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Background: Engagement with self-monitoring of blood pressure (BP) declines, on average, over time but may vary substantially by individual. Objectives: We aimed to describe different 1-year patterns (groups) of self-monitoring of BP behaviors, identify predictors of those groups, and examine the association of self-monitoring of BP groups with BP levels over time. Methods: We analyzed device-recorded BP measurements collected by the Health eHeart Study—an ongoing prospective eCohort study—from participants with a wireless consumer-purchased device that transmitted date- and time-stamped BP data to the study through a full 12 months of observation starting from the first day they used the device. Participants received no instruction on device use. We applied clustering analysis to identify 1-year self-monitoring, of BP patterns. Results: Participants had a mean age of 52 years and were male and White. Using clustering algorithms, we found that a model with three groups fit the data well: persistent daily use (9.1% of participants), persistent weekly use (21.2%), and sporadic use only (69.7%). Persistent daily use was more common among older participants who had higher Week 1 self-monitoring of BP frequency and was associated with lower BP levels than the persistent weekly use or sporadic use groups throughout the year. Conclusion: We identified three distinct self-monitoring of BP groups, with nearly 10% sustaining a daily use pattern associated with lower BP levels. [ABSTRACT FROM AUTHOR]

Diabetes is a major source of morbidity and mortality in the USA with over 1.5 million new cases diagnosed every year.[1] Diabetes is monitored using hemoglobin A1c (HbA1c) as it correlates with burden of disease.[2]-[4] Clinical practice guidelines recommend HbA1c be repeated every 3 months if a patient's value is above 7%, because a higher HbA1c correlates with worse outcomes.[5]-[9] Complications are particularly high in patients with an HbA1c above 10% and even a 1% reduction in HbA1c can reduce complications by 21%.[5] Thus, while regular monitoring of above-goal HbA1c levels is recommended, ensuring patients with an HbA1c over 10% have timely follow-up testing is especially important. Using this trigger, we found that delayed follow-up HbA1c testing is extremely common among those with poorly controlled diabetes, with 45% of patients with an HbA1c over 10% not receiving follow-up testing within 6 months. The initial numerator, representing patients without an appropriate follow-up HbA1c result, was defined as patients without a follow-up clinic visit within 3 months of the index HbA1c test. [Extracted from the article]

Background: Intensive glycemic control is of unclear benefit and carries increased risk for older adults with diabetes. The American Geriatrics Society's (AGS) Choosing Wisely (CW) guideline promotes less aggressive glycemic targets and reduction in pharmacologic therapy for older adults with type II diabetes. Meanwhile, behavioral economic (BE) approaches offer promise in influencing hard-to-change behavior, and previous studies have shown the benefits of using electronic health record (EHR) technology to encourage guideline adherence. Objective: This study aimed to develop and pilot test an intervention that leverages BE with EHR technology to promote appropriate diabetes management in older adults. Design: A pilot study within the New York University Langone Health (NYULH) EHR and Epic system to deliver BE-inspired nudges at five NYULH clinics at varying time points from July 12, 2018, through October 31, 2019. Participants: Clinicians across five practices in the NYULH system whose patients were older adults (age 76 and older) with type II diabetes. Interventions: A BE-EHR module comprising six nudges was developed through a series of design workshops, interviews, user-testing sessions, and clinic visits. BE principles utilized in the nudges include framing, social norming, accountable justification, defaults, affirmation, and gamification. Main Measures: Patient-level CW compliance. Key Results: CW compliance increased 5.1% from a 16-week interval at baseline to a 16-week interval post intervention. From February 14 to June 5, 2018 (prior to the first nudge launch in Vanguard clinics), CW compliance for 1278 patients was mean (95% CI)—16.1% (14.1%, 18.1%). From July 3 to October 22, 2019 (after BE-EHR module launch at all five clinics), CW compliance for 680 patients was 21.2% (18.1%, 24.3%). Conclusions: The BE-EHR module shows promise for promoting the AGS CW guideline and improving diabetes management in older adults. A randomized controlled trial will commence to test the effectiveness of the intervention across 66 NYULH clinics. NIH Trial Registry Number: NCT03409523 [ABSTRACT FROM AUTHOR]

Background: Despite advances in medical therapy for heart failure with reduced ejection fraction (HFrEF), major gaps in medication adherence to guideline-directed medical therapies (GDMT) remain. Greater continuity of care may impact medication adherence and reduced hospitalizations., Methods: We conducted a cross-sectional study of adults with a diagnosis of HF and EF ≤40% with ≥2 outpatient encounters between January 1, 2017 and January 10, 2021, prescribed ≥1 of the following GDMT: 1) Beta Blocker, 2) Angiotensin Converting Enzyme Inhibitor/Angiotensin Receptor Blocker/Angiotensin Receptor Neprilysin Inhibitor, 3) Mineralocorticoid Receptor Antagonist, 4) Sodium Glucose Cotransporter-2 Inhibitor. Continuity of care was calculated using the Bice-Boxerman Continuity of Care Index (COC) and the Usual Provider of Care (UPC) index, categorized by quantile. The primary outcome was adherence to GDMT, defined as average proportion of days covered ≥80% over 1 year. Secondary outcomes included all-cause and HF hospitalization at 1-year. We performed multivariable logistic regression analyses adjusted for demographics, insurance status, comorbidity index, number of visits and neighborhood SES index., Results: Overall, 3,971 individuals were included (mean age 72 years (SD 14), 71% male, 66% White race). In adjusted analyses, compared to individuals in the highest COC quartile, individuals in the third COC quartile had higher odds of GDMT adherence (OR 1.26, 95% CI 1.03-1.53, P = .024). UPC tertile was not associated with adherence (all P > .05). Compared to the highest quantiles, the lowest UPC and COC quantiles had higher odds of all-cause (UPC: OR 1.53, 95%CI 1.23-1.91; COC: OR 2.54, 95%CI 1.94-3.34) and HF (UPC: OR 1.81, 95%CI 1.23-2.67; COC: OR 1.77, 95%CI 1.09-2.95) hospitalizations., Conclusions: Continuity of care was not associated with GDMT adherence among patients with HFrEF but lower continuity of care was associated with increased all-cause and HF-hospitalizations., (Published by Elsevier Inc.)

We investigated the risks of post-acute and chronic adverse kidney outcomes of SARS-CoV-2 infection in the pediatric population via a retrospective cohort study using data from the RECOVER program. We included 1,864,637 children and adolescents under 21 from 19 children's hospitals and health institutions in the US with at least six months of follow-up time between March 2020 and May 2023. We divided the patients into three strata: patients with pre-existing chronic kidney disease (CKD), patients with acute kidney injury (AKI) during the acute phase (within 28 days) of SARS-CoV-2 infection, and patients without pre-existing CKD or AKI. We defined a set of adverse kidney outcomes for each stratum and examined the outcomes within the post-acute and chronic phases after SARS-CoV-2 infection. In each stratum, compared with the non-infected group, patients with COVID-19 had a higher risk of adverse kidney outcomes. For patients without pre-existing CKD, there were increased risks of CKD stage 2+ (HR 1.20; 95% CI: 1.13-1.28) and CKD stage 3+ (HR 1.35; 95% CI: 1.15-1.59) during the post-acute phase (28 days to 365 days) after SARS-CoV-2 infection. Within the post-acute phase of SARS-CoV-2 infection, children and adolescents with pre-existing CKD and those who experienced AKI were at increased risk of progression to a composite outcome defined by at least 50% decline in estimated glomerular filtration rate (eGFR), eGFR <15 mL/min/1.73m 2 , End Stage Kidney Disease diagnosis, dialysis, or transplant., Competing Interests: Dr. Jhaveri is a consultant for AstraZeneca, Seqirus, Dynavax, receives an editorial stipend from Elsevier and Pediatric Infectious Diseases Society and royalties from Up To Date/Wolters Kluwer. Dr. Ruby Patel is the Primary Investigator for FIONA study, no stipend or compensation being given. Dr. Modi reports research funding outside of this work from the National Institutes of Health, Centers for Disease Control, the Patient-Centered Outcomes Research Institute, Travere Therapeutics, and Boehringer Ingelheim. He is also the current director of the Kidney Research Network Data Coordinating Center. Dr. Harshman reports research funding outside of this work from the National Institutes of Health. Dr. Harshman reports research funding outside of this work from Bayer Pharmaceuticals. Dr. Verghese reports research funding outside of this work from the Department of Defense and Viracor Pharmaceuticals. Dr. Dixon reports consultancies with Novartis Pharmaceuticals, Alexion Astra Zeneca Rare Disease, Apellis Pharmaceuticals, and Arrowhead Pharmaceuticals.

Importance: Chronic kidney disease (CKD) affects 37 million adults in the United States, and for patients with CKD, hypertension is a key risk factor for adverse outcomes, such as kidney failure, cardiovascular events, and death., Objective: To evaluate a computerized clinical decision support (CDS) system for the management of uncontrolled hypertension in patients with CKD., Design, Setting, and Participants: This multiclinic, randomized clinical trial randomized primary care practitioners (PCPs) at a primary care network, including 15 hospital-based, ambulatory, and community health center-based clinics, through a stratified, matched-pair randomization approach February 2021 to February 2022. All adult patients with a visit to a PCP in the last 2 years were eligible and those with evidence of CKD and hypertension were included., Intervention: The intervention consisted of a CDS system based on behavioral economic principles and human-centered design methods that delivered tailored, evidence-based recommendations, including initiation or titration of renin-angiotensin-aldosterone system inhibitors. The patients in the control group received usual care from PCPs with the CDS system operating in silent mode., Main Outcomes and Measures: The primary outcome was the change in mean systolic blood pressure (SBP) between baseline and 180 days compared between groups. The primary analysis was a repeated measures linear mixed model, using SBP at baseline, 90 days, and 180 days in an intention-to-treat repeated measures model to account for missing data. Secondary outcomes included blood pressure (BP) control and outcomes such as percentage of patients who received an action that aligned with the CDS recommendations., Results: The study included 174 PCPs and 2026 patients (mean [SD] age, 75.3 [0.3] years; 1223 [60.4%] female; mean [SD] SBP at baseline, 154.0 [14.3] mm Hg), with 87 PCPs and 1029 patients randomized to the intervention and 87 PCPs and 997 patients randomized to usual care. Overall, 1714 patients (84.6%) were treated for hypertension at baseline. There were 1623 patients (80.1%) with an SBP measurement at 180 days. From the linear mixed model, there was a statistically significant difference in mean SBP change in the intervention group compared with the usual care group (change, -14.6 [95% CI, -13.1 to -16.0] mm Hg vs -11.7 [-10.2 to -13.1] mm Hg; P = .005). There was no difference in the percentage of patients who achieved BP control in the intervention group compared with the control group (50.4% [95% CI, 46.5% to 54.3%] vs 47.1% [95% CI, 43.3% to 51.0%]). More patients received an action aligned with the CDS recommendations in the intervention group than in the usual care group (49.9% [95% CI, 45.1% to 54.8%] vs 34.6% [95% CI, 29.8% to 39.4%]; P < .001)., Conclusions and Relevance: These findings suggest that implementing this computerized CDS system could lead to improved management of uncontrolled hypertension and potentially improved clinical outcomes at the population level for patients with CKD., Trial Registration: ClinicalTrials.gov Identifier: NCT03679247.

Background: Electronic health record (EHR) tools can improve prescribing of guideline-recommended therapies for heart failure with reduced ejection fraction (HFrEF), but their effectiveness may vary by physician workload., Objectives: This paper aims to assess whether physician workload modifies the effectiveness of EHR tools for HFrEF., Methods: This was a prespecified subgroup analysis of the BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure) cluster-randomized trial, which compared effectiveness of an alert vs message vs usual care on prescribing of mineralocorticoid antagonists (MRAs). The trial included adults with HFrEF seen in cardiology offices who were eligible for and not prescribed MRAs. Visit volume was defined at the cardiologist-level as number of visits per 6-month study period (high = upper tertile vs non-high = remaining). Analysis at the patient-level used likelihood ratio test for interaction with log-binomial models., Results: Among 2,211 patients seen by 174 cardiologists, 932 (42.2%) were seen by high-volume cardiologists (median: 1,853; Q1-Q3: 1,637-2,225 visits/6 mo; and median: 10; Q1-Q3: 9-12 visits/half-day). MRA was prescribed to 5.5% in the high-volume vs 14.8% in the non-high-volume groups in the usual care arm, 10.3% vs 19.6% in the message arm, and 31.2% vs 28.2% in the alert arm, respectively. Visit volume modified treatment effect (P for interaction = 0.02) such that the alert was more effective in the high-volume group (relative risk: 5.16; 95% CI: 2.57-10.4) than the non-high-volume group (relative risk: 1.93; 95% CI: 1.29-2.90)., Conclusions: An EHR-embedded alert increased prescribing by >5-fold among patients seen by high-volume cardiologists. Our findings support use of EHR alerts, especially in busy practice settings. (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure [BETTER CARE-HF]; NCT05275920)., Competing Interests: Funding Support and Author Disclosures This study was funded by the National Center for Advancing Translational Sciences (NIH/NCATS UL1TR001445). Dr Mukhopadhyay is supported by an American Heart Association Career Development Award (23CDA1042602), outside the scope of the present work. Dr Reynolds has received in-kind donations for unrelated research from Abbott Vascular, Philips, SHL Telemedicine, and Siemens. Dr Phillips has received consulting fees from Novo Nordisk. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024. Published by Elsevier Inc.)

Background: People with memory disorders have difficulty adhering to treatments. With technological advances, it remains important to investigate the potential of health information technology (HIT) in supporting medication adherence among them., Objectives: This review investigates the role of HIT in supporting adherence to medication and therapies among patients with memory issues. It also captures the factors that impact technology adherence interventions., Methods: We searched the literature for relevant publications published until March 15, 2023, using technology to support adherence among patients with memory issues (dementia, Alzheimer's, amnesia, mild cognitive impairment, memory loss, etc.). The review was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We conducted a quality assessment of the papers following the Mixed Methods Appraisal Tool., Results: Fifteen studies were included after carefully reviewing the 3,773 articles in the search. Methodological quality, as appraised, ranged from 80 to 100% with eight studies rated 100%. The studies overall did not have a high risk of bias. Thus, all of the 15 studies were included. Technologies investigated were classified into four groups based on their impact: therapeutic patient education, simplifying treatment regimens, early follow-up visits and short-term treatment goals, and reminder programs. Different technologies were used (automatic drug dispensers or boxes, mobile health-based interventions, game-based interventions, e-health-based interventions, patient portals, and virtual reality). The factors impacting patients' adherence to technology-based treatment and medication were clustered into human-computer interaction and integration challenges., Conclusion: This study contributes to the literature by classifying the technologies that supported medication adherence among patients with memory issues in four groups. It also explores and presents the possible limitations of existing solutions to drive future research in supporting care for people with memory disorders., Competing Interests: None declared., (Thieme. All rights reserved.)

Purpose: Clinic-based or community-based interventions can improve adherence to guideline-directed medication therapies (GDMTs) among patients with heart failure (HF). However, opportunities for such interventions are frequently missed, as providers may be unable to recognise risk patterns for medication non-adherence. Machine learning algorithms can help in identifying patients with high likelihood of non-adherence. While a number of multilevel factors influence adherence, prior models predicting non-adherence have been limited by data availability. We have established an electronic health record (EHR)-based cohort with comprehensive data elements from multiple sources to improve on existing models. We linked EHR data with pharmacy refill data for real-time incorporation of prescription fills and with social determinants data to incorporate neighbourhood factors., Participants: Patients seen at a large health system in New York City (NYC), who were >18 years old with diagnosis of HF or reduced ejection fraction (<40%) since 2017, had at least one clinical encounter between 1 April 2021 and 31 October 2022 and active prescriptions for any of the four GDMTs (beta-blocker, ACEi/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), mineralocorticoid receptor antagonist (MRA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i)) during the study period. Patients with non-geocodable address or outside the continental USA were excluded., Findings to Date: Among 39 963 patients in the cohort, the average age was 73±14 years old, 44% were female and 48% were current/former smokers. The common comorbid conditions were hypertension (77%), cardiac arrhythmias (56%), obesity (33%) and valvular disease (33%). During the study period, 33 606 (84%) patients had an active prescription of beta blocker, 32 626 (82%) had ACEi/ARB/ARNI, 11 611 (29%) MRA and 7472 (19%) SGLT2i. Ninety-nine per cent were from urban metropolitan areas., Future Plans: We will use the established cohort to develop a machine learning model to predict medication adherence, and to support ancillary studies assessing associates of adherence. For external validation, we will include data from an additional hospital system in NYC., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Hospital acquired anemia is common during admission and can result in increased transfusion and length of stay. Recumbent posture is known to lead to lower hemoglobin measurements. We tested to see if an initiative promoting evening lab draws would lead to higher hemoglobin measurements due to more time in upright posture during the day and evening., Methods: We included patients hospitalized on 2 medical units, beginning March 26, 2020 and discharged prior to January 25, 2021. On one of the units, we implemented an initiative to have routine laboratory draws in the evening rather than the morning starting on August 26, 2020. There were 1217 patients on the control unit and 1265 on the intervention unit during the entire study period. First we used a linear mixed-effects model to see if timing of blood draw was associated with hemoglobin level in the pre-intervention period. We then compared levels of hemoglobin before and after the intervention using a difference-in-difference analysis., Results: In the pre-intervention period, evening blood draws were associated with higher hemoglobin compared to morning (0.28; 95% CI, 0.22-0.35). Evening blood draws increased with the intervention (10.3% vs 47.9%, P > 0.001). However, the intervention floor was not associated with hemoglobin levels in difference-in-difference analysis (coefficient of -0.15; 95% CI, -0.51-0.21)., Conclusions: While evening blood draws were associated with higher hemoglobin levels, an intervention that successfully changed timing of routine labs to the evening did not lead to an increase in hemoglobin levels., (© American Association for Clinical Chemistry 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Low medication adherence is a common cause of high blood pressure but is often unrecognized in clinical practice. Electronic data linkages between electronic health records (EHRs) and pharmacies offer the opportunity to identify low medication adherence, which can be used for interventions at the point of care. We developed a multicomponent intervention that uses linked EHR and pharmacy data to automatically identify patients with elevated blood pressure and low medication adherence. The intervention then combines team-based care with EHR-based workflows to address medication nonadherence., Objective: This study aims to describe the design of the Leveraging EHR Technology and Team Care to Address Medication Adherence (TEAMLET) trial, which tests the effectiveness of a multicomponent intervention that leverages EHR-based data and team-based care on medication adherence among patients with hypertension., Methods: TEAMLET is a pragmatic, cluster randomized controlled trial in which 10 primary care practices will be randomized 1:1 to the multicomponent intervention or usual care. We will include all patients with hypertension and low medication adherence who are seen at enrolled practices. The primary outcome is medication adherence, as measured by the proportion of days covered, and the secondary outcome is clinic systolic blood pressure. We will also assess intervention implementation, including adoption, acceptability, fidelity, cost, and sustainability., Results: As of May 2023, we have randomized 10 primary care practices into the study, with 5 practices assigned to each arm of the trial. The enrollment for the study commenced on October 5, 2022, and the trial is currently ongoing. We anticipate patient recruitment to go through the fall of 2023 and the primary outcomes to be assessed in the fall of 2024., Conclusions: The TEAMLET trial will evaluate the effectiveness of a multicomponent intervention that leverages EHR-based data and team-based care on medication adherence. If successful, the intervention could offer a scalable approach to address inadequate blood pressure control among millions of patients with hypertension., Trial Registration: ClinicalTrials.gov NCT05349422; https://clinicaltrials.gov/ct2/show/NCT05349422., International Registered Report Identifier (irrid): DERR1-10.2196/47930., (©Saul Blecker, Antoinette Schoenthaler, Tiffany Rose Martinez, Hayley M Belli, Yunan Zhao, Christina Wong, Cassidy Fitchett, Harris R Bearnot, Devin Mann. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 07.07.2023.)

Purpose: The purpose of the study was to examine differences among adult patients with diabetes who receive care through a telementoring model versus care at an academic specialty clinic on guideline-recommended diabetes care and self-management behaviors., Methods: Endocrinology-focused Extension for Community Healthcare Outcomes (ECHO Endo) patients completed surveys assessing demographics, access to care, health care quality, and self-management behaviors at enrollment and 1 year after program enrollment. Diabetes Comprehensive Care Center (DCCC) patients completed surveys at comparable time points., Results: At baseline, ECHO patients were less likely than DCCC patients to identify English as their primary language, have postsecondary education, and private insurance. One year postenrollment, ECHO patients visited their usual source of diabetic care more frequently. There were no differences in A1C testing or feet checking by health care professionals, but ECHO patients were less likely to report eye exams and smoking status assessment. ECHO and DCCC patients did not differ in consumption of high-fat foods and soda, physical activity, or home feet checks. ECHO patients were less likely to space carbohydrates evenly and test glucose levels and more likely to have smoked cigarettes., Conclusions: Endo ECHO is a suitable alternative to specialty care for patients in underserved communities with restricted access to specialty care. Results support the value of the Project ECHO telementoring model in addressing barriers to high-quality care for underserved communities.

Background: Only half of patients with hypertension have adequately controlled blood pressure. Clinical decision support (CDS) has the potential to overcome barriers to delivering guideline-recommended care and improve hypertension management. However, optimal strategies for scaling CDS have not been well established, particularly in small, independent primary care practices which often lack the resources to effectively change practice routines. Practice facilitation is an implementation strategy that has been shown to support process changes. Our objective is to evaluate whether practice facilitation provided with hypertension-focused CDS can lead to improvements in blood pressure control for patients seen in small primary care practices., Methods/design: We will conduct a cluster randomized control trial to compare the effect of hypertension-focused CDS plus practice facilitation on BP control, as compared to CDS alone. The practice facilitation intervention will include an initial training in the CDS and a review of current guidelines along with follow-up for coaching and integration support. We will randomize 46 small primary care practices in New York City who use the same electronic health record vendor to intervention or control. All patients with hypertension seen at these practices will be included in the evaluation. We will also assess implementation of CDS in all practices and practice facilitation in the intervention group., Discussion: The results of this study will inform optimal implementation of CDS into small primary care practices, where much of care delivery occurs in the U.S. Additionally, our assessment of barriers and facilitators to implementation will support future scaling of the intervention., Clinicaltrials: gov Identifier: NCT05588466., Competing Interests: Declaration of Competing Interest The authors have no conflict of interest to declare., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Chronic kidney disease (CKD) is common and associated with adverse clinical outcomes. Most care for early CKD is provided in primary care, including hypertension (HTN) management. Computerized clinical decision support (CDS) can improve the quality of care for CKD but can also cause alert fatigue for primary care physicians (PCPs). Computable phenotypes (CPs) are algorithms to identify disease populations using, for example, specific laboratory data criteria., Objectives: Our objective was to determine the feasibility of implementation of CDS alerts by developing CPs and estimating potential alert burden., Methods: We utilized clinical guidelines to develop a set of five CPs for patients with stage 3 to 4 CKD, uncontrolled HTN, and indications for initiation or titration of guideline-recommended antihypertensive agents. We then conducted an iterative data analytic process consisting of database queries, data validation, and subject matter expert discussion, to make iterative changes to the CPs. We estimated the potential alert burden to make final decisions about the scope of the CDS alerts. Specifically, the number of times that each alert could fire was limited to once per patient., Results: In our primary care network, there were 239,339 encounters for 105,992 primary care patients between April 1, 2018 and April 1, 2019. Of these patients, 9,081 (8.6%) had stage 3 and 4 CKD. Almost half of the CKD patients, 4,191 patients, also had uncontrolled HTN. The majority of CKD patients were female, elderly, white, and English-speaking. We estimated that 5,369 alerts would fire if alerts were triggered multiple times per patient, with a mean number of alerts shown to each PCP ranging from 0.07-to 0.17 alerts per week., Conclusion: Development of CPs and estimation of alert burden allows researchers to iteratively fine-tune CDS prior to implementation. This method of assessment can help organizations balance the tradeoff between standardization of care and alert fatigue., Competing Interests: None declared., (Thieme. All rights reserved.)

BACKGROUND: Incorporation of pharmacy fill data into the electronic health record has enabled calculations of medication adherence, as measured by proportion of days covered (PDC), to be displayed to clinicians. Although PDC values help identify patients who may be nonadherent to their medications, it does not provide information on the reasons for medication-taking behaviors. OBJECTIVE: To characterize self-reported adherence status to antihypertensive medications among patients with low refill medication adherence. Our secondary objective was to identify the most common reasons for nonadherence and examine the patient sociodemographic characteristics associated with these barriers. METHODS: Participants were adult patients seen in primary care clinics of a large, urban health system and on antihypertensive therapy with a PDC of less than 80% based on 6-month linked electronic health record-pharmacy fill data. We administered a validated medication adherence screener and a survey assessing reasons for antihypertensive medication nonadherence. We used descriptive statistics to characterize these data and logistic and Poisson regression models to assess the relationship between sociodemographic characteristics and adherence barriers. RESULTS: The survey was completed by 242 patients (57% female; 61.2% White; 79.8% not Latino/a or Hispanic). Of these patients, 45% reported missing doses of their medications in the last 7 days. In addition, 48% endorsed having at least 1 barrier to adherence and 38.4% endorsed 2 or more barriers. The most common barriers were being busy and having difficulty remembering to take medications. Compared with White participants, Black participants (incident rate ratio = 2.49; 95% CI = 1.93-3.22) and participants of other races (incident rate ratio = 2.16; 95% CI = 1.62-2.89) experienced a greater number of barriers. CONCLUSIONS: Nearly half of patients with low PDC reported nonadherence in the prior week, suggesting PDC can be used as a screening tool. Augmenting PDC with brief self-report tools can provide insights into the reasons for nonadherence. DISCLOSURES: Dr Kharmats, Ms Martinez, Dr Belli, Ms Zhao, Dr Mann, Dr Schoenthaler, and Dr Blecker received grants from the National Institute of Health/National Heart, Lung, Blood Institute. Dr Voils holds a license by Duke University for the DOSE-Nonadherence measure and is a consultant for New York University Grossman School of Medicine. This research was supported by the NIH (R01HL156355). Dr Kharmats received a postdoctoral training grant from the National Institutes of Health (5T32HL129953-04). Dr Voils was supported by a Research Career Scientist award from the Health Services Research & Development Service of the Department of Veterans Affairs (RCS 14-443). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the United States Government.

Background: Beart failure with reduced ejection fraction (HFrEF) is a leading cause of morbidity and mortality. However, shortfalls in prescribing of proven therapies, particularly mineralocorticoid receptor antagonist (MRA) therapy, account for several thousand preventable deaths per year nationwide. Electronic clinical decision support (CDS) is a potential low-cost and scalable solution to improve prescribing of therapies. However, the optimal timing and format of CDS tools is unknown., Methods and Results: We developed two targeted CDS tools to inform cardiologists of gaps in MRA therapy for patients with HFrEF and without contraindication to MRA therapy: (1) an alert that notifies cardiologists at the time of patient visit, and (2) an automated electronic message that allows for review between visits. We designed these tools using an established CDS framework and findings from semistructured interviews with cardiologists. We then pilot tested both CDS tools (n = 596 patients) and further enhanced them based on additional semistructured interviews (n = 11 cardiologists). The message was modified to reduce the number of patients listed, include future visits, and list date of next visit. The alert was modified to improve noticeability, reduce extraneous information on guidelines, and include key information on contraindications., Conclusions: The BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) trial aims to compare the effectiveness of the alert vs. the automated message vs. usual care on the primary outcome of MRA prescribing. To our knowledge, no study has directly compared the efficacy of these two different types of electronic CDS interventions. If effective, our findings can be rapidly disseminated to improve morbidity and mortality for patients with HFrEF, and can also inform the development of future CDS interventions for other disease states. (Trial registration: Clinicaltrials.gov NCT05275920)., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Guideline-directed medical therapy (GDMT) dramatically improves outcomes in heart failure with reduced ejection fraction (HFrEF). Our goal was to examine GDMT use in community patients with newly diagnosed HFrEF., Methods and Results: We performed a population-based, retrospective cohort study of all Olmsted County, Minnesota, residents with newly diagnosed HFrEF (EF ≤ 40%) 2007-2017. We excluded patients with contraindications to medication initiation. We examined the use of beta-blockers, HF beta-blockers (metoprolol succinate, carvedilol, bisoprolol), angiotensin converting enzyme inhibitors (ACEis), angiotensin receptor blockers (ARBs), angiotensin receptor neprilysin inhibitors (ARNIS), and mineralocorticoid receptor antagonists (MRAs) in the first year after HFrEF diagnosis. We used Cox models to evaluate the association of being seen in an HF clinic with the initiation of GDMT. From 2007 to 2017, 1160 patients were diagnosed with HFrEF (mean age 69.7 years, 65.6% men). Most eligible patients received beta-blockers (92.6%) and ACEis/ARBs/ARNIs (87.0%) in the first year. However, only 63.8% of patients were treated with an HF beta-blocker, and few received MRAs (17.6%). In models accounting for the role of an HF clinic in initiation of these medications, being seen in an HF clinic was independently associated with initiation of new GDMT across all medication classes, with a hazard ratio (95% CI) of 1.54 (1.15-2.06) for any beta-blocker, 2.49 (1.95-3.20) for HF beta-blockers, 1.97 (1.46-2.65) for ACEis/ARBs/ARNIs, and 2.14 (1.49-3.08) for MRAs., Conclusions: In this population-based study, most patients with newly diagnosed HFrEF received beta-blockers and ACEis/ARBs/ARNIs. GDMT use was higher in patients seen in an HF clinic, suggesting the potential benefit of referral to an HF clinic for patients with newly diagnosed HFrEF., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: National registries reveal significant gaps in medical therapy for patients with heart failure and reduced ejection fraction (HFrEF), but may not accurately (or fully) characterize the population eligible for therapy., Objective: We developed an automated, electronic health record-based algorithm to identify HFrEF patients eligible for evidence-based therapy, and extracted treatment data to assess gaps in therapy in a large, diverse health system., Methods: In this cross-sectional study of all NYU Langone Health outpatients with EF ≤ 40% on echocardiogram and an outpatient visit from 3/1/2019 to 2/29/2020, we assessed prescription of the following therapies: beta-blocker (BB), angiotensin converting enzyme inhibitor (ACE-I)/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), and mineralocorticoid receptor antagonist (MRA). Our algorithm accounted for contraindications such as medication allergy, bradycardia, hypotension, renal dysfunction, and hyperkalemia., Results: We electronically identified 2732 patients meeting inclusion criteria. Among those eligible for each medication class, 84.8% and 79.7% were appropriately prescribed BB and ACE-I/ARB/ARNI, respectively, while only 23.9% and 22.7% were appropriately prescribed MRA and ARNI, respectively. In adjusted models, younger age, cardiology visit and lower EF were associated with increased prescribing of medications. Private insurance and Medicaid were associated with increased prescribing of ARNI (OR = 1.40, 95% CI = 1.02-2.00; and OR = 1.70, 95% CI = 1.07-2.67)., Conclusions: We observed substantial shortfalls in prescribing of MRA and ARNI therapy to ambulatory HFrEF patients. Subspecialty care setting, and Medicaid insurance were associated with higher rates of ARNI prescribing. Further studies are warranted to prospectively evaluate provider- and policy-level interventions to improve prescribing of these evidence-based therapies., (© 2022. The Author(s).)

Background: Both community health workers and the Project ECHO model of specialist telementoring are innovative approaches to support primary care providers in the care of complex patients with diabetes. We studied the effect of an intervention that combined these 2 approaches on glycemic control., Methods: Patients with diabetes were recruited from 10 federally qualified health centers in New Mexico. We used electronic health record (EHR) data to compare HbA1c levels prior to intervention enrollment with HbA1c levels after 3 months (early follow-up) and 12 months (late follow-up) following enrollment. We propensity matched intervention patients to comparison patients from other sites within the same electronic health records databases to estimate the average treatment effect., Results: Among 557 intervention patients with HbA1c data, mean HbA1c decreased from 10.5% to 9.3% in the pre- versus postintervention periods (P < .001). As compared to the comparison group, the intervention was associated with a change in HbA1c of -0.2% (95% confidence interval [CI] -0.4%-0.5%) and -0.3 (95% CI -0.5-0.0) in the early and late follow-up cohorts, respectively. The intervention was associated with a significant increase in percentage of patients with HbA1c <8% in the late follow-up cohort (8.1%, 95% CI 2.2%-13.9%) but not the early follow-up cohort (3.6%, 95% CI -1.5% to 8.7%) DISCUSSION: The intervention was associated with a substantial decrease in HbA1c in intervention patients, although this improvement was not different from matched comparison patients in early follow-up. Although combining community health workers with Project ECHO may hold promise for improving glycemic control, particularly in the longer term, further evaluations are needed., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objective: To develop algorithms to identify patients with advanced heart failure (HF) that can be applied to administrative data., Patients and Methods: In a population-based cohort of all residents of Olmsted County, Minnesota, with greater than or equal to 1 HF billing code 2007-2017 (n=8657), we identified all patients with advanced HF (n=847) by applying the gold standard European Society of Cardiology advanced HF criteria via manual medical review by an HF cardiologist. The advanced HF index date was the date the patient first met all European Society of Cardiology criteria. We subsequently developed candidate algorithms to identify advanced HF using administrative data (billing codes and prescriptions relevant to HF or comorbidities that affect HF outcomes), applied them to the HF cohort, and assessed their ability to identify patients with advanced HF on or after their advanced HF index date., Results: A single hospitalization for HF or ventricular arrhythmias identified all patients with advanced HF (sensitivity, 100%); however, the positive predictive value (PPV) was low (36.4%). More stringent definitions, including additional hospitalizations and/or other signs of advanced HF (hyponatremia, acute kidney injury, hypotension, or high-dose diuretic use), decreased the sensitivity but improved the specificity and PPV. For example, 2 hospitalizations plus 1 sign of advanced HF had a sensitivity of 72.7%, specificity of 89.8%, and PPV of 60.5%. Negative predictive values were high for all algorithms evaluated., Conclusion: Algorithms using administrative data can identify patients with advanced HF with reasonable performance., (© 2022 The Authors.)

Structures (context of care delivery) and processes (actions aimed at delivery care) are posited to drive patient outcomes. Despite decades of primary care research, there remains a lack of evidence connecting specific structures/processes to patient outcomes to determine which of the numerous recommended structures/processes to prioritize for implementation. The objective of this study was to identify structures/processes most commonly present in high-performing primary care practices for chronic care management and prevention. We conducted key informant interviews with a national sample of 22 high-performing primary care practices. We identified the 10 most commonly present structures/processes in these practices, which largely enable 2 core functions: mobilizing staff to conduct patient outreach and helping practices avoid gaps in care. Given the costs of implementing and maintaining numerous structures/processes, our study provides a starting list for providers to prioritize and for researchers to investigate further for specific effects on patient outcomes., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc.)

Objective: To evaluate the impact of structured recommendations on follow-up completion for incidental lung nodules (ILNs)., Methods: Patients with ILNs before and after implementation of structured Fleischner recommendations and electronic tracking were sampled randomly. The cohorts were compared for imaging follow-up. Multivariable logistic regression was used to assess appropriate follow-up and loss to follow-up, with independent variables including use of structured recommendations or tracking, age, sex, race, ethnicity, setting of the index test (inpatient, outpatient, emergency department), smoking history, and nodule features., Results: In all, 1,301 patients met final inclusion criteria, including 255 patients before and 1,046 patients after structured recommendations or tracking. Baseline differences were found in the pre- and postintervention groups, with smaller ILNs and younger age after implementing structured recommendations. Comparing pre- versus postintervention outcomes, 40.0% (100 of 250) versus 29.5% (309 of 1,046) of patients had no follow-up despite Fleischner indications for imaging (P = .002), and among the remaining patients, 56.6% (82 of 145) versus 75.0% (553 of 737) followed up on time (P < .001). Delayed follow-up was more frequent before intervention. Differences postintervention were mostly accounted for by nodules ≤8 mm in the outpatient setting (P < .001). In multivariable analysis, younger age, White race, outpatient setting, and larger nodule size showed significant association with appropriate follow-up completion (P < .015), but structured recommendations did not. Similar results applied for loss to follow-up., Discussion: Consistent use of structured reporting is likely key to mitigate selection bias when benchmarking rates of appropriate follow-up of ILN. Emergency department patients and inpatients are at high risk of missed or delayed follow-up despite structured recommendations., (Copyright © 2021 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Objectives: To identify ICD-10-CM diagnostic codes associated with the social determinants of health (SDOH), determine frequency of use of the code for homelessness across time, and examine the frequency of interrupted periods of Medicaid eligibility (ie, Medicaid churn) for beneficiaries with and without this code., Design: Retrospective data analyses of New York State (NYS) Medicaid claims data for years 2006-2017 to determine reliable indicators of SDOH hypothesized to affect Medicaid churn, and for years 2016-2017 to examine frequency of Medicaid churn among patients with and without an indicator for homelessness., Main Outcome Measures: Any interruption in the eligibility for Medicaid insurance (Medicaid churn), assessed via client identification numbers (CIN) for continuity., Methods: Analyses were conducted to assess the frequency of use and pattern of New York State Medicaid claims submission for SDOH codes. Analyses were conducted for Medicaid claims submitted for years 2016-2017 for Medicaid patients with and without a homeless code (ie, ICD-10-CM Z59.0) in 2017., Results: ICD-9-CM / ICD-10-CM codes for lack of housing / homelessness demonstrated linear reliability over time (ie, for years 2006-2017) with increased usage. In 2016-2017, 22.9% of New York Medicaid patients with a homelessness code in 2017 experienced at least one interruption of Medicaid eligibility, while 18.8% of Medicaid patients without a homelessness code experienced Medicaid churn., Conclusions: Medicaid policies would do well to take into consideration the barriers to continued enrollment for the Medicaid population. Measures ought to be enacted to reduce Medicaid churn, especially for individuals experiencing homelessness., Competing Interests: Competing Interests: None declared., (Copyright © 2021, Ethnicity & Disease, Inc.)

Objective: The Endocrinology ECHO intervention utilized a tele-mentoring model that connects primary care providers (PCPs) and community health workers (CHWs) with specialists for training in diabetes care. We evaluated the impact of the Endo ECHO intervention on healthcare utilization and care for Medicaid patients with diabetes in New Mexico., Methods: Between January 2015 and April 2017, patients with complex diabetes from 10 health centers in NM were recruited to receive diabetes care from a PCP and CHW upskilled through Endo ECHO. We matched intervention patients in the NM Medicaid claims database to comparison Medicaid beneficiaries using 5:1 propensity matching. We used a difference-in-difference (DID) approach to compare utilization and processes of care between intervention and comparison patients., Results: Of 541 Medicaid patients enrolled in Endo ECHO, 305 met inclusion criteria and were successfully matched. Outpatient visits increased with Endo ECHO for intervention patients as compared to comparison patients (rate ratio, 1.57; 95% confidence interval &lsqb;CI], 1.43 to 1.72). The intervention was associated with an increase in emergency department (ED) visits (rate ratio, 1.30; 95% CI, 1.04 to 1.63) but no change in hospitalizations (rate ratio, 1.47; 95% CI, 0.95 to 2.23). Among intervention patients, utilization of metformin increased from 57.1% to 60.7%, with a DID between groups of 8.8% (95% CI, 4.0% to 13.6%). We found similar increases in use of statins (DID, 8.5%; 95% CI, 3.2% to 13.8%), angiotensin-converting enzyme inhibitors (DID, 9.5%; 95% CI, 3.5% to 15.4%), or antidepressant therapies (DID, 9.4%; 95% CI, 1.1% to 18.1%)., Conclusion: Patient enrollment in Endo ECHO was associated with increased outpatient and ED utilization and increased uptake of prescription-related quality measures. No impact was observed on hospitalization., (© 2020 American Association of Clinical Endocrinologists. Published by Elsevier, Inc. All rights reserved.)

Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of cases is challenging because of the heterogeneous nature of the disease, which encompasses various phenotypes that may respond differently to treatment. The increasing availability of both structured and unstructured data in the EHR has expanded opportunities for cohort construction. This article reviews the current literature on approaches to identification of heart failure, and looks toward the future of machine learning, big data, and phenomapping., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: Patients increasingly access radiology results through digital portals. We compared patient satisfaction and understanding of radiology results when received through an electronic patient portal versus direct communication from providers., Methods: Patients were invited to participate in an online survey within 7 days of undergoing a radiology examination. Participants received one of two survey versions, based on whether or not they viewed results in the patient portal. The associations between method of result notification and satisfaction with notification timing and self-reported understanding of results were evaluated using χ 2 tests and logistic regression., Results: Of 1,005 survey respondents, 87.8% (882 of 1,005) reported having received their imaging test results, with 486 (48.4%) first being notified through the patient portal and 396 (39.4%) via direct provider communication. Patients reported high levels of satisfaction with timing regardless of whether they first received the results through the patient portal or through direct provider communication (88.8%-89.9%). Patients who first received their results through the patient portal reported a lesser degree of perceived understanding than those who first received their results through direct provider communication (26.7% versus 47.8%; P < .001). Patients were less likely to report clear understanding for advanced imaging (CT or MRI) than ultrasound or x-rays (29.3% versus 40.3% versus 38.2%, respectively; P = .02). Patient characteristics showed no association with understanding in multivariable analysis., Conclusion: As online portal release of radiology results to patients becomes commonplace, efforts may be warranted to improve patient experience when first receiving their radiology results online., (Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Individuals living with complex diabetes experience limited access to endocrine care due to a nationwide shortage of endocrinologists. Project ECHO (Extension for Community Healthcare Outcomes) is an innovative, scalable model of health care that extends specialty care to medically underserved areas through ongoing telementorship of community primary care providers. We evaluated the effects of an endocrine-focused ECHO program (Endo ECHO) on patients with type 1 and complex type 2 diabetes, and report here on changes in patient-reported measures of health care access and quality from baseline to one year aft er program enrollment. Patients were eligible for Endo ECHO if they were 18 years or older with complex diabetes. Aft er participating in Endo ECHO, access to health care and diabetes-related quality of care improved dramatically. Our results suggest that Endo ECHO may be a suitable intervention for extending best practices in diabetes care to medically underserved patients.

Purpose: To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations., Methods and Materials: We searched the electronic health records for patients who underwent chest CT during 2014 and 2017, before and after implementation of a department-wide dictation macro of the Fleischner Society recommendations. We randomly selected 950 unstructured chest CT reports and reviewed manually for ILNs. An NLP tool was trained and validated against the manually reviewed set, for the task of automated detection of ILNs with exclusion of previously known or definitively benign nodules. For ILNs found in the training and validation sets, we assessed whether reported management recommendations agreed with Fleischner Society guidelines. The guideline concordance of management recommendations was compared between 2014 and 2017., Results: The NLP tool identified ILNs with sensitivity and specificity of 91.1% and 82.2%, respectively, in the validation set. Positive and negative predictive values were 59.7% and 97.0%. In reports of ILNs in the training and validation sets before versus after introduction of a Fleischner reporting macro, there was no difference in the proportion of reports with ILNs (108 of 500 [21.6%] versus 101 of 450 [22.4%]; P = .8), or in the proportion of reports with ILNs containing follow-up recommendations (75 of 108 [69.4%] versus 80 of 101 [79.2%]; P = .2]. Rates of recommendation guideline concordance were not significantly different before and after implementation of the standardized macro (52 of 75 [69.3%] versus 60 of 80 [75.0%]; P = .43)., Conclusion: NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management., (Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Background: Mineralocorticoid receptor antagonists (MRA) are an underutilized therapy for heart failure with a reduced ejection fraction (HFrEF), but the current impact of hospitalization on MRA use is not well characterized. The objective of this study was to describe contemporary MRA prescription for heart failure patients before and after the full scope of hospitalizations and the association between MRA discharge prescription and post-hospitalization outcomes., Methods: We conducted a retrospective cohort study at an academic hospital system in 2013-2016. Among 1500 included hospitalizations of 1009 unique patients with HFrEF and without MRA contraindication, the mean age was 71.9 ± 13.6 years and 443 (29.5%) were female. We compared MRA prescription before and after hospitalizations with McNemar's test and between patients with principal and secondary diagnoses of HFrEF with the chi-square test, and association of MRA discharge prescription with 30-day and 180-day mortality and readmissions using generalized estimating equations., Results: MRA prescriptions increased from 303 (20.2%) to 375 (25.0%) at discharge (+4.8%, p < 0.0001). More patients with principal diagnosis of HFrEF compared to those hospitalized for other reasons received MRA (34.9% versus 21.3%, p < 0.0001) and had them initiated (21.8% versus 9.7%, p < 0.0001). MRA prescription at discharge was not associated with mortality or readmission at 30 and 180 days, and there was no interaction with principal/secondary diagnosis., Conclusions: Among hospitalized HFrEF patients, 75% did not receive MRA before or after hospitalization, and nearly 90% of eligible patients did not have MRA initiated. As we found no signal for short-term harm after discharge, hospitalization may represent an opportunity to initiate guideline-directed heart failure therapy.

Background: The Medicare Hospital Readmissions Reduction Program has led to fewer readmissions following hospitalizations with a principal diagnosis of heart failure (HF). Patients with HF are frequently hospitalized for other causes., Objectives: This study sought to compare trends in Medicare risk-adjusted, 30-day readmissions following principal HF hospitalizations and other hospitalizations with HF., Methods: This was a retrospective study of 12,973,853 Medicare hospitalizations with a principal or secondary diagnosis of HF between January 2008 and June 2015. Hospitalizations were categorized as follows: principal HF hospitalizations; principal acute myocardial infarction or pneumonia hospitalizations with secondary HF; and other hospitalizations with secondary HF. The study examined trends in risk-adjusted, 30-day, all-cause readmission rates for each cohort and trends in differences in readmission rates among cohorts by using linear spline regression models., Results: Before passage of the Affordable Care Act in March 2010, risk-adjusted, 30-day readmission rates were stable for all 3 cohorts, with mean monthly rates of 26.1%, 24.9%, and 24.4%, respectively. Risk-adjusted readmission rates started declining after passage of the Affordable Care Act by 1.09% (95% confidence interval [CI]: 0.51% to 1.68%), 1.24% (95% CI: 0.92% to 1.57%), and 1.05% (95% CI: 0.52% to 1.58%) per year, respectively, until implementation of the Hospital Readmissions Reduction Program in October 2012 and then stabilized for all 3 cohorts., Conclusions: Patients with HF are often hospitalized for other causes, and these hospitalizations have high readmission rates. Policy changes led to decreases in readmission rates for both principal and secondary HF hospitalizations. Readmission rates in both groups remain high, suggesting that initiatives targeting all hospitalized patients with HF continue to be warranted., (Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Interventions to reduce readmissions after acute heart failure hospitalization require early identification of patients. The purpose of this study was to develop and test accuracies of various approaches to identify patients with acute decompensated heart failure (ADHF) with the use of data derived from the electronic health record., Methods and Results: We included 37,229 hospitalizations of adult patients at a single hospital during 2013-2015. We developed 4 algorithms to identify hospitalization with a principal discharge diagnosis of ADHF: 1) presence of 1 of 3 clinical characteristics, 2) logistic regression of 31 structured data elements, 3) machine learning with unstructured data, and 4) machine learning with the use of both structured and unstructured data. In data validation, algorithm 1 had a sensitivity of 0.98 and positive predictive value (PPV) of 0.14 for ADHF. Algorithm 2 had an area under the receiver operating characteristic curve (AUC) of 0.96, and both machine learning algorithms had AUCs of 0.99. Based on a brief survey of 3 providers who perform chart review for ADHF, we estimated that providers spent 8.6 minutes per chart review; using this this parameter, we estimated that providers would spend 61.4, 57.3, 28.7, and 25.3 minutes on secondary chart review for each case of ADHF if initial screening were done with algorithms 1, 2, 3, and 4, respectively., Conclusions: Machine learning algorithms with unstructured notes had the best performance for identification of ADHF and can improve provider efficiency for delivery of quality improvement interventions., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Urgent, unplanned hospital readmissions are increasingly being used to gauge the quality of care. We reviewed urgent readmissions to determine which were potentially avoidable and compared rates of all-cause and avoidable readmissions., Methods: In a multicentre, prospective cohort study, we reviewed all urgent readmissions that occurred within six months among patients discharged to the community from 11 teaching and community hospitals between October 2002 and July 2006. Summaries of the readmissions were reviewed by at least four practising physicians using standardized methods to judge whether the readmission was an adverse event (poor clinical outcome due to medical care) and whether the adverse event could have been avoided. We used a latent class model to determine whether the probability that each readmission was truly avoidable exceeded 50%., Results: Of the 4812 patients included in the study, 649 (13.5%, 95% confidence interval [CI] 12.5%-14.5%) had an urgent readmission within six months after discharge. We considered 104 of them (16.0% of those readmitted, 95% CI 13.3%-19.1%; 2.2% of those discharged, 95% CI 1.8%-2.6%) to have had a potentially avoidable readmission. The proportion of patients who had an urgent readmission varied significantly by hospital (range 7.5%-22.5%; χ(2) = 92.9, p < 0.001); the proportion of readmissions deemed avoidable did not show significant variation by hospital (range 1.2%-3.7%; χ(2) = 12.5, p < 0.25). We found no association between the proportion of patients who had an urgent readmission and the proportion of patients who had an avoidable readmission (Pearson correlation 0.294; p = 0.38). In addition, we found no association between hospital rankings by proportion of patients readmitted and rankings by proportion of patients with an avoidable readmission (Spearman correlation coefficient 0.28, p = 0.41)., Interpretation: Urgent readmissions deemed potentially avoidable were relatively uncommon, comprising less than 20% of all urgent readmissions following hospital discharge. Hospital-specific proportions of patients who were readmitted were not related to proportions with a potentially avoidable readmission.

In the era of "comparative effectiveness" research, each of the major stakeholders in healthcare-payors, patients, providers, and government-face a similar challenge. When making a decision about whether a new device, drug, or a diagnostic modality should be considered for use or coverage, what choices are best supported by the evidence? Medical evidence is defined by randomized controlled trials and by observational studies that vary greatly in their design, the accuracy of their analyses, and the relevance of their conclusions and recommendations. Hence, key decision makers increasingly rely on systematic reviews and meta-analyses to facilitate the interpretation and application of research evidence. Knowing how to evaluate meta-analyses and understanding the potential pitfalls of the method are crucial for those involved in designing drug benefits. The authors highlight the process, strengths, and weaknesses of meta-analysis and explain how to judge the value of the results.