Nyman, M.

Background Electronic health records (EHRs) serve as a critical tool for clinical documentation, yet their complexity has raised concerns about readability and usability for healthcare providers and patients. Readability scores, such as the Flesch-Kincaid (FK) Grade Level, offer a quantitative method to assess documentation clarity. In this study, the authors evaluated the impact of an EHR transition on the readability of clinical notes across multiple medical specialties. Methods All clinical notes recorded between 2016 and 2019 at a large healthcare organization were evaluated across specialties. The organization transitioned EHR systems during the study period, in May 2018. FK Grade Level scores were calculated for each clinical consultation note and stratified by specialty and EHR. Results Across 10 medical specialties, 630,246 clinical notes were evaluated. The median FK grade levels from notes composed in the new EHR were similar to those composed in the previous EHR (8.228 vs 8.240; p=0.58). The variation in FK grade level as measured by standard deviation was lower in the new EHR compared with the old EHR (1.405 vs 1.764; p <0.01). Conclusions Calculation of FK grade level of clinical notes is a feasible method to assess clinical note readability. Study results showed that clinical documentation readability may not be strongly associated with underlying EHR. Migrating to a new EHR alone should not be expected to improve readability of clinical notes.

The objective was to assess the value of routinely collected patient-reported health-related social needs (HRSNs) measures for predicting utilization and health outcomes. The authors identified Mayo Clinic patients with cancer, diabetes, or heart failure. The HRSN measures were collected as part of patient-reported screenings from June to December 2019 and outcomes (hospitalization, 30-day readmission, and death) were ascertained in 2020. For each outcome and disease combination, 4 models were used: gradient boosting machine (GBM), random forest (RF), generalized linear model (GLM), and elastic net (EN). Other predictors included clinical factors, demographics, and area-based HRSN measures—area deprivation index (ADI) and rurality. Predictive performance for models was evaluated with and without the routinely collected HRSN measures as change in area under the curve (AUC). Variable importance was also assessed. The differences in AUC were mixed. Significant improvements existed in 3 models of death for cancer (GBM: 0.0421, RF: 0.0496, EN: 0.0428), 3 models of hospitalization (GBM: 0.0372, RF: 0.0640, EN: 0.0441), and 1 of death (RF: 0.0754) for diabetes, and 1 model of readmissions (GBM: 0.1817), and 3 models of death (GBM: 0.0333, RF: 0.0519, GLM: 0.0489) for heart failure. Age, ADI, and the Charlson comorbidity index were the top 3 in variable importance and were consistently more important than routinely collected HRSN measures. The addition of routinely collected HRSN measures resulted in mixed improvement in the predictive performance of the models. These findings suggest that existing factors and the ADI are more important for prediction in these contexts. More work is needed to identify predictors that consistently improve model performance.

Introduction: Increasing evidence unravels the role of loneliness and social connection in cardiovascular diseases potentially mediating accelerated biological aging. The present study aims to explore the association between social connection and biological aging as determinate by artificial intelligence-enabled electrocardiography (ECG). Methods: This is an observational retrospective cohort of adults aged ≥18 seen at Mayo Clinic from 2019-2022 who completed the social determinants of health questionnaire and had a 12-lead ECG within one year of completing the questionnaire. Five questions were asked to understand each patient’s social connection . The answers were scored to assess their level of social connection from socially isolated (score of 0) to socially connected (score of 4). The biological age was predicted from ECGs using the previously developed convolutional neural network (AI-ECG age). Delta age (Δage) was defined as AI-ECG age minus chronological age, where positive values reflect an older than expected age. Results: We included 280,323 subjects (chronological age 59.7±16.4 years, 51% female). The average AI-ECG age was 59.5±13.5 year. Better social connection status correlated with lower delta age (β=-0.33 (95%CI, -0.38 and -0.28, p<0.001, adjusted to chronological age and sex). Conversely, individuals reporting the least social connection were an average of 2 years older than expected whereas those with the most social connection were 2 years younger than expected by AI-ECG (Figure 1). Conclusions: Social connection is strongly associated with slower biological aging compared to chronological aging, independent of the conventional cardiovascular risk factors. This observation underscores the need to address social connection as a healthcare determinant.

Introduction: The physiological changes associated with aging have deleterious effects on cardiovascular function. In addition, increasing evidence showed the effect of exercise on improving cardiovascular health and reducing the aging process. The present study sought to demonstrate the association between exercise and biological age, estimated by our previously reported method using artificial intelligence-enabled electrocardiography (ECG). Methods: This retrospective observational study enrolled adults aged ≥18 years who have been evaluated at Mayo Clinic between 2019-2022 and completed the exercise questionnaire. The total time spent in moderate to strenuous exercise was calculated by multiplying the number of days engaging in exercise per week by the average minutes per day. The biological age was determined by applying our previously developed convolutional neural network model on the 12-lead ECG records (AI-ECG). The ECG records were within one year of subjects’ visits (in case of multiple visits, the closest one to the questionnaire was chosen). Positive Age-Gap (AI-ECG minus chronological age) reflects a person identified as being older by AI-ECG compared to his/her chronological age. Results: A total of 268,002 subjects (51% female) were recruited (mean age 59.7±16.4 years). Of those, 25.7% were not engaged in any exercise. The median day of engaging in exercise was 3 (Interquartile range, 2,5) per week, and the average time of exercising was 31.3±30 minutes/day. Age-Gap diminished progressively with increased duration of moderate to strenuous exercise (Figure 1). Linear regression analysis revealed exercise is an independent predictor of Age-Gap, (β= -0.043, 95%CI, -0.045 and -0.041, p<0.001). Conclusions: Engaging in moderate to strenuous exercise is associated with slower biological aging relative to chronological age as determined by AI-enabled ECG.

Background: Health systems are increasingly recognizing the importance of collecting social determinants of health (SDoH) data. However, gaps remain in our understanding of facilitators or barriers to collection. To address these gaps, we evaluated a real-world implementation of a SDoH screening tool. Methods: We conducted a retrospective analysis of the implementation of the SDoH screening tool at Mayo Clinic in 2019. The outcomes are: (1) completion of screening and (2) the modality used (MyChart: filled out on patient portal; WelcomeTablet: filled out by patient on a PC-tablet; EpicCare: data obtained directly by provider and entered in chart). We conducted logistic regression for completion and multinomial logistic regression for modality. The factors of interest included race and ethnicity, use of an interpreter, and whether the visit was for primary care. Results: Overall, 58.7% (293,668/499,931) of screenings were completed. Patients using interpreters and racial/ethnic minorities were less likely to complete the screening. Primary care visits were associated with an increase in completion compared with specialty care visits. Patients who used an interpreter, racial and ethnic minorities, and primary care visits were all associated with greater WelcomeTablet and lower MyChart use. Conclusion: Patient and system-level factors were associated with completion and modality. The lower completion and greater WelcomeTablet use among patients who use interpreters and racial and ethnic minorities points to the need to improve screening in these groups and that the availability of the WelcomeTablet may have prevented greater differences. The higher completion in primary care visits may mean more outreach is needed for specialists.

Background: Concerns for side-effects makes the identification of treatment targets of hypertension in the very elderly challenging. We sought to utilize natural language processing (NLP) to evaluate the prevalence of prespecified side-effects in a cohort of very elderly (≥80 years) patients. Methods and Results: We conducted a two-year, cross-sectional study involving 5169 very elderly patients with and without hypertension seen by primary or specialist providers. Hypertension patients had to have a prior hypertension ICD-9 code in the electronic medical records problem list and ≥2 office visits in 2008 and 2009. We used NLP to analyze extracted free text reports from last clinic visits between 2008 and 2009. Of the 10 prespecified side-effects, hypotension, fatigue, and dizziness were the commonest three conditions and hypotension was significantly higher in the HTN patients. Of the 2835 patients with no prior hypertension history and 2334 hypertension patients identified, the prevalence of each prespecified item in both groups were: acute renal failure (0.3% v 0.6%, p=0.13); syncope (2.8% v 2.2%, p = 0.17), dizziness (3.1% v 4.0%, p=0.09), history of fall (1.0% v 0.9%, p=0.65), fatigue (5.4% v 4.5%, p=0.19), femoral neck fracture (0.18% v 0.04%, p=0.16), palpitations (1.2% v 1.5%, p=0.35), hypotension (4.1% v 6.6%, p<0.001), hyperkalemia (1.1% v 1.7%, p=0.04), hypokalemia (0.4% v 0.9%, p=0.01). At least one presumed side-effect was present in 17.5% of all 5169 patients. Among hypertension patients, there was no significant difference in the prevalence of the side-effects among patients with BP above (578) or below 140/90 (1756) except for acute renal failure (1.21% vs 0.40%, p = 0.03). Conclusion: Analysis of free-text outpatient notes using NLP showed that hypotension and potassium abnormalities were significantly more prevalent in very elderly patients with hypertension compared to similarly aged patients with no hypertension history.

Background: Left ventricular hypertrophy (LVH) is considered a marker of stage B heart failure according to the ACC/AHA classification. Current guidelines recommend beta-blocker (BB) therapy for treatment but randomized control trials suggest that Angiotensin Receptor Blocker (ARB) therapy may be superior. Purpose: To compare survival among cohorts of LVH patients classified according to use of BB, ARB or neither (NONE) class of medications. Methods: A retrospective analysis of hypertension patients seen in the outpatient clinics was done. LVH was defined using the most stringent electrocardiogram (ECG) criteria whereas patients with minimal and moderate voltage criteria were excluded. Using hypertension medications from the year 2009 outpatient visits notes, 869 LVH patients were classified into BB (604), ARB (115) and NONE (150) cohort groups. Cox proportional hazard models were built. Results: Survival at 5 years was 85.2% with 17 deaths and 98 censored patients, (unadjusted hazard ratio HR 0.53, p=0.03) in the ARB cohort and 75.5 % with 148 deaths, 456 censored patients (HR 0.91, p=0.61) in the BB group compared to 74.0% in the NONE cohort with 39 deaths and 111 censored patients. After adjusting for age, sex, atrial fibrillation/flutter, heart failure, chronic kidney disease (CKD), coronary artery disease (CAD), acute myocardial infarction, stroke, obesity, diabetes, sleep apnea and pulmonary hypertension, HR was 0.40, p=0.006 for ARB and HR=0.80, p=0.278 for BB compared to the NONE group. At baseline, there was no significant difference in age, sex, and the prevalence in heart failure, stroke, obesity, sleep apnea, and pulmonary hypertension in all three groups although the BB group had significantly highest prevalence of atrial fibrillation/flutter, CAD, CKD and the lowest prevalence of diabetes. Conclusion: There was a significant mortality reduction benefit to hypertension patients with ECG diagnosed LVH treated with ARB which was not seen in similar patients on BB when compared to those treated with none of both medication classes. This effect persisted even after adjusting for cardiovascular comorbidities.

Objective: To evaluate the performance of an Electronic Health Record (EHR) integrated risk score for COVID-19 positive outpatients to predict 30-day risk of hospitalization. Patients and Methods: A retrospective observational study of 67 470 patients with COVID-19 confirmed by polymerase chain reaction (PCR) test between March 12, 2020 and February 8, 2021. Risk scores were calculated based on data in the chart at the time of the incident infection. Results: The Mayo Clinic COVID-19 risk score consisted of 13 components included age, sex, chronic lung disease, congenital heart disease, congestive heart failure, coronary artery disease, diabetes mellitus, end stage liver disease, end stage renal disease, hypertension, immune compromised, nursing home resident, and pregnant. Univariate analysis showed all components, except pregnancy, have significant ( P < .001) association with admission. The Mayo Clinic COVID-19 risk score showed a Receiver Operating Characteristic Area Under Curve (AUC) of 0.837 for the prediction of admission for this large cohort of COVID-19 positive patients. Conclusion: The Mayo Clinic COVID-19 risk score is a simple score that is easily integrated into the EHR with excellent predictive performance for severe COVID-19. It can be leveraged to stratify risk for severe COVID-19 at initial contact, when considering therapeutics or in the allocation of vaccine supply.

Objective Persistent post-COVID symptoms are estimated to occur in up to 10% of patients who have had COVID-19. These lingering symptoms may persist for weeks to months after resolution of the acute illness. This study aimed to add insight into our understanding of certain post-acute conditions and clinical findings. The primary purpose was to determine the persistent post COVID impairments prevalence and characteristics by collecting post COVID illness data utilizing Patient-Reported Outcomes Measurement Information System (PROMIS ® ). The resulting measures were used to assess surveyed patients physical, mental, and social health status. Methods A cross-sectional study and 6-months Mayo Clinic COVID recovered registry data were used to evaluate continuing symptoms severity among the 817 positive tested patients surveyed between March and September 2020. The resulting PROMIS ® data set was used to analyze patients post 30 days health status. The e-mailed questionnaires focused on fatigue, sleep, ability to participate in social roles, physical function, and pain. Results The large sample size (n = 817) represented post hospitalized and other managed outpatients. Persistent post COVID impairments prevalence and characteristics were determined to be demographically young (44 years), white (87%), and female (61%). Dysfunction as measured by the PROMIS ® scales in patients recovered from acute COVID-19 was reported as significant in the following domains: ability to participate in social roles (43.2%), pain (17.8%), and fatigue (16.2%). Conclusion Patient response on the PROMIS ® scales was similar to that seen in multiple other studies which used patient reported symptoms. As a result of this experience, we recommend utilizing standardized scales such as the PROMIS ® to obtain comparable data across the patients’ clinical course and define the disease trajectory. This would further allow for effective comparison of data across studies to better define the disease process, risk factors, and assess the impact of future treatments.

Background: Blood pressure (BP) measurement is a national core quality measure for hypertension care and hypertension control rates remain a major performance measure. It remains unclear whether improving measurement directly results in better control rates in hypertension management. Purpose: To utilize electronic medical records to explore the relationship between measurement of blood pressure and hypertension control rates among 4 eligible primary care providers groups - Primary Care Internal Medicine (PCIM), Family medicine (FM), General Internal Medicine (GIM), Preventive Medicine (PREV) - and 2 eligible specialist groups - Cardiology (CARD), Nephrology (NEPH) at an academic group medical practice. Methods: A two year cross sectional study involving adults seen by eligible primary or specialist providers at 2 or more office visits with a hypertension ICD-9 code in two years with at least one office visit in the measurement year. We measured the proportion of patients who had their BP measured at their last hypertension visit (LHV) in 2009 and 2010. We compared measurement rates with control rates as defined by the proportion of hypertension patients with BP < 140/90 mmHg at their LHV. Results: In 2009, 84.2% of 17989 patients (95% CI 83.6 - 84.7%) had their BP measured at their LHV compared to 85.3% (95% CI 84.8 - 85.8%) of 17992 patients in 2010, an increase of 1.2% (p=0.0026). Measurement rates varied widely across the different provider groups. PCIM, a high performing group, had significant improvement in measurement rates from 2009 to 2010 and demonstrated improvement in control rates. Lower performing groups, Nephrology and GIM, had significant improvement in measurement but did not see a direct translation to improved control rates. There was an associated worsening of the control rates for Preventive medicine along with a decrease in the measurement rates from 2009 to 2010. Conclusion: High BP measurement rates are associated with high hypertension control rates. Prioritizing BP measurement may result in improved intermediate outcomes in hypertension management.