Efficacy of a Digital Intervention to Increase Annual Wellness Visit Scheduling Amid COVID-19 Backlog.

BACKGROUND: The Medicare Annual Wellness Visit is a preventive visit that is largely underutilized, a problem further compounded by the COVID-19 pandemic. METHODS: We implemented a digital outreach intervention to improve Annual Wellness Visit scheduling in our health system. Using a bulk outreach functionality in the electronic medical record, we sent a message to patients due for an Annual Wellness Visit and analyzed the efficacy of this message on scheduling rates while also assessing its impact by race. RESULTS: Patients who read the message were 40% more likely to schedule an Annual Wellness Visit (OR 1.42; 95% CI, 1.34 - 1.50) compared to those who did not read the message. DISCUSSION: After this intervention, Annual Wellness Visit scheduling rates increased by 50% for White patients and 325% for Black patients versus prepandemic rates in 2019.

Independent Predictors for Hospitalization-Associated Radiation therapy Interruptions.

Purpose: Radiation treatment interruption associated with unplanned hospitalization remains understudied. The intent of this study was to benchmark the frequency of hospitalization-associated radiation therapy interruptions (HARTI), characterize disease processes causing hospitalization during radiation, identify factors predictive for HARTI, and localize neighborhood environments associated with HARTI at our academic referral center. Methods and Materials: This retrospective review of electronic health records provided descriptive statistics of HARTI event rates at our institutional practice. Uni- and multivariable logistic regression models were developed to identify significant factors predictive for HARTI. Causes of hospitalization were established from primary discharge diagnoses. HARTI rates were mapped according to patient residence addresses. Results: Between January 1, 2015, and December 31, 2017, 197 HARTI events (5.3%) were captured across 3729 patients with 727 total missed treatments. The 3 most common causes of hospitalization were malnutrition/dehydration (n = 28; 17.7%), respiratory distress/infection (n = 24; 13.7%), and fever/sepsis (n = 17; 9.7%). Factors predictive for HARTI included African-American race (odds ratio [OR]: 1.48; 95% confidence interval [CI], 1.07-2.06; P = .018), Medicaid/uninsured status (OR: 2.05; 95% CI, 1.32-3.15; P = .0013), Medicare coverage (OR: 1.7; 95% CI, 1.21-2.39; P = .0022), lung (OR: 5.97; 95% CI, 3.22-11.44; P < .0001), and head and neck (OR: 5.6; 95% CI, 2.96-10.93; P < .0001) malignancies, and prescriptions >20 fractions (OR: 2.23; 95% CI, 1.51-3.34; P < .0001). HARTI events clustered among Medicaid/uninsured patients living in urban, low-income, majority African-American neighborhoods, and patients from middle-income suburban communities, independent of race and insurance status. Only the wealthiest residential areas demonstrated low HARTI rates. Conclusions: HARTI disproportionately affected socioeconomically disadvantaged urban patients facing a high treatment burden in our catchment population. A complementary geospatial analysis also captured the risk experienced by middle-income suburban patients independent of race or insurance status. Confirmatory studies are warranted to provide scale and context to guide intervention strategies to equitably reduce HARTI events.

Real-world implementation evaluation of an electronic health record-integrated consumer informatics tool that collects patient-generated contextual data.

OBJECTIVE: Use the RE-AIM framework to examine the implementation of a patient contextual data (PCD) Tool designed to share patients' needs, values, and preferences with care teams ahead of clinical encounters. MATERIALS & METHODS: Observational study that follows initial PCD Tool scaling across primary care at a Midwestern academic health network. Program invitations, enrollment, patient submissions, and clinician views were tracked over a 1-year study period. Logistic regression modeled the likelihood of using the PCD Tool, accounting for patient covariates. RESULTS: Of 58,874 patients who could be contacted by email, 9,183 (15.6%) became PCD Tool users. Overall, 76% of primary care providers had patients who used the PCD Tool. Older age, female gender, non-minority race, patient portal activation, and Medicare coverage were significantly associated with increased likelihood of use. Number of office visits, medical issues, and behavioral health conditions also associated with use. Primary care staff viewed 18.7% of available PCD Tool summaries, 1.1% to 57.6% per clinic. DISCUSSION: The intervention mainly reached non-minority patients and patients who used more health services. Given the requirement for an email address on file, some patients may have been underrepresented. Overall, patient reach and adoption and clinician adoption, implementation, and maintenance of this Tool were modest but stable, consistent with a non-directive approach to fostering adoption by introducing the Tool in the absence of clear expectations for use. CONCLUSION: Healthcare organizations must implement effective methods to increase the reach, adoption, implementation, and maintenance of PCD tools across all patient populations. Assisting people, particularly racial minorities, with PCD Tool registration and actively supporting clinician use are critical steps in implementing technology that facilitates care.

Assessment of oral and overall health parameters using the SillHa Oral Wellness System.

OBJECTIVE: The study evaluated use of a multipoint saliva analyzer to assess patient wellness in a contemporary dental practice setting. STUDY DESIGN: Unstimulated saliva from a diverse 104 patient cohort was analyzed using the SillHa Oral Wellness System. The device measures the following 7 analytes present in the patient's oral rinse: cariogenic bacteria, acidity, buffer capacity, blood, leukocytes, protein, and ammonia. Data obtained were compared with validated clinical assessment data independently provided by credentialed dental professionals. RESULTS: Measured leukocyte and protein levels were higher in patients with periodontal disease and/or deep gingival pockets. Patients with a history of cancer and/or diabetes presented with higher ammonia and lower leukocyte levels. Acidity levels were higher in patients using multiple xerogenic medications and lower in patients with a history of sleep apnea. Sex differences showed female patients exhibiting higher acidity, lower buffer capacity, and lower ammonia than male patients. Increasing age is associated with elevated buffer capacity. CONCLUSIONS: Multipoint saliva analyzers such as the one used in this study, along with clinical practice examination and medical history, can provide rapid salivary component analysis that augments treatment planning. A follow-up multisite study would provide the opportunity to test this analyzer in clinical sites with different practice workflows.

Association Between Nonsurgical Weight Loss From Body Mass Index >40 to Body Mass Index <40 and Complications and Readmissions Following Total Hip Arthroplasty.

BACKGROUND: Elevated body mass index (BMI) is a risk factor for adverse outcomes following total hip arthroplasty (THA). It is unknown if preoperative weight loss to a BMI <40 kg/m2 is associated with reduced risk of adverse outcomes. METHODS: We retrospectively reviewed elective, primary THA performed at an academic center from 2015 to 2019. Patients were split into groups based on their BMI trajectory prior to THA: BMI consistently <40 ("BMI <40"); BMI >40 at the time of surgery ("BMI >40"); and BMI >40 within 2 years preoperatively, but <40 at the time of surgery ("Weight Loss"). Length of stay (LOS), 30-day readmissions, and complications as defined by Centers for Medicare and Medicaid Services were compared between groups using parsimonious regression models and Fisher's exact testing. Adjusted analyses controlled for sex, age, and American Society of Anesthesiologists class. RESULTS: In total, 1589 patients were included (BMI <40: 1387, BMI >40: 96, Weight Loss: 106). The rate of complications in each group was 3.5%, 6.3%, and 8.5% and the rate of 30-day readmissions was 3.0%, 4.2%, and 7.5%, respectively. Compared to the BMI <40 group, the weight loss group had a significantly higher risk of 30-day readmission (odds ratio [OR] 2.70, 95% confidence interval [CI] 1.19-6.17, P = .02), higher risk of any complication (OR 2.47, 95% CI 1.09-5.59, P = .03), higher risk of mechanical complications (OR 3.07, 95% CI 1.14-8.25, P = .03), and longer median LOS (16% increase, P = .002). The BMI >40 group had increased median LOS (10% increase, P = .03), but no difference in readmission or complications (P > .05) compared to BMI <40. CONCLUSION: Weight loss from BMI >40 to BMI <40 prior to THA was associated with increased risk of readmission and complications compared to BMI <40, whereas BMI >40 was not. LEVEL OF EVIDENCE: Level III - Retrospective Cohort Study.

Do In-Hospital Rothman Index Scores Predict Postdischarge Adverse Events and Discharge Location After Total Knee Arthroplasty?

BACKGROUND: There have been efforts to reduce adverse events and unplanned readmissions after total joint arthroplasty. The Rothman Index (RI) is a real-time, composite measure of medical acuity for hospitalized patients. We aimed to examine the association among in-hospital RI scores and complications, readmissions, and discharge location after total knee arthroplasty (TKA). We hypothesized that RI scores could be used to predict the outcomes of interest. METHODS: This is a retrospective study of an institutional database of elective, primary TKA from July 2018 until December 2019. Complications and readmissions were defined per Centers for Medicare and Medicaid Services. Analysis included multivariate regression, computation of the area under the curve (AUC), and the Youden Index to set RI thresholds. RESULTS: The study cohort's (n = 957) complications (2.4%), readmissions (3.6%), and nonhome discharge (13.7%) were reported. All RI metrics (minimum, maximum, last, mean, range, 25th%, and 75th%) were significantly associated with increased odds of readmission and home discharge (all P < .05). RI scores were not significantly associated with complications. The optimal RI thresholds for increased risk of readmission were last ≤ 71 (AUC = 0.65), mean ≤ 67 (AUC = 0.66), or maximum ≤ 80 (AUC = 0.63). The optimal RI thresholds for increased risk of home discharge were minimum ≥ 53 (AUC = 0.65), mean ≥ 69 (AUC = 0.65), or maximum ≥ 81 (AUC = 0.60). CONCLUSION: RI values may be used to predict readmission or home discharge after TKA.

Addressing depression and behavioral health needs through a digital program at scale.

Depression and anxiety disorders are prevalent mental health conditions; yet they are often unrecognized, under-addressed and/or under-treated, and specialty treatment for these conditions is oftentimes difficult to access. By acting either as a bridge to therapy or as a form of therapy, digital tools, such as those that provide internet-based cognitive behavioral therapy (iCBT), may help clinicians support their patients' mental health needs. At one academic health system, a digital mental health program was deployed in primary care and outpatient behavioral health programs to help patients meet needs identified through screening or clinical visits. Over the first two years of operation, 138 clinicians (40% of eligible clinicians) prescribed the program to 2,228 unique patients, from which 1,117 (48.9%) enrolled. Patients who enrolled tended to be younger and healthier than non-enrollees. On average, enrolled patients spent 114.6 minutes within the iCBT program. Clinical improvement was assessed using pre- and post PHQ-9 and GAD-7 scores for depression and anxiety, respectively. Pre/Post scores were compared using Wilcoxon Rank Sum test. Patients with at least moderate depression had an average 23% reduction in PHQ-9 scores (median change -3(interquartile range 7), p<0.001) and those with at least moderate anxiety had a 26% reduction in GAD-7 scores (-4(7), p<0.001). Improvements were clinically and statistically significant. Future steps include performing a cost analysis to understand whether models utilizing iCBT are net cost-saving for health systems.

Racial Disparities in Incidence and Outcomes Among Patients With COVID-19.

Importance: Initial public health data show that Black race may be a risk factor for worse outcomes of coronavirus disease 2019 (COVID-19). Objective: To characterize the association of race with incidence and outcomes of COVID-19, while controlling for age, sex, socioeconomic status, and comorbidities. Design, Setting, and Participants: This cross-sectional study included 2595 consecutive adults tested for COVID-19 from March 12 to March 31, 2020, at Froedtert Health and Medical College of Wisconsin (Milwaukee), the largest academic system in Wisconsin, with 879 inpatient beds (of which 128 are intensive care unit beds). Exposures: Race (Black vs White, Native Hawaiian or Pacific Islander, Native American or Alaska Native, Asian, or unknown). Main Outcomes and Measures: Main outcomes included COVID-19 positivity, hospitalization, intensive care unit admission, mechanical ventilation, and death. Additional independent variables measured and tested included socioeconomic status, sex, and comorbidities. Reverse transcription polymerase chain reaction assay was used to test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Results: A total of 2595 patients were included. The mean (SD) age was 53.8 (17.5) years, 978 (37.7%) were men, and 785 (30.2%) were African American patients. Of the 369 patients (14.2%) who tested positive for COVID-19, 170 (46.1%) were men, 148 (40.1%) were aged 60 years or older, and 218 (59.1%) were African American individuals. Positive tests were associated with Black race (odds ratio [OR], 5.37; 95% CI, 3.94-7.29; P = .001), male sex (OR, 1.55; 95% CI, 1.21-2.00; P = .001), and age 60 years or older (OR, 2.04; 95% CI, 1.53-2.73; P = .001). Zip code of residence explained 79% of the overall variance in COVID-19 positivity in the cohort (ρ = 0.79; 95% CI, 0.58-0.91). Adjusting for zip code of residence, Black race (OR, 1.85; 95% CI, 1.00-3.65; P = .04) and poverty (OR, 3.84; 95% CI, 1.20-12.30; P = .02) were associated with hospitalization. Poverty (OR, 3.58; 95% CI, 1.08-11.80; P = .04) but not Black race (OR, 1.52; 95% CI, 0.75-3.07; P = .24) was associated with intensive care unit admission. Overall, 20 (17.2%) deaths associated with COVID-19 were reported. Shortness of breath at presentation (OR, 10.67; 95% CI, 1.52-25.54; P = .02), higher body mass index (OR per unit of body mass index, 1.19; 95% CI, 1.05-1.35; P = .006), and age 60 years or older (OR, 22.79; 95% CI, 3.38-53.81; P = .001) were associated with an increased likelihood of death. Conclusions and Relevance: In this cross-sectional study of adults tested for COVID-19 in a large midwestern academic health system, COVID-19 positivity was associated with Black race. Among patients with COVID-19, both race and poverty were associated with higher risk of hospitalization, but only poverty was associated with higher risk of intensive care unit admission. These findings can be helpful in targeting mitigation strategies for racial disparities in the incidence and outcomes of COVID-19.

Location as Destiny: Identifying Geospatial Disparities in Radiation Treatment Interruption by Neighborhood, Race, and Insurance.

PURPOSE: Radiation therapy interruption (RTI) worsens cancer outcomes. Our purpose was to benchmark and map RTI across a region in the United States with known cancer outcome disparities. METHODS AND MATERIALS: All radiation therapy (RT) treatments at our academic center were cataloged. Major RTI was defined as ≥5 unplanned RT appointment cancellations. Univariate and multivariable logistic and linear regression analyses identified associated factors. Major RTI was mapped by patient residence. A 2-sided P value <.0001 was considered statistically significant. RESULTS: Between 2015 and 2017, a total of 3754 patients received RT, of whom 3744 were eligible for analysis: 962 patients (25.8%) had ≥2 RT interruptions and 337 patients (9%) had major RTI. Disparities in major RTI were seen across Medicaid versus commercial/Medicare insurance (22.5% vs 7.2%; P < .0001), low versus high predicted income (13.0% vs 5.9%; P < .0001), Black versus White race (12.0% vs 6.6%; P < .0001), and urban versus suburban treatment location (12.0% vs 6.3%; P < .0001). On multivariable analysis, increased odds of major RTI were seen for Medicaid patients (odds ratio [OR], 3.35; 95% confidence interval [CI], 2.25-5.00; P < .0001) versus those with commercial/Medicare insurance and for head and neck (OR, 3.74; 95% CI, 2.56-5.46; P < .0001), gynecologic (OR, 3.28; 95% CI, 2.09-5.15; P < .0001), and lung cancers (OR, 3.12; 95% CI, 1.96-4.97; P < .0001) compared with breast cancer. Major RTI was mapped to urban, majority Black, low-income neighborhoods and to rural, majority White, low-income regions. CONCLUSIONS: Radiation treatment interruption disproportionately affects financially and socially vulnerable patient populations and maps to high-poverty neighborhoods. Geospatial mapping affords an opportunity to correlate RT access on a neighborhood level to inform potential intervention strategies.

A Growing Opportunity: Community Gardens Affiliated with US Hospitals and Academic Health Centers.

BACKGROUND: Community gardens can reduce public health disparities through promoting physical activity and healthy eating, growing food for underserved populations, and accelerating healing from injury or disease. Despite their potential to contribute to comprehensive patient care, no prior studies have investigated the prevalence of community gardens affiliated with US healthcare institutions, and the demographic characteristics of communities served by these gardens. METHODS: In 2013, national community garden databases, scientific abstracts, and public search engines (e.g., Google Scholar) were used to identify gardens. Outcomes included the prevalence of hospital-based community gardens by US regions, and demographic characteristics (age, race/ethnicity, education, household income, and obesity rates) of communities served by gardens. RESULTS: There were 110 healthcare-based gardens, with 39 in the Midwest, 25 in the South, 24 in the Northeast, and 22 in the West. Compared to US population averages, communities served by healthcare-based gardens had similar demographic characteristics, but significantly lower rates of obesity (27% versus 34%, p < .001). CONCLUSIONS: Healthcare-based gardens are located in regions that are demographically representative of the US population, and are associated with lower rates of obesity in communities they serve.

Still a long way to go for the Lower Mississippi Delta.

It is difficult to separate the demographic, social, and economic changes that have occurred in the Delta. The complex fabric that forms the Delta cannot be broken into parts for simple analysis. Healthcare issues cannot be separated from economic issues, and neither of these issues can be separated from social, political, and other factors of race and power that form the fabric of the Delta. While this analysis disaggregates the data into separate and distinct sections, the reader should be aware of the complex interactions of the performance measures. The clear interaction of health and economic data cannot be overstated and neither can the relationships between education, productivity, employment, income, and social progress. Health is one aspect of investing in human capital and, like education, has its support in the basic mix of public and private goods. Social goods require social investments, and public safety, education, and health are frequent exceptions to the rules of the marketplace. In many areas of the Delta, the allocation of scarce federal and state financial resources to address the problems of the Delta has served to relieve some of the region's distress. The commitment to long-term intervention has, however, varied widely over time.