Screening for Atrial Fibrillation in Older Adults at Primary Care Visits: VITAL-AF Randomized Controlled Trial.

BACKGROUND: Undiagnosed atrial fibrillation (AF) may cause preventable strokes. Guidelines differ regarding AF screening recommendations. We tested whether point-of-care screening with a handheld single-lead ECG at primary care practice visits increases diagnoses of AF. METHODS: We randomized 16 primary care clinics 1:1 to AF screening using a handheld single-lead ECG (AliveCor KardiaMobile) during vital sign assessments, or usual care. Patients included were ages ≥65 years. Screening results were provided to primary care clinicians at the encounter. All confirmatory diagnostic testing and treatment decisions were made by the primary care clinician. New AF diagnoses during the 1-year follow-up were ascertained electronically and manually adjudicated. Proportions and incidence rates were calculated. Effect heterogeneity was assessed. RESULTS: Of 30 715 patients without prevalent AF (n=15 393 screening [91% screened], n=15 322 control), 1.72% of individuals in the screening group had new AF diagnosed at 1 year versus 1.59% in the control group (risk difference, 0.13% [95% CI, -0.16 to 0.42]; P=0.38). In prespecified subgroup analyses, new AF diagnoses in the screening and control groups were greater among those aged ≥85 years (5.56% versus 3.76%, respectively; risk difference, 1.80% [95% CI, 0.18 to 3.30]). The difference in newly diagnosed AF between the screening period and the previous year was marginally greater in the screening versus control group (0.32% versus -0.12%; risk difference, 0.43% [95% CI, -0.01 to 0.84]). The proportion of individuals with newly diagnosed AF who were initiated on oral anticoagulants was not different in the screening (n=194, 73.5%) and control (n=172, 70.8%) arms (risk difference, 2.7% [95% CI, -5.5 to 10.4]). CONCLUSIONS: Screening for AF using a single-lead ECG at primary care visits did not affect new AF diagnoses among all individuals aged 65 years or older compared with usual care. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03515057.

Accuracy and variability of cardiologist interpretation of single lead electrocardiograms for atrial fibrillation: The VITAL-AF trial.

BACKGROUND: Screening for atrial fibrillation (AF) using consumer-based devices capable of producing a single lead electrocardiogram (1L ECG) is increasing. There are limited data on the accuracy of physician interpretation of these tracings. The goal of this study is to assess the sensitivity, specificity, confidence, and variability of cardiologist interpretation of point-of-care 1L ECGs. METHODS: Fifteen cardiologists reviewed point-of-care handheld 1L ECGs collected from patients aged 65 years or older enrolled in the VITAL-AF clinical trial [NCT035115057] who underwent cardiac rhythm assessments with a 1L ECG using an AliveCor KardiaMobile device. Random sampling of 1L ECGs for cardiologist review was stratified by the AliveCor algorithm interpretation. A 12L ECG performed on the same day for clinical purposes was used as the gold standard. Cardiologists each reviewed a common sample of 200 1L ECG tracings and completed a survey associated with each tracing. Cardiologists were blinded to both the AliveCor algorithm and same day 12L ECG interpretation. For each tracing, study cardiologists were asked to assess the rhythm (sinus rhythm, AF, unclassifiable), report their assessment of the quality of the tracing, and rate their confidence in rhythm interpretation. The outcomes included the sensitivity, specificity, variability, and confidence in physician interpretation. Variables associated with each measure were identified using multivariable regression. RESULTS: The average sensitivity for AF was 77.4% (range 50%-90.6%, standard deviation [SD]=11.4%) and the average specificity was 73.0% (range 41.3%-94.6%, SD = 15.4%). The mean variability was 30.8% (range 0%-76.2%, SD = 23.2%). The average reviewer confidence of 1L ECG rhythm assessment was 3.6 out of 5 (range 2.5-4.2, SD = 0.6). Patient and tracing factors associated with sensitivity, specificity, variability, and confidence were identified and included age, body mass index, and presence of artifact. CONCLUSION: Cardiologist interpretation of point-of-care handheld 1L ECGs has modest diagnostic sensitivity and specificity with substantial variability for AF classification despite high confidence. Variability in cardiologist interpretation of 1L ECGs highlights the importance of confirmatory testing for diagnosing AF.

Cytisine Versus Varenicline for Smoking Cessation in a Primary Care Setting: A Randomized Non-inferiority Trial.

INTRODUCTION: A smoking-cessation program was implemented as a randomized non-inferiority trial in primary care practices in Croatia and Slovenia to investigate whether a standard 4-week treatment with cytisine was at least as effective and feasible as a standard 12-week treatment with varenicline in helping smokers quit. AIMS AND METHODS: Out of 982 surveyed smokers, 377 were recruited to the non-inferiority trial: 186 were randomly assigned to cytisine and 191 to varenicline treatment. The primary cessation outcome was 7-day abstinence after 24 weeks, while the primary feasibility outcome was defined by adherence to the treatment plan. We also compared the rates of adverse events between the two treatment groups. RESULTS: The cessation rate after 24 weeks was 32.46% (62/191) in the varenicline group and 23.12% (43/186) in the cytisine group (odds ratio [OR]: 95%, credible interval [CI]: 0.39 to 0.98). Of 191 participants assigned to varenicline treatment 59.16% (113) were adherent, while 70.43% (131 of 186) were adherent in the cytisine group (OR: 1.65, 95% CI: 1.07 to 2.56). Participants assigned to cytisine experienced fewer total (incidence rate ratio [IRR]: 0.59, 95% CI: 0.43 to 0.81) and fewer severe or more extreme adverse events (IRR: 0.72, 95% CI: 0.35 to 1.47). CONCLUSIONS: This randomized non-inferiority trial (n = 377) found the standard 4-week cytisine treatment to be less effective than the standard 12-week varenicline treatment for smoking cessation. However, adherence to the treatment plan, ie, feasibility, was higher, and the rate of adverse events was lower among participants assigned to cytisine treatment. IMPLICATIONS: The present study found the standard 12 weeks of varenicline treatment to be more effective than the standard 4 weeks of cytisine treatment for smoking cessation in a primary care setting in Croatia and Slovenia. Participants assigned to cytisine, however, had a higher adherence to the treatment plan and a lower rate of adverse events. Estimates from the present study may be especially suitable for generalizations to high-smoking prevalence populations in Europe. Given the much lower cost of cytisine treatment, its lower rate of adverse events, and higher feasibility (but its likely lower effectiveness with the standard dosage regimen), future analyses should assess the cost-effectiveness of the two treatments for health policy considerations.

Automated Electronic Phenotyping of Cardioembolic Stroke.

BACKGROUND AND PURPOSE: Oral anticoagulation is generally indicated for cardioembolic strokes, but not for other stroke causes. Consequently, subtype classification of ischemic stroke is important for risk stratification and secondary prevention. Because manual classification of ischemic stroke is time-intensive, we assessed the accuracy of automated algorithms for performing cardioembolic stroke subtyping using an electronic health record (EHR) database. METHODS: We adapted TOAST (Trial of ORG 10172 in Acute Stroke Treatment) features associated with cardioembolic stroke for derivation in the EHR. Using administrative codes and echocardiographic reports within Mass General Brigham Biobank (N=13 079), we iteratively developed EHR-based algorithms to define the TOAST cardioembolic stroke features, revising regular expression algorithms until achieving positive predictive value ≥80%. We compared several machine learning-based statistical algorithms for discriminating cardioembolic stroke using the feature algorithms applied to EHR data from 1598 patients with acute ischemic strokes from the Massachusetts General Hospital Ischemic Stroke Registry (2002-2010) with previously adjudicated TOAST and Causative Classification of Stroke subtypes. RESULTS: Regular expression-based feature extraction algorithms achieved a mean positive predictive value of 95% (range, 88%-100%) across 11 echocardiographic features. Among 1598 patients from the Massachusetts General Hospital Ischemic Stroke Registry, 1068 had any cardioembolic stroke feature within predefined time windows in proximity to the stroke event. Cardioembolic stroke tended to occur at an older age, with more TOAST-based comorbidities, and with atrial fibrillation (82.3%). The best model was a random forest with 92.2% accuracy and area under the receiver operating characteristic curve of 91.1% (95% CI, 87.5%-93.9%). Atrial fibrillation, age, dilated cardiomyopathy, congestive heart failure, patent foramen ovale, mitral annulus calcification, and recent myocardial infarction were the most discriminatory features. CONCLUSIONS: Machine learning-based identification of cardioembolic stroke using EHR data is feasible. Future work is needed to improve the accuracy of automated cardioembolic stroke identification and assess generalizability of electronic phenotyping algorithms across clinical settings.

The Implementation and Acceptability of a Mobile Application for Screening for Atrial Fibrillation at Home.

Introduction: Although patients are able to easily record electrocardiograms using consumer devices, these are typically not shared with their clinicians. This article discusses the development and acceptability of a mobile application (app) that integrates with the electronic health record to facilitate screening for atrial fibrillation (AF). Methods: After app development and implementation, we compared workflows with and without the mobile app. Seven older adults used it during a prospective twice-daily 2-week home-based AF screening protocol and completed an acceptability survey with Likert scale responses. Results: Compliance with the screening protocol was 82%. Acceptability and usability was favorable. Patients reported confidence in the connection between the app and their medical record. Discussion: The availability of apps to capture data and facilitate a connection with health systems is critical. The app developed is a feasible solution for older patients with AF to self-monitor and report results to their health provider.

Colorectal Cancer Screening Patient Navigation for Patients with Mental Illness and/or Substance Use Disorder: Pilot Randomized Control Trial.

OBJECTIVE: Colorectal cancer (CRC) is the second leading cause of cancer death in the US. Screening has decreased CRC mortality. However, disadvantaged patients, particularly those with mental illness or substance use disorder (SUD), are less likely to be screened. The aim of this trial was to evaluate the impact of a patient navigation program on CRC screening in patients with mental illness and/or SUD. METHODS: A pilot randomized nonblinded controlled trial was conducted from January to June 2017 in an urban community health center serving a low-income population. We randomized 251 patients aged 50-74 years with mental illness and/or SUD diagnosis overdue for CRC screening to intervention (n = 126) or usual care (n = 125) stratified by mental illness, SUD, or dual diagnosis. Intervention group patients received a letter followed by a phone call from patient navigators. Navigators helped patients overcome their individual barriers to CRC screening including: education, scheduling, explanation of bowel preparation, lack of transportation or accompaniment to appointments. If patient refused colonoscopy, navigators offered fecal occult blood testing. The main measure was proportion of patients completing CRC screening in intervention and usual care groups. RESULTS: Navigators contacted 85 patients (67%) in the intervention group and 26 declined to participate. In intention-to treat analysis, more patients in the intervention group received CRC screening than in the usual care group, 19% versus 10.4% (p = .04). Among 56 intervention patients who received navigation, 19 completed screening (33.9% versus 10.4% in the control group, p = .001). In the subgroup of patients with SUD, 20% in the intervention group were screened compared to none in the usual care group (p = .05). CONCLUSIONS: A patient navigation program improved CRC screening rates in patients with mental illness and/or SUD. Larger studies in diverse care settings are needed to demonstrate generalizability and explore which modality of CRC screening is most acceptable and which navigator activities are most effective for this vulnerable population. TRIALS REGISTRATION NUMBER: 2016P001322.

Design and rationale of a pragmatic trial integrating routine screening for atrial fibrillation at primary care visits: The VITAL-AF trial.

Given the preventable morbidity and mortality associated with atrial fibrillation (AF), increased awareness of undiagnosed AF, and advances in mobile electrocardiogram (ECG) technology, there is a critical need to assess the effectiveness of using such technology to routinely screen for AF in clinical practice. VITAL-AF is a pragmatic trial that will test whether screening for AF using a single-lead handheld ECG in individuals 65 years or older during primary care visits will lead to an increased rate of AF detection. The study is a cluster-randomized trial, with 8 primary care practices randomized to AF screening and 8 primary care practices randomized to usual care. We anticipate studying approximately 16,000 patients in each arm. During the 1-year enrollment period, practice medical assistants will screen eligible patients who agree to participate during office visits using a single-lead ECG device. Automated screening results are documented in the electronic health record, and patients can discuss screening results with their provider during the scheduled visit. All single-lead ECGs are overread by a cardiologist. Screen-detected AF is managed at the discretion of the patient's physician. The primary study end point is incident AF during the screening period. Key secondary outcomes include new oral anticoagulation prescriptions, incident ischemic stroke, and major hemorrhage during a 24-month period following the study start. Outcomes are ascertained based on electronic health record documentation and are manually adjudicated. The results of this pragmatic trial may help identify a model for widespread adoption of AF screening as part of routine clinical practice.

Proactive Population Health Strategy to Offer Tobacco Dependence Treatment to Smokers in a Primary Care Practice Network.

BACKGROUND: Population-based strategies can expand the reach of tobacco cessation treatment beyond clinical encounters. OBJECTIVE: To determine the effect of two population-based tobacco cessation strategies, compared with usual care, on providing tobacco treatment outside of clinical encounters. DESIGN: 3-arm pragmatic randomized controlled trial. PARTICIPANTS: Current smokers ≥ 18 years old with a primary care provider at one of five community health centers in Massachusetts were identified via the electronic health record (n = 5225) and recruited using automated phone calls. INTERVENTIONS: One intervention group involved engagement with a health system-based tobacco coach (internal care coordination), and the other connected patients to a national quitline (external community referral). MEASUREMENTS: Proportion of smokers with documentation of any evidence-based cessation treatment in the 6 months after enrollment. KEY RESULTS: Of 639 individuals who responded to the proactive treatment offer, 233 consented and were randomized 1:1:1 to study arm. At 6-month follow-up, the pooled intervention group, compared with usual care, had higher documentation of any smoking cessation treatment (63% vs. 34%, p < 0.001), cessation medication prescription (52% vs. 30%, p = 0.002), and counseling (47% vs. 9%, p < 0.001). Internal care coordination was more effective than external community referral at connecting smokers to any cessation treatment (76% vs. 50%, p = 0.001) and at providing cessation medication (66% vs. 39%, p < 0.001), but comparable at linking smokers to cessation counseling resources. CONCLUSIONS: Smokers responding to a population-based, proactive outreach strategy had better provision of tobacco cessation treatment when referred to either a health system-based or community-based program compared with usual care. The health system-based strategy outperformed the quitline-based one in several measures. Future work should aim to improve population reach and test the effect on smoking cessation rates. TRIAL REGISTRATION: ClinicalTrials.gov NCT03612895.

Impact of a Population Health Management Intervention on Disparities in Cardiovascular Disease Control.

BACKGROUND: Healthcare systems use population health management programs to improve the quality of cardiovascular disease care. Adding a dedicated population health coordinator (PHC) who identifies and reaches out to patients not meeting cardiovascular care goals to these programs may help reduce disparities in cardiovascular care. OBJECTIVE: To determine whether a program that used PHCs decreased racial/ethnic disparities in LDL cholesterol and blood pressure (BP) control. DESIGN: Retrospective difference-in-difference analysis. PARTICIPANTS: Twelve thousdand five hundred fifty-five primary care patients with cardiovascular disease (cohort for LDL analysis) and 41,183 with hypertension (cohort for BP analysis). INTERVENTION: From July 1, 2014-December 31, 2014, 18 practices used an information technology (IT) system to identify patients not meeting LDL and BP goals; 8 practices also received a PHC. We examined whether having the PHC plus IT system, compared with having the IT system alone, decreased racial/ethnic disparities, using difference-in-difference analysis of data collected before and after program implementation. MAIN MEASURES: Meeting guideline concordant LDL and BP goals. KEY RESULTS: At baseline, there were racial/ethnic disparities in meeting LDL (p = 0.007) and BP (p = 0.0003) goals. Comparing practices with and without a PHC, and accounting for pre-intervention LDL control, non-Hispanic white patients in PHC practices had improved odds of LDL control (OR 1.20 95% CI 1.09-1.32) compared with those in non-PHC practices. Non-Hispanic black (OR 1.15 95% CI 0.80-1.65) and Hispanic (OR 1.29 95% CI 0.66-2.53) patients saw similar, but non-significant, improvements in LDL control. For BP control, non-Hispanic white patients in PHC practices (versus non-PHC) improved (OR 1.13 95% CI 1.05-1.22). Non-Hispanic black patients (OR 1.17 95% CI 0.94-1.45) saw similar, but non-statistically significant, improvements in BP control, but Hispanic (OR 0.90 95% CI 0.59-1.36) patients did not. Interaction testing confirmed that disparities did not decrease (p = 0.73 for LDL and p = 0.69 for BP). CONCLUSIONS: The population health management intervention did not decrease disparities. Further efforts should explicitly target improving both healthcare equity and quality. Clinical Trials #: NCT02812303 ( ClinicalTrials.gov ).

Electronic physician notifications to improve guideline-based anticoagulation in atrial fibrillation: a randomized controlled trial.

BACKGROUND: Oral anticoagulants reduce the risk of stroke in patients with atrial fibrillation. However, many patients with atrial fibrillation at elevated stroke risk are not treated with oral anticoagulants. OBJECTIVE: To test whether electronic notifications sent to primary care physicians increase the proportion of ambulatory patients prescribed oral anticoagulants. DESIGN: Randomized controlled trial conducted from February to May 2017 within 18 practices in an academic primary care network. PARTICIPANTS: Primary care physicians (n = 175) and their patients with atrial fibrillation, at elevated stroke risk, and not prescribed oral anticoagulants. INTERVENTION: Patients of each physician were randomized to the notification or usual care arm. Physicians received baseline email notifications and up to three reminders with patient information, educational material and primary care guidelines for anticoagulation management, and surveys in the notification arm. MAIN MEASURES: The primary outcome was the proportion of patients prescribed oral anticoagulants at 3 months in the notification (n = 972) vs. usual care (n = 1364) arms, compared using logistic regression with clustering by physician. Secondary measures included survey-based physician assessment of reasons why patients were not prescribed oral anticoagulants and how primary care physicians might be influenced by the notification. KEY RESULTS: Over 3 months, a small proportion of patients were newly prescribed oral anticoagulants with no significant difference in the notification (3.9%, 95% CI 2.8-5.3%) and usual care (3.2%, 95% CI 2.4-4.2%) arms (p = 0.37). The most common, non-exclusive reasons why patients were not on oral anticoagulants included atrial fibrillation was transient (30%) or paroxysmal (12%), patient/family declined (22%), high bleeding risk (20%), fall risk (19%), and frailty (10%). For 95% of patients, physicians stated they would not change their management after reviewing the alert. CONCLUSIONS: Electronic physician notification did not increase anticoagulation in patients with atrial fibrillation at elevated stroke risk. Primary care physicians did not prescribe anticoagulants because they perceived the bleeding risk was too high or stroke risk was too low. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT02950285.

Defining Team Effort Involved in Patient Care from the Primary Care Physician's Perspective.

BACKGROUND: A better understanding of the attributes of patients who require more effort to manage may improve risk adjustment approaches and lead to more efficient resource allocation, improved patient care and health outcomes, and reduced burnout in primary care clinicians. OBJECTIVE: To identify and characterize high-effort patients from the physician's perspective. DESIGN: Cohort study. PARTICIPANTS: Ninety-nine primary care physicians in an academic primary care network. MAIN MEASURES: From a list of 100 randomly selected patients in their panels, PCPs identified patients who required a high level of team-based effort and patients they considered complex. For high-effort patients, PCPs indicated which factors influenced their decision: medical/care coordination, behavioral health, and/or socioeconomic factors. We examined differences in patient characteristics based on PCP-defined effort and complexity. KEY RESULTS: Among 9594 eligible patients, PCPs classified 2277 (23.7 %) as high-effort and 2676 (27.9 %) as complex. Behavioral health issues were the major driver of effort in younger patients, while medical/care coordination issues predominated in older patients. Compared to low-effort patients, high-effort patients were significantly (P < 0.01 for all) more likely to have higher rates of medical (e.g. 23.2 % vs. 6.3 % for diabetes) and behavioral health problems (e.g. 9.8 % vs. 2.9 % for substance use disorder), more frequent primary care visits (10.9 vs. 6.0 visits), and higher acute care utilization rates (25.8 % vs. 7.7 % for emergency department [ED] visits and 15.0 % vs. 3.9 % for hospitalization). Almost one in five (18 %) patients who were considered high-effort were not deemed complex by the same PCPs. CONCLUSIONS: Patients defined as high-effort by their primary care physicians, not all of whom were medically complex, appear to have a high burden of psychosocial issues that may not be accounted for in current chronic disease-focused risk adjustment approaches.

Targeted Reminder Phone Calls to Patients at High Risk of No-Show for Primary Care Appointment: A Randomized Trial.

BACKGROUND: No-shows, or missed appointments, are a problem for many medical practices. They result in fragmented care and reduce access for all patients. OBJECTIVE: To determine whether telephone reminder calls targeted to patients at high risk of no-show can reduce no-show rates. DESIGN: Single-center randomized controlled trial. PARTICIPANTS: A total of 2247 primary care patients in a hospital-based primary care clinic at high risk of no-show (>15 % risk) for their appointment in 7 days. INTERVENTION: Seven days prior to their appointment, intervention arm patients were placed in a calling queue to receive a reminder phone call from a patient service coordinator. Coordinators were trained to engage patients in concrete planning. All patients received an automated phone call (usual care). MAIN MEASURES: Primary outcome was no-show rate. Secondary outcomes included arrival rate, cancellation rate, reschedule rate, time to cancellation, and change in revenue. KEY RESULTS: The no-show rate in the intervention arm (22.8 %) was significantly lower (absolute risk difference -6.4 %, p < 0.01, 95 % CI [-9.8 to -3.0 %]) than that in the control arm (29.2 %). Arrival, cancellation, and reschedule rates did not differ significantly. In the intervention arm, rescheduling and cancellations occurred further in advance of the appointment (mean difference, 0.35 days; 95 % CI [0.07-0.64]; p = 0.01). Reimbursement did not differ significantly. CONCLUSIONS: A phone call 7 days prior to an appointment led to a significant reduction in no-shows and increased reimbursement among patients at high risk of no-show. The use of targeted interventions may be of interest to practices taking on increased accountability for population health.

Randomized trial of a health IT tool to support between-visit-based laboratory monitoring for chronic disease medication prescriptions.

BACKGROUND: Lack of timely medication intensification and inadequate medication safety monitoring are two prevalent and potentially modifiable barriers to effective and safe chronic care. Innovative applications of health information technology tools may help support chronic disease management. OBJECTIVE: To examine the clinical impact of a novel health IT tool designed to facilitate between-visit ordering and tracking of future laboratory testing. DESIGN AND PARTICIPANTS: Clinical trial randomized at the provider level (n = 44 primary care physicians); patient-level outcomes among 3,655 primary care patients prescribed 5,454 oral medicines for hyperlipidemia, diabetes, and/or hypertension management over a 12-month period. MAIN MEASURES: Time from prescription to corresponding follow-up laboratory testing; proportion of follow-up time that patients achieved corresponding risk factor control (A1c, LDL); adverse event laboratory monitoring 4 weeks after medicine prescription. KEY RESULTS: Patients whose physicians were allocated to the intervention (n = 1,143) had earlier LDL laboratory assessment compared to similar patients (n = 703) of control physicians [adjusted hazard ratio (aHR): 1.15 (1.01-1.32), p = 0.04]. Among patients with elevated LDL (486 intervention, 324 control), there was decreased time to LDL goal in the intervention group [aHR 1.26 (0.99-1.62)]. However, overall there were no significant differences between study arms in time spent at LDL or HbA1c goal. Follow-up safety monitoring (e.g., creatinine, potassium, or transaminases) was relatively infrequent (ranging from 7 % to 29 % at 4 weeks) and not statistically different between arms. Intervention physicians indicated that lack of reimbursement for non-visit-based care was a barrier to use of the tool. CONCLUSIONS: A health IT tool to support between-visit laboratory monitoring improved the LDL testing interval but not LDL or HbA1c control, and it did not alter safety monitoring. Adoption of innovative tools to support physicians in non-visit-based chronic disease management may be limited by current visit-based financial and productivity incentives.

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network.

BACKGROUND: Improving the ability to risk-stratify patients is critical for efficiently allocating resources within healthcare systems. OBJECTIVE: The purpose of this study was to evaluate a physician-defined complexity prediction model against outpatient Charlson score (OCS) and a commercial risk predictor (CRP). DESIGN: Using a cohort in which primary care physicians reviewed 4302 of their adult patients, we developed a predictive model for estimated physician-defined complexity (ePDC) and categorized our population using ePDC, OCS and CRP. PARTICIPANTS: 143,372 primary care patients in a practice-based research network participated in the study. MAIN MEASURES: For all patients categorized as complex in 2007 by one or more risk-stratification method, we calculated the percentage of total person time from 2008-2011 for which eligible cancer screening was incomplete, HbA1c was ≥ 9 %, and LDL was ≥ 130 mg/dl (in patients with cardiovascular disease). We also calculated the number of emergency department (ED) visits and hospital admissions per person year (ppy). KEY RESULTS: There was modest agreement among individuals classified as complex using ePDC compared with OCS (36.7 %) and CRP (39.6 %). Over 4 follow-up years, eligible ePDC-complex patients had higher proportions (p < 0.001) of time with: incomplete cervical (17.8 % vs. 13.3 % for OCS; 19.4 % vs. 11.2 % for CRP), breast (21.4 % vs. 14.9 % for OCS; 22.7 % vs. 15.0 % for CRP), and colon (25.9 % vs. 18.7 % for OCS; 27.0 % vs. 18.2 % for CRP) cancer screening; HbA1c ≥ 9 % (15.6 % vs. 8.1 % for OCS; 15.9 % vs. 6.9 % for CRP); and LDL ≥ 130 mg/dl (12.4 % vs. 7.9 % for OCS; 11.8 % vs 9.0 % for CRP). ePDC-complex patients had higher rates (p < 0.003) of: ED visits (0.21 vs. 0.11 ppy for OCS; 0.17 vs. 0.15 ppy for CRP), and admissions in patients 45-64 and ≥ 65 years old (0.11 vs. 0.10 ppy AND 0.24 vs. 0.21 ppy for OCS). CONCLUSION: Our measure for estimated physician-defined complexity compared favorably to commonly used risk-prediction approaches in identifying future suboptimal quality and utilization outcomes.

Appointment "no-shows" are an independent predictor of subsequent quality of care and resource utilization outcomes.

BACKGROUND: Identifying individuals at high risk for suboptimal outcomes is an important goal of healthcare delivery systems. Appointment no-shows may be an important risk predictor. OBJECTIVES: To test the hypothesis that patients with a high propensity to "no-show" for appointments will have worse clinical and acute care utilization outcomes compared to patients with a lower propensity. DESIGN: We calculated the no-show propensity factor (NSPF) for patients of a large academic primary care network using 5 years of outpatient appointment data. NSPF corrects for patients with fewer appointments to avoid over-weighting of no-show visits in such patients. We divided patients into three NSPF risk groups and evaluated the association between NSPF and clinical and acute care utilization outcomes after adjusting for baseline patient characteristics. PARTICIPANTS: A total of 140,947 patients who visited a network practice from January 1, 2007, through December 31, 2009, and were either connected to a primary care physician or to a primary care practice, based on a previously validated algorithm. MAIN MEASURES: Outcomes of interest were incomplete colorectal, cervical, and breast cancer screening, and above-goal hemoglobin A1c (HbA1c) and low-density lipoprotein (LDL) levels at 1-year follow-up, and hospitalizations and emergency department visits in the subsequent 3 years. KEY RESULTS: Compared to patients in the low NSPF group, patients in the high NSPF group (n=14,081) were significantly more likely to have incomplete preventive cancer screening (aOR 2.41 [2.19-.66] for colorectal, aOR 1.85 [1.65-.08] for cervical, aOR 2.93 [2.62-3.28] for breast cancer), above-goal chronic disease control measures (aOR 2.64 [2.22-3.14] for HbA1c, aOR 1.39 [1.15-1.67] for LDL], and increased rates of acute care utilization (aRR 1.37 [1.31-1.44] for hospitalization, aRR 1.39 [1.35-1.43] for emergency department visits). CONCLUSIONS: NSPF is an independent predictor of suboptimal primary care outcomes and acute care utilization. NSPF may play an important role in helping healthcare systems identify high-risk patients.

Building Equity Improvement into Quality Improvement: Reducing Socioeconomic Disparities in Colorectal Cancer Screening as Part of Population Health Management.

BACKGROUND: Improving colorectal cancer (CRC) screening rates for patients from socioeconomically disadvantaged backgrounds is a recognized public health priority. OBJECTIVE: Our aim was to determine if implementation of a system-wide screening intervention could reduce disparities in the setting of improved overall screening rates. DESIGN: This was an interrupted time series (ITS) analysis before and after a population management intervention. PARTICIPANTS: Patients eligible for CRC screening (age 52-75 years without prior total colectomy) in an 18-practice research network from 15 June 2009 to 15 June 2012 participated in the study. INTERVENTION: The Technology for Optimizing Population Care (TopCare) intervention electronically identified patients overdue for screening and facilitated contact by letter or telephone scheduler, with or without physician involvement. Patients identified by algorithm as high risk for non-completion entered into intensive patient navigation. MAIN MEASURES: Patients were dichotomized as ≤ high school diploma (≤ HS), an indicator of socioeconomic disadvantage, vs. >HS diploma (> HS). The monthly disparity between ≤ HS and > HS with regard to CRC screening completion was examined. KEY RESULTS: At baseline, 72% of 47,447 eligible patients had completed screening, compared with 75% of 51,442 eligible patients at the end of follow-up (p < 0.001). CRC screening completion was lower in ≤ HS vs. >HS patients in June 2009 (65.7% vs. 74.5%, p < 0.001) and remained lower in June 2012 (69.4% vs. 76.7%, p < 0.001). In the ITS analysis, which accounts for secular trends, TopCare was associated with a significant decrease in the CRC screening disparity (0.7%, p < 0.001). The effect of TopCare represents approximately 99 additional ≤ HS patients screened above prevailing trends, or 26 life-years gained had these patients remained unscreened. CONCLUSIONS: A multifaceted population management intervention sensitive to the needs of vulnerable patients modestly narrowed disparities in CRC screening, while also increasing overall screening rates. Embedding interventions for vulnerable patients within larger population management systems represents an effective approach to increasing overall quality of care while also decreasing disparities.

The longitudinal impact of patient navigation on equity in colorectal cancer screening in a large primary care network.

BACKGROUND: The long-term effects of interventions to improve colorectal (CRC) screening in vulnerable populations are uncertain. The authors evaluated the impact of patient navigation (PN) on the equity of CRC prevention over a 5-year period. METHODS: A culturally tailored CRC screening PN program was implemented in 1 community health center (CHC) in 2007. In a primary care network, CRC screening rates from 2006 to 2010 among eligible patients from the CHC with PN were compared with the rates from other practices without PN. Multivariable logistic regression models for repeated measures were used to assess differences over time. RESULTS: Differences in CRC screening rates diminished among patients at the CHC with PN and at other practices between 2006 (49.2% vs 62.5%, respectively; P < .001) and 2010 (69.2% vs 73.6%, respectively; P < .001). The adjusted rate of increase over time was higher at the CHC versus other practices (5% vs 3.4% per year; P < .001). Among Latino patients at the CHC compared with other practices, lower CRC screening rates in 2006 (47.5% vs 52.1%, respectively; P = .02) were higher by 2010 (73.5% vs 67.3%, respectively; P < .001). Similar CRC screening rates among non-English speakers at the CHC and at other practices in 2006 (44.3% vs 44.7%, respectively; P = .79) were higher at the CHC by 2010 (70.6% vs 58.6%, respectively; P < .001). Adjusted screening rates increased more over time for Latino and non-English speakers at the CHC compared with other practices (both P < .001). CONCLUSIONS: A PN program increased CRC screening rates in a CHC and improved equity in vulnerable patients. Long-term funding of PN programs has the potential to reduce cancer screening disparities.

Predicting post-stroke cognitive impairment using electronic health record data.

Importance: Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification. Objective: To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records. Design: Cohort study with patients enrolled between 2003-2016 with follow-up through 2022. Setting: Primary care practices affiliated with two academic medical centers. Participants: Individuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort). Exposures: Predictors of PSCI were ascertained from the electronic health record. Main Outcome: The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included. Results: The analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts. Conclusions and Relevance: Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.

Predicting post-stroke cognitive impairment using electronic health record data.

BACKGROUND: Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification. AIMS: To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records. METHODS: Cohort study that included primary care patients from two academic medical centers. Patients were aged 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003 and 2016 (development/internal validation cohort) or 2010 and 2022 (external validation cohort). Predictors of PSCI were ascertained from the electronic health record. The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using International Classification of Diseases, Ninth/Tenth Revision (ICD-9/10) codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included. RESULTS: The analysis included 332 incident diagnoses of PSCI in the development cohort (n = 3741), and 161 and 128 incident diagnoses in the internal (n = 1925) and external (n = 2237) validation cohorts, respectively. The C-statistic for predicting PSCI was 0.731 (95% confidence interval (CI): 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-23 points) risk groups. The hazard ratios (HRs) for incident PSCI were significantly different by risk categories in internal (high, HR: 6.2, 95% CI: 4.1-9.3; Intermediate, HR: 2.7, 95% CI: 1.8-4.1) and external (high, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR: 2.8, 95% CI: 1.9-4.3) validation cohorts. CONCLUSION: Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts. DATA ACCESS STATEMENT: Mass General Brigham data contain protected health information and cannot be shared publicly. The data processing scripts used to perform analyses will be made available to interested researchers upon reasonable request to the corresponding author.