Outpatient Emergency Department Utilization: Measurement and Prediction: A Dissertation

Approximately half of all emergency department (ED) visits are primary-care sensitive (PCS) – meaning that they could potentially be avoided with timely, effective primary care. Reducing undesirable types of healthcare utilization (including PCS ED use) requires the ability to define, measure, and predict such use in a population. In this retrospective, observational study, we quantified ED use in 2 privately insured populations and developed ED risk prediction models. One dataset, obtained from a Massachusetts managed-care network (MCN), included data from 2009-11. The second was the MarketScan database, with data from 2007-08. The MCN study included 64,623 individuals enrolled for at least 1 base-year month and 1 prediction-year month in Massachusetts whose primary care provider (PCP) participated in the MCN. The MarketScan study included 15,136,261 individuals enrolled for at least 1 base-year month and 1 prediction-year month in the 50 US states plus DC, Puerto Rico, and the US Virgin Islands. We used medical claims to identify principal diagnosis codes for ED visits, and scored each according to the New York University Emergency Department algorithm. We defined primary-care sensitive (PCS) ED visits as those in 3 subcategories: nonemergent, emergent but primary-care treatable, and emergent but preventable/avoidable. We then: 1) defined and described the distributions of 3 ED outcomes: any ED use; number of ED visits; and a new outcome, based on the NYU algorithm, that we call PCS ED use; 2) built and validated predictive models for these outcomes using administrative claims data; 3) compared the performance of models predicting any ED use, number of ED visits, and PCS ED use; 4) enhanced these models by adding enrollee characteristics from electronic medical records, neighborhood characteristics, and payor/provider characteristics, and explored differences in performance between the original and enhanced models. In the MarketScan sample, 10.6% of enrollees had at least 1 ED visit, with about half of utilization scored as PCS. For the top risk group (those in the 99.5th percentile), the model’s sensitivity was 3.1%, specificity was 99.7%, and positive predictive value (PPV) was 49.7%. The model predicting PCS visits yielded sensitivity of 3.8%, specificity of 99.7%, and PPV of 40.5% for the top risk group. In the MCN sample, 14.6% (±0.1%) had at least 1 ED visit during the prediction period, with an overall rate of 18.8 (±0.2) visits per 100 persons and 7.6 (±0.1) PCS ED visits per 100 persons. Measuring PCS ED use with a threshold-based approach resulted in many fewer visits counted as PCS, discarding information unnecessarily. Out of 45 practices, 5 to 11 (11-24%) had observed values that were statistically significantly different from their expected values. Models predicting ED utilization using age, sex, race, morbidity, and prior use only (claims-based models) had lower R2 (ranging from 2.9% to 3.7%) and poorer predictive ability than the enhanced models that also included payor, PCP type and quality, problem list conditions, and covariates from the EMR, Census tract, and MCN provider data (enhanced model R2 ranged from 4.17% to 5.14%). In adjusted analyses, age, claims-based morbidity score, any ED visit in the base year, asthma, congestive heart failure, depression, tobacco use, and neighborhood poverty were strongly associated with increased risk for all 3 measures (all P\u3c.001)

Exploration of Self-Care Following Distribution of Acute Management Tool for Elder Heart Failure Patients in Clinic Setting

The aim of this study was to develop a broad understanding of heart failure patients’ perceptions about their lived experiences. An acute symptom management tool, Red Flags I Need to Know: Heart Failure Action Plan (Health Net Federal Services, 2011), was distributed to the patients prior to initiation of the project. The problem of heart failure rehospitalization is significant. Cost of treatment for heart disease in the United States exceeds all other conditions. The national excessive 30-day readmission rate in elders post-discharge is 24.8%. Pay-for-performance initiatives will reduce reimbursement for excessive readmissions beginning FY 2013. The project was a mixed method, qualitative, and quantitative study. Psychometric quality-of-life outcome measures from the Patient Care Outcome Scale (POS) provided empirical data. An ANOVA analysis determined differences between patients, caregivers, and staff in outcome measures. Glaser and Strauss’s (2009) grounded theory guided the qualitative analysis of elder HF patients (N = 10) in a clinic setting. The transactional model of stress and adaptation (Lazarus, DeLongis, Folkman, & Gruen, 1985) gave meaning to patient adherence. Quantitative comparisons of patient, staff, and caregiver scores were not significantly different; patients and caregivers did not check overwhelming symptoms. However, when only patient and staff responses were compared, patients reported experiencing significantly higher scores of severe symptoms than staff, F(1, 9) = 6.644, p = .03. Patient scores of three individual questions were significantly higher than staff. This result suggested staff was not recognizing all symptoms patients experienced. Several main themes that emerged from qualitative findings were extreme fatigue, anxiety, and fragmented healthcare systems. Staff was not recognizing all the pain and other symptoms experienced by patients in this sample. Limitations were small sample size and all patients did not have caregivers. It is recommended that the study be replicated with (a) a larger sample of more diverse participants, (b) all participants do in fact have caregivers, and (c) the project be conducted over a longer period of time. It is also recommended that care and watchfulness will be practiced when assessing patient symptoms in the future. Dissemination of the acute management tool is recommended for all HF patients at discharge transition

Effective implementation and monitoring of telehealth and telecare in Ireland: learning from international best practice.

This document synthesises the information provided in a number of papers relating to Telecare/Telehealth commissioned by and developed for the National Disability Authority between 2014 and 2017. The papers in question were developed by researchers in Work Research Centre (WRC), the National Disability Authority and the University of Ulster, and this report has taken key learning and information from each of them to create this composite briefing paper

Care Management and Readmission among Elderly African American Patients with Chronic Illnesses

More than 70 million Americans aged 50 years and older suffer from at least 1 or more chronic condition and use the emergency department frequently. By 2025, chronic illness will affect an estimated 164 million Americans or nearly 49% of the population. The rapid rise in chronic illness is due to a combination of an aging population, longer life expectancies, and poor lifestyle choices. This quantitative study provided a statistical analysis on the impact of care management on readmissions among African Americans between the ages of 65 and 80, with diabetes, hypertension, asthma, or multiple chronic conditions. Logistic regression was used to address the gap in the literature on unplanned readmission for an elderly population living in an urban community. The chronic care model was used as the theoretical framework of a systematic approach to improve relationships between patients and the clinical team. Retrospective data analysis (n = 577) from the years 2016–2018 supported a predictive association between care management and lower rates of readmission for an at-risk population. Findings from the analysis showed care management had a significant impact and positive association for diabetes, hypertension, and multiple chronic conditions. The asthma cohort had minimal association with care management due to other outside therapeutic resources. Factors that affect poverty in neighborhoods, living alone, and aging can affect a patient’s chance of being readmitted, however, the linkage of care management provides an alternative to improve social change by reducing psychological, physical, and financial stress for readmissions

Predictive risk models to identify people with chronic conditions at risk of hospitalisation

A disproportionately large percentage of health care costs and utilisation is spent on a small fraction of the population with complex and chronic conditions (Panattoni et al., 2011). It is widely agreed that effective and accessible primary health care (PHC) is central to reducing potentially avoidable hospitalisations (PAHs) associated with chronic disease. Predictive risk modelling is one method that is used to identify individuals who may be at risk of a hospitalisation event. The Predictive Risk Model (PRM) is a tool for identifying at-risk patients, so that appropriate preventive care can be provided, to avoid both exacerbation and complications of existing conditions, and acute events that may lead to hospitalisation. This Policy Issue Review identifies a selection of currently available PRMs, focusing on those applied in a PHC setting; and examines evidence of reliability in targeting patients with complex and chronic conditions

INFLUENCES ON SELF-CARE IN WOMEN WITH HEART FAILURE: A PILOT STUDY

Background: Heart Failure self-care becomes exceedingly difficult to perform as the disease progresses; therefore social support becomes important in facilitating heart failure self-care. Woman with heart failure represent a significant and growing vulnerable population. Women tend to have lower self-confidence in providing self-care, experience greater negative emotions, decreased social support, experience more adverse psychosocial factors affecting self-care and experience greater psychosocial adversity than do men. Self-care is vital in managing heart failure and social support greatly facilitates self-care behaviors. Purpose: The purpose of this pilot study was to gain a deeper understanding about the sources of perceived social support and how those sources influenced heart failure self-care behavior in women in two generational cohorts. Understanding sources of social support and how they influenced heart failure self-care is necessary in order to develop future interventions that might enhance social support and subsequently create more positive self-care behaviors in women with heart failure. Methods: This study used a cross-sectional, mixed method variant, explanatory concurrent design. A total of 16 female study participants were recruited from two different cohorts, those born from 1925 to 1942 and those born from 1943 to 1960. The Multidimensional Scale of Perceived Social Support, the European Heart Failure Self-Care Behavioral Scale – 9, the Duke Activity Status Index, the Standardized Mini Mental State Exam, the Geriatric Depression Scale - Short Form, the Self-Assessed New York Heart Association Functional Class questionnaire and a demographic form were used along with a semi-structured interview, which sought to elaborate instrument findings. Dominant themes were highlighted in order to help explain quantitative instrument results. Findings: Significant differences were found between cohorts for the Multidimensional Scale of Perceived Social Support and between the Multidimensional Scale of Perceived Social Support and the Geriatric Depression Scale - Short Form and the Standardized Mini Mental State Exam. Interview data indicated that: (1) “special persons” were friends, family or other person that helped the most, (2) distance to support network was a factor in receiving support, (3) religion/spirituality was used as coping mechanism and source of support, (4) participants viewed self-care as those things that they can only do themselves without help from others, (5) participants felt that they didn’t need help with self-care even though they did and (6) participants received mostly instrumental support from support source. Conclusion: Research that explores females with heart failure, their social support networks, and self-care practices with a particular emphasis on their place in history through cohort grouping is suggested to gain a better understanding of heart failure self-care. Understanding cohort differences in terms of socio demographic and other factors could uncover unique differences among cohorts which could lead to more targeted interventions

A study protocol of external validation of eight COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting

BACKGROUND: The COVID-19 pandemic has a large impact worldwide and is known to particularly affect the older population. This paper outlines the protocol for external validation of prognostic models predicting mortality risk after presentation with COVID-19 in the older population. These prognostic models were originally developed in an adult population and will be validated in an older population (≥ 70 years of age) in three healthcare settings: the hospital setting, the primary care setting, and the nursing home setting.METHODS: Based on a living systematic review of COVID-19 prediction models, we identified eight prognostic models predicting the risk of mortality in adults with a COVID-19 infection (five COVID-19 specific models: GAL-COVID-19 mortality, 4C Mortality Score, NEWS2 + model, Xie model, and Wang clinical model and three pre-existing prognostic scores: APACHE-II, CURB65, SOFA). These eight models will be validated in six different cohorts of the Dutch older population (three hospital cohorts, two primary care cohorts, and a nursing home cohort). All prognostic models will be validated in a hospital setting while the GAL-COVID-19 mortality model will be validated in hospital, primary care, and nursing home settings. The study will include individuals ≥ 70 years of age with a highly suspected or PCR-confirmed COVID-19 infection from March 2020 to December 2020 (and up to December 2021 in a sensitivity analysis). The predictive performance will be evaluated in terms of discrimination, calibration, and decision curves for each of the prognostic models in each cohort individually. For prognostic models with indications of miscalibration, an intercept update will be performed after which predictive performance will be re-evaluated.DISCUSSION: Insight into the performance of existing prognostic models in one of the most vulnerable populations clarifies the extent to which tailoring of COVID-19 prognostic models is needed when models are applied to the older population. Such insight will be important for possible future waves of the COVID-19 pandemic or future pandemics.</p

A study protocol of external validation of eight COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting

BACKGROUND: The COVID-19 pandemic has a large impact worldwide and is known to particularly affect the older population. This paper outlines the protocol for external validation of prognostic models predicting mortality risk after presentation with COVID-19 in the older population. These prognostic models were originally developed in an adult population and will be validated in an older population (≥ 70 years of age) in three healthcare settings: the hospital setting, the primary care setting, and the nursing home setting.METHODS: Based on a living systematic review of COVID-19 prediction models, we identified eight prognostic models predicting the risk of mortality in adults with a COVID-19 infection (five COVID-19 specific models: GAL-COVID-19 mortality, 4C Mortality Score, NEWS2 + model, Xie model, and Wang clinical model and three pre-existing prognostic scores: APACHE-II, CURB65, SOFA). These eight models will be validated in six different cohorts of the Dutch older population (three hospital cohorts, two primary care cohorts, and a nursing home cohort). All prognostic models will be validated in a hospital setting while the GAL-COVID-19 mortality model will be validated in hospital, primary care, and nursing home settings. The study will include individuals ≥ 70 years of age with a highly suspected or PCR-confirmed COVID-19 infection from March 2020 to December 2020 (and up to December 2021 in a sensitivity analysis). The predictive performance will be evaluated in terms of discrimination, calibration, and decision curves for each of the prognostic models in each cohort individually. For prognostic models with indications of miscalibration, an intercept update will be performed after which predictive performance will be re-evaluated.DISCUSSION: Insight into the performance of existing prognostic models in one of the most vulnerable populations clarifies the extent to which tailoring of COVID-19 prognostic models is needed when models are applied to the older population. Such insight will be important for possible future waves of the COVID-19 pandemic or future pandemics.</p