known history of QTc-prolongation, 170 (44%) did not have a documented history of QTc-prolongation and 199 (52%) had an unknown history (no ECG documented). Thirty-nine patients (10%) had at least one laboratory-related risk factor at the time of prescribing, specifically hypokalemia (16 patients), hypomagnesemia (8 patients) or hypocalcemia (19 patients). Of these 39 patients with laboratory risk factors, only 6 patients (15%) had their risk acknowledged or addressed by a clinician. Additionally, eight patients' most recent QTc was ≥500 ms and none had an ECG checked at the time the prescription was ordered., Conclusion: Despite national recommendations, medication monitoring and risk mitigation is infrequent when prescribing QTc-prolonging medications in the ambulatory care setting. These findings call for additional research to better understand this gap, including reasons for the gap and consequences on patient outcomes., (© 2023 John Wiley & Sons Ltd.)

Background: The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods., Main Text: This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly., Conclusions: Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically., (© 2024. The Author(s).)

Implementation science theories, models, and frameworks (TMF) should help users understand complex issues in translating research into practice, guide selection of appropriate implementation strategies, and evaluate implementation outcomes. They should also be sensitive to evidence from projects that apply the framework, evolve based on those experiences, and be accessible to a range of users. This paper describes these issues as they relate to the Practical, Robust Implementation and Sustainability Model (PRISM). PRISM was created to assess key multilevel contextual factors related to the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) outcomes of health interventions. We describe key aspects of PRISM and how it has been applied, evolved, and adapted across settings, time, and content areas. Since its development in 2008 PRISM has been used in over 200 publications, with increased use in recent years. It has been used for a wide variety of purposes and more recent applications have focused on increasing its accessibility for non-researcher groups and more rapid and iterative application for use in learning heath systems. PRISM has been applied to address health equity issues including representation, representativeness, and co-creation activities in both US and non-US settings. We describe common types of adaptations made by implementation teams when applying PRISM to fit with the resources and priorities of diverse and low-resource settings. We conclude by summarizing lessons learned and providing recommendations for future research and practice using PRISM., Competing Interests: Conflict of interestThere are no conflicts of interest to disclose., (© The Author(s) 2024.)

Background: Reasons for suboptimal prescribing for heart failure with reduced ejection fraction (HFrEF) have been identified, but it is unclear if they remain relevant with recent advances in healthcare delivery and technologies. This study aimed to identify and understand current clinician-perceived challenges to prescribing guideline-directed HFrEF medications., Methods: We conducted content analysis methodology, including interviews and member-checking focus groups with primary care and cardiology clinicians. Interview guides were informed by the Cabana Framework., Results: We conducted interviews with 33 clinicians (13 cardiology specialists, 22 physicians) and member checking with 10 of these. We identified four levels of challenges from the clinician perspective. Clinician level challenges included misconceptions about guideline recommendations, clinician assumptions (e.g., drug cost or affordability), and clinical inertia. Patient-clinician level challenges included misalignment of priorities and insufficient communication. Clinician-clinician level challenges were primarily between generalists and specialists, including lack of role clarity, competing priorities of providing focused versus holistic care, and contrasting confidence regarding safety of newer drugs. Policy and system/organisation level challenges included insufficient access to timely/reliable patient data, and unintended care gaps for medications without financially incentivized metrics., Conclusion: This study presents current challenges faced by cardiology and primary care which can be used to strategically design interventions to improve guideline-directed care for HFrEF. The findings support the persistence of many challenges and also sheds light on new challenges. New challenges identified include conflicting perspectives between generalists and specialists, hesitancy to prescribe newer medications due to safety concerns, and unintended consequences related to value-based reimbursement metrics for select medications., (© 2023 John Wiley & Sons Ltd.)

Objectives: In a randomized controlled trial, we found that applying implementation science (IS) methods and best practices in clinical decision support (CDS) design to create a locally customized, "enhanced" CDS significantly improved evidence-based prescribing of β blockers (BB) for heart failure compared with an unmodified commercially available CDS. At trial conclusion, the enhanced CDS was expanded to all sites. The purpose of this study was to evaluate the real-world sustained effect of the enhanced CDS compared with the commercial CDS., Methods: In this natural experiment of 28 primary care clinics, we compared clinics exposed to the commercial CDS (preperiod) to clinics exposed to the enhanced CDS (both periods). The primary effectiveness outcome was the proportion of alerts resulting in a BB prescription. Secondary outcomes included patient reach and clinician adoption (dismissals)., Results: There were 367 alerts for 183 unique patients and 171 unique clinicians (pre: March 2019-August 2019; post: October 2019-March 2020). The enhanced CDS increased prescribing by 26.1% compared with the commercial (95% confidence interval [CI]: 17.0-35.1%), which is consistent with the 24% increase in the previous study. The odds of adopting the enhanced CDS was 81% compared with 29% with the commercial (odds ratio: 4.17, 95% CI: 1.96-8.85). The enhanced CDS adoption and effectiveness rates were 62 and 14% in the preperiod and 92 and 10% in the postperiod., Conclusion: Applying IS methods with CDS best practices was associated with improved and sustained clinician adoption and effectiveness compared with a commercially available CDS tool., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: To increase uptake of implementation science (IS) methods by researchers and implementers, many have called for ways to make it more accessible and intuitive. The purpose of this paper is to describe the iPRISM webtool (Iterative, Practical, Robust Implementation and Sustainability Model) and how this interactive tool operationalizes PRISM to assess and guide a program's (a) alignment with context, (b) progress on pragmatic outcomes, (c) potential adaptations, and (d) future sustainability across the stages of the implementation lifecycle., Methods: We used an iterative human-centered design process to develop the iPRISM webtool., Results: We conducted user-testing with 28 potential individual and team-based users who were English and Spanish speaking from diverse settings in various stages of implementing different types of programs. Users provided input on all aspects of the webtool including its purpose, content, assessment items, visual feedback displays, navigation, and potential application. Participants generally expressed interest in using the webtool and high likelihood of recommending it to others. The iPRISM webtool guides English and Spanish-speaking users through the process of iteratively applying PRISM across the lifecycle of a program to facilitate systematic assessment and alignment with context. The webtool summarizes assessment responses in graphical and tabular displays and then guides users to develop feasible and impactful adaptations and corresponding action plans. Equity considerations are integrated throughout., Conclusions: The iPRISM webtool can intuitively guide individuals and teams from diverse settings through the process of using IS methods to iteratively assess and adapt different types of programs to align with the context across the implementation lifecycle. Future research and application will continue to develop and evaluate this IS resource., (© 2023. BioMed Central Ltd.)

Objective: To compare the effectiveness of 2 clinical decision support (CDS) tools to avoid prescription of nonsteroidal anti-inflammatory drugs (NSAIDs) in patients with heart failure (HF): a "commercial" and a locally "customized" alert., Methods: We conducted a retrospective cohort study of 2 CDS tools implemented within a large integrated health system. The commercial CDS tool was designed according to third-party drug content and EHR vendor specifications. The customized CDS tool underwent a user-centered design process informed by implementation science principles, with input from a cross disciplinary team. The customized CDS tool replaced the commercial CDS tool. Data were collected from the electronic health record via analytic reports and manual chart review. The primary outcome was effectiveness, defined as whether the clinician changed their behavior and did not prescribe an NSAID., Results: A random sample of 366 alerts (183 per CDS tool) was evaluated that represented 355 unique patients. The commercial CDS tool was effective for 7 of 172 (4%) patients, while the customized CDS tool was effective for 81 of 183 (44%) patients. After adjusting for age, chronic kidney disease, ejection fraction, NYHA class, concurrent prescription of an opioid or acetaminophen, visit type (inpatient or outpatient), and clinician specialty, the customized alerts were at 24.3 times greater odds of effectiveness compared to the commercial alerts (OR: 24.3 CI: 10.20-58.06)., Conclusion: Investing additional resources to customize a CDS tool resulted in a CDS tool that was more effective at reducing the total number of NSAID orders placed for patients with HF compared to a commercially available CDS tool., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction/background: Patients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. Machine learning can overcome many limitations of traditional methods to capture complex relationships in data and lead to a more comprehensive understanding of the underpinnings driving underprescribing. Here, we used machine learning methods and routinely available electronic health record data to identify predictors of prescribing., Methods: We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications for adults with HFrEF: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), evidence-based beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). The models with the best predictive performance were used to identify the top 20 characteristics associated with prescribing each medication type. Shapley values were used to provide insight into the importance and direction of the predictor relationships with medication prescribing., Results: For 3,832 patients meeting the inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB, and 40% an MRA. The best-predicting model for each medication type was a random forest (area under the curve: 0.788-0.821; Brier score: 0.063-0.185). Across all medications, top predictors of prescribing included prescription of other evidence-based medications and younger age. Unique to prescribing an ARNI, the top predictors included lack of diagnoses of chronic kidney disease, chronic obstructive pulmonary disease, or hypotension, as well as being in a relationship, nontobacco use, and alcohol use., Discussion/conclusions: We identified multiple predictors of prescribing for HFrEF medications that are being used to strategically design interventions to address barriers to prescribing and to inform further investigations. The machine learning approach used in this study to identify predictors of suboptimal prescribing can also be used by other health systems to identify and address locally relevant gaps and solutions to prescribing., Competing Interests: DCM reports consulting fees from Biomarin, Abbott/Pharmacylics, Novartis, Pear Therapeutics, Seres, and Sarepta. LAA reports consulting fees from Abbott, ACI Clinical, Amgen, Boston Scientific, Cytokinetics, and Novartis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Kim, Suresh, Rosenberg, Tan, Malone, Allen, Kao, Anderson, Tiwari and Trinkley.)

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping., (© 2023. The Author(s).)

Background: Drug-induced long-QT syndrome (diLQTS) is a major concern among patients who are hospitalized, for whom prediction models capable of identifying individualized risk could be useful to guide monitoring. We have previously demonstrated the feasibility of machine learning to predict the risk of diLQTS, in which deep learning models provided superior accuracy for risk prediction, although these models were limited by a lack of interpretability., Objective: In this investigation, we sought to examine the potential trade-off between interpretability and predictive accuracy with the use of more complex models to identify patients at risk for diLQTS. We planned to compare a deep learning algorithm to predict diLQTS with a more interpretable algorithm based on cluster analysis that would allow medication- and subpopulation-specific evaluation of risk., Methods: We examined the risk of diLQTS among 35,639 inpatients treated between 2003 and 2018 with at least 1 of 39 medications associated with risk of diLQTS and who had an electrocardiogram in the system performed within 24 hours of medication administration. Predictors included over 22,000 diagnoses and medications at the time of medication administration, with cases of diLQTS defined as a corrected QT interval over 500 milliseconds after treatment with a culprit medication. The interpretable model was developed using cluster analysis (K=4 clusters), and risk was assessed for specific medications and classes of medications. The deep learning model was created using all predictors within a 6-layer neural network, based on previously identified hyperparameters., Results: Among the medications, we found that class III antiarrhythmic medications were associated with increased risk across all clusters, and that in patients who are noncritically ill without cardiovascular disease, propofol was associated with increased risk, whereas ondansetron was associated with decreased risk. Compared with deep learning, the interpretable approach was less accurate (area under the receiver operating characteristic curve: 0.65 vs 0.78), with comparable calibration., Conclusions: In summary, we found that an interpretable modeling approach was less accurate, but more clinically applicable, than deep learning for the prediction of diLQTS. Future investigations should consider this trade-off in the development of methods for clinical prediction., (©Steven T Simon, Katy E Trinkley, Daniel C Malone, Michael Aaron Rosenberg. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.12.2022.)

Many health systems are working to become learning health systems (LHSs), which aim to improve the value of health care by rapidly, continuously generating evidence to apply to practice. However, challenges remain to advance toward the aspirational goal of becoming a fully mature LHS. While some important challenges have been well described (i.e., building system-level supporting infrastructure and the accessibility of inclusive, integrated, and actionable data), other key challenges are underrecognized, including balancing evaluation rapidity with rigor, applying principles of health equity and classic ethics, focusing on external validity and reproducibility (generalizability), and designing for sustainability. Many LHSs focus on continuous learning cycles, but with limited consideration of issues related to the rapidity of these learning cycles, as well as the sustainability or generalizability of solutions. Some types of data have been consistently underrepresented, including patient-reported outcomes and preferences, social determinants, and behavioral and environmental data, the absence of which can exacerbate health disparities. A promising approach to addressing many challenges that LHSs face may be found in dissemination and implementation (D&I) science. With an emphasis on multilevel dynamic contextual factors, representation of implementation partner engagement, pragmatic research, sustainability, and generalizability, D&I science methods can assist in overcoming many of the challenges facing LHSs. In this article, the authors describe the current state of LHSs and challenges to becoming a mature LHS, propose solutions to current challenges, focusing on the contributions of D&I science with other methods, and propose key components and characteristics of a mature LHS model that others can use to plan and develop their LHSs., (Copyright © 2022 by the Association of American Medical Colleges.)

Traditional research focuses on efficacy or effectiveness of interventions but lacks evaluation of strategies needed for equitable uptake, scalable implementation, and sustainable evidence-based practice transformation. The purpose of this introductory review is to describe key implementation science (IS) concepts as they apply to medication management and pharmacy practice, and to provide guidance on literature review with an IS lens. There are five key ingredients of IS, including: (1) evidence-based intervention; (2) implementation strategies; (3) IS theory, model, or framework; (4) IS outcomes and measures; and (5) stakeholder engagement, which is key to a successful implementation. These key ingredients apply across the three stages of IS research: (1) pre-implementation; (2) implementation; and (3) sustainment. A case example using a combination of IS models, PRISM (Practical, Robust Implementation and Sustainability model) and RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance), is included to describe how an IS study is designed and conducted. This case is a cluster randomized trial comparing two clinical decision support tools to improve guideline-concordant prescribing for patients with heart failure and reduced ejection fraction. The review also includes information on the Standards for Reporting Implementation Studies (StaRI), which is used for literature review and reporting of IS studies,as well as IS-related learning resources., Competing Interests: Conflict of interest statement: The authors declare no conflicts of interest.

Introduction: Many health care institutions are working to improve depression screening and management with the use of the Patient Health Questionnaire 9 (PHQ-9). Clinical decision support (CDS) within the EHR is one strategy, but little is known about effective approaches to design or implement such CDS. The purpose of this study is to compare implementation outcomes of two versions of a CDS tool to improve PHQ-9 administration for patients with depression., Methods: This was a retrospective, observational study comparing two versions of a CDS. Version 1 interrupted clinician workflow, and version 2 did not interrupt workflow. Outcomes of interest included reach, adoption, and effectiveness. PHQ-9 administration was determined by chart review. Chi-square tests were used to evaluate associations between PHQ-9 administration with versions 1 and 2., Results: Version 1 resulted in PHQ-9 administration 77 times (15.3% of 504 unique encounters) compared with 49 times (9.8% of 502 unique encounters) with version 2 ( P  = .011)., Discussion: An interruptive CDS tool may be more effective at increasing PHQ-9 administration, but a noninterruptive CDS tool may be preferred to minimize alert fatigue despite a decrease in effectiveness., Competing Interests: Disclosures: K.E.T. was supported by the National Heart Lung and Blood Institute K12 Training grant (no. K12HL137862). The authors declare that there is no conflict of interest., (© 2021 CPNP. The Mental Health Clinician is a publication of the College of Psychiatric and Neurologic Pharmacists.)

Background: Limited consideration of clinical decision support (CDS) design best practices, such as a user-centered design, is often cited as a key barrier to CDS adoption and effectiveness. The application of CDS best practices is resource intensive; thus, institutions often rely on commercially available CDS tools that are created to meet the generalized needs of many institutions and are not user centered. Beyond resource availability, insufficient guidance on how to address key aspects of implementation, such as contextual factors, may also limit the application of CDS best practices. An implementation science (IS) framework could provide needed guidance and increase the reproducibility of CDS implementations., Objective: This study aims to compare the effectiveness of an enhanced CDS tool informed by CDS best practices and an IS framework with a generic, commercially available CDS tool., Methods: We conducted an explanatory sequential mixed methods study. An IS-enhanced and commercial CDS alert were compared in a cluster randomized trial across 28 primary care clinics. Both alerts aimed to improve beta-blocker prescribing for heart failure. The enhanced alert was informed by CDS best practices and the Practical, Robust, Implementation, and Sustainability Model (PRISM) IS framework, whereas the commercial alert followed vendor-supplied specifications. Following PRISM, the enhanced alert was informed by iterative, multilevel stakeholder input and the dynamic interactions of the internal and external environment. Outcomes aligned with PRISM's evaluation measures, including patient reach, clinician adoption, and changes in prescribing behavior. Clinicians exposed to each alert were interviewed to identify design features that might influence adoption. The interviews were analyzed using a thematic approach., Results: Between March 15 and August 23, 2019, the enhanced alert fired for 61 patients (106 alerts, 87 clinicians) and the commercial alert fired for 26 patients (59 alerts, 31 clinicians). The adoption and effectiveness of the enhanced alert were significantly higher than those of the commercial alert (62% vs 29% alerts adopted, P<.001; 14% vs 0% changed prescribing, P=.006). Of the 21 clinicians interviewed, most stated that they preferred the enhanced alert., Conclusions: The results of this study suggest that applying CDS best practices with an IS framework to create CDS tools improves implementation success compared with a commercially available tool., Trial Registration: ClinicalTrials.gov NCT04028557; http://clinicaltrials.gov/ct2/show/NCT04028557., (©Katy E Trinkley, Miranda E Kroehl, Michael G Kahn, Larry A Allen, Tellen D Bennett, Gary Hale, Heather Haugen, Simeon Heckman, David P Kao, Janet Kim, Daniel M Matlock, Daniel C Malone, Robert L Page 2nd, Jessica Stine, Krithika Suresh, Lauren Wells, Chen-Tan Lin. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 22.03.2021.)

Background: Major gaps exist in the routine initiation and dose up-titration of guideline-directed medical therapies (GDMT) for patients with heart failure with reduced ejection fraction. Without novel approaches to improve prescribing, the cumulative benefits of heart failure with reduced ejection fraction treatment will be largely unrealized. Direct-to-consumer marketing and shared decision making reflect a culture where patients are increasingly involved in treatment choices, creating opportunities for prescribing interventions that engage patients., Methods: The EPIC-HF (Electronically Delivered, Patient-Activation Tool for Intensification of Medications for Chronic Heart Failure with Reduced Ejection Fraction) trial randomized patients with heart failure with reduced ejection fraction from a diverse health system to usual care versus patient activation tools-a 3-minute video and 1-page checklist-delivered electronically 1 week before, 3 days before, and 24 hours before a cardiology clinic visit. The tools encouraged patients to work collaboratively with their clinicians to "make one positive change" in heart failure with reduced ejection fraction prescribing. The primary endpoint was the percentage of patients with GDMT medication initiations and dose intensifications from immediately preceding the cardiology clinic visit to 30 days after, compared with usual care during the same period., Results: EPIC-HF enrolled 306 patients, 290 of whom attended a clinic visit during the study period: 145 were sent the patient activation tools and 145 were controls. The median age of patients was 65 years; 29% were female, 11% were Black, 7% were Hispanic, and the median ejection fraction was 32%. Preclinic data revealed significant GDMT opportunities, with no patients on target doses of β-blocker, sacubitril/valsartan, and mineralocorticoid receptor antagonists. From immediately preceding the cardiology clinic visit to 30 days after, 49.0% in the intervention and 29.7% in the control experienced an initiation or intensification of their GDMT ( P =0.001). The majority of these changes were made at the clinician encounter itself and involved dose uptitrations. There were no deaths and no significant differences in hospitalization or emergency department visits at 30 days between groups., Conclusions: A patient activation tool delivered electronically before a cardiology clinic visit improved clinician intensification of GDMT. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03334188.

Background: Pharmacists in ambulatory care can utilize population health approaches to identify patients needing disease management and improve outcomes. However, population health is only effective when identified patients are successfully outreached and show to appointments., Objective: Describe a population health approach utilized by pharmacists in primary care, report outcomes of outreach attempts and scheduled appointments, and determine whether patient and referral characteristics predict no-show appointments., Methods: Retrospective cohort study of patients referred for pharmacist management of hypertension or chronic pain through population health between 2013-2016. Outreach attempt and appointments outcomes were collected. Patient and referral characteristics were analyzed to determine whether predictive of no-show appointments using logistic regression., Results: Of 450 outreach attempts, 250 (56%) patients were not reached, 164 (36%) scheduled appointments, and 36 (8%) were reached but declined an appointment. Of 164 patients with appointments, 71 (43%) no-showed. Patients with higher systolic blood pressure were more likely to no-show (OR: 1.02, 95% CI: 1.00-1.04). Other characteristics were not predictive of no-show appointments., Conclusion: Successful outreach and showed appointments are essential components of successful population health programs. Using multiple outreach modalities and further identifying factors predictive of no-show appointments to refine the current approach may lead to increased efficiency.

Objective: Clinical decision support (CDS) alerts built into the electronic health record (EHR) have the potential to reduce the risk of drug-induced long QT syndrome (diLQTS) in susceptible patients. However, the degree to which providers incorporate this information into prescription behavior and the impact on patient outcomes is often unknown., Methods: We examined provider response data over a period from October 8, 2016 until November 8, 2018 for a CDS alert deployed within the EHR from a 13-hospital integrated health care system that fires when a patient with a QTc ≥ 500 ms within the past 14 days is prescribed a known QT-prolonging medication. We used multivariate generalized estimating equations to analyze the impact of therapeutic alternatives, relative risk of diLQTS for specific medications, and patient characteristics on provider response to the CDS and overall patient mortality., Results: The CDS alert fired 15,002 times for 7,510 patients for which the most common response (51.0%) was to override the alert and order the culprit medication. In multivariate models, we found that patient age, relative risk of diLQTS, and presence of alternative agents were significant predictors of adherence to the CDS alerts and that nonadherence itself was a predictor of mortality. Risk of diLQTS and presence of an alternative agent are major factors in provider adherence to a CDS to prevent diLQTS; however, provider nonadherence was associated with a decreased risk of mortality., Conclusion: Surrogate endpoints, such as provider adherence, can be useful measures of CDS value but attention to hard outcomes, such as mortality, is likely needed., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Lisinopril and losartan manufacturer labels recommend twice-daily dosing (BID) if once-daily (QDay) is insufficient to lower blood pressure (BP)., Methods and Results: Retrospective cohort study of patients taking QDay lisinopril and losartan who experienced a dose-doubling (index date). A text-processing tool categorized BID and QDay groups at the index date based on administration instructions. We excluded: pregnant/hospice, regimens other than BID/QDay, and without BP measurements -6 months/+12 months of the index date. The most proximal BP measurements -6 months and +2 weeks to 12 months of the index date were used to evaluate BP differences. Propensity scores were generated, and differences in BP and adverse events (angioedema, acute kidney injury, hyperkalemia) between BID/QDay groups were analyzed within dosing cohorts using inverse propensity of treatment-weighted regression models. Of 11,210 and 6,051 patients who met all criteria for lisinopril and losartan, 784 (7.0%) and 453 (7.5%) were taking BID, respectively. BID patients were older and had higher comorbidity and medication burdens. There were no differences in systolic/diastolic BP between BID and QDay, with absolute differences in mean systolic BP ranging from -1.8 to 0.7 mmHg and diastolic BP ranging from -1.1 to 0.1 mmHg (all 95% confidence intervals [CI] cross 0). Lisinopril 10mg BID was associated with an increased odds of angioedema compared to lisinopril 20mg QDay (odds ratio 2.27, 95%CI 1.13-4.58)., Conclusions: Adjusted models do not support improved effectiveness or safety of BID lisinopril and losartan., Competing Interests: The authors have declared that no competing interests exist.

Background: Heart failure with reduced ejection fraction (HFrEF) benefits from initiation and intensification of multiple pharmacotherapies. Unfortunately, there are major gaps in the routine use of these drugs. Without novel approaches to improve prescribing, the cumulative benefits of HFrEF treatment will be largely unrealized. Direct-to-consumer marketing and shared decision making reflect a culture where patients are increasingly involved in treatment choices, creating opportunities for prescribing interventions that engage patients., Hypothesis: Encouraging patients to engage providers in HFrEF prescribing decisions will improve the use of guideline-directed medical therapies., Design: The Electronically delivered, Patient-activation tool for Intensification of Chronic medications for Heart Failure with reduced ejection fraction (EPIC-HF) trial randomizes patients with HFrEF to usual care versus patient-activation tools-a 3-minute video and 1-page checklist-delivered prior to cardiology clinic visits that encourage patients to work collaboratively with their clinicians to intensify HFrEF prescribing. The study assesses the effectiveness of the EPIC-HF intervention to improve guideline-directed medical therapy in the month after its delivery while using an implementation design to also understand the reach, adoption, implementation, and maintenance of this approach within the context of real-world care delivery. Study enrollment was completed in January 2020, with a total 305 patients. Baseline data revealed significant opportunities, with <1% of patients on optimal HFrEF medical therapy., Summary: The EPIC-HF trial assesses the implementation, effectiveness, and safety of patient engagement in HFrEF prescribing decisions. If successful, the tool can be easily disseminated and may inform similar interventions for other chronic conditions., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles., Objective: This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations., Methods: We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach., Results: Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise., Conclusions: Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success., (©Katy E Trinkley, Michael G Kahn, Tellen D Bennett, Russell E Glasgow, Heather Haugen, David P Kao, Miranda E Kroehl, Chen-Tan Lin, Daniel C Malone, Daniel D Matlock. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.10.2020.)

Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or prespecified statistical interactions. However, the application of ML to healthcare data has been met with mixed results, especially when using administrative datasets such as the electronic health record. The black box nature of many ML algorithms contributes to an erroneous assumption that these algorithms can overcome major data issues inherent in large administrative healthcare data. As with other research endeavors, good data and analytic design is crucial to ML-based studies. In this paper, we will provide an overview of common misconceptions for ML, the corresponding truths, and suggestions for incorporating these methods into healthcare research while maintaining a sound study design., Competing Interests: No authors reported conflicts of interest., (© The Association for Clinical and Translational Science 2020.)

Background: Medication nonadherence and unaffordability are prevalent, burdensome issues in primary care. In response, technology companies are capitalizing on clinical decision support (CDS) to deliver patient-specific information regarding medication adherence and costs to clinicians using electronic health records (EHRs). To maximize adoption and usability, these CDS tools should be designed with consideration of end users' values and preferences., Objective: This article evaluates primary care clinicians' values and preferences for a medication adherence and cost CDS., Methods: We conducted semistructured interviews with primary care clinicians with prescribing privileges and EHR access to identify clinicians' perceptions of and approaches to assessing medication adherence and costs, and to determine perceived values and preferences for medication adherence and cost CDS. Interviews were conducted until saturation of responses was reached. ATLAS.ti was used for thematic analysis., Results: Among 26 clinicians interviewed, themes identified included a high value, but moderate need for a medication adherence CDS and high value and need for cost CDS. Clinicians expressed the cost CDS would provide actionable solutions and greatly impact patient care. Another theme identified was a desire for medication adherence and cost CDS to be separate tools yet integrated into workflow. The majority of clinicians preferred a medication adherence CDS that integrated claims data and actively displayed data using color-coded adherence categories within patients' medication lists in the EHR. For the cost CDS, clinicians preferred medication out-of-pocket costs and a list of cheaper or payor-preferred alternatives to display within the order queue of the EHR., Conclusion: We identified valuable insights regarding clinician values and preferences for medication adherence and cost CDS. Overall, primary care clinicians feel CDS for medication adherence and cost are valuable and prefer them to be separate. These insights should be used to inform the design, implementation, and EHR integration of future medication and cost CDS tools., Competing Interests: C.G.D. reports other from Amgen Inc, other from Amarin Corporation, outside the submitted work., (Georg Thieme Verlag KG Stuttgart · New York.)

Patients with congestive heart failure (CHF) require complex medical management across the continuum of care. Electronic health records (EHR) are currently used for traditional tasks of documentation, reviewing and managing test results, computerized order entry, and billing. Unfortunately many clinicians view EHR as merely digitized versions of paper charts, which create additional work and cognitive burden without improving quality or efficiency of care. In fact, EHR are revolutionizing the care of chronic diseases such as CHF. This review describes how appropriate use of technologies offered by EHR can help standardize CHF care, promote adherence to evidence-based guidelines, optimize workflow efficiency, improve performance metrics, and facilitate patient engagement. This review discusses a number of tools including documentation templates, telehealth and telemedicine, health information exchange, order sets, clinical decision support, registries, and analytics. Where available, evidence of their potential utility in management of CHF is presented. Together these EHR tools can also be used to enhance quality improvement, patient management, and clinical research as part of a learning health care system model. This review describes how existing EHR tools can support patients, cardiologists, and care teams to deliver consistent, high-quality, coordinated, patient-centered, and guideline-concordant care of CHF., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Introduction: Options for the treatment of the behavioral and psychological symptoms of dementia (BPSD) are limited. Atypical antipsychotics are often used but have questionable efficacy and are generally considered high risk. Therefore, the objective of this study is to evaluate the efficacy and safety of using any atypical antipsychotic for the treatment of BPSD among outpatients., Methods: Retrospective observational study of an academic outpatient memory disorders clinic. Participants included any community-dwelling patient with a diagnosis of dementia, not trauma induced, with documented BPSD treated with an atypical antipsychotic for at least 2 weeks. Medical records were reviewed from January 1, 1990 to March 23, 2010. Safety outcomes were documented from the time of antipsychotic initiation, and behavioral/psychological efficacy outcomes were documented beginning 2 weeks after antipsychotic therapy was initiated, until the last documentation available., Results: A total of 87 distinct antipsychotic treatment periods for 81 unique patients were included. Antipsychotic treatment was continued for more than a year in 33% of patients and only 17% of patients discontinued antipsychotic treatment over the entire period. The behavioral/psychological outcomes improved for 24 (28%) treatments, remained stable for 17 (20%) treatments, and worsened for 46 (53%) treatments. Adverse events were reported by 53% of patients, with the most common adverse events being metabolic, fall related, type, and vascular. Few adverse events were severe. The odds ratio of adverse events per every 90-day increase in duration of treatment was 1.20 ( P = 0.02)., Conclusion: Antipsychotic treatment improved behavioral/psychological symptoms for less than one-third of patients and increased the potential risk of adverse events for more than half of patients.

Total medication burden (antihypertensive and nonantihypertensive medications) may be associated with poor systolic blood pressure (SBP) control. We investigated the association of baseline medication burden and clinical outcomes and whether the effect of the SBP intervention varied according to baseline medication burden in SPRINT (Systolic Blood Pressure Intervention Trial). Participants were randomized to intensive or standard SBP goal (below 120 or 140 mm Hg, respectively); n=3769 participants with high baseline medication burden (≥5 medications) and n=5592 with low burden (<5 medications)., Primary Outcome: differences in SBP., Secondary Outcomes: 8-item Morisky Medication Adherence Scale and modified Treatment Satisfaction Questionnaire for Medications measured at baseline and 12 months and incident cardiovascular disease events and serious adverse events throughout the trial. Participants in the intensive group with high versus low medication burden were less likely to achieve their SBP goal at 12 months (risk ratio, 0.91; 95% CI, 0.85-0.97) but not in the standard group (risk ratio, 0.98; 95% CI, 0.93-1.03; P interaction <0.001). High medication burden was associated with increased cardiovascular disease events (hazard ratio, 1.39; 95% CI, 1.14-1.70) and serious adverse events (hazard ratio, 1.34; 95% CI, 1.24-1.45), but the effect of intensive versus standard treatment did not vary between medication burden groups ( P interaction >0.5). Medication burden had minimal association with adherence or satisfaction. High baseline medication burden was associated with worse intensive SBP control and higher rates of cardiovascular disease events and serious adverse events. The relative benefits and risks of intensive SBP goals were similar regardless of medication burden., Clinical Trial Registration- Url: http://www., Clinicaltrials: gov. Unique identifier: NCT01206062.

Objective: Clinical pharmacists use population health methods to generate chronic disease management referrals for patients with uncontrolled chronic conditions. The purpose of this study was to compare primary care providers' (PCPs) referral responses for 4 pharmacist-managed indications and to identify provider and patient characteristics that are predictive of PCP response., Design: Retrospective cohort study., Setting: This study occurred in an academic internal medicine clinic., Participants: Clinical pharmacy referrals generated through a population health approach between 2012 and 2016 for hypertension, chronic pain, depression, and benzodiazepine management were included., Main Outcome Measures: Proportion of referrals accepted, left pending, or rejected and influencing provider and patient characteristics., Results: Of 1769 referrals generated, PCPs accepted 869 (49%), left pending 300 (17%), and rejected 600 (34%). Compared with referrals for hypertension, benzodiazepine management, and depression, chronic pain referrals had the lowest likelihood of rejection (odds ratio [OR] 0.31; 95% CI 0.19-0.49). Depression referrals had an equal likelihood of being accepted or rejected (OR 1.04; 95% CI 0.66-1.64). Provider characteristics were not significantly associated with referral response, but residents were more likely to accept referrals. Patient characteristics associated with lower referral rejection included black race (OR 0.39; 95% CI 0.18-0.87), higher systolic blood pressure (OR 0.98; 95% CI 0.97-0.99), and missed visits (OR 0.24; 95% CI 0.07-0.81)., Conclusion: The majority of referrals for clinical pharmacists in primary care settings were responded to, varying mostly between acceptance and rejection. There was variability in referral acceptance across indications, and some patient characteristics were associated with increased referral acceptance., (Copyright © 2019 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Background: To improve user-centred design efforts and efficiency; there is a need to disseminate information on modern day clinician preferences for technologies such as computerised clinical decision support (CDS)., Objective: To describe clinician perceptions regarding beneficial features of CDS for chronic medications in primary care., Methods: This study included focus groups and clinicians individually describing their ideal CDS. Three focus groups were conducted including prescribing clinicians from a variety of disciplines. Outcome measures included identification of favourable features and unintended consequences of CDS for chronic medication management in primary care. We transcribed recordings, performed thematic qualitative analysis and generated counts when possible., Results: There were 21 participants who identified four categories of beneficial CDS features during the group discussion: non-interruptive alerts, clinically relevant and customisable support, presentation of pertinent clinical information and optimises workflow. Non-interruptive alerts were broadly defined as passive alerts that a user chooses to review, whereas interruptive were active or disruptive alerts that interrupted workflow and one is forced to review before completing a task. The CDS features identified in the individual descriptions were consistent with the focus group discussion, with the exception of non-interruptive alerts. In the individual descriptions, 12 clinicians preferred interruptive CDS compared with seven clinicians describing non-interruptive CDS., Conclusion: Clinicians identified CDS for chronic medications beneficial when they are clinically relevant and customisable, present pertinent clinical information (eg, labs, vitals) and improve their workflow. Although clinicians preferred passive, non-interruptive alerts, most acknowledged that these may not be widely seen and may be less effective. These features align with literature describing best practices in CDS design and emphasise those features clinicians prioritise, which should be considered when designing CDS for medication management in primary care. These findings highlight the disparity between the current state of CDS design and clinician-stated design features associated with beneficial CDS., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background:: Barriers exist for patients transitioning from one health-care setting to another, or to home, and health-care systems are falling short of meeting patient needs during this time. Community pharmacist incorporation poses a solution to the current communication breakdown and high rates of medication errors during transitions of care (TOC). The purpose of this study was to determine community pharmacists' involvement in and perceptions of TOC services., Methods:: Cross-sectional study using electronic surveys nationwide to pharmacists employed by a community pharmacy chain., Results:: Of 7236 pharmacists surveyed, 546 (7.5%) responded. Only 33 (6%) pharmacists reported their pharmacy participates in TOC services. Most pharmacists (81.5%) reported receiving discharge medication lists. The most common reported barrier to TOC participation is lack of electronic integration with surrounding hospitals (51.1%). Most pharmacists agreed that (1) it is valuable to receive discharge medication lists (83.3%), (2) receiving discharge medication lists is beneficial for patients' health (89.1%), (3) discharge medication list receipt improves medication safety (88.8%)., Conclusions:: Most pharmacists reported receiving discharge medication lists and reported discharge medication lists are beneficial, but less than half purposefully used medication lists. To close TOC gaps, health-care providers must collaborate to overcome barriers for successful TOC services.

Registries are fundamental to the success of population health initiatives to improve care and outcomes for patients, including those with depression. The purpose of this article is to describe the design and clinical implementation of a depression registry as part of a collaborative care for depression intervention at 2 large academic outpatient internal medicine practices. The primary objective of the registry was to identify and track patients with depression and monitor antidepressant therapy. Secondary objectives of the registry were to assist in addressing pay-for-performance and value-based reimbursement metrics for depression screening and remission. The registry design and variables for inclusion in the registry were defined with input from clinicians, institutional leadership, and data analysts. For implementation, specific clinical workflows were established and responsible team roles were designated.

Guidelines recommend patient follow-up within 2 weeks of antidepressant initiation or uptitration to minimize treatment discontinuation and suicidal ideation risks; however, time constraints and lack of systematic processes remain barriers in primary care. A pharmacist-led multidisciplinary telemonitoring service aimed to address these barriers. This was a retrospective, observational study of adults with depression initiated or uptitrated on an antidepressant between May and October 2016. Outcomes included the proportion of eligible patients successfully contacted, adherence, adverse effects, suicidal ideations, and pharmacist interventions. Clinical pharmacists successfully reached 258 of 380 (68%) patients and provided follow-up in 298 calls. Patients endorsed antidepressant nonadherence during 56 (19%) calls, adverse effects in 81 (27%) calls, and suicidal ideations in 13 (4%) calls. Pharmacists provided 109 total interventions for 102 patients. The clinical pharmacist-led multidisciplinary antidepressant telemonitoring service is an alternative resource to monitor patients after antidepressant initiation or titration in primary care settings.

Background: Despite strong recommendations to use metformin as first-line therapy for type 2 diabetes (T2DM), its use has been suboptimal, likely due to concerns of lactic acidosis. This study compared the association of acidosis in patients with T2DM prescribed metformin with those prescribed other antihyperglycemic medications or no medications., Methods: This was a retrospective cohort study of patients with newly diagnosed T2DM utilizing an administrative database, which includes medical and prescription claims. Eligible patients had a diagnosis of T2DM, had continuous health plan enrollment 3 months prior to study enrollment and during the study period, and were at least 18 years of age. Mutually exclusive exposure groups were metformin only, other antihyperglycemic medications, and no medication. Acidosis cases were stratified by exposure group and risk factors for lactic acidosis (chronic obstructive pulmonary disease, hepatic dysfunction, alcohol abuse, heart failure, renal insufficiency, age of 80 years or older, and a history of acidosis). Degree of renal insufficiency was not available. Associations between exposure and acidosis were estimated, and risk factors evaluated., Results: A total of 132,780 patients met inclusion criteria: 24,936 (20%) metformin only group, 15,059 (11%) other antihyperglycemic medication group, and 92,785 (70%) no medication group. Acidosis was observed in 1.45 per 10,000 patient months (0.78 metformin, 1.59 other antihyperglycemic medication, 1.51 no medication). The unadjusted relative risk of acidosis was 0.5 for patients prescribed metformin only compared with the other exposure groups (95% confidence interval = 0.2-1.2). There was no significant difference in risk of acidosis between exposure groups, irrespective of risk factors for lactic acidosis., Conclusions: Risk of acidosis was similar with metformin only compared with those prescribed other antihyperglycemic medications or no medication. These results support expanded use of metformin for T2DM. Additional studies are needed to understand the impact of risk factor severity on risk of lactic acidosis., Competing Interests: Conflict of interest statement: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background There are limited options for the treatment of behavioral and psychological symptoms of dementia (BPSD). Objective Evaluate the efficacy and safety of using atypical antipsychotics for BPSD among patients residing in long-term care. Setting Long term care community facility in the United States. Methods Retrospective observational study of patients residing in a long-term care facility with a diagnosis of dementia not trauma-induced with documented BPSD treated with an atypical antipsychotic for at least 2 weeks. Paper medical records were reviewed from January 1, 1990 until March 23, 2010. Main outcome measure Behavioral/psychological efficacy outcomes were documented beginning 2 weeks after atypical antipsychotic therapy was initiated and safety outcomes were documented from the time of atypical antipsychotic initiation, until the last documentation available. Efficacy and safety outcomes were documented as part of routine clinical practice based on the responsible clinician. Results A total of 85 distinct atypical antipsychotic treatment periods for 73 unique patients were included. Nearly 50% of patients continued atypical antipsychotic treatment for at least 1 year and 5.6% of treatments were discontinued due to an adverse event. Patients' behavioral/psychological outcomes improved for 52 (61%) treatments, remained stable for 17 (20%) treatments, and worsened for 16 (19%) treatments. Adverse events were reported by 57% of patients, with the most common adverse events being metabolic, fall related, and extrapyramidal symptoms. The odds ratio for an adverse event was 1.08 (p = 0.03) for every 90 day increase in duration of treatment. Conclusion In patients who reside in a long-term care setting, atypical antipsychotic treatment improved BPSD, but also increased the potential risk of adverse events.

The purpose of this study was to identify and characterize adverse drug events (ADEs) in a primary care setting using an electronic health record (EHR). This prospective, observational study enrolled patients with any medication change who were seen at an outpatient internal medicine clinic. Patients were evaluated for ADEs by EHR review and telephone interview. ADEs were independently assessed for causality, severity, preventability, and ameliorability by a physician and a pharmacist using a grading instrument. There were 1368 unique medication changes for 701 individuals who completed the study (1.95 changes per person). Of the 226 suspected ADEs, 68 (58%) were deemed to be "definite" or "probable" following causality assessment; 21% were preventable and 40% ameliorable. Only 2 ADEs were serious or life-threatening. Compared with prior reports, ADEs in primary care have decreased in frequency and severity, yet the occurrence of preventable and ameliorable ADEs has increased.

This retrospective cohort study compared administration of lisinopril twice daily and once daily for hypertension. Data were collected from an ambulatory electronic health record between 2011 and 2014. Patients previously receiving lisinopril 20 mg were placed into the once-daily cohort if changed to 40 mg once daily or into the twice-daily cohort if changed to 20 mg twice daily. Efficacy outcome measures were change in systolic blood pressure and diastolic blood pressure and achievement of blood pressure control (<140/90 mm Hg). Of 90 patients included (45 per cohort), the mean age was 61.8 years and 17.8% were black. Once- and twice-daily administrations were associated with blood pressure reductions of 6.2/1.5 mm Hg and 16.5/5.9 mm Hg, with a 10.2/4.3 mm Hg greater reduction with twice-daily administration (systolic blood pressure, P=.016; diastolic blood pressure, P=.068). Twice-daily lisinopril dosing was associated with greater systolic blood pressure reductions compared with the same total daily dose administered once daily., (©2017 Wiley Periodicals, Inc.)

Purpose: A pharmacist-managed chronic pain clinic (PMCPC) in a primary care setting is described., Summary: As primary care providers (PCPs) may be unprepared or lack time to manage high-risk patients receiving opioids for chronic nonmalignant pain, alternative models of care are needed. The University of Colorado PMCPC is integrated into an internal medicine outpatient clinic. The PMCPC is staffed by 1 clinical pharmacist, with pharmacy students and residents also performing clinic duties. The pharmacy team reviews health records to determine eligibility for PMCPC services and documents referral requests in the electronic health record (EHR); on PCP acceptance of a referral, the pharmacy team assumes primary responsibility for the patient's pain management under a collaborative practice agreement. Using a collaborative drug therapy management (CDTM) protocol, the pharmacy team conducts patient assessments, including an assessment for signs of aberrant drug-taking behaviors; provides initial and ongoing counseling and education; and makes recommendations to the PCP for opioid dosage adjustments and regimen additions and discontinuations. Experience at the clinic to date indicates that the PMCPC model is feasible and accepted by PCPs and patients., Conclusion: A PMCPC based in a primary care setting was established to improve the care of patients with chronic nonmalignant pain who are prescribed opioid therapy for a period of 3 months or longer. Clinic patients are referred to the clinic through the EHR and managed by a pharmacist under a CDTM protocol., (Copyright © 2017 by the American Society of Health-System Pharmacists, Inc. All rights reserved.)

Introduction: Benzodiazepines are prescribed inappropriately in up to 40% of outpatients. The purpose of this study is to describe a collaborative team-based care model in which clinical pharmacists work with primary care providers (PCPs) to improve the safe use of benzodiazepines for anxiety and sleep disorders and to assess the preliminary results of the impact of the clinical service on patient outcomes., Methods: Adult patients were eligible if they received care from the academic primary care clinic, were prescribed a benzodiazepine chronically, and were not pregnant or managed by psychiatry. Outcomes included baseline PCP confidence and knowledge of appropriate benzodiazepine use, patient symptom severity, and medication changes., Results: Twenty-five of 57 PCPs responded to the survey. PCPs reported greater confidence in diagnosing and treating generalized anxiety and panic disorders than sleep disorder and had variable knowledge of appropriate benzodiazepine prescribing. Twenty-nine patients had at least 1 visit. Over 44 total patient visits, 59% resulted in the addition or optimization of a nonbenzodiazepine medication and 46% resulted in the discontinuation or optimization of a benzodiazepine. Generalized anxiety symptom severity scores significantly improved (-2.0; 95% confidence interval (CI): -3.57 to -0.43)., Conclusion: Collaborative team-based models that include clinical pharmacists in primary care can assist in optimizing high-risk benzodiazepine use. Although these findings suggest improvements in safe medication use and symptoms, additional studies are needed to confirm these preliminary results.