An analysis of clinical process measures for acute healthcare delivery in Appalachia: The Roane Medical Center experience

OBJECTIVE: To survey management of selected emergency healthcare needs in a Tennessee community hospital. MATERIALS AND METHODS: In this descriptive report, discharges and associated standard process measures were retrospectively studied for Roane Medical Center (RMC) in Harriman, Tennessee (pop. 6,757). Hospital data were extracted from a nationwide database of short-term acute care hospitals to measure 16 quality performance measures in myocardial infarction (MI), heart failure, and pneumonia during the 14 month interval ending March 2005. The data also permitted comparisons with state and national reference groups. RESULTS: Of RMC patients with myocardial infarction (MI), 94% received aspirin on arrival, a figure higher than both state (85%) and national (91%) averages. Assessment of left ventricular dysfunction among heart failure patients was also higher at RMC (98%) than the state (74%) or national (79%) average. For RMC pneumonia patients, 79% received antibiotics within 4 h of admission, which compared favorably with State (76%) and national (75%) average. RMC scored higher on 13 of 16 clinical process measures (p<0.01, sign test analysis, >95% CI) compared to state and national averages. DISCUSSION: Although acute health care needs are often met with limited resources in medically underserved regions, RMC performed above state and national average for most process measures assessed in this review. Our data were derived from one facility and the associated findings may not be applicable in other healthcare settings. Further studies are planned to track other parameters and specific clinical outcomes at RMC, as well as to identify specific institutional policies that facilitate attainment of target quality measures

Economic burden of illness of acute coronary syndromes: medical and productivity costs

<p>Abstract</p> <p>Background</p> <p>The significant economic burden associated with acute coronary syndromes (ACS) provides a need to evaluate both medical costs and productivity costs, according to evolving guideline-driven ACS treatment strategies, medical management (MM), percutaneous coronary intervention (PCI), or coronary artery bypass graft (CABG).</p> <p>Methods</p> <p>Commercially insured individuals, aged 18-64, with an emergency room (ER) visit or hospitalization accompanied by an ACS diagnosis (index event) were identified from a large claims database between 01/2004 and 12/2005 with a 1-year follow-up period. Patients who had an ACS diagnosis in the 12 months prior to their index event were excluded. Patients were divided into 3 groups according to treatment strategies during the index event: MM, PCI, or CABG. A subset of patients was identified for the productivity cost analysis exploring short-term disability and absenteeism costs. Multivariate generalized linear models were performed to examine the ACS costs by 3 different treatment strategies.</p> <p>Results</p> <p>A total of 10,487 patients were identified for the medical cost analysis. The total 1-year medical costs (index event costs plus the 1-year follow-up costs) were lowest for MM patients ($34,087), followed by PCI patients ($52,673) and CABG patients ($86,914). Of the 3,080 patients in the productivity costs analysis, 2,454 patients were identified in the short-term disability cohort and 626 patients were identified in the absenteeism cohort. Both the estimated mean total 1-year short-term disability and absenteeism costs were highest for CABG patients ($17,335, $14,960, respectively) compared to MM patients ($6,048, $9,826, respectively) and PCI patients ($9,221, $9,460, respectively).</p> <p>Conclusions</p> <p>Both total 1-year medical costs and 1-year productivity costs are substantial for working-aged individuals with ACS. These costs differ according to the type of treatment strategy, with CABG having higher costs compared to either PCI or MM.</p

Duration of hospital participation in a nationwide stroke registry is associated with improved quality of care

BACKGROUND: There are several proven therapies for patients with ischemic stroke or transient ischemic attack (TIA), including prophylaxis of deep venous thrombosis (DVT) and initiation of antithrombotic medications within 48 h and at discharge. Stroke registries have been promoted as a means of increasing use of such interventions, which are currently underutilized. METHODS: From 1999 through 2003, 86 U.S. hospitals participated in Ethos, a voluntary web-based acute stroke treatment registry. Detailed data were collected on all patients admitted with a diagnosis of TIA or ischemic stroke. Rates of optimal treatment (defined as either receipt or a valid contraindication) were examined within each hospital as a function of its length of time in registry. Generalized estimating equations were used to adjust for patient and hospital characteristics. RESULTS: A total of 16,301 patients were discharged with a diagnosis of stroke or TIA from 50 hospitals that participated for more than 1 year. Rates of optimal treatment during the first 3 months of participation were as follows: 92.5% for antithrombotic medication within 48 h, 84.6% for antithrombotic medications at discharge, and 77.1% for DVT prophylaxis. Rates for all treatments improved with duration of participation in the registry (p < 0.05), with the most dramatic improvements in the first year. CONCLUSION: In a large cohort of patients with stroke or TIA, three targeted quality-improvement measures improved among hospitals participating in a disease-specific registry. Although the changes could be attributed to interventions other than the registry, these findings demonstrate the potential for hospital-level interventions to improve care for patients with stroke and TIA

Determinants of per diem Hospital Costs in Mental Health

INTRODUCTION:An understanding of differences in hospital costs between patient groups is relevant for the efficient organisation of inpatient care. The main aim of this study was to confirm the hypothesis that eight a priori identified cost drivers influence per diem hospital costs. A second aim was to explore further variables that might influence hospital costs. METHODS:The study included 667 inpatient episodes consecutively discharged in 2014 at the psychiatric hospital of the Medical Centre-University of Freiburg. Fifty-one patient characteristics were analysed. Per diem costs were calculated from the hospital perspective based on a detailed documentation of resource use. Mixed-effects maximum likelihood regression and an ensemble of conditional inference trees were used to analyse data. RESULTS:The study confirmed the a priori hypothesis that not being of middle age (33-64 years), danger to self, involuntary admission, problems in the activities of daily living, the presence of delusional symptoms, the presence of affective symptoms, short length of stay and the discharging ward affect per diem hospital costs. A patient classification system for prospective per diem payment was suggested with the highest per diem hospital costs in episodes having both delusional symptoms and involuntary admissions and the lowest hospital costs in episodes having neither delusional symptoms nor somatic comorbidities. CONCLUSION:Although reliable cost drivers were identified, idiosyncrasies of mental health care complicated the identification of clear and consistent differences in hospital costs between patient groups. Further research could greatly inform current discussions about inpatient mental health reimbursement, in particular with multicentre studies that might find algorithms to split patients in more resource-homogeneous groups

Roles and practices of general practitioners and psychiatrists in management of depression in the community

BACKGROUND: Little is known about depressed patients' profiles and how they are managed. The aim of the study is to compare GPs and psychiatrists for 1°) sociodemographic and clinical profile of their patients considered as depressed 2°) patterns of care provision. METHODS: The study design is an observational cross-sectional study on a random sample of GPs and psychiatrists working in France. Consecutive inclusion of patients seen in consultation considered as depressed by the physician. GPs enrolled 6,104 and psychiatrists 1,433 patients. Data collected: sociodemographics, psychiatric profile, environmental risk factors of depression and treatment. All clinical data were collected by participating physicians; there was no direct independent clinical assessment of patients to check the diagnosis of depressive disorder. RESULTS: Compared to patients identified as depressed by GPs, those identified by psychiatrists were younger, more often urban (10.5% v 5.4% – OR = 2.4), educated (42.4% v 25.4% – OR = 3.9), met DSM-IV criteria for depression (94.6% v 85.6% – OR = 2.9), had been hospitalized for depression (26.1% v 15.6% – OR = 2.0) and were younger at onset of depressive problems (all adjusted p < .001). No difference was found for psychiatric and somatic comorbidity, suicide attempt and severity of current depression. Compared to GPs, psychiatrists more often prescribed tricyclics and very novel antidepressants (7.8% v 2.3% OR = 5.0 and 6.8% v 3.0% OR = 3.8) with longer duration of antidepressant treatment. GPs' patients received more "non-conventional" treatment (8.8% v 2.4% OR = 0.3) and less psychotherapy (72.2% v 89.1% OR = 3.1) (all adjusted p < .001). CONCLUSION: Differences between patients mainly concerned educational level and area of residence with few differences regarding clinical profile. Differences between practices of GPs and psychiatrists appear to reflect more the organization of the French care system than the competence of providers

Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan

<p>Abstract</p> <p>Objective</p> <p>In-hospital mortality is an important performance measure for quality improvement, although it requires proper risk adjustment. We set out to develop in-hospital mortality prediction models for acute hospitalization using a nation-wide electronic administrative record system in Japan.</p> <p>Methods</p> <p>Administrative records of 224,207 patients (patients discharged from 82 hospitals in Japan between July 1, 2002 and October 31, 2002) were randomly split into preliminary (179,156 records) and test (45,051 records) groups. Study variables included Major Diagnostic Category, age, gender, ambulance use, admission status, length of hospital stay, comorbidity, and in-hospital mortality. ICD-10 codes were converted to calculate comorbidity scores based on Quan's methodology. Multivariate logistic regression analysis was then performed using in-hospital mortality as a dependent variable. C-indexes were calculated across risk groups in order to evaluate model performances.</p> <p>Results</p> <p>In-hospital mortality rates were 2.68% and 2.76% for the preliminary and test datasets, respectively. C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay.</p> <p>Conclusion</p> <p>Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy. These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures.</p

Association between Frequency Domain Heart Rate Variability and Unplanned Readmission to Hospital in Geriatric Patients

<p>Abstract</p> <p>Background</p> <p>An accurate prediction of unplanned readmission (UR) after discharge from hospital can facilitate physician's decision making processes for providing better quality of care in geriatric patients. The objective of this study was to explore the association of cardiac autonomic functions as measured by frequency domain heart rate variability (HRV) and 14-day UR in geriatric patients.</p> <p>Methods</p> <p>Patients admitted to the geriatric ward of a regional hospital in Chiayi county in Taiwan were followed prospectively from July 2006 to June 2007. Those with invasive tubes and those who were heavy smokers, heavy alcohol drinkers, on medications that might influence HRV, or previously admitted to the hospital within 30 days were excluded. Cardiac autonomic functions were evaluated by frequency domain indices of HRV. Multiple logistic regression was used to assess the association between UR and HRV indices adjusted for age and length of hospitalization.</p> <p>Results</p> <p>A total of 78 patients met the inclusion criteria and 15 of them were readmitted within 14 days after discharge. The risk of UR was significantly higher in patients with lower levels of total power (OR = 1.39; 95% CI = 1.04-2.00), low frequency power (LF) (OR = 1.22; 95% CI = 1.03-1.49), high frequency power (HF) (OR = 1.27; 95% CI = 1.02-1.64), and lower ratios of low frequency power to high frequency power (LF/HF ratio) (OR = 1.96; 95% CI = 1.07-3.84).</p> <p>Conclusion</p> <p>This is the first study to evaluate the association between frequency domain heart rate variability and the risk of UR in geriatric patients. Frequency domain heart rate variability indices measured on admission were significantly associated with increased risk of UR in geriatric patients. Additional studies are required to confirm the value and feasibility of using HRV indices on admission as a non-invasive tool to assist the prediction of UR in geriatric patients.</p

A Spoonful of Math Helps the Medicine Go Down: An Illustration of How Healthcare can Benefit from Mathematical Modeling and Analysis

<p>Abstract</p> <p>Objectives</p> <p>A recent joint report from the Institute of Medicine and the National Academy of Engineering, highlights the benefits of--indeed, the need for--mathematical analysis of healthcare delivery. Tools for such analysis have been developed over decades by researchers in Operations Research (OR). An OR perspective typically frames a complex problem in terms of its essential mathematical structure. This article illustrates the use and value of the tools of operations research in healthcare. It reviews one OR tool, queueing theory, and provides an illustration involving a hypothetical drug treatment facility.</p> <p>Method</p> <p>Queueing Theory (QT) is the study of waiting lines. The theory is useful in that it provides solutions to problems of waiting and its relationship to key characteristics of healthcare systems. More generally, it illustrates the strengths of modeling in healthcare and service delivery.</p> <p>Queueing theory offers insights that initially may be hidden. For example, a queueing model allows one to incorporate randomness, which is inherent in the actual system, into the mathematical analysis. As a result of this randomness, these systems often perform much worse than one might have guessed based on deterministic conditions. Poor performance is reflected in longer lines, longer waits, and lower levels of server utilization.</p> <p>As an illustration, we specify a queueing model of a representative drug treatment facility. The analysis of this model provides mathematical expressions for some of the key performance measures, such as average waiting time for admission.</p> <p>Results</p> <p>We calculate average occupancy in the facility and its relationship to system characteristics. For example, when the facility has 28 beds, the average wait for admission is 4 days. We also explore the relationship between arrival rate at the facility, the capacity of the facility, and waiting times.</p> <p>Conclusions</p> <p>One key aspect of the healthcare system is its complexity, and policy makers want to design and reform the system in a way that affects competing goals. OR methodologies, particularly queueing theory, can be very useful in gaining deeper understanding of this complexity and exploring the potential effects of proposed changes on the system without making any actual changes.</p