The Implementation and Acceptability of a Combined Mobile Application with a COVID-19 at-Home Test Kit.

Introduction: Testing facilities for COVID-19 were stood up around the country during the pandemic, but could not handle the demand. This study aimed to combine a mobile application (App) with an at-home test kit to facilitate home-based testing. Methods: After integrating an App with an at-home testing service, we measured the time between sample collection and notification of results. We recruited 92 volunteers to utilize the platform. Results: Sixty-one percent (55/92) responded to the survey. Median sample collection-to-result time was 2.2 days (IQR = 1.3-3.2). Eighty-two percent (45/55) found the self-test kit and App easy to use. Eighty-four percent agreed that the combined solution is an acceptable way to receive health care services. Discussion: Decreasing testing time and providing timely test results improve care access and decrease the risk of infection. Combining a tailored App with an at-home testing service is a feasible solution to reaching that goal.

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.

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.

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.

Unveiling readability challenges: An extensive analysis of consent document accessibility in clinical trials.

Background: Clinical research trials rely on informed consent forms (ICFs) to explain all aspects of the study to potential participants. Despite efforts to ensure the readability of ICFs, concerns about their complexity and participant understanding persist. There is a noted gap between Institutional Review Board (IRB) standards and the actual readability levels of ICFs, which often exceed the recommended 8th-grade reading level. This study evaluates the readability of over five thousand ICFs from ClinicalTrials.gov in the USA to assess their literacy levels. Methods: We analyzed 5,239 US-based ICFs from ClinicalTrials.gov using readability metrics such as the Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, and the percentage of difficult words. We examined trends in readability levels across studies initiated from 2005 to 2024. Results: Most ICFs exceeded the recommended 8th-grade reading level, with an average Flesch-Kincaid Grade Level of 10.99. While 91% of the ICFs were written above the 8th-grade level, there was an observable improvement in readability, with fewer studies exceeding a 10th-grade reading level in recent years. Conclusions: The study reveals a discrepancy between the recommended readability levels and actual ICFs, highlighting a need for simplification. Despite a trend toward improvement in more recent years, ongoing efforts are necessary to ensure ICFs are comprehensible to participants of varied educational backgrounds, reinforcing the ethical integrity of the consent process.

Factors associated with non-adherence to dual-energy x-ray absorptiometry screening during the COVID-19 pandemic in an academic medical center.

This study explored why some elderly females do not adhere to their bone density tests. It found that factors like age, race, marital status, insurance type, social vulnerability index, and vaccination status influence completion of these tests. Addressing these differences could improve the management of bone health in older adults. PURPOSE: This study investigated factors influencing the cancellation of dual-energy x-ray absorptiometry (DXA) scans among females aged 65 and above during the COVID-19 pandemic. METHODS: Utilizing a dataset of 19,066 females from 2021 to 2023, the research employed chi-squared tests and logistic regression analyses to examine demographic, socio-economic, and health-related determinants of DXA scan adherence. RESULTS: Key findings revealed that younger seniors, White patients, married individuals, those with commercial/private or Medicare insurance, and vaccinated persons were more likely to complete DXA scans. In contrast, Asian and African American females, along with those from higher Social Vulnerability Index areas, showed lower completion rates. CONCLUSION: These results highlight the need for tailored strategies to improve osteoporosis screening adherence, focusing on identified demographic groups to enhance overall healthcare outcomes in osteoporosis management.

Implementing virtual desktops for clinical research at an academic health center: a case report.

Objectives: To address the challenges of sharing clinical research data through the implementation of cloud-based virtual desktops, enhancing collaboration among researchers while maintaining data security. Materials and Methods: This case study details the deployment of virtual desktops at UMass Chan Medical School (UMass Chan). The process involved forming a Research Informatics Steering Executive workgroup, identifying key requirements, implementing Amazon WorkSpaces, and establishing configurable data management for research support. Results: Key lessons include the significance of collaboration, balancing user-friendliness and functionality, flexibility in data management, maximizing virtual desktop efficiency within budget constraints, and continuous user feedback. The implementation of virtual desktops supports secure collaborative research, advancing medical knowledge and improving healthcare outcomes. Discussion: The structured approach to implementing virtual desktops addresses data security, regulatory compliance, and real-time collaboration challenges. Continuous feedback and iterative improvements have enhanced the system's effectiveness. Conclusion: The successful implementation of virtual desktops at UMass Chan demonstrates the potential for such systems to support secure, collaborative research, offering insights for similar initiatives in other academic health centers.

Efficacy and cost-effectiveness of an automated screening algorithm in an inpatient clinical trial.

INTRODUCTION: Screening and recruitment for clinical trials can be costly and time-consuming. Inpatient trials present additional challenges because enrollment is time sensitive based on length of stay. We hypothesized that using an automated prescreening algorithm to identify eligible subjects would increase screening efficiency and enrollment and be cost-effective compared to manual review of a daily admission list. METHODS: Using a before-and-after design, we compared time spent screening, number of patients screened, enrollment rate, and cost-effectiveness of each screening method in an inpatient diabetes trial conducted at Massachusetts General Hospital. Manual chart review (CR) involved reviewing a daily list of admitted patients to identify eligible subjects. The automated prescreening (APS) method used an algorithm to generate a daily list of patients with glucose levels ≥ 180 mg/dL, an insulin order, and/or admission diagnosis of diabetes mellitus. The census generated was then manually screened to confirm eligibility and eliminate patients who met our exclusion criteria. We determined rates of screening and enrollment and cost-effectiveness of each method based on study sample size. RESULTS: Total screening time (prescreening and screening) decreased from 4 to 2 h, allowing subjects to be approached earlier in the course of the hospital stay. The average number of patients prescreened per day increased from 13 ± 4 to 30 ± 16 (P < 0.0001). Rate of enrollment increased from 0.17 to 0.32 patients per screening day. Developing the computer algorithm added a fixed cost of US$3000 to the study. Based on our screening and enrollment rates, the algorithm was cost-neutral after enrolling 12 patients. Larger sample sizes further favored screening with an algorithm. By contrast, higher recruitment rates favored individual CR. LIMITATIONS: Because of the before-and-after design of this study, it is possible that unmeasured factors contributed to increased enrollment. CONCLUSION: Using a computer algorithm to identify eligible patients for a clinical trial in the inpatient setting increased the number of patients screened and enrolled, decreased the time required to enroll them, and was less expensive. Upfront investment in developing a computerized algorithm to improve screening may be cost-effective even for relatively small trials, especially when the recruitment rate is expected to be low.

COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study.

BACKGROUND: Public health scientists have used spatial tools such as web-based Geographical Information System (GIS) applications to monitor and forecast the progression of the COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV-2 variants and incorporate the social determinants of health with street-level granularity can facilitate the identification of local outbreaks, highlight variant-specific geospatial epidemiology, and inform effective interventions. We developed a novel dashboard, the University of Massachusetts' Graphical user interface for Geographic Information (MAGGI) variant tracking system that combines GIS, health-associated sociodemographic data, and viral genomic data to visualize the spatiotemporal incidence of SARS-CoV-2 variants with street-level resolution while safeguarding protected health information. The specificity and richness of the dashboard enhance the local understanding of variant introductions and transmissions so that appropriate public health strategies can be devised and evaluated. OBJECTIVE: We developed a web-based dashboard that simultaneously visualizes the geographic distribution of SARS-CoV-2 variants in Central Massachusetts, the social determinants of health, and vaccination data to support public health efforts to locally mitigate the impact of the COVID-19 pandemic. METHODS: MAGGI uses a server-client model-based system, enabling users to access data and visualizations via an encrypted web browser, thus securing patient health information. We integrated data from electronic medical records, SARS-CoV-2 genomic analysis, and public health resources. We developed the following functionalities into MAGGI: spatial and temporal selection capability by zip codes of interest, the detection of variant clusters, and a tool to display variant distribution by the social determinants of health. MAGGI was built on the Environmental Systems Research Institute ecosystem and is readily adaptable to monitor other infectious diseases and their variants in real-time. RESULTS: We created a geo-referenced database and added sociodemographic and viral genomic data to the ArcGIS dashboard that interactively displays Central Massachusetts' spatiotemporal variants distribution. Genomic epidemiologists and public health officials use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by selecting a combination of data features such as variant subtype, subject zip codes, or date of COVID-19-positive sample collection. Furthermore, they use it to scale time and space to visualize association patterns between socioeconomics, social vulnerability based on the Centers for Disease Control and Prevention's social vulnerability index, and vaccination rates. We launched the system at the University of Massachusetts Chan Medical School to support internal research projects starting in March 2021. CONCLUSIONS: We developed a COVID-19 variant surveillance dashboard to advance our geospatial technologies to study SARS-CoV-2 variants transmission dynamics. This real-time, GIS-based tool exemplifies how spatial informatics can support public health officials, genomics epidemiologists, infectious disease specialists, and other researchers to track and study the spread patterns of SARS-CoV-2 variants in our communities.

Linking electronic health record-extracted psychosocial data in real-time to risk of readmission for heart failure.

BACKGROUND: Knowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care. OBJECTIVE: We hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days. METHODS: We identified 15 psychosocial predictors of readmission. Eleven of these were extracted from the EHR (six from structured data sources and five from unstructured clinical notes). We then analyzed their association with the likelihood of hospital readmission within the next 30 days among 729 patients admitted for HF. Finally, we developed a multivariable predictive model to recognize individuals at high risk for readmission. RESULTS: We found five characteristics-dementia, depression, adherence, declining/refusal of services, and missed clinical appointments-that were associated with an increased risk for hospital readmission: the first four features were captured from unstructured clinical notes, while the last item was captured from a structured data source. CONCLUSIONS: Unstructured clinical notes contain important knowledge on the relationship between psychosocial risk factors and an increased risk of readmission for HF that would otherwise have been missed if only structured data were considered. Gathering this EHR-based knowledge can be automated, thus enabling timely and targeted care.

Implementation of the Connect for Health pediatric weight management program: study protocol and baseline characteristics.

We are implementing Connect for Health, a primary care-based intervention to improve family-centered outcomes for children, ages 2-12 years, in organizations that care for low-income children. We will use the 'Reach-Effectiveness-Adoption-Implementation-Maintenance' framework to guide our mixed-methods evaluation to examine the effectiveness of stakeholder-informed strategies in supporting program adoption and child outcomes. We also describe characteristics of children, ages 2-12 years with a BMI ≥85th percentile and obesity-related care practices. During the period prior to implementation, 26,161 children with a BMI ≥85th percentile were seen for a primary care visit and a majority lacked recommended diagnosis codes, referrals and laboratory evaluations. The findings suggest the need to augment current approaches to increase uptake of proven-effective weight management programs. Clinical trial registration number: NCT04042493 (Clinicaltrials.gov), Registered on 2 August 2019; https://clinicaltrials.gov/ct2/show/NCT04042493.

Queuing theory to guide the implementation of a heart failure inpatient registry program.

OBJECTIVE The authors previously implemented an electronic heart failure registry at a large academic hospital to identify heart failure patients and to connect these patients with appropriate discharge services. Despite significant improvements in patient identification and connection rates, time to connection remained high, with an average delay of 3.2 days from the time patients were admitted to the time connections were made. Our objective for this current study was to determine the most effective solution to minimize time to connection. DESIGN We used a queuing theory model to simulate 3 different potential solutions to decrease the delay from patient identification to connection with discharge services. MEASUREMENTS The measures included average rate at which patients were being connected to the post discharge heart failure services program, average number of patients in line, and average patient waiting time. RESULTS Using queuing theory model simulations, we were able to estimate for our current system the minimum rate at which patients need to be connected (262 patients/mo), the ideal patient arrival rate (174 patients/mo) and the maximal patient arrival rate that could be achieved by adding 1 extra nurse (348 patients/mo). CONCLUSIONS Our modeling approach was instrumental in helping us characterize key process parameters and estimate the impact of adding staff on the time between identifying patients with heart failure and connecting them with appropriate discharge services.

Lessons from implementing a combined workflow-informatics system for diabetes management.

Shortcomings surrounding the care of patients with diabetes have been attributed largely to a fragmented, disorganized, and duplicative health care system that focuses more on acute conditions and complications than on managing chronic disease. To address these shortcomings, we developed a diabetes registry population management application to change the way our staff manages patients with diabetes. Use of this new application has helped us coordinate the responsibilities for intervening and monitoring patients in the registry among different users. Our experiences using this combined workflow-informatics intervention system suggest that integrating a chronic disease registry into clinical workflow for the treatment of chronic conditions creates a useful and efficient tool for managing disease.

Designing healthcare information technology to catalyse change in clinical care.

The gap between best practice and actual patient care continues to be a pervasive problem in our healthcare system. Efforts to improve on this knowledge-performance gap have included computerised disease management programs designed to improve guideline adherence. However, current computerised reminder and decision support interventions directed at changing physician behaviour have had only a limited and variable effect on clinical outcomes. Further, immediate pay-for-performance financial pressures on institutions have created an environment where disease management systems are often created under duress, appended to existing clinical systems and poorly integrated into the existing workflow, potentially limiting their real-world effectiveness. The authors present a review of disease management as well as a conceptual framework to guide the development of more effective health information technology (HIT) tools for translating clinical information into clinical action.

Chronic disease outcomes from primary care population health program implementation.

OBJECTIVES: We implemented a health information technology-enabled population health management program for chronic disease management in academic hospital-affiliated primary care practices, then compared quality-of-care outcome measures among practices assigned a central population health coordinator (PHC) and those not assigned a PHC. STUDY DESIGN: Quasi-experimental. METHODS: Central PHCs were nonrandomly assigned to 8 of 18 practices. They met with physicians, managed lists of patients not at goal in chronic disease registries, and performed administrative tasks. In non-PHC practices, existing staff remained responsible for these tasks. The primary outcome was difference-in-differences over the 6-month follow-up period between PHC and non-PHC practices for outcome measures for diabetes (low-density lipoprotein cholesterol [LDL-C], glycated hemoglobin [A1C], and blood pressure [BP] goal attainment), cardiovascular disease (LDL-C goal attainment), and hypertension (BP goal attainment). Secondary outcomes included process measures only (obtaining LDL-C, A1C, and BP readings) and cancer screening test completion. RESULTS: The difference in the percentage point (PP) increase in outcome measures over follow-up was greater in PHC practices than non-PHC practices for all measures among patients with diabetes (LDL-C, 4.6 PP; A1C, 4.8 PP; BP, 4.7 PP), cardiovascular disease (LDL-C, 3.3 PP), and hypertension (BP, 2.3 PP) (adjusted P all <.001). Changes in cancer screening outcomes, which were not a focus of PHC efforts, were similar between PHC and non-PHC practices. CONCLUSIONS: Use of central PHCs led to greater improvement in short-term chronic disease outcome measures compared with patients in practices not assigned a central PHC.

Development and Implementation of a Collaborative Team Care Model for Effective Insulin Use in an Academic Medical Center Primary Care Network.

Improving glycemic control across a primary care diabetes population is challenging. This article describes the development, implementation, and outcomes of the Diabetes Care Collaborative Model (DCCM), a collaborative team care process focused on promoting effective insulin use targeting patients with hyperglycemia in a patient-centered medical home model. After a pilot, the DCCM was implemented in 18 primary care practices affiliated with an academic medical center. Its implementation was associated with improvements in glycemic control and increase in insulin prescription longitudinally and across the entire population, with a >1% reduction in the proportion of glycated hemoglobin >9% at 2 years after the implementation compared with the 2 years prior ( P < .001). Facilitating factors included diverse stakeholder engagement, institutional alignment of priorities, awarding various types of credits for participation and implementation to providers, and a strong theoretical foundation using the principles of the collaborative care model.

Can Text Messages Improve Attendance to Primary Care Appointments in Underserved Populations?

We explored whether text message (TM) reminders could be used at a community health center (CHC) to improve primary care appointment attendance in adult patients. Over six months, we allocated 8,425 appointments to intervention and 2,679 to control. The proportion of no-shows in the intervention was 18.0% vs. 19.8% in control (p = .106). Among intervention appointments, 1,431 did not have a cell phone, 4,955 did not respond to the consent TM, and 231 declined TMs. The proportion of no-shows for the 1,309 appointments who received TM was 13.7% compared with 20.2% in a matched control group (p = .001). However, of 81 surveyed patients who did not respond to the consent TM, 64 (93%) wished to receive TMs. In conclusion, patients who received TM demonstrated improved attendance to their appointments. TM might be an effective supplemental appointment reminder method in a subpopulation of CHC patients and it should be explored in future research.

Patient Navigation for Comprehensive Cancer Screening in High-Risk Patients Using a Population-Based Health Information Technology System: A Randomized Clinical Trial.

IMPORTANCE: Patient navigation (PN) to improve cancer screening in low-income and racial/ethnic minority populations usually focuses on navigating for single cancers in community health center settings. OBJECTIVE: We evaluated PN for breast, cervical, and colorectal cancer screening using a population-based information technology (IT) system within a primary care network. DESIGN, SETTING, AND PARTICIPANTS: Randomized clinical trial conducted from April 2014 to December 2014 in 18 practices in an academic primary care network. All patients eligible and overdue for cancer screening were identified and managed using a population-based IT system. Those at high risk for nonadherence with completing screening were identified using an electronic algorithm (language spoken, number of overdue tests, no-show visit history), and randomized to a PN intervention (n = 792) or usual care (n = 820). Navigators used the IT system to track patients, contact them, and provide intense outreach to help them complete cancer screening. MAIN OUTCOMES AND MEASURES: Mean cancer screening test completion rate over 8-month trial for each eligible patient, with all overdue cancer screening tests combined using linear regression models. Secondary outcomes included the proportion of patients completing any and each overdue cancer screening test. RESULTS: Among 1612 patients (673 men and 975 women; median age, 57 years), baseline patient characteristics were similar among randomized groups. Of 792 intervention patients, patient navigators were unable to reach 151 (19%), deferred 246 (38%) (eg, patient declined, competing comorbidity), and navigated 202 (32%). The mean proportion of patients who were up to date with screening among all overdue screening examinations was higher in the intervention vs the control group for all cancers combined (10.2% vs 6.8%; 95% CI [for the difference], 1.5%-5.2%; P < .001), and for breast (14.7% vs 11.0%; 95% CI, 0.2%-7.3%; P = .04), cervical (11.1% vs 5.7%; 95% CI, 0.8%-5.2%; P = .002), and colon (7.6% vs 4.6%; 95% CI, 0.8%-5.2%; P = .01) cancer compared with control. The proportion of overdue patients who completed any cancer screening during follow-up was higher in the intervention group (25.5% vs 17.0%; 95% CI, 4.7%-12.7%; P < .001). The intervention group had more patients completing screening for breast (23.4% vs 16.6%; 95% CI, 1.8%-12.0%; P = .009), cervical (14.4% vs 8.6%; 95% CI, 1.6%-10.5%; P = .007), and colorectal (13.7% vs 7.0%; 95% CI, 3.2%-10.4%; P < .001) cancer. CONCLUSIONS AND RELEVANCE: Patient navigation as part of a population-based IT system significantly increased screening rates for breast, cervical, and colorectal cancer in patients at high risk for nonadherence with testing. Integrating patient navigation into population health management activities for low-income and racial/ethnic minority patients might improve equity of cancer care. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT02553538.

A population management system for improving colorectal cancer screening in a primary care setting.

RATIONALE: Provision of colorectal cancer (CRC) screening in primary care is suboptimal; failure to observe screening guidelines poses unnecessary risks to patients and doctors. AIMS AND OBJECTIVES: Implement a population management system for CRC screening; evaluate impact on compliance with evidence-based guidelines. DESIGN: A quasi-experimental, prospective quality improvement study design using pre-post-analyses with concurrent controls. SETTING: Six suites within an academic primary care practice. PARTICIPANTS: 5320 adults eligible for CRC screening treated by 70 doctors. INTERVENTION: In three intervention suites, doctors reviewed real-time rosters of patients due for CRC screening and chose practice delegate outreach or default reminder letter. Delegates tracked overdue patients, made outreach calls, facilitated test ordering, obtained records and documented patient deferral, exclusion or decline. In three control suites, doctors followed usual preventive care practices. MAIN OUTCOME MEASURES: CRC screening compliance (including documented decline, deferral or exclusion) and CRC screening completion rates over 5 months. RESULTS: At baseline, there was no significant difference in CRC screening compliance (I: 80.4% and C: 79.6%, P = 0.439) and CRC screening completion rates (I: 78.3% and C: 77.3%, P = 0.398) between intervention and control groups. Post-intervention, compliance rates (I: 88.1% and C: 80.5%, P < 0.01) and completion rates (I: 81.0% and C: 78.1%, P < 0.05) were significantly higher in the intervention group. CONCLUSIONS: A population management system using closed-loop communication may improve CRC screening compliance and completion rates within academic primary care practices. Team-based care using well-designed IT systems can enable sharing of patient care responsibilities and improve patient outcomes.