Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs

Reducing potentially preventable readmissions has been identified as an important issue for decreasing Medicare costs and improving quality of care provided by hospitals. Based on previous research by medical professionals, preventable readmissions are caused by such factors as flawed patient discharging process, inadequate follow-ups after discharging, and noncompliance of patients on discharging and follow up instructions. It is also found that the risk of preventable readmission also may relate to some patient's characteristics, such as age, health condition, diagnosis, and even treatment specialty. In this study, using both general demographic information and individual past history of readmission records, we develop a risk prediction model based on hierarchical nonlinear mixed effect framework to extract significant prognostic factors associated with patient risk of 30-day readmission. The effectiveness of our proposed approach is validated based on a real dataset from four VA facilities in the State of Michigan. Simultaneously explaining both patient and population based variations of readmission process, such an accurate model can be used to recognize patients with high likelihood of discharging non-compliances, and then targeted post-care actions can be designed to reduce further rehospitalization.Comment: 6 pages, to be submitted in IEEE CASE 201

A Quality Initiative to Reduce Pneumonia Readmissions and Mortality in Older Adults

The United States (U.S.) healthcare system journey for making improvements in the quality and value of healthcare continues. Hospital organizations are required to compose and make publically available their health quality outcome data. The publication transparency and increased availability of local, regional and national health quality metrics, including readmission and mortality rates, to governmental agencies, health plans, investors, other hospitals, providers and potential patient and families’ knowledge, creates a competitive pressure for a hospital to assure their quality outcomes data are the best. Despite breakthrough improvements using innovative care models that target vulnerable and potentially high cost of care areas such as individuals with chronic illnesses, complex health and social needs, children, and frail elders, have been seen, there remains a need for quality improvement (QI) initiatives to reduce particularly avoidable hospital readmissions and mortality. A Midwest hospital system identified that their 30-day pneumonia (PNA) readmission rate for FY2017 was higher than the national median and the peer hospitals Centers for Medicare and Medicaid Services (CMS) benchmark percentage. The assumption was if there are more programs and resources available to the PNA patient then there should be better health outcomes. This project evaluated the differences in the PNA patient outcomes, mortality and readmission rates based on the number of hospital readmission reduction strategies (RRS) identified and available for the PNA Medicare patients among three of the Midwest hospital system acute care facilities. The results of the Chi-square test of independence performed to examine differences between the total number of RRS in FY 2018 and FY 2019 and readmission and mortality rates was significant for readmissions, χ2 (3, N= 107) = 25.15, p \u3c .001, and mortality χ2 (3, N= 58) = 34.93, p

What Is the Additive Value of Nutritional Deficiency to Va-Fi in the Risk Assessment For Heart Failure Patients?

OBJECTIVES: to assess the impact of adding the Prognostic Nutritional Index (PNI) to the U.S. Veterans Health Administration frailty index (VA-FI) for the prediction of time-to-death and other clinical outcomes in Veterans hospitalized with Heart Failure. METHODS: A retrospective cohort study of veterans hospitalized for heart failure (HF) from October 2015 to October 2018. Veterans ≥50 years with albumin and lymphocyte counts, needed to calculate the PNI, in the year prior to hospitalization were included. We defined malnutrition as PNI ≤43.6, based on the Youden index. VA-FI was calculated from the year prior to the hospitalization and identified three groups: robust (≤0.1), prefrail (0.1-0.2), and frail (\u3e0.2). Malnutrition was added to the VA-FI (VA-FI-Nutrition) as a 32 RESULTS: We identified 37,601 Veterans hospitalized for HF (mean age: 73.4 ± 10.3 years, BMI: 31.3 ± 7.4 kg/m CONCLUSION: Adding PNI to VA-FI provides a more accurate and comprehensive assessment among Veterans hospitalized for HF. Clinicians should consider adding a specific nutrition algorithm to automated frailty tools to improve the validity of risk prediction in patients hospitalized with HF

Factors That Lead To Hospital Readmissions and Interventions that Reduce Them: Moving Toward a Faith Community Nursing Intervention

Abstract Hospital readmissions affect over 80 percent of all Medicare enrollees. Hospitals have a responsibility to their Medicare patients to keep them safe after discharge in their homes and communities. With changes in the Medicare reimbursement model, hospitals are examining efficient methods of decreasing avoidable re-admissions. A Faith Community Nurse Transitional Care Program may be just the answer to improve patient’s discharge experience, ensure post-discharge support and reduce re-hospitalization. Methods In preparations for testing a Faith Community Nurse Transitional Care Program Model, a systematic integrative review was needed. Using PRISMA, a search was done, inclusion criteria identified, and articles retrieved. Sixty-two articles were collected, compared, and combined using a descriptive matrix template. Results Chronic diseases such as heart failure, COPD, diabetes mellitus, cancer, stroke and/or psychosis, depression, and lower mental health status have the highest risks. Patient variables include Medicare and Medicaid payer status, markers of frailty and elderly with complex medical, social and financial needs. Lack of caregiver or social support, poor health literacy, inability to navigate the health care system, are non-clinical needs leading to readmissions. Methods or interventions leading to decreased readmissions are early discharge planning, case management, self-management skills enhanced, medication education, and standardized tools. Conclusions A systematic integrative review process identified factors that increase and reduce hospital readmissions. The key findings revealed that certain disease, patient, and non-clinical variables can predict patient’s risks for readmission. Interventions done before discharge and after discharge can impact hospital readmissions. A new Faith Community Nurse Transition Care Program meets many of the criteria for decreasing readmission as identified in the literature. In addition, Faith Community Nurses can provide whole health care, which includes care of the physical, psychological, social, and spiritual dimensions of the person

An Analytics Approach To Reducing Hospital Readmission

One of the significant sources of waste in the Unites States health care systems is preventable hospital readmission. About 2.3 million Medicare fee-for-service beneficiaries are re-hospitalized within 30 days after discharge which incurs an annual cost of $17 billion. However, it is reported by the Medicare Payment Advisory Commission that about 75% of such readmissions can and should be avoided because they are the results of factors such as poor planning for follow up care transitions, inadequate communication of discharge instructions, and failure to reconcile and coordinate medications. Hence, reducing unnecessary rehospitalization through care transition and systems engineering principles has attracted policymakers and health organizations as a way to simultaneously improve quality of care and reduce costs. In this dissertation we investigated predictive and prescriptive analytics approaches for discharge planning and hospital readmission problem. Motivated by the gaps in research, we first develop a new readmission metric based on administrative data that can identify potentially avoidable readmissions from all other types of readmission. The approach is promising and uses a comprehensive risk adjustment, Diagnostic Cost Group Hierarchical Condition Category, to assess the clinical relevance between a readmission and its initial hospitalizations. Next, we tackle the difficulties around selecting an appropriate readmission time interval by proposing a generic Continuous Time Markov Chain (CTMC) approach conceptualizing the movements of patients after discharge. We found that cutoff point defining readmission time interval must not depend on the instantaneous risk of readmission but rather it has to be based on quality of inpatient or outpatient care received. We further assert that the government endorsed 30 day time window which has been used for profiling hospitals and public reporting is not appropriate for chronic conditions such as chronic obstructive pulmonary disease. Thus, we propose a special case of the CTMC method and obtain the optimal cut point that best stratifies among inpatient and outpatient care episodes. Third, we proposed a novel tree based prediction method, phase time survival forest (PTSF), for patient risk of readmission that combines good aspects of traditional classification methods and timing based models. The method is simple to implement and can be able to (1) model the effect of partially known information (censored observations) into the risk of readmission, and (2) directly incorporate patient\u27s history of readmission and risk factors changes over time. The latter property is highly favorable especially when repeated measurements of patient factors or recurrent readmissions are likely. The basic idea is quite generic and it works by modifying the traditional replicate based bootstrap samples to account for correlations among repeated records of a subject. We demonstrated the superiority of our model over current solutions with respect to various accuracy and misclassification criteria. Further, to confirm that the high discrimination ability of our proposal is irrespective to overfitting, we performed internal and external validation with 2011-12 Veterans Health Administration data from inpatients hospitalized for heart failure, acute myocardial infarction, pneumonia, or chronic obstructive pulmonary disease in the Mid West facilities. Results indicated improved discrimination power compared to the literature (c statistics greater than 80%) and good calibration. Overall, the current research outlined a successful multifaceted analytics framework that enables medical decision makers to systematically characterize, predict, and reduce avoidable readmissions and contribute to patient care quality improvements

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

Background: There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been little research into how to make the most of SBDH information from EHRs. Methods: A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results: Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (NLP) technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion: Despite known associations between SBDH and disease, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive ModelComment: 32 pages, 5 figure

Frailty Screening in Cardiac Surgery Patients: Improving Risk Stratification

Frailty has been noted throughout the literature to have a negative effect on patient outcomes especially in patients undergoing major surgical interventions such as cardiothoracic surgery. Preoperative assessments have historically included assessment of all body systems, however fails to evaluate patients for baseline physical functioning or frailty. The American College of Cardiology has recommended frailty screening on all cardiac surgery patients; however, facilities have failed to educate staff providing care to this population on the impact of frailty and use of commonly used frailty screening tools. This project hypothesized that Cardiothoracic Surgery Nurses and providers would have improved knowledge and confidence regarding the description and impact of frailty and use of frailty screening tools after receiving education. The project outcomes found that nurses and providers had significantly improved knowledge and confidence regarding description and impact of frailty. Knowledge and confidence regarding completion of frailty screening tools (Katz-6 and Lawton Independent Activities of Daily Living) improved also. Providers (100%) acknowledged that the educational intervention would change their current practice