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.

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.

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.

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.

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.

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.

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.

Health IT-assisted population-based preventive cancer screening: a cost analysis.

OBJECTIVES: Novel health information technology (IT)-based strategies harnessing patient registry data seek to improve care at a population level. We analyzed costs from a randomized trial of 2 health IT strategies to improve cancer screening compared with usual care from the perspective of a primary care network. STUDY DESIGN: Monte Carlo simulations were used to compare costs across management strategies. METHODS: We assessed the cost of the software, materials, and personnel for baseline usual care (BUC) compared with augmented usual care (AUC [ie, automated patient outreach]) and augmented usual care with physician input (AUCPI [ie, outreach mediated by physicians' knowledge of their patient panels]) over 1 year. RESULTS: AUC and AUCPI each reduced the time physicians spent on cancer screening by 6.5 minutes per half-day clinical session compared with BUC without changing cancer screening rates. Assuming the value of this time accrues to the network, total costs of cancer screening efforts over the study year were $3.83 million for AUC, $3.88 million for AUCPI, and $4.10 million for BUC. AUC was cost-saving relative to BUC in 87.1% of simulations. AUCPI was cost-saving relative to BUC in 82.5% of simulations. Ongoing per patient costs were lower for both AUC ($35.63) and AUCPI ($35.58) relative to BUC ($39.51). CONCLUSIONS: Over the course of the study year, the value of reduced physician time devoted to preventive cancer screening outweighed the costs of the interventions. Primary care networks considering similar interventions will need to capture adequate physician time savings to offset the costs of expanding IT infrastructure.

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.

Clinical preventability of 30-day readmission after percutaneous coronary intervention.

BACKGROUND: Early readmission after PCI is an important contributor to healthcare expenditures and a target for performance measurement. The extent to which 30-day readmissions after PCI are preventable is unknown yet essential to minimizing their occurrence. METHODS AND RESULTS: PCI patients readmitted to hospital at which PCI was performed within 30 days of discharge at the Massachusetts General Hospital and Brigham and Women's Hospital were identified, and their medical records were independently reviewed by 2 physicians. Each reviewer used an ordinal scale (0, not; 1, possibly; 2, probably; and 3, definitely preventable) to rate clinical preventability, and a total sum score ≥2 was considered preventable. Characteristics of preventable and unpreventable readmissions were compared, and predictors of clinical preventability were assessed by using multivariate logistic regression. Of 9288 PCIs performed, 9081 (97.8%) patients survived to initial hospital discharge and 1007 (11.1%) were readmitted to the index hospital within 30 days. After excluding repeat readmissions, 893 readmissions were reviewed. Fair agreement between physician reviewers was observed (weighted κ statistic 0.44 [95% CI 0.39 to 0.49]). After aggregation of scores, 380 (42.6%) readmissions were deemed preventable and 513 (57.4%) were deemed not preventable. Common causes of preventable readmissions included staged PCI without new symptoms (14.7%), vascular/bleeding complications of PCI (10.0%), and congestive heart failure (9.7%). CONCLUSIONS: Nearly half of 30-day readmissions after PCI may have been prevented by changes in clinical decision-making. Focusing on these readmissions may reduce readmission rates.

Non-visit-based cancer screening using a novel population management system.

BACKGROUND: Advances in information technology (IT) now permit population-based preventive screening, but the best methods remain uncertain. We evaluated whether involving primary care providers (PCPs) in a visit-independent population management IT application led to more effective cancer screening. METHODS: We conducted a cluster-randomized trial involving 18 primary care practice sites and 169 PCPs from June 15, 2011, to June 14, 2012. Participants included adults eligible for breast, cervical, and/or colorectal cancer screening. In practices randomized to the intervention group, PCPs reviewed real-time rosters of their patients overdue for screening and provided individualized contact (via a letter, practice delegate, or patient navigator) or deferred screening (temporarily or permanently). In practices randomized to the comparison group, overdue patients were automatically sent reminder letters and transferred to practice delegate lists for follow-up. Intervention patients without PCP action within 8 weeks defaulted to the automated control version. The primary outcome was adjusted average cancer screening completion rates over 1-year follow-up, accounting for clustering by physician or practice. RESULTS: Baseline cancer screening rates (80.8% vs 80.3%) were similar among patients in the intervention (n = 51,071) and comparison group (n = 52,799). Most intervention providers used the IT application (88 of 101, 87%) and users reviewed 7984 patients overdue for at least 1 cancer screening (73% sent reminder letter, 6% referred directly to a practice delegate or patient navigator, and 21% deferred screening). In addition, 6128 letters were automatically sent to patients in the intervention group (total of 12,002 letters vs 16,378 letters in comparison practices; P < .001). Adjusted average cancer screening rates did not differ among intervention and comparison practices for all cancers combined (81.6% vs 81.4%; P = .84) nor breast (82.7% vs 82.7%; P = .96), cervical (84.1% vs 84.7%; P = .60), or colorectal cancer (77.8% vs 76.2%; P = .33). CONCLUSIONS: Involving PCPs in a visit-independent population management IT application resulted in similar cancer screening rates compared with an automated reminder system, but fewer patients were sent reminder letters. This suggests that PCPs were able to identify and exclude from contact patients who would have received automated reminder letters but not undergone screening.

Causes of short-term readmission after percutaneous coronary intervention.

BACKGROUND: Rehospitalization within 30 days after an admission for percutaneous coronary intervention (PCI) is common, costly, and a future target for Medicare penalties. Causes of readmission after PCI are largely unknown. METHODS AND RESULTS: To illuminate the causes of PCI readmissions, patients with PCI readmitted within 30 days of discharge between 2007 and 2011 at 2 hospitals were identified, and their medical records were reviewed. Of 9288 PCIs, 9081 (97.8%) were alive at the end of the index hospitalization. Of these, 893 patients (9.8%) were readmitted within 30 days of discharge and included in the analysis. Among readmitted patients, 341 patients (38.1%) were readmitted for evaluation of recurrent chest pain or other symptoms concerning for angina, whereas 59 patients (6.6%) were readmitted for staged PCI without new symptoms. Complications of PCI accounted for 60 readmissions (6.7%). For cases in which chest pain or other symptoms concerning for angina prompted the readmission, 21 patients (6.2%) met criteria for myocardial infarction, and repeat PCI was performed in 54 patients (15.8%). The majority of chest pain patients (288; 84.4%) underwent ≥1 diagnostic imaging test, most commonly coronary angiography, and only 9 (2.6%) underwent target lesion revascularization. CONCLUSIONS: After PCI, readmissions within 30 days were seldom related to PCI complications but often for recurrent chest pain. Readmissions with recurrent chest pain infrequently met criteria for myocardial infarction but were associated with high rates of diagnostic testing.

Development and implementation of a real-time 30-day readmission predictive model.

Hospitals are under great pressure to reduce readmissions of patients. Being able to reliably predict patients at increased risk for rehospitalization would allow for tailored interventions to be offered to them. This requires the creation of a functional predictive model specifically designed to support real-time clinical operations. A predictive model for readmissions within 30 days of discharge was developed using retrospective data from 45,924 MGH admissions between 2/1/2012 and 1/31/2013 only including factors that would be available by the day after admission. It was then validated prospectively in a real-time implementation for 3,074 MGH admissions between 10/1/2013 and 10/31/2013. The model developed retrospectively had an AUC of 0.705 with good calibration. The real-time implementation had an AUC of 0.671 although the model was overestimating readmission risk. A moderately discriminative real-time 30-day readmission predictive model can be developed and implemented in a large academic hospital.

Applying operations research to optimize a novel population management system for cancer screening.

OBJECTIVE: To optimize a new visit-independent, population-based cancer screening system (TopCare) by using operations research techniques to simulate changes in patient outreach staffing levels (delegates, navigators), modifications to user workflow within the information technology (IT) system, and changes in cancer screening recommendations. MATERIALS AND METHODS: TopCare was modeled as a multiserver, multiphase queueing system. Simulation experiments implemented the queueing network model following a next-event time-advance mechanism, in which systematic adjustments were made to staffing levels, IT workflow settings, and cancer screening frequency in order to assess their impact on overdue screenings per patient. RESULTS: TopCare reduced the average number of overdue screenings per patient from 1.17 at inception to 0.86 during simulation to 0.23 at steady state. Increases in the workforce improved the effectiveness of TopCare. In particular, increasing the delegate or navigator staff level by one person improved screening completion rates by 1.3% or 12.2%, respectively. In contrast, changes in the amount of time a patient entry stays on delegate and navigator lists had little impact on overdue screenings. Finally, lengthening the screening interval increased efficiency within TopCare by decreasing overdue screenings at the patient level, resulting in a smaller number of overdue patients needing delegates for screening and a higher fraction of screenings completed by delegates. CONCLUSIONS: Simulating the impact of changes in staffing, system parameters, and clinical inputs on the effectiveness and efficiency of care can inform the allocation of limited resources in population management.

Assessing hospital readmission risk factors in heart failure patients enrolled in a telemonitoring program.

The purpose of this study was to validate a previously developed heart failure readmission predictive algorithm based on psychosocial factors, develop a new model based on patient-reported symptoms from a telemonitoring program, and assess the impact of weight fluctuations and other factors on hospital readmission. Clinical, demographic, and telemonitoring data was collected from 100 patients enrolled in the Partners Connected Cardiac Care Program between July 2008 and November 2011. 38% of study participants were readmitted to the hospital within 30 days. Ten different heart-failure-related symptoms were reported 17,389 times, with the top three contributing approximately 50% of the volume. The psychosocial readmission model yielded an AUC of 0.67, along with sensitivity 0.87, specificity 0.32, positive predictive value 0.44, and negative predictive value 0.8 at a cutoff value of 0.30. In summary, hospital readmission models based on psychosocial characteristics, standardized changes in weight, or patient-reported symptoms can be developed and validated in heart failure patients participating in an institutional telemonitoring program. However, more robust models will need to be developed that use a comprehensive set of factors in order to have a significant impact on population health.

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.

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.