Showing papers in "Medical Care in 2010"
TL;DR: Favorable results were found in therapeutic and safety outcomes, and meta-analyses conducted for hemoglobin A1c, LDL cholesterol, blood pressure, and adverse drug events were significant, favoring pharmacists' direct patient care over comparative services.
Abstract: Background:One approach postulated to improve the provision of health care is effective utilization of team-based care including pharmacists.Objective:The objective of this study was to conduct a comprehensive systematic review with focused meta-analyses to examine the effects of pharmacist-provided
733 citations
TL;DR: Clinical and social factors available within hours of hospital presentation and extractable from an EMR predicted mortality and readmission at 30 days increased the model's accuracy, suggesting that such factors could enhance risk adjustment models designed to compare hospital readmission rates.
Abstract: Background:A real-time electronic predictive model that identifies hospitalized heart failure (HF) patients at high risk for readmission or death may be valuable to clinicians and hospitals who care for these patients.Methods:An automated predictive model for 30-day readmission and death was derived
440 citations
TL;DR: In settings where considerable uncertainty surrounds the data or the causal mechanisms underlying the treatment assignment and outcome process, it is suggested that researchers report a panel of results under various specifications of statistical models to assess the sensitivity of the results to model assumptions that are often not supported by strong subject-matter knowledge.
Abstract: Epidemiologic studies are increasingly used to investigate the safety and effectiveness of medical products and interventions. Appropriate adjustment for confounding in such studies is challenging because exposure is determined by a complex interaction of patient, physician, and healthcare system factors. The challenges of confounding control are particularly acute in studies using healthcare utilization databases where information on many potential confounding factors is lacking and the meaning of variables is often unclear. We discuss advantages and disadvantages of different approaches to confounder control in healthcare databases. In settings where considerable uncertainty surrounds the data or the causal mechanisms underlying the treatment assignment and outcome process, we suggest that researchers report a panel of results under various specifications of statistical models. Such reporting allows the reader to assess the sensitivity of the results to model assumptions that are often not supported by strong subject-matter knowledge.
355 citations
TL;DR: Heart failure was predicted more than 6 months before clinical diagnosis, with AUC of about 0.76, using logistic regression and Boosting, and SVM had the poorest performance, possibly because of imbalanced data.
Abstract: Background:Electronic health record (EHR) databases contain vast amounts of information about patients. Machine learning techniques such as Boosting and support vector machine (SVM) can potentially identify patients at high risk for serious conditions, such as heart disease, from EHR data. However,
318 citations
TL;DR: The effectiveness of CHWs in many health care areas requires further research that addresses the methodologic limitations of prior studies and that contributes to translating research into practice.
Abstract: Objectives:We conducted a systematic review on outcomes and costs of community health worker (CHW) interventions. CHWs are increasingly expected to improve health outcomes cost-effectively for the underserved.Research Design:We searched Medline, Cochrane Collaboration resources, and the Cumulative I
217 citations
TL;DR: The pilot demonstrated the potential utility of the design, which addressed the major concerns of both users and data holders, and distributed networks are capable of addressing nearly all anticipated uses of routinely collected electronic healthcare data.
Abstract: Background:Comparative effectiveness research, medical product safety evaluation, and quality measurement will require the ability to use electronic health data held by multiple organizations. There is no consensus about whether to create regional or national combined (eg, “all payer”) databases for
211 citations
TL;DR: Low adherence to AH agents is correlated with a higher risk of vascular events, hospitalization, and greater healthcare costs, and an increased level of adherence should provide a better health status for individuals and a net economic gain.
Abstract: Background:Cardiovascular diseases (CVD) represent a heavy economic burden on individuals, health services, and society. Low adherence to antihypertensive (AH) agents is acknowledged as a major contributor to the lack of blood pressure control, and may have a significant impact on clinical outcomes
204 citations
TL;DR: Assessment of Medicare payments for and outcomes of patients discharged from acute care to an IRF, a SNF, or home after an inpatient diagnosis of stroke or hip fracture between January 2002 and June 2003 finds that when there is a choice between IRF and SNF care for stroke and hip fracture patients, the marginal patient is better off going to anIRF for postacute care.
Abstract: Background—Elderly patients who leave an acute care hospital after a stroke or a hip fracture may be discharged home or undergo post-acute rehabilitative care in an inpatient rehabilitation facility (IRF) or skilled nursing facility (SNF). Since 15% of Medicare expenditures are for these types of post-acute care, it is important to understand their relative costs and the health outcomes they produce. Objective—To assess Medicare payments for and outcomes of patients discharged from acute care to an IRF, a SNF, or home after an inpatient diagnosis of stroke or hip fracture between January 2002 and June 2003. Research Design—This is an observational study based on Medicare administrative data. We adjust for observable differences in patient severity across post-acute care sites and we use instrumental variables estimation to account for unobserved patient selection. Study Outcomes—Mortality, return to community residence, and total Medicare post-acute payments by 120 days after acute care discharge. Results—Relative to discharge home, IRFs improve health outcomes for hip fracture patients. SNFs reduce mortality for hip fracture patients, but increase rates of institutionalization for stroke patients. Both sites of care are far more expensive than discharge to home. Conclusions—When there is a choice between IRF and SNF care for stroke and hip fracture patients, the marginal patient is better off going to an IRF for post-acute care. However, given the marginal cost of an IRF stay compared to returning home, the gains to these patients should be considered in light of the additional costs.
190 citations
TL;DR: The results indicate that the multiple PS method is a feasible method to adjust for observed pretreatment differences in nonrandomized studies where the number of pret treatment differences is large and multiple treatments are compared.
Abstract: Background and Objective: The propensity score method (PS) has proven to be an effective tool to reduce bias in nonrandomized studies, especially when the number of (potential) confounders is large and dimensionality problems arise. The PS method introduced by Rosenbaum and Rubin is described in detail for studies with 2 treatment options. Since in clinical practice we are often interested in the comparison of multiple interventions, there was a need to extend the PS method to multiple treatments. It has been shown that in theory a multiple PS method is possible. So far, its practical application is rare and a practical introduction lacking. Methods: A practical guideline to illustrate the use of the multiple PS method is provided with data from a mental health study. The multiple PS is estimated with a multinomial logistic regression analysis. The multiple PS is the probability of assignment to each treatment category. Subsequently, to estimate the treatment effects while controlling for initial differences, the multiple PSs, calculated for each treatment category, are included as extra predictors in the regression analysis. Results: With the multiple PS method, balance was achieved in all relevant pretreatment variables. The corrected estimated treatment effects were somewhat different from the results without control for initial differences. Conclusions: Our results indicate that the multiple PS method is a feasible method to adjust for observed pretreatment differences in nonrandomized studies where the number of pretreatment differences is large and multiple treatments are compared.
189 citations
TL;DR: The findings suggest that diseases affect preference-based HRQoL differently in a hospital-based LTC population compared with previous studies in the general population.
Abstract: Background:Population-based diagnosis- and condition-specific health-related quality of life (HRQoL) scores are required for decision-making and research purposes. These HRQoL scores do not exist for hospital-based long-term care (LTC) residents.Objective:To estimate the impact of 60 diseases and 15
185 citations
TL;DR: Despite relatively comprehensive claims data on patients, physicians, and out-of-pocket costs, the overall ability to explain adherence remains poor.
Abstract: Background: Although many patient, physician, and payment predictors of adherence have been described, knowledge of their relative strength and overall ability to explain adherence is limited. Objectives: To measure the contributions of patient, physician, and payment predictors in explaining adherence to statins. Research Design: Retrospective cohort study using administrative data. Subjects: A total of 14,257 patients insured by Horizon Blue Cross Blue Shield of New Jersey who were newly prescribed a statin cholesterol-lowering medication. Measures: Adherence to statin medication was measured during the year after the initial prescription, based on proportion of days covered. The impact of patient, physician, and payment predictors of adherence were evaluated using multivariate logistic regression. The explanatory power of these models was evaluated with C statistics, a measure of the goodness of fit. Results: Overall, 36.4% of patients were fully adherent. Older patient age, male gender, lower neighborhood percent black composition, higher median income, and fewer number of emergency department visits were significant patient predictors of adherence. Having a statin prescribed by a cardiologist, a patient’s primary care physician, or a US medical graduate were significant physician predictors of adherence. Lower copayments also predicted adherence. All of our models had low explanatory power. Multivariate models including patient covariates only had greater explanatory power (C 0.613) than models with physician variables only (C 0.566) or copayments only (C 0.543). A fully specified model had only slightly more explanatory power (C 0.633) than the model with patient characteristics alone. Conclusions: Despite relatively comprehensive claims data on patients, physicians, and out-of-pocket costs, our overall ability to explain adherence remains poor. Administrative data likely do not capture many complex mechanisms underlying adherence.
TL;DR: It is demonstrated that a comprehensive approach using all 3 approaches for analyzing the competing risks problem provided useful insights on the effect of an intervention on the relative and absolute risks of multiple competing outcomes.
Abstract: Background: An evaluation of the effect of a healthcare intervention (or an exposure) must consider multiple possible outcomes, including the primary outcome of interest and other outcomes such as adverse events or mortality. The determination of the likelihood of benefit from an intervention, in the presence of other competing outcomes, is a competing risks problem. Although statistical methods exist for quantifying the probability of benefit from an intervention while accounting for competing events, these methods have not been widely adopted by clinical researchers. Objectives: (1) To demonstrate the importance of considering competing risks in the evaluation of treatment effectiveness, and (2) to review appropriate statistical methods, and recommend how they might be applied. Research Design and Methods: We reviewed 3 statistical approaches for analyzing the competing risks problem: (a) causespecific hazard (CSH), (b) cumulative incidence function (CIF), and (c) event-free survival (EFS). We compare these methods using a simulation study and a reanalysis of a randomized clinical trial. Results: Simulation studies evaluating the statistical power to detect the effect of intervention under different scenarios showed that: (1) CSH approach is best for detecting the effect of an intervention if the intervention only affects either the primary outcome or the competing event; (2) EFS approach is best only when the intervention affects both primary and competing events in the same manner; and (3) CIF approach is best when the intervention affects both primary and competing events, but in opposite directions. Using data from a randomized controlled trial, we demonstrated that a comprehensive approach using all 3 approaches provided useful insights on the effect of an intervention on the relative and absolute risks of multiple competing outcomes. Conclusions: CSH is the fundamental measure of outcome in competing risks problems. It is appropriate for evaluating treatment effects in the presence of competing events. Results of CSH analysis for primary and competing outcomes should always be reported even when EFS or CIF approaches are called for. EFS is appropriate for evaluating the composite effect of an intervention, only when combining different endpoints is clinically and biologically meaningful, and the treatment has similar effects on all event types. CIF is useful for evaluating the likelihood of benefit from an intervention over a meaningful period. CIF should be used for absolute risk calculations instead of the widely used complement of the KaplanMeier (1 KM) estimator.
TL;DR: The evidence presented supports the validity of the NYU ED visit severity algorithm for differentiating ED visits based on need for hospitalization and/or mortality risk; therefore, it can contribute to evidence-based policies aimed at reducing the use of the ED for nonemergencies.
Abstract: Background:Differentiating between appropriate and inappropriate resource use represents a critical challenge in health services research. The New York University Emergency Department (NYU ED) visit severity algorithm attempts to classify visits to the ED based on diagnosis, but it has not been form
TL;DR: It is possible to use health transitions defined by the MAHC system to estimate the MIDs for preference-based health index scores, and these estimates are quite comparable to published values estimated from empirical data using anchor-based methods.
Abstract: Objective To estimate minimally important differences (MIDs) for the EQ-5D, Health Utilities Index Mark II (HUI2), HUI3, and SF-6D health index scores using health-state transitions defined by each instrument's multiattribute health classification (MAHC) system. Methods We assume that changes in preference scores associated with the smallest health transitions defined by an MAHC system are minimally important. Any transitions between 2 health states defined by an MAHC system which differ in only one health dimension or attribute and by only one functional level are considered "smallest health transitions." Thus, each such health transition provides 1 MID estimate. The MID for each of the 4 instruments was estimated using all the hypothetical smallest health transitions defined by its MAHC system. Results Based on our definitions, the total number of smallest health transitions was 405 for the EQ-5D, 127,600 for the HUI2, 6,382,800 for the HUI3, and 86,700 for the SF-6D. The mean (standard deviation) MID estimate was 0.040 (0.026) for the EQ-5D (US algorithm), 0.082 (0.032) for the EQ-5D (UK algorithm), 0.045 (0.039) for the HUI2, 0.032 (0.027) for the HUI3, and 0.027 (0.028) for the SF-6D. The effect sizes of these MID estimates ranged from 0.11 to 0.37. These MID estimates are quite comparable to published values estimated from empirical data using anchor-based methods. Conclusions It is possible to use health transitions defined by the MAHC system to estimate the MIDs for preference-based health index scores. This study provides new information regarding MID estimates for the 4 health indices examined.
TL;DR: Costs for HAI were considerable from hospital and societal perspectives and suggests that HAI prevention expenditures would be balanced by savings in medical costs, lives saved and available hospital days that could be used by overcrowded hospitals to enhance available services.
Abstract: Background:Hospitals will increasingly bear the costs for healthcare-acquired conditions such as infection. Our goals were to estimate the costs attributable to healthcare-acquired infection (HAI) and conduct a sensitivity analysis comparing analytic methods.Methods:A random sample of high-risk adul
TL;DR: These data do not support the claim that a substantial number of adult respondents intentionally under-report nicotine consumption in face-to-face interviews, and the remarkable accuracy of self-reports of nicotine consumption seen here justifies confidence in self- Reports of this behavior in such surveys.
Abstract: Background:Many researchers rely on high-quality face-to-face national surveys conducted by the federal government to estimate the prevalence of nicotine product use, but some scholars have suggested that adults' self-reports in such surveys are intentionally distorted by social desirability respons
TL;DR: In this nationally representative cohort, middle-aged and elderly adults with cardiovascular disease who reported cutting back on medication use because of cost were more likely to report being hospitalized over a subsequent 2-year period after they had reported medication underuse.
Abstract: Context
It is well-documented that the financial burden of out-of-pocket expenditures for prescription drugs often leads people with medication-sensitive chronic illnesses to restrict their use of these medications. Less is known about the extent to which such cost-related medication underuse is associated with increases in subsequent hospitalizations and deaths.
TL;DR: An ePRO-based rapid learning cancer clinic is feasible, providing real-time research-quality data to support comparative effectiveness research.
Abstract: Background:“Rapid learning healthcare” presents a new infrastructure to support comparative effectiveness research. By leveraging heterogeneous datasets (eg, clinical, administrative, genomic, registry, and research), health information technology, and sophisticated iterative analyses, rapid learnin
TL;DR: Patients who enter medical care late in their HIV disease have substantially higher direct medical treatment expenditures than those who enter at earlier stages, and successful efforts to link patients with medical care earlier in the disease course may yield cost savings.
Abstract: Antiretroviral therapy has reduced morbidity and mortality among persons with human immunodeficiency virus (HIV).1–3 However, not all persons living with HIV access available therapies. Between 1996 and 2005, HIV surveillance data from 34 US states showed that 38.5% were diagnosed with acquired immune deficiency syndrome (AIDS) within 1 year of testing seropositive for HIV infection.4 Among members of a large United States managed care organization, 43% of newly diagnosed cases of HIV infection were late presenters, defined as first entering care with a CD4 lymphocyte count less than 200 cells/mm3.5 In 5 studies, the proportion with CD4 counts <200 cells/mm3 at first presentation ranged from 24% to 43%.6 Late entry into HIV care may occur because the person is unaware of his or her serostatus, or because persons who know they have HIV infection defer seeking treatment, due to stigma, mistrust, inaccessibility, or unaffordability of care.
Late entry into care is harmful.7–9 Compared with patients who enter care early in the course of their HIV infection, those who present late have a worse prognosis, shorter survival, and less benefit from highly active antiretroviral therapy.8,10,11 From a public health perspective, early recognition and treatment of individuals with HIV infection decreases the risk of HIV transmission.12–14
Late presentation also has a major effect on healthcare utilization and expenditure. In a study of 241 Canadian patients from 1996 to 2001, direct medical care costs were 200% higher for late presenters (CD4 count <200 cells/mm3) compared with early presenters in the year following HIV diagnosis.15
Recent progress in HIV treatment, new testing methods, and increasing healthcare costs require an updated evaluation of medical expenditures of individuals who enter care late. This study compares direct medical care expenditures by CD4 count at presentation in a large multistate HIV cohort between 2000 and 2007.
TL;DR: To maximize health care quality, developers, implementers and certifiers of EHRs should focus on increasing the adoption of robust EHR systems and increasing the use of specific features rather than simply aiming to deploy an EHR regardless of functionality.
Abstract: Background:Electronic health records (EHRs) are widely viewed as useful tools for supporting the provision of high quality healthcare. However, evidence regarding their effectiveness for this purpose is mixed, and existing studies have generally considered EHR usage a binary factor and have not cons
TL;DR: Childhood cancer survivors have higher levels of unemployment because of health or being between jobs, and high-risk survivors may need vocational assistance.
Abstract: Background
Adult childhood cancer survivors report high levels of unemployment although it is unknown whether this is due to health or employability limitations.
TL;DR: Hospital occupancy, nurse staffing levels, weekend admission, and seasonal influenza all appear to be independently associated with in-hospital mortality, but to varying degrees in the current sample.
Abstract: Background:Hospital occupancy, nurse staffing levels, weekend admission, and seasonal influenza have all been shown to be associated with in-hospital mortality. Yet, no study has simultaneously compared the strength of associations of these 4 factors with in-hospital mortality.Objective:To compare t
TL;DR: Among Latino and Asian Americans with mental disorders, LEP contributes to disparities in access to care and longer duration of untreated disorders.
Abstract: Objective:The impact of language proficiency as a potential contributor to ethnic disparities in mental health care has received less attention than other factors. Data from the National Latino and Asian American Study were examined to assess the impact of limited English proficiency (LEP) on access
TL;DR: If a brief measure is desired, the 3-item PEG is more responsive than the SF bodily pain scale, and both anchor and distribution-based methods should be considered when determining clinically important change.
Abstract: Background:Comparative responsiveness data are needed to inform choices about pain outcome measures.Objectives:To compare responsiveness of pain intensity, pain-related function, and composite measures, using data from a randomized trial and observational study.Research Design:Analysis of responsive
TL;DR: The threat to the comparability of international clinical morbidity is growing with the development of many country-specific ICD-10 versions, and one solution is to develop a meta-database including all country- specific modifications to ensure more efficient use of people and resources, decrease omissions and errors but most importantly provide a platform for future ICD updates.
Abstract: Background:The United States is about to make a major nationwide transition from ICD-9-CM coding of hospital discharges to ICD-10-CM, a country-specific modification of the World Health Organization's ICD-10. As this transition occurs, the WHO is already in the midst of developing ICD-11. Given this
TL;DR: Modest concentration into higher-volume centers has taken place over the past decade, but improvements within volume categories have driven most of the generalized decreases in mortality.
Abstract: Background: For more than a decade, health policy groups have recommended concentrating care for certain high-risk oncologic procedures into high-volume centers. The degree to which practice patterns and outcomes have changed over that time period is unclear. Objective: To evaluate temporal trends in the mortality and concentration of high-risk oncologic procedures. Research Design: Retrospective cohort study using data from the Nationwide Inpatient Sample on 93,108 adult patients undergoing pancreatectomy, esophagectomy, gastrectomy, or major lung resection for organ-specific cancers from 1997 to 2006. The main outcome measure was in-hospital mortality. Results: Risk- and volume-adjusted mortality decreased over time for all 4 procedures (P < 0.05). When hospitals were categorized into terciles of case volume, mortality gaps persisted between high-and low-volume centers in all procedures throughout the study period and did not significantly narrow over time. Patient volumes shifted toward high-volume centers over time for all procedures (P <0.001), although at the end of the study period, low-volume centers still cared for one-quarter to one-third of patients undergoing each of the studied procedures. Most of the overall improvements in mortality were attributable to decreasing mortality within volume categories as opposed to the effects of care concentration. Conclusions: Modest concentration into higher-volume centers has taken place over the past decade, but improvements within volume categories have driven most of the generalized decreases in mortality. Significant outcome gaps between high- and low-volume centers still persist; further centralization may retain the potential to lower overall mortality.
TL;DR: Access indicators represent the ability to navigate the US health care system but have a differential impact on AA groups, and should be integrated into interventions designed to improve cancer screening rates.
Abstract: Background: Asian Americans (AA) have the lowest rates of cancer screening of all ethnic groups. Reasons for these low rates of screening frequently include low acculturation levels. However, screening rates remain low for most AA populations despite differences in acculturation levels, suggesting presence of other important modifiers such as access barriers. Objectives: To compare the relative impact of access versus acculturation on breast and cervical cancer screening for AA subgroups. Research Design: Multiple regressions models, controlling for sociodemographics, were developed for each AA subgroup. Subjects: Women ages 18 and older from the 2003 California Health Interview Survey were included in this study. We included women with Chinese, Filipino, Japanese, Korean, South Asian, and Vietnamese origins. Measures: The dependent variables included clinical breast examination in the past year, mammogram in the past 2 years, and Pap test in the past 3 years. Independent variables included AA subgroup, access indicators, acculturation indicators, and other sociodemographics. Results: Access explained more variation that acculturation alone in cancer screening for most AA women. The exceptions were in mammograms for Japanese, Koreans and South Asians and Pap test among Japanese. No insurance reduced the likelihood of clinical breast examination for immigrant Chinese and Filipinos, and no usual source of care reduced likelihood of Pap test for Japanese and South Asians compared with US born. Conclusions: Access indicators represent the ability to navigate the US health care system but have a differential impact on AA groups. These differences should be integrated into interventions designed to improve cancer screening rates.
TL;DR: German physicians had the least time allocated and needed for most types of appointment and British physicians felt they had the most control over time in their office and spending sufficient time with patients.
Abstract: Background: As physicians are pressured to deliver an increasing number of preventive services, follow guidelines, engage in evidence-based practice, and deliver patient-centered care in managerially driven organizations, they struggle with how much control they have over their time. Methods: A secondary analysis was conducted with data from 3 parallel studies of clinical decision making in Germany, the United Kingdom, and the United States with 128 physicians per country. Physicians reported how much time they were allocated and how much time they needed for high-quality care for new patient appointments, routine consultations, and complete physicals. They also reported how much control they had over their time in the office and spending adequate time with patients. Results: German, British, and American physicians were allocated (on average) 16/11/32 minutes for a new patient appointment, 6/10/18 minutes for a routine visit, and 12/20/36 minutes for a complete physical, but felt that they needed more time. Over half of German and American physicians felt that they always or usually had control over the hours they were required to be in their office or spending sufficient time with their patients while less than half of British physicians felt this way. Conclusion: German physicians had the least time allocated and needed for most types of appointment. American physicians had the most time allocated and needed for each type of appointment. However, British physicians felt they had the least control over time in their office and spending sufficient time with patients.
TL;DR: There was a nonlinear negative relationship between hospital EOL treatment intensity and 30-day mortality among all admissions, although patients with higher PPD derived the greatest benefit, and admission to higher E OL treatment intensity hospitals is associated with small gains in postadmission survival.
Abstract: Greater spending on acute care and physician services among Medicare beneficiaries in the last 6 months of life has gained substantial traction as a measure of inefficiency.1–3 The general assumption is that the provision of intensive and expensive care to those who died was a waste of resources. Indeed, Fisher et al reported that Medicare beneficiaries who lived in hospital referral regions with higher last 6-months-of-life spending did not live longer.4,5 These investigators recently demonstrated marked variation in the use of acute care services in the last 6 months of life among cohorts of Medicare beneficiaries loyal to “highly respected” hospitals.1 The hospital is a much more proximate measure than the hospital referral region, yet we do not know whether higher “end-of-life” (EOL) hospital treatment intensity impacts survival.
The purpose of the current study is to examine whether there is a survival benefit of admission to a high EOL intensity hospital. In a departure from the approach used by other investigators, we define EOL treatment intensity as the use of intensive care and life-sustaining treatments among patients who had a high probability of dying upon admission, rather than among those who died. By using intensive care and life-sustaining treatments, rather than spending, we measure treatment provided with life-saving (or prolonging), not palliative, intent. By using a cohort of patients who have a high probability of dying, rather than those who died, we capture treatment decisions for patients who may be dying that are made under conditions of prognostic uncertainty, rather than treatment decisions that (in hindsight) were made for patients who actually died.6,7 Furthermore, if higher treatment intensity reduces mortality, then the patients making up the decedent cohorts at higher compared with lower intensity hospitals would differ. Using noncomparable denominators would introduce a systematic bias into the intensity measurement that could confound the relationship between treatment intensity and survival.
TL;DR: Formalizing nurse assessment of discharge readiness could facilitate identification of patients at risk for readmission or ED utilization before discharge when anticipatory interventions could prevent avoidable postdischarge utilization.
Abstract: Background:Prevention of hospital readmission and emergency department (ED) utilization will be a crucial strategy in reducing health care costs. There has been limited research on nurse assessment and patient perceptions of discharge readiness in relation to postdischarge outcomes.Objectives:To inv