Timeliness of Follow-Up to a Positive Fecal Immunochemical Test Result Among Community Health Center Patients

Background/Aims: Colorectal cancer is the fourth most common cancer and the third-leading cause of cancer death in the United States. Fecal testing, including fecal immunochemical testing (FIT), has been proven to reduce mortality from colorectal cancer. Such mortality reductions can only be achieved, however, if those with abnormal test results receive follow-up colonoscopies. Completion rates for follow-up colonoscopy are low, especially in community health centers, where many Hispanics receive care. As part of the Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) project, we examined rates of adherence to follow-up colonoscopy, time to colonoscopy completion and characteristics of patients who complete colonoscopy after abnormal FIT results compared to those who do not. Methods: Virginia Garcia Memorial Health Center was the clinic partner in this project. Project staff reviewed charts from patients who had abnormal FIT results following the STOP CRC outreach program. Reviews of electronic medical charts ascertained patient demographic characteristics, referral to colonoscopy (yes/no), date of referral and reason (if not made), colonoscopy completion (yes/no) and date of colonoscopy and reason (if not completed). Bivariate analyses and regression analyses were used to examine associations and complete mediator analysis. Results: A total of 56 patients had abnormal FIT results; 29 (52%) were Hispanic and 31 (55%) were female. Forty-five (80%) patients received referral for colonoscopy, with a median time to referral of 2 days. Of the 56 patients, 32 (57%) had evidence of a completed colonoscopy in their medical charts. Latinos were less likely than non-Latino whites to have completed a colonoscopy (44.8% vs. 70.4%). The median time to colonoscopy completion was 62 days. Females were less likely than males to complete their colonoscopy within 60 days of a positive FIT result (odds ratio: 0.21, 95% confidence interval: 0.05–0.96). Mediation analysis indicated that time to referral was not a mediator between patient-level factors and completion of follow-up colonoscopy. Conclusion: Our findings suggest improvements are needed to increase rates of follow-up colonoscopy completion, especially among females and Hispanic patients. Future research might explore the role that clinic- and patient-level factors play in colonoscopy completion

Timeliness of Follow-Up to a Positive Fecal Immunochemical Test Result Among Community Health Center Patients

Background/Aims: Colorectal cancer is the fourth most common cancer and the third-leading cause of cancer death in the United States. Fecal testing, including fecal immunochemical testing (FIT), has been proven to reduce mortality from colorectal cancer. Such mortality reductions can only be achieved, however, if those with abnormal test results receive follow-up colonoscopies. Completion rates for follow-up colonoscopy are low, especially in community health centers, where many Hispanics receive care. As part of the Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) project, we examined rates of adherence to follow-up colonoscopy, time to colonoscopy completion and characteristics of patients who complete colonoscopy after abnormal FIT results compared to those who do not. Methods: Virginia Garcia Memorial Health Center was the clinic partner in this project. Project staff reviewed charts from patients who had abnormal FIT results following the STOP CRC outreach program. Reviews of electronic medical charts ascertained patient demographic characteristics, referral to colonoscopy (yes/no), date of referral and reason (if not made), colonoscopy completion (yes/no) and date of colonoscopy and reason (if not completed). Bivariate analyses and regression analyses were used to examine associations and complete mediator analysis. Results: A total of 56 patients had abnormal FIT results; 29 (52%) were Hispanic and 31 (55%) were female. Forty-five (80%) patients received referral for colonoscopy, with a median time to referral of 2 days. Of the 56 patients, 32 (57%) had evidence of a completed colonoscopy in their medical charts. Latinos were less likely than non-Latino whites to have completed a colonoscopy (44.8% vs. 70.4%). The median time to colonoscopy completion was 62 days. Females were less likely than males to complete their colonoscopy within 60 days of a positive FIT result (odds ratio: 0.21, 95% confidence interval: 0.05–0.96). Mediation analysis indicated that time to referral was not a mediator between patient-level factors and completion of follow-up colonoscopy. Conclusion: Our findings suggest improvements are needed to increase rates of follow-up colonoscopy completion, especially among females and Hispanic patients. Future research might explore the role that clinic- and patient-level factors play in colonoscopy completion

Predicting the Risk of Emergency Department Visits in Medicaid Members: Development and Temporal Validation of a Model

Background/Aims: We developed and validated a model to predict the risk of emergency department (ED) visits in adult Medicaid members so that case-managers can identify the highest-risk patients and intervene. We then validated the model on newly enrolled Medicaid patients. Methods: To develop the prediction model, we assembled a retrospective cohort of adult Medicaid members (18–64 years old) enrolled at Kaiser Permanente Northwest between 2010 and 2013. We measured patient characteristics during the 90 days before the start of follow-up that might predict ED visits. We followed patients for up to 180 days to identify the first ED visit. To validate the model, we assembled a distinct cohort of adult Medicaid members who joined Kaiser Permanente Northwest in the first quarter of 2014. We developed and validated separate models for men and women using Cox regression. Results: We observed 2,587 patients who visited the ED during the 180-day follow-up. The overall 180-day risk of an ED visit was 13.9 per 100 (men) and 17.4 per 100 (women). The models discriminated the high- and low-risk patients adequately: concordance or c-statistic was 0.72 (men) and 0.71 (women), respectively. The model’s 10 predictor characteristics explained 35.2% of the variation in ED visits in men and 29.6% of the variation in women. Model calibration (agreement between observed and predicted) revealed that the mean predicted risks in the highest-risk patients underestimated the observed risks of an ED visit by approximately 11 per 100. The model for women validated adequately in the newly enrolled cohort because the c-statistic remained constant while the model for men disappointed because the c-statistic dropped by 0.05. For both men and women, the models continued to underestimate the absolute risk of an ED visit in the highest-risk patients. Conclusion: The models identified the highest-risk patients with only 90 days of clinical history, and the models validated on new Medicaid patients. Time invested in managing the highest-risk patients may offer a superior return on investment compared with a strategy that does not stratify because the highest-risk patients suffer a disproportionate excess risk. The return on time invested may be even higher if recurrent ED visits are considered

Validation of Colorectal Cancer Screening in the Electronic Health Record for Identifying Patients Due for Screening in a Pragmatic Trial

Background/Aims: In 2015 an estimated 143,000 adults in the United States will be diagnosed with colorectal cancer, and 52,000 will die from the disease. Despite this knowledge, colorectal cancer screening rates remain low. Electronic health records (EHR) hold much promise for helping to close this gap by identifying eligible individuals who are overdue for or have never completed colorectal cancer screening; however, important shortfalls to this approach remain. Records of colonoscopy completion are frequently missing in the EHR. Variation in workflow and documentation can lead to incomplete capture of colorectal cancer screening and testing events. Through the Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) study, we conducted a data validation to understand the data being captured in the EHR and completeness of the data. Methods: We selected a stratified random sample of 800 study participants from 26 participating clinics to compare the EHR to chart audit data. A trained validation specialist completed the abstraction qualifying data of eligible and ineligible patients. Results: Comparing EHR data to chart audits, we found 88% (459/520) of individuals were correctly classified as eligible for program inclusion. EHR data correctly identified 96% (269/280) of excluded patients. Of the patients incorrectly classified as eligible, 83.6% (51/61) of disagreements were due to evidence of a prior colonoscopy or referral that was not captured in recognizable fields in the EHR. Conclusion: If our goal to increase CRC screening uptake is achieved, there will be an even greater need to improve data capture of all screening events, document completion of diagnostic testing after a positive test, conduct surveillance exams and provide appropriate outreach to patients needing repeated screening. While the need for better population-based data is not unique to CRC screening, it provides an important example of using population-based data for not only tracking needed care but also directly delivering needed interventions

Methods for scaling up an outreach intervention to increase colorectal cancer screening rates in rural areas

Abstract Background Mailed fecal immunochemical test (FIT) outreach and patient navigation are evidence-based practices shown to improve rates of colorectal cancer (CRC) and follow-up in various settings, yet these programs have not been broadly adopted by health systems and organizations that serve diverse populations. Reasons for low adoption rates are multifactorial, and little research explores approaches for scaling up a complex, multi-level CRC screening outreach intervention to advance equity in rural settings. Methods SMARTER CRC, a National Cancer Institute Cancer Moonshot project, is a cluster-randomized controlled trial of a mailed FIT and patient navigation program involving 3 Medicaid health plans and 28 rural primary care practices in Oregon and Idaho followed by a national scale-up trial. The SMARTER CRC intervention combines mailed FIT outreach supported by clinics, health plans, and vendors and patient navigation for colonoscopy following an abnormal FIT result. We applied the framework from Perez and colleagues to identify the intervention’s components (including functions and forms) and scale-up dissemination strategies and worked with a national advisory board to support scale-up to additional organizations. The team is recruiting health plans, primary care clinics, and regional and national organizations in the USA that serve a rural population. To teach organizations about the intervention, activities include Extension for Community Healthcare Outcomes (ECHO) tele-mentoring learning collaboratives, a facilitation guide and other materials, a patient navigation workshop, webinars, and individualized technical assistance. Our primary outcome is program adoption (by component), measured 6 months after participation in an ECHO learning collaborative. We also assess engagement and adaptations (implemented and desired) to learn how the multicomponent intervention might be modified to best support broad scale-up. Discussion Findings may inform approaches for adapting and scaling evidence-based approaches to promote CRC screening participation in underserved populations and settings. Trial registration Registered at ClinicalTrials.gov (NCT04890054) and at the NCI’s Clinical Trials Reporting Program (CTRP no.: NCI-2021–01032) on May 11, 2021

Effectiveness of automated and live phone reminders after mailed-FIT outreach in a pilot randomized trial

The effectiveness of annual mailed fecal immunochemical testing (FIT) outreach is highest when return rates are optimized, which is aided by patient reminders. In a pilot patient-randomized controlled trial in two western Washington clinics of the Sea Mar Community Health Centers, we compared the effectiveness of two phone-based approaches to mailed FIT outreach reminders. In fall 2016, patients ages 50–75, due for colorectal cancer screening, and with a visit in the previous year at either of two clinics, were mailed an introductory letter and FIT. Those who did not return the FIT within 3 weeks (N = 427) were randomized to receive either: a) a series of up to 6 automated phone reminders; or b) the combination of automated and live phone reminders (up to 6 in total). The sole outcome was FIT return within 6 months after the FIT mailing. FIT completion rates were similar in the groups assigned to receive automated calls vs automated plus live calls (40% vs 39%; p = 0.89). The effectiveness of FIT reminder mode differed by language preference (p for interaction = 0.03): among Spanish-preferring patients (n = 106), FIT return rates were higher in the automated-only group than to the auto- plus live-call group (62% vs 39%, p = 0.02). Among English-preferring patients, no difference in modes was observed (n = 279, 32% vs 34%, p = 0.74). We observed no added benefit of live reminder calls in a mailed FIT plus automated call reminder program; our findings may inform efforts to efficiently optimize mailed-FIT outreach programs.ClinicalTrials.gov identifier NCT01742065 Keywords: Colorectal neoplasms, Early detection of cancer, Community health centers, Languag

The use of individual and multilevel data in the development of a risk prediction model to predict patients’ likelihood of completing colorectal cancer screening

Promotion of colorectal cancer (CRC) screening can be expensive and unnecessary for many patients. The use of predictive analytics promises to help health systems target the right services to the right patients at the right time while improving population health. Multilevel data at the interpersonal, organizational, community, and policy levels, is rarely considered in clinical decision making but may be used to improve CRC screening risk prediction. We compared the effectiveness of a CRC screening risk prediction model that uses multilevel data with a more conventional model that uses only individual patient data.We used a retrospective cohort to ascertain the one-year occurrence of CRC screening. The cohort was determined from a Health Maintenance Organization, in Oregon. Eligible patients were 50–75 years old, health plan members for at least one year before their birthday in 2018 and were due for screening. We created a risk model using logistic regression first with data available in the electronic health record (EHR), and then added multilevel data.In a cohort of 59,249 patients, 36.1% completed CRC screening. The individual level model included 14 demographic, clinical and encounter based characteristics, had a bootstrap-corrected C-statistic of 0.722 and sufficient calibration. The multilevel model added 9 variables from clinical setting and community characteristics, and the bootstrap-corrected C-statistic remained the same with continued sufficient calibration.The predictive power of the CRC screening model did not improve after adding multilevel data. Our findings suggest that multilevel data added no improvement to the prediction of the likelihood of CRC screening

Predicting costs of care in heart failure patients

Abstract Background Identifying heart failure patients most likely to suffer poor outcomes is an essential part of delivering interventions to those most likely to benefit. We sought a comprehensive account of heart failure events and their cumulative economic burden by examining patient characteristics that predict increased cost or poor outcomes. Methods We collected electronic medical data from members of a large HMO who had a heart failure diagnosis and an echocardiogram from 1999–2004, and followed them for one year. We examined the role of demographics, clinical and laboratory findings, comorbid disease and whether the heart failure was incident, as well as mortality. We used regression methods appropriate for censored cost data. Results Of the 4,696 patients, 8% were incident. Several diseases were associated with significantly higher and economically relevant cost changes, including atrial fibrillation (15% higher), coronary artery disease (14% higher), chronic lung disease (29% higher), depression (36% higher), diabetes (38% higher) and hyperlipidemia (21% higher). Some factors were associated with costs in a counterintuitive fashion (i.e. lower costs in the presence of the factor) including age, ejection fraction and anemia. But anemia and ejection fraction were also associated with a higher death rate. Conclusions Close control of factors that are independently associated with higher cost or poor outcomes may be important for disease management. Analysis of costs in a disease like heart failure that has a high death rate underscores the need for economic methods to consider how mortality should best be considered in costing studies.</p