Utilization of a multidisciplinary hospital-based approach to reduce readmission rates.

Background: Hospital readmissions remain a significant and pressing issue in our healthcare system. In 2010, the Affordable Care Act helped establish the Hospital Readmissions Reduction Program, which incentivized reducing readmission rates by instituting penalties. Hospital readmission, specifically unplanned, refers to a patient returning to the hospital shortly after discharge due to the same or a related medical condition, signaling potential issues in initial care, discharge processes, or post-hospitalization management. For this study, we defined readmission as a return to the hospital within 30 days. In 2018, Staten Island University Hospital started a multidisciplinary and coordinated initiative to reduce patient readmissions. The approach involved the departments of emergency medicine, medicine, cardiology, case management, nursing, pharmacy, and transitional care management. This study aimed to determine if this approach reduced 30-day readmissions. Methods: This case-control retrospective study reviewed electronic health records between January 2018 and November 2019. Readmission rates within 30 days of index discharge were compared between patients who received transitional care management before and after establishing a multidisciplinary communication of transitional care. Readmission rates were unadjusted and adjusted for patient demographics and predisposed risk for readmission and compared across demographics and select clinical characteristics. Results: A total of 772 patients were included in the analyses; 323 were in the control group (41.8%), and 449 were in the intervention group (58.2%). After the hospital adopted the workflow for multidisciplinary communication of transitional care, there was 45.2% less adjusted incidence of readmission, or approximately seven fewer overall readmissions per 100 patients (16.4% readmission vs 9.0% readmission; incident rate ratio, 0.55; 95% CI: 0.34-0.88). Conclusions: Multidisciplinary communication approaches led by emergency medicine can help reduce readmissions significantly. Adopting a structured communication workflow can enhance co-managing patients with a high risk of readmission between the emergency department and hospital medicine teams.

Augmenting a Transitional Care Model With Artificial Intelligence Decreased Readmissions.

OBJECTIVES: Evaluate if augmenting a transitions of care delivery model with insights from artificial intelligence (AI) that applied clinical and exogenous social determinants of health data would reduce rehospitalization in older adults. DESIGN: Retrospective case-control study. SETTING AND PARTICIPANTS: Adult patients discharged from integrated health system between November 1, 2019, and February 31, 2020, and enrolled in a rehospitalization reduction transitional care management program. INTERVENTION: An AI algorithm utilizing multiple data sources including clinical, socioeconomic, and behavioral data was developed to predict patients at highest risk for readmitting within 30 days and provide care navigators five care recommendations to prevent rehospitalization. METHODS: Adjusted incidence of rehospitalization was estimated with Poisson regression and compared between transitional care management enrollees that used AI insights and matched enrollees for whom AI insights were not used. RESULTS: Analyses included 6371 hospital encounters between November 2019 and February 2020 across 12 hospitals. Of the encounters 29.3% were identified by AI as being medium-high risk for re-hospitalizing within 30 days, for which AI provided transitional care recommendations to the transitional care management team. The navigation team completed 40.2% of AI recommendations for these high-risk older adults. These patients had overall 21.0% less adjusted incidence of 30-day rehospitalization compared with matched control encounters, or 69 fewer rehospitalizations per 1000 encounters (95% CI 0.65‒0.95). CONCLUSIONS AND IMPLICATIONS: Coordinating a patient's care continuum is critical for safe and effective transition of care. This study found that augmenting an existing transition of care navigation program with patient insights from AI reduced rehospitalization more than without AI insights. Augmenting transitional care with insights from AI could be a cost-effective intervention to improve transitional care outcomes and reduce unnecessary rehospitalization. Future studies should examine cost-effectiveness of augmenting transitional care models of care with AI when hospitals and post-acute providers partner with AI companies.

Postpartum navigation decreases severe maternal morbidity most among Black women.

BACKGROUND: Postpartum care is crucial for addressing conditions associated with severe maternal morbidity and mortality. Examination of programs that affect these outcomes for women at high risk, including disparate populations, is needed. OBJECTIVE: This study aimed to examine whether a postpartum navigation program decreases all-cause 30-day postpartum hospitalizations and hospitalizations because of severe maternal morbidity identified using the US Centers for Disease Control and Prevention guidelines. The effect of this program was explored across patient demographics, including race and ethnicity. STUDY DESIGN: This was a retrospective cohort study that used health records of women who delivered at 3 large hospitals in the New York metropolitan area (Queens and Long Island) between April 2020 and November 2021 and who were at high risk of severe maternal morbidity. The incidence rates of 30-day postpartum all-cause hospitalization and hospitalization because of severe maternal morbidity were compared between women who were and were not enrolled in a novel postpartum transitional care management program. Navigation included standardized assessments, development of care plans, clinical management, and connection to clinical and social services that would extend beyond the postpartum period. Because the program prioritized enrolling women of the greatest risk, the risk-adjusted incidence was estimated using multivariate Poisson regression and stratified across patient demographics. RESULTS: Patient health records of 5819 women were included for analysis. Of note, 5819 of 19,258 deliveries (30.2%) during the study period were identified as having a higher risk of severe maternal morbidity. This was consistent with the incidence of high-risk pregnancies for tertiary hospitals in the New York metropolitan area. The condition most identified for risk of severe maternal morbidity at the time of delivery was hypertension (3171/5819 [54.5%]). The adjusted incidence of all-cause rehospitalization was 20% lower in enrollees than in nonenrollees (incident rate ratio, 0.80; 95% confidence interval, 0.67-0.95). Rehospitalization was decreased the most among Black women (incident rate ratio, 0.57; 95% confidence interval, 0.42-0.80). The adjusted incidence of rehospitalization because of indicators of severe maternal morbidity was 56% lower in enrollees than in nonenrollees (incident rate ratio, 0.44; 95% confidence interval, 0.24-0.77). Furthermore, it decreased most among Black women (incident rate ratio, 0.23; 95% confidence interval, 0.07-0.73). CONCLUSION: High-risk medical conditions at the time of delivery increased the risk of postpartum hospitalization, including hospitalizations because of severe maternal morbidity. A postpartum navigation program designed to identify and resolve clinical and social needs reduced postpartum hospitalizations and racial disparities with hospitalizations. Hospitals and healthcare systems should adopt this type of care model for women at high risk of severe maternal morbidity. Cost analyses are needed to evaluate the financial effect of postpartum navigation programs for women at high risk of severe maternal morbidity or mortality, which could influence reimbursement for these types of services. Further evidence and details of novel postpartum interventional models are needed for future studies.

Associations Between Physical Activity Vital Sign in Patients and Health Care Utilization in a Health Care System, 2018-2020.

BACKGROUND: Physical inactivity is a risk factor for many chronic conditions. This retrospective cohort study examined associations between physical activity (PA) with health care utilization (HU). METHODS: A PA vital sign was recorded in clinics from January 2018 to December 2020. Patients were categorized as inactive, insufficiently active, or sufficiently active by US PA aerobic guidelines. Associations between PA vital sign and visits (inpatient admissions, emergency department, urgent care, and primary care) were estimated using population average regression by visit type. RESULTS: 23,721 patients had at least one PA vital sign recorded, with a mean age of 47.3 years and mean body mass index (BMI) of 28; 52% were female and 63% were White. Sufficiently active patients were younger, male, White, and had lower BMI than insufficiently active patients. Achieving 150 minutes per week of moderate to vigorous PA per 1000 patient years was associated with 34 fewer emergency department visits (P < .001), 19 fewer inpatient admissions (P < .001), and 38 fewer primary care visits (P < .001) compared with inactive patients. Stronger associations between lower PA and higher HU were present among those who were older or had a higher comorbidity. BMI, sex, ethnicity, and race did not modify the association between PA and HU. CONCLUSIONS: Meeting aerobic guidelines was associated with reduced HU for inpatient, primary care, and emergency department visits.

An mHealth Platform for Augmenting Behavioral Health in Primary Care: Longitudinal Feasibility Study.

BACKGROUND: The collaborative care model is a well-established system of behavioral health care within primary care settings. There is potential for mobile health (mHealth) technology to augment collaborative behavioral health care in primary care settings, thereby improving scalability, efficiency, and clinical outcomes. OBJECTIVE: We aimed to assess the feasibility of engaging with and the preliminary clinical outcomes of an mHealth platform that was used to augment an existing collaborative care program in primary care settings. METHODS: We performed a longitudinal, single-arm feasibility study of an mHealth platform that was used to augment collaborative care. A total of 3 behavioral health care managers, who were responsible for coordinating disease management in 6 primary care practices, encouraged participants to use a mobile app to augment the collaborative model of behavioral health care. The mHealth platform's functions included asynchronous chats with the behavioral health care managers, depression self-report assessments, and psychoeducational content. The primary outcome was the feasibility of engagement, which was based on the number and type of participant-generated actions that were completed in the app. The primary clinical end point was a comparison of the baseline and final assessments of the Patient Health Questionnaire-9. RESULTS: Of the 245 individuals who were referred by their primary care provider for behavioral health services, 89 (36.3%) consented to app-augmented behavioral health care. Only 12% (11/89) never engaged with the app during the study period. Across all participants, we observed a median engagement of 7 (IQR 12; mean 10.4; range 0-130) actions in the app (participants: n=78). The chat function was the most popular, followed by psychoeducational content and assessments. The subgroup analysis revealed no significant differences in app usage by age (P=.42) or sex (P=.84). The clinical improvement rate in our sample was 73% (32/44), although follow-up assessments were only available for 49% (44/89) of participants. CONCLUSIONS: Our preliminary findings indicate the moderate feasibility of using mHealth technology to augment behavioral health care in primary care settings. The results of this study are applicable to improving the design and implementation of mobile apps in collaborative care.

Value-Based Pay-for-Performance Gaps in the Care Delivery Framework for a Large-Scale Health System.

Many health systems are engaging in pay-for-performance agreements with payers that focus primarily on improving ambulatory preventive screenings. These also are referred to as gaps in care. Gaps in care are typically measured by the Healthcare Effectiveness Data and Information Set measures of health care quality. To address gaps in care effectively, the physician-led Gaps in Care program at Northwell Health works to improve processes related to measurement, data attribution, patient outreach, and patient engagement. Following a structured framework to address patient gaps in care is a successful strategy for accomplishing complex value-based care delivery.

Concurrent Validity of a Self-Reported Physical Activity "Vital Sign" Questionnaire With Adult Primary Care Patients.

INTRODUCTION: No tool currently used by primary health care providers to assess physical activity has been evaluated for its ability to determine whether or not patients achieve recommended levels of activity. The purpose of this study was to assess concurrent validity of physical activity self-reported to the brief (<30 sec) Physical Activity "Vital Sign" questionnaire (PAVS) compared with responses to the lengthier (3-5 min), validated Modifiable Activity Questionnaire (MAQ). METHODS: Agreement between activity reported to the PAVS and MAQ by primary care patients at 2 clinics in 2014 was assessed by using percentages and κ coefficients. Agreement consisted of meeting or not meeting the 2008 Aerobic Physical Activity Guidelines for Americans (PA Guidelines) of the US Department of Health and Human Services. We compared self-reported usual minutes per week of moderate-to-vigorous physical activity among patients at a primary care clinic in 2014 who reported to PAVS and to MAQ by using Pearson correlation and Bland-Altman plots of agreement. RESULTS: Among 269 consenting patients who reported physical activity, PAVS results agreed with those of MAQ 89.6% of the time and demonstrated good agreement in identifying patients who did not meet PA Guidelines recommendations (κ = 0.55, ρ = 0.57; P < .001). Usual minutes per week of moderate-to-vigorous physical activity reported to PAVS had a high positive correlation with the same reported to MAQ (r = 0.71; P < .001). CONCLUSION: PAVS may be a valid tool for identifying primary care patients who need counseling about physical activity. PAVS should be assessed further for agreement with repeated objective measures of physical activity in the patient population.

Predictive Validity of an Adult Physical Activity "Vital Sign" Recorded in Electronic Health Records.

BACKGROUND: Few have examined predictive relationships between physical activity (PA) and health using electronic health records (EHRs) of patient-reported PA. OBJECTIVE: Assess initial predictive validity of the Physical Activity "Vital Sign" (PAVS) recorded in EHRs with BMI and disease burden. METHODS: EHRs were from November 2011 to November 2013 (n = 34,712). Differences in not meeting Physical Activity Guidelines (PAG) were tested using chi-square analysis between being normal weight versus overweight/obese, and scoring below versus above the 50th percentile of the Charlson Comorbidity Index (CCI). Repeated measures logistic regression was used to determine odds of BMI and CCI classifications according to responses to the PAVS as not meeting PAG. RESULTS: Patients who did not meet PAG according to the PAVS were more likely than normal weight patients to have a higher BMI (BMI 25.0-29.9, OR = 1.19, P = .001; BMI 30.0-34.9, OR = 1.39, P < .0001; BMI 35.0-39.9, OR = 2.42, P < .0001; BMI ≥ 40, OR = 3.7, P < .0001) and also higher disease burden (above 50th percentile for CCI, OR = 1.8, P < .0001). CONCLUSIONS: The strong association of the PAVS found with patient BMI and moderately-strong association with disease burden supports initial predictive validity of the PAVS recorded in EHRs. PA recorded in EHRs may be vastly useful for assessing patient disease and cost burdens attributed independently to PA behavior.

Validity of two brief primary care physical activity questionnaires with accelerometry in clinic staff.

BACKGROUND: To date, no physical activity (PA) questionnaires intended for primary care have been compared against a criterion measure of PA and current (2008) aerobic PA recommendations of the American College of Sports Medicine/American Heart Association (ACSM/AHA). AIM: This study evaluated preliminary evidence for criterion validity of two brief (<1 min) PA questionnaires with accelerometry, and their ability to identify if individuals meet ACSM/AHA PA recommendations. METHODS: 45 health clinic staff wore an accelerometer for seven consecutive days and afterwards completed two brief PA questionnaires, the Physical Activity Vital Sign (PAVS), and the Speedy Nutrition and Physical Activity Assessment (SNAP). Agreement and descriptive statistics were calculated between the PAVS or SNAP and accelerometry in order to measure each questionnaire's ability to quantify the number of days participants achieved ⩾ 30 min of moderate-vigorous PA (MVPA) performed in bouts of ⩾ 10 continuous minutes. Participants with <5 days of ⩾ 30 bout-min of MVPA were considered insufficiently active according to PA recommendations. FINDINGS: There was a significant positive correlation between number of days with ⩾ 30 bout-min MVPA and the PAVS (r = 0.52, P < 0.001), and SNAP ( r= 0.31, P < 0.05). The PAVS had moderate agreement with accelerometry for identifying if individuals met or did not meet PA recommendations (κ = 0.46, P < 0.001), whereas SNAP had poor agreement (κ = 0.12, P < 0.05). CONCLUSIONS: This study provides preliminary evidence of criterion validity of the PAVS and SNAP with accelerometry and agreement identifying if respondents meet current (2008) ACSM/AHA aerobic PA recommendations. The PAVS and SNAP should be evaluated further for repeatability, and in populations varying in PA levels, age, gender, and ethnicity.

Healthy eating vital sign: a new assessment tool for eating behaviors.

Introduction. Most dietary questionnaires are not created for use in a clinical setting for an adult health exam. We created the Healthy Eating Vital Sign (HEVS) to assess eating behaviors associated with excess weight. This study investigated the validity and reliability of the HEVS. Methods. Using a cross-sectional study design, participants responded to the HEVS and the Block Food Frequency Questionnaire (BFFQ). We analyzed the data descriptively, and, with Pearson's correlation and Cronbach coefficient alpha. Results. We found moderate correlation (rho > 0.3) between multiple items of the HEVS and BFFQ. The Cronbach's alpha was 0.49. Conclusion. Our results support the criterion validity and internal reliability of the HEVS as compared to the BFFQ. The HEVS can help launch a dialogue between patients and providers to monitor and potentially manage dietary behaviors associated with many chronic health conditions, including obesity.