Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

Background: Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection. Objective: The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes. Methods: Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles. Results: Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%). Conclusions: The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation

An APN-led COPD Discharge Education Program to Decrease 30-day Readmission Rates

The purpose of this project was to implement an APN-led COPD discharge education program to decrease 30-day readmission rates. This Doctorate of Nursing (DNP) project combined strategies obtained in the literature search and blended these into a cutting-edge and state-of-the-art discharge education program at a major medical center. The significance of chronic obstructive pulmonary disease (COPD) readmission rates include financial implications, a large number of Medicare patients who return to the hospital within 30 days, poor quality of patient care, and poorly coordinated discharge processes. An APN-led transitional care COPD education discharge plan was implemented on the pulmonary floor at a major medical center in New Jersey. Consented patients admitted to OMC pulmonary floor and who received their pulmonary care from Pulmonary & Allergy Associates (PAA) were asked to participate in this quality initiative. This quality initiative was conducted on 18 patients with COPD from October 2015 to January 2016. Patients included in this quality initiative received 1-hour; face-to-face visits by me, three days a week during the 12-week program and totaled 15 hours per week. The primary project outcomes were decreased 30-day readmission rates during the 12-week program. Secondary project outcomes were the implementation of patient discharge education including the following: 7-day pulmonary follow-up; signs and symptoms which require an emergency pulmonary visit; importance of influenza and pneumococcal vaccination; proper inhaler technique utilizing the 10-second breath hold with “teach back” method; importance of physical activity and pulmonary rehabilitation (PR); home oxygen needs; home nebulizer needs; importance of proper nutrition; assessment of anxiety, depression, and gastrointestinal reflux (GERD); and assessment for the safest discharge location based on the patient’s risk for readmission. The clinical significance of this initiative is a suitable approach to decrease 30-day readmission rates resulting in improved quality of care, a multidisciplinary transition of care approach to the patient with COPD, decreased financial burdens for this medical center, and implementation of pulmonary evidence based guidelines

An APN-led COPD Discharge Education Program to Decrease 30-day Readmission Rates

The purpose of this project was to implement an APN-led COPD discharge education program to decrease 30-day readmission rates. This Doctorate of Nursing (DNP) project combined strategies obtained in the literature search and blended these into a cutting-edge and state-of-the-art discharge education program at a major medical center. The significance of chronic obstructive pulmonary disease (COPD) readmission rates include financial implications, a large number of Medicare patients who return to the hospital within 30 days, poor quality of patient care, and poorly coordinated discharge processes. An APN-led transitional care COPD education discharge plan was implemented on the pulmonary floor at a major medical center in New Jersey. Consented patients admitted to OMC pulmonary floor and who received their pulmonary care from Pulmonary & Allergy Associates (PAA) were asked to participate in this quality initiative. This quality initiative was conducted on 18 patients with COPD from October 2015 to January 2016. Patients included in this quality initiative received 1-hour; face-to-face visits by me, three days a week during the 12-week program and totaled 15 hours per week. The primary project outcomes were decreased 30-day readmission rates during the 12-week program. Secondary project outcomes were the implementation of patient discharge education including the following: 7-day pulmonary follow-up; signs and symptoms which require an emergency pulmonary visit; importance of influenza and pneumococcal vaccination; proper inhaler technique utilizing the 10-second breath hold with “teach back” method; importance of physical activity and pulmonary rehabilitation (PR); home oxygen needs; home nebulizer needs; importance of proper nutrition; assessment of anxiety, depression, and gastrointestinal reflux (GERD); and assessment for the safest discharge location based on the patient’s risk for readmission. The clinical significance of this initiative is a suitable approach to decrease 30-day readmission rates resulting in improved quality of care, a multidisciplinary transition of care approach to the patient with COPD, decreased financial burdens for this medical center, and implementation of pulmonary evidence based guidelines

Clinical Practice Guideline for Post Discharge Primary Care Follow-up Program for COPD

AbstractApproximately 15.7 million individuals throughout the United States have been diagnosed with chronic obstructive lung disease (COPD), with 700,000 individuals hospitalized annually in the United States. An estimated one in five patients are readmitted within 30 days, raising the costs above $15 billion annually. The gap in practice was a lack of adequate clinical practice guidelines associated with discharge to primary care in this acute care facility. The purpose of this project was to use the expertise of the multidisciplinary team of pulmonologists, acute care nurse practitioners, respiratory therapists, and pharmacists to develop a clinical practice guideline using evidence-based literature and professional organization guidelines and recommendations. The guiding practice focused question was whether an interprofessional team can update COPD guidelines for post discharge care for the management of this high-risk population. The IOWA conceptual framework was used to direct the process. Key points that emerged from the literature were the importance of promoting self-management, and training on home care including medication compliance and standing orders for prescription refills and home oxygen therapy. The team advocated patient access to their health care team 24 hours a day through the telehealth platform. Consensus was documented with the AGREE II. The highest scores were rigor of development (92%) and scope and purpose (90%). Stakeholder involvement received the lowest score of 81%, reflecting lack of patient input. Upon execution, the guideline could improve nursing practice in the support of COPD patients and caregivers in the home environment and to improve health status and prevent readmissions of patients with COPD, promoting social change

A study of unplanned 30-day hospital readmissions in the United States : early prediction and potentially modifiable risk factor identification

Unplanned hospital readmissions greatly impair patients' quality of life and have imposed a significant economic burden on American society. The pressure to reduce costs and improve healthcare quality has triggered the development of readmission reduction interventions. However, existing solutions focus on complementing inpatient care with enhanced care transition and post-discharge interventions, which are initiated near or after discharge when clinicians' impact on inpatient care is ending. Preventive intervention during hospitalization is an under-explored area, which holds the potential for reducing readmission risk. Nevertheless, it is challenging for clinicians to predict readmission risk at the early stage of inpatient care because little data is available. Existing readmission predictive models tend to incorporate variables whose values are only available near or after discharge. As a result, these models cannot be used for the early prediction of readmission. Another challenge is that there is no universal solution to reduce readmissions during hospitalization. Patients can be readmitted for any reason, and their heterogeneous social and clinical factors can further complicate the planning of interventions. The objective of this project was to improve the timeliness of readmission preventive intervention through a data-driven approach. A systematic review of the literature was performed to collect reported risk factors for unplanned 30-day hospital readmission. Using various predictive modeling and exploratory analysis methods, we have developed an early prediction model of readmission and have identified potentially modifiable readmission risk factors, which may be used to guide the development of readmission preventive interventions during hospitalization for different patients

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions

ArticleInPressOne of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory- predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies

Variables to Predict Risk of Hospital Readmission

As the healthcare industry transitions toward accountable care and payment reform, creative approaches to healthcare is imperative. Poorly coordinated care and shorter hospital stays have resulted in higher rates of readmissions. This has large implications for hospitals and health systems

A Chronic Obstructive Pulmonary Disease Pilot Using Risk Stratification to Improve Resource Allocation and Reduce Readmissions

Background: Chronic Obstructive Pulmonary Disease (COPD) impacts 250 million people, is associated with high hospital readmission rates, and costs over $50 billion annually. Purpose: Apply risk stratification identifying higher risk patients to prioritize complex, time-consuming interventions and resources. Methods: Patients hospitalized with COPD were risk stratified using PEARL. Moderate-high risk patients were referred to specialty nurse practitioners, who used real-time interventions and motivational interviewing during intense weekly visits over 30 days targeting self-management, patient-specific risks, and resources. Results: No patients were readmitted or died during the pilot using risk stratification with patient-specific tertiary preventive care to communicate resource allocation. Impact: This process provided recommendations for expansion throughout the healthcare facility, other chronic health conditions, budgets and policy for value-based care, and further research

SOCIAL DETERMINANTS OF HEALTH AND CHRONIC OBSTRUCTIVE PULMONARY DISEASE READMISSIONS: SYSTEM CHARACTERISTICS, COMMUNITY FACTORS, AND PRIMARY CARE.

Social determinants of health (SDH) are the conditions in which people are born, grow, live, work, and age. Located within the paradigm of SDH are the domains of economic stability, neighborhood and physical environment, education, food, community and social context, and health care systems. All variables that influence the health outcomes of mortality, morbidity, life expectancy, health care expenditures, health status, and functional limitations. Chronic obstructive pulmonary disease (COPD) is a prevalent chronic condition impacting the nation, and the world. Social determinants of health have not been studied widely in relation to chronic obstructive pulmonary disease, hence, this dissertation examines the nexus of SDH and COPD with the hope of advancing health equity