Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial.

BACKGROUND: As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with high likelihood of palliative care need. METHODS: 42 care team units in 9 clusters were randomized to 7 wedges, each lasting 42 days. For care teams in treatment wedges, palliative care specialists review identified patients, making recommendations to primary care providers when appropriate. Care teams in control wedges receive palliative care under the standard of care. DISCUSSION: This pragmatic trial therefore integrates machine learning into clinical decision making, instead of simply reporting theoretical predictive performance. Such integration has the possibility to decrease time to palliative care, improving patient quality of life and symptom burden. TRIAL REGISTRATION: Clinicaltrials.gov NCT04604457 , restrospectively registered 10/26/2020. PROTOCOL: v0.5, dated 9/23/2020.

Association of neighborhood socioeconomic disadvantage and cognitive impairment.

INTRODUCTION: We investigated the association of the area deprivation index (ADI) with cognitive decline, mild cognitive impairment (MCI), and dementia in older adults (≥50 years old). ADI is a neighborhood socioeconomic disadvantage measure assessed at the census block group level. METHODS: The study included 4699 participants, initially without dementia, with available ADI values for 2015 and at least one study visit in 2008 through 2018. Using logistic regression and Cox proportional hazards models with age as the time scale, we assessed the odds for MCI and the risk for dementia, respectively. RESULTS: In cognitively unimpaired (CU) adults at baseline, the risk for progression to dementia increased for every decile increase in the ADI state ranking (hazard ratio = 1.06, 95% confidence interval (1.01-1.11), P = .01). Higher ADI values were associated with subtly faster cognitive decline. DISCUSSION: In older CU adults, higher baseline neighborhood socioeconomic deprivation levels were associated with progression to dementia and slightly faster cognitive decline. HIGHLIGHTS: The study used area deprivation index, a composite freely available neighborhood deprivation measure. Higher levels of neighborhood deprivation were associated with greater mild cognitive impairment odds. Higher neighborhood deprivation levels were associated with higher dementia risk.

Correction to: Neighborhood socioeconomic disadvantage is associated with multimorbidity in a geographically-defined community.

An amendment to this paper has been published and can be accessed via the original article.

Neighborhood socioeconomic disadvantage is associated with multimorbidity in a geographically-defined community.

BACKGROUND: Persons with low socioeconomic status may be disproportionately at risk for multimorbidity. METHODS: Adults aged ≥20 years on 4/1/2015 from 7 counties in Minnesota were identified using the Rochester Epidemiology Project (population-based sample). A composite measure of neighborhood socioeconomic disadvantage, the area deprivation index (ADI), was estimated at the census block group level (n = 251). The prevalence of 21 chronic conditions was obtained to calculate the proportion of persons with multimorbidity (≥2 chronic conditions) and severe multimorbidity (≥5 chronic conditions). Hierarchical logistic regression was used to estimate the association of ADI with multimorbidity and severe multimorbidity using odds ratios (OR). RESULTS: Among 198,941 persons (46.7% male, 30.6% aged ≥60 years), the age- and sex-standardized (to the United States 2010 census) median prevalence (Q1, Q3) was 23.4% (21.3%, 25.9%) for multimorbidity and 4.8% (4.0%, 5.7%) for severe multimorbidity. Compared with persons in the lowest quintile of ADI, persons in the highest quintile had a 50% increased risk of multimorbidity (OR 1.50, 95% CI 1.39-1.62) and a 67% increased risk of severe multimorbidity (OR 1.67, 95% CI 1.51-1.86) after adjusting for age, sex, race, and ethnicity. Associations were stronger after further adjustment for individual level of education; persons in the highest quintile had a 78% increased risk of multimorbidity (OR 1.78, 95% CI 1.62-1.96) and a 92% increased risk of severe multimorbidity (OR 1.92, 95% CI 1.72-2.13). There was evidence of interactions between ADI and age, between ADI and sex, and between ADI and education. After age 70 years, no difference in the risk of multimorbidity was observed across quintiles of ADI. The pattern of increasing multimorbidity with increasing ADI was more pronounced in women. Finally, there was less variability across quintiles of ADI for the most highly educated group. CONCLUSIONS: Higher ADI was associated with increased risk of multimorbidity, and the associations were strengthened after adjustment for individual level of education, suggesting that neighborhood context plays a role in health above and beyond individual measures of socioeconomic status. Furthermore, associations were more pronounced in younger persons and women, highlighting the importance of interventions to prevent chronic conditions in younger women, in particular.

Correction to: The effect of routine training on the self-efficacy of informal caregivers of colorectal cancer patients.

The "NCI Grant No. 5R25CA116339, Outcomes Research Branch of the National Cancer Institute, National Institutes of Health" is not included in the Funding information. The below is the correct "Funding/Support".

The impact of positive contextual factors on the association between adverse family experiences and obesity in a National Survey of Children.

Adverse family experiences (AFEs) are associated with childhood obesity. We evaluated whether certain positive contextual factors reduce the risk of obesity and overweight among children exposed to AFEs in a nationally representative sample. Using data derived from the National Survey of Children's Health 2011-12 (N = 43,864), we calculated the distribution of positive contextual factors (very good/excellent maternal mental health, neighborhood and school safety, and child resilience) and AFEs across weight status. The AFEs composite score was modeled as a categorical measure (0 or ≥1 AFEs). Positive contextual factors, AFEs and their interactions were evaluated in weighted, adjusted, multinomial logistic regression models predicting the odds of overweight and obesity. Children exposed to lack of very good/excellent maternal mental health and at least one AFE were at risk for overweight (OR = 1.43; 95% CI: 1.16, 1.76) and obesity (OR = 1.53; 95% CI: 1.22, 1.93). Unsafe school or neighborhood environment and exposure to 1 or more AFEs was. associated with overweight (OR = 1.32; 95% CI: 1.08, 1.61) and obesity (OR = 1.66; 95% CI: 1.34, 2.05). Lack of child resilience and exposure to 1 or more AFEs was associated with an increased risk of obesity (OR = 1.45; 95% CI: 1.17, 1.90) and overweight (OR = 1.29; 95% CI: 1.06, 1.57). These odds of obesity and overweight all decreased when positive contextual factors were present. Among children exposed to AFEs, overweight and obesity risk is reduced with positive contextual factors. Optimizing the early childhood environment can impact obesity risk.

The effect of routine training on the self-efficacy of informal caregivers of colorectal cancer patients.

PURPOSE: Little is known about the degree to which caregiver training as part of routine clinical care influences caregiver self-efficacy. The objective of this study was to examine the relationship between training during routine clinical cancer care and self-efficacy among caregivers of colorectal cancer patients. METHODS: Caregivers completed a self-administered questionnaire about their experiences with training for specific patient problems and about their task-specific and general caregiving self-efficacy. Associations between training and self-efficacy were examined for each problem using multivariate logistic regression adjusted for caregiver age, race, care burden, education, perception of patient's health, and patient stage of disease. RESULTS: Four hundred seventeen caregivers completed the survey (70% response rate), of whom 374 (90%) were female and 284 (68%) were the patient's spouse/partner. Overall, 77 (38%) reported inadequate training for pain, 80 (38%) for bowel, 121 (48%) for fatigue, 65 (26%) for medication administration, and 101 (40%) for other symptoms. The odds of having low self-efficacy were significantly higher among those with perceptions of inadequate training across the following cancer-related problems: pain 10.10 (3.36, 30.39), bowel 5.04 (1.98, 12.82), fatigue 8.45 (3.22, 22.15), managing medications 9.00 (3.30, 24.51), and other 3.87 (1.68, 8.93). CONCLUSIONS: Caregivers commonly report inadequate training in routine colorectal cancer care. Significant and consistent associations between training adequacy and self-efficacy were found. This study supports the value of training caregivers in common cancer symptoms. Further work on how and when to provide caregiver training to best impact self-efficacy is needed.

Telephone triage utilization among patients with limited English proficiency.

BACKGROUND: Communication between patients with limited English proficiency (LEP) and telephone triage services has not been previously explored. The purpose of this study was to determine the utilization characteristics of a primary care triage call center by patients with LEP. METHODS: This was a retrospective cohort study of the utilization of a computer-aided, nurse-led telephone triage system by English proficiency status of patients empaneled to a large primary care practice network in the Midwest United States. Interpreter Services (IS) need was used as a proxy for LEP. RESULTS: Call volumes between the 587 adult patients with LEP and an age-frequency matched cohort of English-Proficient (EP) patients were similar. Calls from patients with LEP were longer and more often made by a surrogate. Patients with LEP received recommendations for higher acuity care more frequently (49.4% versus 39.0%; P < 0.0004), and disagreed with recommendations more frequently (30.1% versus 20.9%; P = 0.0004). These associations remained after adjustment for comorbidities. Patients with LEP were also less likely to follow recommendations (60.9% versus 69.4%; P = 0.0029), even after adjusting for confounders (adjusted odds ratio [AOR] = 0.65; 95% confidence interval [CI], 0.49, 0.85; P < 0.001). CONCLUSION: Patients with LEP who utilized a computer-aided, nurse-led telephone triage system were more likely to receive recommendations for higher acuity care compared to EP patients. They were also less likely to agree with, or follow, recommendations given. Additional research is needed to better understand how telephone triage can better serve patients with LEP.

Adverse family experiences and obesity in children and adolescents in the United States.

While exposure to adverse family experiences (AFEs), subset of adverse childhood experiences (ACEs), has been associated with childhood obesity, less is known about the impact of exposures to each type of AFE. Using 2011-2012 National Survey of Children's Health data, we evaluated associations between exposure to individual AFEs and overweight/obesity status in children 10years or older, adjusting for socio-demographic factors. Caregivers reported their child's height, weight, and exposure to nine AFEs; body mass index (BMI) was classified by Center for Disease Control and Prevention's (CDC) guidelines. At Mayo Clinic, we calculated frequencies and weighted estimates of socio-demographic factors and AFEs. Unadjusted and adjusted weighted multinomial logistic regression models were employed to assess the independent associations of each AFE and the different AFE composite scores with BMI category. Exposure to two or more AFEs was independently associated with increased odds of overweight (odds ratio [OR], 1.33; 95% confidence interval [CI], 1.13, 1.56) and obese (OR, 1.45; 95% CI, 1.21, 1.73) status after adjustment for age, household income, parents' education-level, race and sex. Death of parent (OR, 1.59; 95% CI, 1.18, 2.15) and hardship due to family income (OR, 1.26; 95% CI, 1.06, 1.50) were independently associated with obesity status with adjustment for other AFEs and socio-demographic factors. Our results suggest that, in addition to cumulative exposure to AFEs, exposure to certain childhood experiences are more strongly associated with childhood obesity than others. Death of parent and hardship due to family income are individual AFEs, which are strongly predictive of obesity.

Health Information Brokers in the General Population: An Analysis of the Health Information National Trends Survey 2013-2014.

BACKGROUND: Health information exchanged between friends or family members can influence decision making, both for routine health questions and for serious health issues. A health information broker is a person to whom friends and family turn for advice or information on health-related topics. Characteristics and online behaviors of health information brokers have not previously been studied in a national population. OBJECTIVE: The objective of this study was to examine sociodemographic characteristics, health information seeking behaviors, and other online behaviors among health information brokers. METHODS: Data from the Health Information National Trends Survey (2013-2014; n=3142) were used to compare brokers with nonbrokers. Modified Poisson regression was used to examine the relationship between broker status and sociodemographics and online information seeking. RESULTS: Over half (54.8%) of the respondents were consulted by family or friends for advice or information on health topics (ie, they acted as health information brokers). Brokers represented 54.1% of respondents earning <$20,000 yearly and 56.5% of respondents born outside the United States. Women were more likely to be brokers (PR 1.34, 95% CI 1.23-1.47) as were those with education past high school (PR 1.42, CI 1.22-1.65). People aged ≥75 were less likely to be brokers as compared to respondents aged 35-49 (PR 0.81, CI 0.67-0.99). Brokers used the Internet more frequently for a variety of online behaviors such as seeking health information, creating and sharing online content, and downloading health information onto a mobile device; and also reported greater confidence in obtaining health information online. CONCLUSIONS: More than 50% of adults who responded to this national survey, including those with low income and those born abroad, were providing health information or advice to friends and family. These individuals may prove to be effective targets for initiatives supporting patient engagement and disease management, and may also be well-positioned within their respective social networks to propagate health messages.

Use of E-Cigarettes Among Current Smokers: Associations Among Reasons for Use, Quit Intentions, and Current Tobacco Use.

INTRODUCTION: Research has documented growing availability and use of e-cigarettes in the United States over the last decade. METHODS: We conducted a national panel survey of current adult cigarette smokers to assess attitudes, beliefs, and behaviors relating to e-cigarette use in the United States (N = 2,254). RESULTS: Among current cigarette smokers, 20.4% reported current use of e-cigarettes on some days and 3.7% reported daily use. Reported reasons for e-cigarette use included: quit smoking (58.4%), reduce smoking (57.9%), and reduce health risks (51.9%). No significant differences in sociodemographic characteristics between e-cigarette users and nonusers were observed. Prior quit attempts were reported more frequently among e-cigarette users (82.8%) than nonusers (74.0%). Intention to quit was reported more frequently among e-cigarette users (64.7%) than nonusers (46.8%). Smokers intending to quit were more likely to be e-cigarette users than those not intending to quit (odds ratio [OR] = 1.90, CI =1.36-2.65). Those who used e-cigarettes to try to quit smoking (OR = 2.25, CI = 1.25-4.05), reduce stress (OR = 3.66, CI = 1.11-12.09), or because they cost less (OR = 3.42, CI = 1.64-7.13) were more likely to report decreases in cigarette smoking than those who did not indicate these reasons. Smokers who reported using e-cigarettes to quit smoking (OR = 16.25, CI = 8.32-31.74) or reduce stress (OR = 4.30, CI = 1.32-14.09) were significantly more likely to report an intention to quit than those who did not indicate those reasons for using e-cigarettes. CONCLUSIONS: Nearly a quarter of smokers in our study reported e-cigarettes use, primarily motivated by intentions to quit or reduce smoking. These findings identify a clinical and public health opportunity to re-engage smokers in cessation efforts.

Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D's Real-Time Capabilities and Cross-Hospital Transferability.

Background: Postoperative ileus (POI) after colorectal surgery leads to increased morbidity, costs, and hospital stays. Identifying POI risk for early intervention is important for improving surgical outcomes especially given the increasing trend towards early discharge after surgery. While existing studies have assessed POI risk with regression models, the role of deep learning's remains unexplored. Methods: We assessed the performance and transferability (brutal force/instance/parameter transfer) of Gated Recurrent Unit with Decay (GRU-D), a longitudinal deep learning architecture, for real-time risk assessment of POI among 7,349 colorectal surgeries performed across three hospital sites operated by Mayo Clinic with two electronic health records (EHR) systems. The results were compared with atemporal models on a panel of benchmark metrics. Results: GRU-D exhibits robust transferability across different EHR systems and hospital sites, showing enhanced performance by integrating new measurements, even amid the extreme sparsity of real-world longitudinal data. On average, for labs, vitals, and assisted living status, 72.2%, 26.9%, and 49.3% respectively lack measurements within 24 hours after surgery. Over the follow-up period with 4-hour intervals, 98.7%, 84%, and 95.8% of data points are missing, respectively. A maximum of 5% decrease in AUROC was observed in brutal-force transfer between different EHR systems with non-overlapping surgery date frames. Multi-source instance transfer witnessed the best performance, with a maximum of 2.6% improvement in AUROC over local learning. The significant benefit, however, lies in the reduction of variance (a maximum of 86% decrease). The GRU-D model's performance mainly depends on the prediction task's difficulty, especially the case prevalence rate. Whereas the impact of training data and transfer strategy is less crucial, underscoring the challenge of effectively leveraging transfer learning for rare outcomes. While atemporal Logit models show notably superior performance at certain pre-surgical points, their performance fluctuate significantly and generally underperform GRU-D in post-surgical hours. Conclusion: GRU-D demonstrated robust transferability across EHR systems and hospital sites with highly sparse real-world EHR data. Further research on built-in explainability for meaningful intervention would be highly valuable for its integration into clinical practice.

Effect of an Artificial Intelligence Decision Support Tool on Palliative Care Referral in Hospitalized Patients: A Randomized Clinical Trial.

CONTEXT: Palliative care services are commonly provided to hospitalized patients, but accurately predicting who needs them remains a challenge. OBJECTIVES: To assess the effectiveness on clinical outcomes of an artificial intelligence (AI)/machine learning (ML) decision support tool for predicting patient need for palliative care services in the hospital. METHODS: The study design was a pragmatic, cluster-randomized, stepped-wedge clinical trial in 12 nursing units at two hospitals over a 15-month period between August 19, 2019, and November 17, 2020. Eligible patients were randomly assigned to either a medical service consultation recommendation triggered by an AI/ML tool predicting the need for palliative care services or usual care. The primary outcome was palliative care consultation note. Secondary outcomes included: hospital readmissions, length of stay, transfer to intensive care and palliative care consultation note by unit. RESULTS: A total of 3183 patient hospitalizations were enrolled. Of eligible patients, A total of 2544 patients were randomized to the decision support tool (1212; 48%) and usual care (1332; 52%). Of these, 1717 patients (67%) were retained for analyses. Patients randomized to the intervention had a statistically significant higher incidence rate of palliative care consultation compared to the control group (IRR, 1.44 [95% CI, 1.11-1.92]). Exploratory evidence suggested that the decision support tool group reduced 60-day and 90-day hospital readmissions (OR, 0.75 [95% CI, 0.57, 0.97]) and (OR, 0.72 [95% CI, 0.55-0.93]) respectively. CONCLUSION: A decision support tool integrated into palliative care practice and leveraging AI/ML demonstrated an increased palliative care consultation rate among hospitalized patients and reductions in hospitalizations.

Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications.

Translation of predictive modeling algorithms into routine clinical care workflows faces challenges in the form of varying data quality-related issues caused by the heterogeneity of electronic health record (EHR) systems. To better understand these issues, we retrospectively assessed and compared the variability of data produced from two different EHR systems. We considered three dimensions of data quality in the context of EHR-based predictive modeling for three distinct translational stages: model development (data completeness), model deployment (data variability), and model implementation (data timeliness). The case study was conducted based on predicting post-surgical complications using both structured and unstructured data. Our study discovered a consistent level of data completeness, a high syntactic, and moderate-high semantic variability across two EHR systems, for which the quality of data is context-specific and closely related to the documentation workflow and the functionality of individual EHR systems.

Associations of Neighborhood Socioeconomic Disadvantage With Chronic Conditions by Age, Sex, Race, and Ethnicity in a Population-Based Cohort.

OBJECTIVE: To determine the association of socioeconomic status at the census block group level with chronic conditions and to determine whether the associations differ by age, sex, race, or ethnicity. METHODS: Adults aged 20 years and older on April 1, 2015, from 7 counties in southern Minnesota were identified using the Rochester Epidemiology Project records-linkage system. We estimated the prevalence of 19 chronic conditions (7 cardiometabolic, 7 other somatic, and 5 mental health conditions) at the individual level and a composite measure of neighborhood socioeconomic disadvantage (the area deprivation index [ADI]) at the census block group level (n=249). RESULTS: Among the 197,578 persons in our study, 46.7% (92,373) were male, 49.5% (97,801) were aged 50 years and older, 12.3% (24,316) were of non-White race, and 5.3% (10,546) were Hispanic. The risk of most chronic conditions increased with increasing ADI. For each cardiometabolic condition and most other somatic and mental health conditions, the pattern of increasing risk across ADI quintiles was attenuated, or there was no association across quintiles of ADI in the oldest age group (aged ≥70 years). Stronger associations between ADI and several cardiometabolic, other somatic, and mental health conditions were observed in women. CONCLUSION: Higher ADI was associated with increased risk of most chronic conditions, with more pronounced associations in younger persons. For some chronic conditions, the associations were stronger in women. Our findings underscore the importance of recognizing the overall and potentially differential impact of area-level deprivation on chronic disease outcomes for diverse populations.

Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial.

BACKGROUND: Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care. METHODS: To that aim, we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary's Hospital & Methodist Hospital both within Mayo Clinic Rochester in Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care, and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months of recruitment unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance. DISCUSSION: This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice while retaining scientific rigor. TRIAL REGISTRATION: ClinicalTrials.gov NCT03976297 . Registered on 6 June 2019, prior to trial start.

Early Detection of Post-Surgical Complications using Time-series Electronic Health Records.

Models predicting health complications are increasingly attempting to reflect the temporally changing nature of patient status. However, both the practice of medicine and electronic health records (EHR) have yet to provide a true longitudinal representation of a patient's medical history as relevant data is often asynchronous and highly missing. To match the stringent requirements of many static time models, time-series data has to be truncated, and missing values in samples have to be filled heuristically. However, these data preprocessing procedures may unconsciously misinterpret real-world data, and eventually lead into failure in practice. In this work, we proposed an augmented gated recurrent unit (GRU), which formulate both missingness and timeline signals into GRU cells. Real patient data of post-operative bleeding (POB) after Colon and Rectal Surgery (CRS) was collected from Mayo Clinic EHR system to evaluate the effectiveness of proposed model. Conventional models were also trained with imputed dataset, in which event missingness or asynchronicity were approximated. The performance of proposed model surpassed current state-of-the-art methods in this POB detection task, indicating our model could be more eligible to handle EHR datasets.

Improving the delivery of palliative care through predictive modeling and healthcare informatics.

OBJECTIVE: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. MATERIALS AND METHODS: Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team. RESULTS: Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. CONCLUSIONS: A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.

Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook.

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.

Using Shared Decision-Making Tools and Patient-Clinician Conversations About Costs.

OBJECTIVE: To determine how shared decision-making (SDM) tools used during clinical encounters that raise cost as an issue impact the incidence of cost conversations between patients and clinicians. PATIENTS AND METHODS: A randomly selected set of 220 video recordings of clinical encounters were analyzed. Videos were obtained from eight practice-based randomized clinical trials and one quasi-randomized clinical trial (pre- and post-) comparing care with and without SDM tools. The secondary analysis took place in 2018 from trials ran between 2007 and 2015. RESULTS: Most patient participants were white (85%), educated (38% completed college), middle-aged (mean age 56 years), and female (61%). There were 105 encounters with and 115 without the SDM tool. Encounters with SDM tools were more likely to include both general cost conversations (62% vs 36%, odds ratio [OR]: 9.6; 95% CI: 4 to 26) as well as conversations on medication costs specifically (89% vs 51%, P=.01). However, clinicians using SDM tools were less likely to address cost issues during the encounter (37% vs 51%, P=.04). Encounters with patients with less than a college degree were also associated with a higher incidence of cost conversations. CONCLUSION: Using SDM tools that raise cost as an issue increased the occurrence of cost conversations but was less likely to address cost issues or offer potential solutions to patients' cost concerns. This result suggests that SDM tools used during the consultation can trigger cost conversations but are insufficient to support them.