Cardiovascular health assessment in routine cancer follow-up in community settings: survivor risk awareness and perspectives.

BACKGROUND: Guidelines recommend cardiovascular risk assessment and counseling for cancer survivors. For effective implementation, it is critical to understand survivor cardiovascular health (CVH) profiles and perspectives in community settings. We aimed to (1) Assess survivor CVH profiles, (2) compare self-reported and EHR-based categorization of CVH factors, and (3) describe perceptions regarding addressing CVH during oncology encounters. METHODS: This cross-sectional analysis utilized data from an ongoing NCI Community Oncology Research Program trial of an EHR heart health tool for cancer survivors (WF-1804CD). Survivors presenting for routine care after potentially curative treatment recruited from 8 oncology practices completed a pre-visit survey, including American Heart Association Simple 7 CVH factors (classified as ideal, intermediate, or poor). Medical record abstraction ascertained CVD risk factors and cancer characteristics. Likert-type questions assessed desired discussion during oncology care. RESULTS: Of 502 enrolled survivors (95.6% female; mean time since diagnosis = 4.2 years), most had breast cancer (79.7%). Many survivors had common cardiovascular comorbidities, including high cholesterol (48.3%), hypertension or high BP (47.8%) obesity (33.1%), and diabetes (20.5%); 30.5% of survivors received high cardiotoxicity potential cancer treatment. Less than half had ideal/non-missing levels for physical activity (48.0%), BMI (18.9%), cholesterol (17.9%), blood pressure (14.1%), healthy diet (11.0%), and glucose/ HbA1c (6.0%). While > 50% of survivors had concordant EHR-self-report categorization for smoking, BMI, and blood pressure; cholesterol, glucose, and A1C were unknown by survivors and/or missing in the EHR for most. Most survivors agreed oncology providers should talk about heart health (78.9%). CONCLUSIONS: Tools to promote CVH discussion can fill gaps in CVH knowledge and are likely to be well-received by survivors in community settings. TRIAL REGISTRATION: NCT03935282, Registered 10/01/2020.

Child Opportunity Index and Pediatric Intensive Care Outcomes: A Multicenter Retrospective Study in the United States.

OBJECTIVES: To evaluate for associations between a child's neighborhood, as categorized by Child Opportunity Index (COI 2.0), and 1) PICU mortality, 2) severity of illness at PICU admission, and 3) PICU length of stay (LOS). DESIGN: Retrospective cohort study. SETTING: Fifteen PICUs in the United States. PATIENTS: Children younger than 18 years admitted from 2019 to 2020, excluding those after cardiac procedures. Nationally-normed COI category (very low, low, moderate, high, very high) was determined for each admission by census tract, and clinical features were obtained from the Virtual Pediatric Systems LLC (Los Angeles, CA) data from each site. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 33,901 index PICU admissions during the time period, median patient age was 4.9 years and PICU mortality was 2.1%. There was a higher percentage of admissions from the very low COI category (27.3%) than other COI categories (17.2-19.5%, p < 0.0001). Patient admissions from the high and very high COI categories had a lower median Pediatric Index of Mortality 3 risk of mortality (0.70) than those from the very low, low, and moderate COI groups (0.71) ( p < 0.001). PICU mortality was lowest in the very high (1.7%) and high (1.9%) COI groups and highest in the moderate group (2.5%), followed by very low (2.3%) and low (2.2%) ( p = 0.001 across categories). Median PICU LOS was between 1.37 and 1.50 days in all COI categories. Multivariable regression revealed adjusted odds of PICU mortality of 1.30 (95% CI, 0.94-1.79; p = 0.11) for children from a very low versus very high COI neighborhood, with an odds ratio [OR] of 0.996 (95% CI, 0.993-1.00; p = 0.05) for mortality for COI as an ordinal value from 0 to 100. Children without insurance coverage had an OR for mortality of 3.58 (95% CI, 2.46-5.20; p < 0.0001) as compared with those with commercial insurance. CONCLUSIONS: Children admitted to a cohort of U.S. PICUs were often from very low COI neighborhoods. Children from very high COI neighborhoods had the lowest risk of mortality and observed mortality; however, odds of mortality were not statistically different by COI category in a multivariable model. Children without insurance coverage had significantly higher odds of PICU mortality regardless of neighborhood.

The Association of Frailty and Neighborhood Disadvantage with Emergency Department Visits and Hospitalizations in Older Adults.

BACKGROUND: Risk stratification and population management strategies are critical for providing effective and equitable care for the growing population of older adults in the USA. Both frailty and neighborhood disadvantage are constructs that independently identify populations with higher healthcare utilization and risk of adverse outcomes. OBJECTIVE: To examine the joint association of these factors on acute healthcare utilization using two pragmatic measures based on structured data available in the electronic health record (EHR). DESIGN: In this retrospective observational study, we used EHR data to identify patients aged ≥ 65 years at Atrium Health Wake Forest Baptist on January 1, 2019, who were attributed to affiliated Accountable Care Organizations. Frailty was categorized through an EHR-derived electronic Frailty Index (eFI), while neighborhood disadvantage was quantified through linkage to the area deprivation index (ADI). We used a recurrent time-to-event model within a Cox proportional hazards framework to examine the joint association of eFI and ADI categories with healthcare utilization comprising emergency visits, observation stays, and inpatient hospitalizations over one year of follow-up. KEY RESULTS: We identified a cohort of 47,566 older adults (median age = 73, 60% female, 12% Black). There was an interaction between frailty and area disadvantage (P = 0.023). Each factor was associated with utilization across categories of the other. The magnitude of frailty's association was larger than living in a disadvantaged area. The highest-risk group comprised frail adults living in areas of high disadvantage (HR 3.23, 95% CI 2.99-3.49; P < 0.001). We observed additive effects between frailty and living in areas of mid- (RERI 0.29; 95% CI 0.13-0.45; P < 0.001) and high (RERI 0.62, 95% CI 0.41-0.83; P < 0.001) neighborhood disadvantage. CONCLUSIONS: Considering both frailty and neighborhood disadvantage may assist healthcare organizations in effectively risk-stratifying vulnerable older adults and informing population management strategies. These constructs can be readily assessed at-scale using routinely collected structured EHR data.

Incidence, severity and detection of blood pressure and heart rate perturbations in postoperative ward patients after noncardiac surgery.

STUDY OBJECTIVE: We sought to determine changes in continuous mean and systolic blood pressure and heart rate in a cohort of non-cardiac surgical patients recovering on the postoperative ward. Furthermore, we estimated the proportion of vital signs changes that would remain undetected with intermittent vital signs checks. DESIGN: Retrospective cohort. SETTING: Post-operative general ward. PATIENTS: 14,623 adults recovering from non-cardiac surgical procedures. INTERVENTIONS & MEASUREMENTS: Using a wireless, noninvasive monitor, we recorded postoperative blood pressure and heart rate at 15-s intervals and encouraged nursing intervention as clinically indicated. MAIN RESULTS: 7% of our cohort of 14,623 patients spent >15 sustained minutes with a MAP <65 mmHg, and 23% had MAP <75 mmHg for 15 sustained minutes. Hypertension was more common, with 67% of patients spending at least 60 sustained minutes with MAP >110 mmHg. Systolic pressures <90 mmHg were present for 15 sustained minutes in about a fifth of all patients, and 40% of patients had pressures >160 mmHg sustained for 30 min. 40% of patients were tachycardic with heart rates >100 beats/min for at least continuous 15 min and 15% of patients were bradycardic at a threshold of <50 beats/min for 5 sustained minutes. Conventional vital sign assessments at 4-h intervals would have missed 54% of mean pressure episodes <65 mmHg sustained >15 min, 20% of episodes of mean pressures >130 mmHg sustained >30 min, 36% of episodes of heart rate > 120 beats/min sustained <10 min, and 68% of episodes of heart rate sustained <40 beats per minute for >3 min. CONCLUSIONS: Substantial hemodynamic disturbances persisted despite implementing continuous portable ward monitoring coupled with nursing alarms and interventions. A significant proportion of these changes would have gone undetected using traditional intermittent monitoring. Better understanding of effective responses to alarms and appropriate interventions on hospital wards remains necessary.

Endometrial cancer survivors' perceptions of their cardiovascular disease risk (results from WF-1804CD AH-HA).

OBJECTIVE: Despite considerable burden of cardiovascular disease (CVD), data on endometrial cancer survivors' CVD perceptions are lacking. We assessed survivors' perspectives on addressing CVD risk during oncology care. METHODS: This cross-sectional analysis utilized data from an ongoing trial of an EHR heart health tool (R01CA226078 & UG1CA189824) conducted through the NCI Community Oncology Research Program (NCORP, WF-1804CD). Endometrial cancer survivors post-potentially curative treatment were recruited from community practices and completed a pre-visit baseline survey, including American Heart Association Simple 7 CVD factors. Likert-type questions assessed confidence in understanding CVD risk, CVD risk perception, and desired discussion during oncology care. Medical record abstraction ascertained data on CVD and cancer characteristics. RESULTS: Survivors (N = 55, median age = 62; 62% 0-2 years post-diagnosis) were predominately white, non-Hispanic (87%). Most agreed/strongly agreed heart disease poses a risk to their health (87%) and oncology providers should talk to patients about heart health (76%). Few survivors reported smoking (12%) but many had poor/intermediate values for blood pressure (95%), body mass index (93%), fasting glucose/A1c (60%), diet (60%), exercise (47%) and total cholesterol (53%). 16% had not seen a PCP in the last year; these survivors were more likely to report financial hardship (22% vs 0%; p = 0.02). Most reported readiness to take steps to maintain or improve heart health (84%). CONCLUSIONS: Discussions of CVD risk during routine oncology care are likely to be well received by endometrial cancer survivors. Strategies are needed to implement CVD risk assessment guidelines and to enhance communication and referrals with primary care. Clinical Trials #: NCT03935282.

Patient Perspectives on a Targeted Text Messaging Campaign to Encourage Screening for Diabetes: Qualitative Study.

BACKGROUND: A sizeable proportion of prediabetes and diabetes cases among adults in the United States remain undiagnosed. Patient-facing clinical decision support (CDS) tools that leverage electronic health records (EHRs) have the potential to increase diabetes screening. Given the widespread mobile phone ownership across diverse groups, text messages present a viable mode for delivering alerts directly to patients. The use of unsolicited text messages to offer hemoglobin A1c (HbA1c) screening has not yet been studied. It is imperative to gauge perceptions of "cold texts" to ensure that information and language are optimized to promote engagement with text messages that affect follow-through with health behaviors. OBJECTIVE: This study aims to gauge the perceptions of and receptiveness to text messages to inform content that would facilitate engagement with text messages intended to initiate a mobile health (mHealth) intervention for targeted screening. Messages were designed to invite those not already diagnosed with diabetes to make a decision to take part in HbA1c screening and walk them through the steps required to perform the behavior based solely on an automated text exchange. METHODS: In total, 6 focus groups were conducted at Wake Forest Baptist Health (WFBH) between September 2019 and February 2020. The participants were adult patients without diabetes who had completed an in-person visit at the Family and Community Medicine Clinic within the previous year. We displayed a series of text messages and asked the participants to react to the message content and suggest improvements. Content was deductively coded with respect to the Health Belief Model (HBM) and inductively coded to identify other emergent themes that could potentially impact engagement with text messages. RESULTS: Participants (N=36) were generally receptive to the idea of receiving a text-based alert for HbA1c screening. Plain language, personalization, and content, which highlighted perceived benefits over perceived susceptibility and perceived severity, were important to participants' understanding of and receptiveness to messages. The patient-physician relationship emerged as a recurring theme in which patients either had a desire or held an assumption that their provider would be working behind the scenes throughout each step of the process. Participants needed further clarification to understand the steps involved in following through with HbA1c screening and receiving results. CONCLUSIONS: Our findings suggest that patients may be receptive to text messages that alert them to a risk of having an elevated HbA1c in direct-to-patient alerts that use cold texting. Using plain and positive language, integrating elements of personalization, and defining new processes clearly were identified by participants as modifiable content elements that could act as facilitators that would help overcome barriers to engagement with these messages. A patient's relationship with their provider and the financial costs associated with texts and screening may affect receptiveness and engagement in this process.

Impact of the COVID-19 Pandemic on Diabetes Care Among a North Carolina Patient Population.

In this study, researchers reviewed electronic health record data to assess whether the coronavirus disease 2019 pandemic was associated with disruptions in diabetes care processes of A1C testing, retinal screening, and nephropathy evaluation among patients receiving care with Wake Forest Baptist Health in North Carolina. Compared with the pre-pandemic period, they found an increase of 13-21 percentage points in the proportion of patients delaying diabetes care for each measure during the pandemic. Alarmingly, delays in A1C testing were greatest for individuals with the most severe disease and may portend an increase in diabetes complications.

Using Electronic Health Records for the Learning Health System: Creation of a Diabetes Research Registry.

Electronic health records (EHRs) were originally developed for clinical care and billing. As such, the data are not collected, organized, and curated in a fashion that is optimized for secondary use to support the Learning Health System. Population health registries provide tools to support quality improvement. These tools are generally integrated with the live EHR, are intended to use a minimum of computing resources, and may not be appropriate for some research projects. Researchers may require different electronic phenotypes and variable definitions from those typically used for population health, and these definitions may vary from study to study. Establishing a formal registry that is mapped to the Observation Medical Outcomes Partnership common data model provides an opportunity to add custom mappings and more easily share these with other institutions. Performing preprocessing tasks such as data cleaning, calculation of risk scores, time-to-event analysis, imputation, and transforming data into a format for statistical analyses will improve efficiency and make the data easier to use for investigators. Research registries that are maintained outside the EHR also have the luxury of using significant computational resources without jeopardizing clinical care data. This paper describes a virtual Diabetes Registry at Atrium Health Wake Forest Baptist and the plan for its continued development.

Characteristics of Adult Primary Care Patients Who Use the Patient Portal: A Cross-Sectional Analysis.

BACKGROUND: The patient portal allows patients to engage with their health care team beyond the clinical encounter. While portals can improve patient outcomes, there may be disparities in which patients access the portal by sociodemographic factors. Understanding the characteristics of patients who use the portal could help design future interventions to expand portal adoption. OBJECTIVES: This study aimed to (1) examine the socioeconomic factors, comorbid conditions, and health care utilization among patients of a large academic primary care network who are users and non-users of the patient portal; and (2) describe the portal functions most frequently utilized. METHODS: We included all adult patients at Atrium Health Wake Forest Baptist who had at least two primary care visits between 2018 and 2019. Patients' demographics, comorbidities, health care utilization, and portal function usage were extracted from the electronic health record and merged with census data (income, education, and unemployment) from the American Community Survey. A myWakeHealth portal user was defined as a patient who used a bidirectional portal function at least once during the study period. We used multivariable logistic regression to determine which patient characteristics were independently associated with being a portal user. RESULTS: Of the 178,720 patients who met inclusion criteria, 32% (N = 57,122) were users of myWakeHealth. Compared to non-users, users were more likely to be 18 to 64 years of age, female, non-Hispanic White, married, commercially insured, have higher disease burden, and have lower health care utilization. Patients residing in areas with the highest educational attainment had 51% higher odds of being a portal user than the lowest (p <0.001). Among portal users, the most commonly used function was messaging clinic providers. CONCLUSION: We found that patient demographics and area socioeconomic factors were associated with patient portal adoption. These findings suggest that efforts to improve portal adoption should be targeted at vulnerable patients.

Low Peak Inspiratory Flow Rates are Common Among COPD Inpatients and are Associated with Increased Healthcare Resource Utilization: A Retrospective Cohort Study.

Background: Patients with chronic obstructive pulmonary disease (COPD) can have low peak inspiratory flow (PIF), especially after hospitalization for acute exacerbation of COPD (AECOPD). Purpose: To characterize patients hospitalized for AECOPD, and to assess the prevalence of low PIF, changes in PIF after hospitalization, and the association of low PIF with healthcare resource utilization (HRU) outcomes. Patients and Methods: A retrospective cohort study was conducted using electronic health record data of hospitalized COPD patients in the Wake Forest Baptist Health system (01/01/2017 through 06/30/2020). Patients with a first eligible AECOPD hospitalization (index hospitalization) who were discharged before 05/31/2020 were included. PIF was measured using the In-Check DIAL™ at both medium-low resistance (R-2) and high resistance (R-5) during the index hospitalization. For R-2 and R-5, PIF was divided into low PIF (< 60 L/min; < 30 L/min) and high PIF (≥ 60 L/min; ≥ 30 L/min) groups. The primary outcome was the prevalence of low PIF. The stability of PIF after hospitalization was described. Adjusted regression models evaluated associations between low PIF and subsequent 30-day readmissions, 90-day readmissions, and HRU outcomes, including hospitalizations, emergency department visits, inpatient days, and intensive care unit (ICU) days. Results: In total, 743 patients with PIF measured at R-2 and R-5 during a AECOPD hospitalization were included. The prevalence of low PIF was 56.9% at R-2 and 14.7% at R-5. PIF values were relatively stable after hospitalization. Adjusted analyses showed significant increases in HRU (all-cause hospitalizations [31%], COPD hospitalizations [33%], COPD inpatient days [46%], and COPD ICU days [24%]) during the follow-up period among patients with low PIF (< 60 L/min) at R-2. The 30- and 90-day readmission risks were similar between patients with low PIF and high PIF. Conclusion: Low PIF is common among patients hospitalized for AECOPD, relatively stable after hospital discharge, and associated with increased HRU.

Associations Between Weight and Lower Respiratory Tract Disease Outcomes in Hospitalized Children.

OBJECTIVE: To identify associations between weight status and clinical outcomes in children with lower respiratory tract infection (LRTI) or asthma requiring hospitalization. METHODS: We performed a retrospective cohort study of 2 to 17 year old children hospitalized for LRTI and/or asthma from 2009 to 2019 using electronic health record data from the PEDSnet clinical research network. Children <2 years, those with medical complexity, and those without a calculable BMI were excluded. Children were classified as having underweight, normal weight, overweight, or class 1, 2, or 3 obesity based on Body Mass Index percentile for age and sex. Primary outcomes were need for positive pressure respiratory support and ICU admission. Subgroup analyses were performed for children with a primary diagnosis of asthma. Outcomes were modeled with mixed-effects multivariable logistic regression incorporating age, sex, and payer as fixed effects. RESULTS: We identified 65 132 hospitalizations; 6.7% with underweight, 57.8% normal weight, 14.6% overweight, 13.2% class 1 obesity, 5.0% class 2 obesity, and 2.8% class 3 obesity. Overweight and obesity were associated with positive pressure respiratory support (class 3 obesity versus normal weight odds ratio [OR] 1.62 [1.38-1.89]) and ICU admission (class 3 obesity versus normal weight OR 1.26 [1.12-1.42]), with significant associations for all categories of overweight and obesity. Underweight was also associated with positive pressure respiratory support (OR 1.39 [1.24-1.56]) and ICU admission (1.40 [1.30-1.52]). CONCLUSIONS: Both underweight and overweight or obesity are associated with increased severity of LRTI or asthma in hospitalized children.

Creating learning health systems and the emerging role of biomedical informatics.

INTRODUCTION: The nature of information used in medicine has changed. In the past, we were limited to routine clinical data and published clinical trials. Today, we deal with massive, multiple data streams and easy access to new tests, ideas, and capabilities to process them. Whereas in the past getting information for decision-making was a challenge, now, it is how to analyze, evaluate and prioritize all that is readily available through the multitude of data-collecting devices. Clinicians must become adept with the tools needed to deal with the era of big data, requiring a major change in how we learn to make decisions. Major change is often met with resistance and questions about value. A Learning Health System is an enabler to encourage the development of such tools and demonstrate value in improved decision-making. METHODS: We describe how we are developing a Biomedical Informatics program to help our medical institution's evolution as an academic Learning Health System, including strategy, training for house staff and examples of the role of informatics from operations to research. RESULTS: We described an array of learning health system implementations and educational programs to improve healthcare and prepare a cadre of physicians with basic information technology skills. The programs have been well accepted with, for example, increasing interest and enrollment in the educational programs. CONCLUSIONS: We are now in an era when large volumes of a wide variety of data are readily available. The challenge is not so much in the acquisition of data, but in assessing the quality, relevance and value of the data. The data we can get may not be the data we need. In the past, sources of data were limited, and trial results published in journals were the major source of evidence for decision making. The advent of powerful analytics systems has changed the concept of evidence. Clinicians will have to develop the skills necessary to work in the era of big data. It is not reasonable to expect that all clinicians will also be data scientists. However, understanding the role of AI and predictive analytics, and how to apply them, will become progressively more important. Programs such as the one being implemented at Wake Forest fill that need.

Weight Status and Risk of Inpatient Admission for Children With Lower Respiratory Tract Disease.

OBJECTIVES: To identify associations between weight category and hospital admission for lower respiratory tract disease (LRTD), defined as asthma, community-acquired pneumonia, viral pneumonia, or bronchiolitis, among children evaluated in pediatric emergency departments (PEDs). METHODS: We performed a retrospective cohort study of children 2 to <18 years of age evaluated in the PED at 6 children's hospitals within the PEDSnet clinical research network from 2009 to 2019. BMI percentile of children was classified as underweight, healthy weight, overweight, and class 1, 2, or 3 obesity. Children with complex chronic conditions were excluded. Mixed-effects multivariable logistic regression was used to assess associations between BMI categories and hospitalization or 7- and 30-day PED revisits, adjusted for covariates (age, sex, race and ethnicity, and payer). RESULTS: Among 107 446 children with 218 180 PED evaluations for LRTD, 4.5% had underweight, 56.4% had healthy normal weight, 16.1% had overweight, 14.6% had class 1 obesity, 5.5% had class 2 obesity, and 3.0% had class 3 obesity. Underweight was associated with increased risk of hospital admission compared with normal weight (odds ratio [OR] 1.76; 95% confidence interval [CI] 1.69-1.84). Overweight (OR 0.87; 95% CI 0.85-0.90), class 1 obesity (OR 0.88; 95% CI 0.85-0.91), and class 2 obesity (OR 0.91; 95% CI 0.87-0.96) had negative associations with hospital admission. Class 1 and class 2, but not class 3, obesity had small positive associations with 7- and 30-day PED revisits. CONCLUSIONS: We found an inverse relationship between patient weight category and risk for hospital admission in children evaluated in the PED for LRTD.

Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study.

BACKGROUND: Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. METHODS: A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children's hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children's Hospital, Cincinnati, OH, Seattle Children's Hospital, Seattle, WA, and Children's Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. RESULTS: Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. CONCLUSIONS: Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

Addressing cancer survivors' cardiovascular health using the automated heart health assessment (AH-HA) EHR tool: Initial protocol and modifications to address COVID-19 challenges.

BACKGROUND: The purpose of this paper is to describe the Automated Heart-Health Assessment (AH-HA) study protocol, which demonstrates an agile approach to cancer care delivery research. This study aims to assess the effect of a clinical decision support tool for cancer survivors on cardiovascular health (CVH) discussions, referrals, completed visits with primary care providers and cardiologists, and control of modifiable CVH factors and behaviors. The COVID-19 pandemic has caused widespread disruption to clinical trial accrual and operations. Studies conducted with potentially vulnerable populations, including cancer survivors, must shift towards virtual consent, data collection, and study visits to reduce risk for participants and study staff. Studies examining cancer care delivery innovations may also need to accommodate the increased use of virtual visits. METHODS/DESIGN: This group-randomized, mixed methods study will recruit 600 cancer survivors from 12 National Cancer Institute Community Oncology Research Program (NCORP) practices. Survivors at intervention sites will use the AH-HA tool with their oncology provider; survivors at usual care sites will complete routine survivorship visits. Outcomes will be measured immediately after the study visit, with follow-up at 6 and 12 months. The study was amended during the COVID-19 pandemic to allow for virtual consent, data collection, and intervention options, with the goal of minimizing participant-staff in-person contact and accommodating virtual survivorship visits. CONCLUSIONS: Changes to the study protocol and procedures allow important cancer care delivery research to continue safely during the COVID-19 pandemic and give sites and survivors flexibility to conduct study activities in-person or remotely.

What Oncologists Want: Identifying Challenges and Preferences on Diagnosis Data Entry to Reduce EHR-Induced Burden and Improve Clinical Data Quality.

PURPOSE: Accurate recording of diagnosis (DX) data in electronic health records (EHRs) is important for clinical practice and learning health care. Previous studies show statistically stable patterns of data entry in EHRs that contribute to inaccurate DX, likely because of a lack of data entry support. We conducted qualitative research to characterize the preferences of oncological care providers on cancer DX data entry in EHRs during clinical practice. METHODS: We conducted semistructured interviews and focus groups to uncover common themes on DX data entry preferences and barriers to accurate DX recording. Then, we developed a survey questionnaire sent to a cohort of oncologists to verify the generalizability of our initial findings. We constrained our participants to a single specialty and institution to ensure similar clinical backgrounds and clinical experience with a single EHR system. RESULTS: A total of 12 neuro-oncologists and thoracic oncologists were involved in the interviews and focus groups. The survey developed from these two initial thrusts was distributed to 19 participants yielding a 94.7% survey response rate. Clinicians reported similar user interface experiences, barriers, and dissatisfaction with current DX entry systems including repetitive entry operations, difficulty in finding specific DX options, time-consuming interactions, and the need for workarounds to maintain efficiency. The survey revealed inefficient DX search interfaces and challenging entry processes as core barriers. CONCLUSION: Oncologists seem to be divided between specific DX data entry and time efficiency because of current interfaces and feel hindered by the burdensome and repetitive nature of EHR data entry. Oncologists' top concern for adopting data entry support interventions is ensuring that it provides significant time-saving benefits and increasing workflow efficiency. Future interventions should account for time efficiency, beyond ensuring data entry effectiveness.

Mental Health and Benzodiazepine Use Among Patients on Chronic Opioid Therapy.

INTRODUCTION: Higher daily doses of opioids as well as co-prescription of benzodiazepines have been associated with risk of overdose. The current study characterizes prescribing patterns in a family medicine practice with regard to patient mental health diagnoses, benzodiazepine prescriptions, morphine milligram equivalent opioid dose, and patient demographics. METHODS: Patients on chronic opioid therapy were studied in 2018 and 2019. Mental health diagnoses, opioid dose, benzodiazepine prescriptions and demographic characteristics were extracted from the electronic health record. Data were compared between years and logistic regression was used to determine which patient characteristics were associated with likelihood of decreased opioid dose. RESULTS: A total of 387 patients were prescribed chronic opioid therapy in 2018, and 231 in 2019. In 2018, 49.9% of patients prescribed chronic opioids had mental health diagnoses. In 2019, this proportion rose to 92.2%. In 2019, 205 of the original 387 patients were still with the practice but were not prescribed chronic opioids. Among the factors studied, psychiatric diagnosis and higher opioid dose were associated with a significantly lower likelihood of tapering doses. DISCUSSION: As practices taper or de-prescribe opioids, or implement harm reduction methods such as de-prescribing benzodiazepines, it is important to understand patient characteristics and their relationship to success with tapering. This study adds to the evidence that odds of successfully tapering opioids may be significantly impacted by patients' mental health diagnosis and opioid dose.

Cardiovascular Assessment Tool for Breast Cancer Survivors and Oncology Providers: Usability Study.

BACKGROUND: Cardiovascular health is of increasing concern to breast cancer survivors and their health care providers, as many survivors are more likely to die from cardiovascular disease than cancer. Implementing clinical decision support tools to address cardiovascular risk factor awareness in the oncology setting may enhance survivors' attainment or maintenance of cardiovascular health. OBJECTIVE: We sought to evaluate survivors' awareness of cardiovascular risk factors and examine the usability of a novel electronic health record enabled cardiovascular health tool from the perspective of both breast cancer survivors and oncology providers. METHODS: Breast cancer survivors (n=49) recruited from a survivorship clinic interacted with the cardiovascular health tool and completed pre and posttool assessments about cardiovascular health knowledge and perceptions of the tool. Oncologists, physician assistants, and nurse practitioners (n=20) who provide care to survivors also viewed the cardiovascular health tool and completed assessments of perceived usability and acceptability. RESULTS: Enrolled breast cancer survivors (84% White race, 4% Hispanic ethnicity) had been diagnosed 10.8 years ago (SD 6.0) with American Joint Committee on Cancer stage 0, I, or II (45/49, 92%). Prior to viewing the tool, 65% of survivors (32/49) reported not knowing their level for one or more cardiovascular health factors (range 0-4). On average, only 45% (range 0%-86%) of survivors' known cardiovascular health factors were at an ideal level. More than 50% of survivors had ideal smoking status (45/48, 94%) or blood glucose level (29/45, 64%); meanwhile, less than 50% had ideal blood pressure (12/49, 24%), body mass index (12/49, 24%), cholesterol level (17/35, 49%), diet (7/49, 14%), and physical activity (10/49. 20%). More than 90% of survivors thought the tool was easy to understand (46/47, 98%), improved their understanding (43/47, 91%), and was helpful (45/47, 96%); overall, 94% (44/47 survivors) liked the tool. A majority of survivors (44/47, 94%) thought oncologists should discuss cardiovascular health during survivorship care. Most (12/20, 60%) oncology providers (female: 12/20, 60%; physicians: 14/20, 70%) had been practicing for more than 5 years. Most providers agreed the tool provided useful information (18/20, 90%), would help their effectiveness (18/20, 90%), was easy to use (20/20, 100%), and presented information in a useful format (19/20, 95%); and 85% of providers (17/20) reported they would use the tool most or all of the time when providing survivorship care. CONCLUSIONS: These usability data demonstrate acceptability of a cardiovascular health clinical decision support tool in oncology practices. Oncology providers and breast cancer survivors would likely value the integration of such apps in survivorship care. By increasing awareness and communication regarding cardiovascular health, electronic health record-enabled tools may improve survivorship care delivery for breast cancer and ultimately patient outcomes.

Developing a Data Quality Standard Primer for Cardiovascular Risk Assessment from Electronic Health Record Data Using the DataGauge Process.

The learning health systems aim to support the needs of patients with chronic diseases, which require methods that account for electronic health recorded (EHR) data limitations. EHR data is often used to calculate cardiovascular risk scores. However, it is unclear whether EHR data presents high enough quality to provide accurate estimates. Still, there is currently no open standard available to assess data quality for such applications. We applied the DataGauge process to develop a data quality standard based on expert clinical, analytical and informatics knowledge by conducting four interviews and one focus group that produced 61 individual data quality requirements. These requirements covered all standard data quality dimensions and uncovered 705 quality issues in EHR data for 456 patients. These requirements will be expanded and further validated in future work. Our work initiates the development of open and explicit data quality standards for specific secondary uses of clinical data.

Sex and race differences in safety and effectiveness of the HEART pathway accelerated diagnostic protocol for acute chest pain.

BACKGROUND: The HEART Pathway is an accelerated diagnostic protocol for Emergency Department patients with possible acute coronary syndrome. The objective was to compare the safety and effectiveness of the HEART Pathway among women vs men and whites vs non-whites. METHODS: A subgroup analysis of the HEART Pathway Implementation Study was conducted. Adults with chest pain were accrued from November 2013 to January 2016 from 3 Emergency Departments in North Carolina. The primary outcomes were death and myocardial infarction (MI) and hospitalization rates at 30 days. Logistic regression evaluated for interactions of accelerated diagnostic protocol implementation with sex or race and changes in outcomes within subgroups. RESULTS: A total of 8,474 patients were accrued, of which 53.6% were female and 34.0% were non-white. The HEART Pathway identified 32.6% of females as low-risk vs 28.5% of males (P = 002) and 35.6% of non-whites as low-risk vs 28.0% of whites (P < .0001). Among low-risk patients, death or MI at 30 days occurred in 0.4% of females vs 0.5% of males (P = .70) and 0.5% of non-whites vs 0.3% of whites (P = .69). Hospitalization at 30 days was reduced by 6.6% in females (aOR: 0.74, 95% CI: 0.64-0.85), 5.1% in males (aOR: 0.87, 95% CI: 0.75-1.02), 8.6% in non-whites (aOR: 0.72, 95% CI: 0.60-0.86), and 4.5% in whites (aOR: 0.83, 95% CI: 0.73-0.94). Interactions were not significant. CONCLUSION: Women and non-whites are more likely to be classified as low-risk by the HEART Pathway. HEART Pathway implementation is associated with decreased hospitalizations and a very low death and MI rate among low-risk patients regardless of sex or race.