Examining the Effects of Approaches on Reducing Hospital Utilization: The Patient-Centered Medical Home, Continuity of Care, and the Inpatient Palliative Consultation at the End-of-Life

Background: It has become a national priority to reduce the high health care expenditure in the United States while improving the quality of care. Hospital care is taking up one-third of the healthcare spending, and services offered in hospitals are costly compared to others. Only one-twentieth of the patients with high-needs account for about half of the health care spending. They consuming a high level of hospital services if their conditions are not well-managed in the outpatient settings. Therefore, it is important to examine the effectiveness of the approaches that have the potentials to reduce costly care utilization through improvements in the quality of care. This dissertation thesis focused on examining the effects of three approaches to reduce hospital utilization. The three approaches include the patient-centered medical homes (PCMH), better continuity of care (COC), and the early use of inpatient palliative consultation (IPC) at the end of life. Methods: Andersen’s Behavioral model of health care utilization was used to guide the modeling process of the three individual studies. The first study used data from the Medical Expenditure Panel Survey Household Component (MEPS-HC). Respondents who reported having a usual source of care other than the emergency department (ED) were included, and they were classified into three levels of PCMH groups by their baseline-year care features from 11 selected items. The outcomes were the second-year hospital admissions and ED visits due to the ambulatory care sensitive conditions (ACSCs). Logistic regressions that accounted for survey weights were used. The second study was conducted among a nationally representative Taiwan Population who were admitted for the first time for the five conditions. The outcomes are the numbers of all-cause and condition-specific hospitalizations during the follow-up year after discharge, and the primary explanatory variable was the outpatient COC. Multivariable generalized estimation equation models with a negative binomial distribution and log link were used. The third study used Nebraska Hospital Discharge Data linked with death certificates to identify the inpatient services received by the Nebraska Decedents due to the top six causes of death. The use of IPC was classified by the time receiving it as early use and late use, and the comparison group was the decedents who never encountered IPC. The outcomes were end-of-life events including hospice discharge, place of death, intensive care utilization, life-sustaining treatment, length of stay and total inpatient charges. Mixed-effect logistic regressions, logistic regression, negative binomial regression, and generalized linear model with log link and gamma distribution were used for those outcomes respectively. Results: The highest level of PCMH primary care was associated with lower risks of having admissions and ED visits due to ACSCs. However, individual attributes of PCMH did not have the same effects. The patients with better COC have significantly fewer all-cause hospitalizations for all the conditions. The COC only worked in patients with ACSC conditions in reducing the condition-specific hospitalizations. The early use of IPC was associated with lower likelihoods of dying in the hospitals, receiving intensive care and the life-sustaining treatment. The use of IPC at either the early or late time was associated with higher odds of being discharged to hospice care, and less length of stay in the inpatient settings and less total inpatient charges. Conclusion: Approaches such as PCMH, improving continuity of care and the early use of palliative care are promising in reducing the costly hospital services and improving the quality of care. These approaches are replicable to any value-based programs for cost-reduction, quality improvement, and improving population health outcomes

Does Better Disease Management in Primary Care Reduce Hospital Costs?

We apply cross-sectional and panel data methods to a database of 5 million patients in 8,000 English general practices to examine whether better primary care management of 10 chronic diseases is associated with reduced hospital costs. We find that only primary care performance in stroke care is associated with lower hospital costs. Our results suggest that the 10% improvement in the general practice quality of stroke care between 2004/5 and 2007/8 reduced 2007/8 hospital expenditure by about £130 million in England. The cost savings are due mainly to reductions in emergency admissions and outpatient visits, rather than to lower costs for patients treated in hospital or to reductions in elective admissions.Quality; disease management; primary care; hospital costs; ambulatory care sensitive conditions; preventative care.

An economic model of long-term use of celecoxib in patients with osteoarthritis

<p>Abstract</p> <p>Background</p> <p>Previous evaluations of the cost-effectiveness of the cyclooxygenase-2 selective inhibitor celecoxib (Celebrex, Pfizer Inc, USA) have produced conflicting results. The recent controversy over the cardiovascular (CV) risks of rofecoxib and other coxibs has renewed interest in the economic profile of celecoxib, the only coxib now available in the United States. The objective of our study was to evaluate the long-term cost-effectiveness of celecoxib compared with nonselective nonsteroidal anti-inflammatory drugs (nsNSAIDs) in a population of 60-year-old osteoarthritis (OA) patients with average risks of upper gastrointestinal (UGI) complications who require chronic daily NSAID therapy.</p> <p>Methods</p> <p>We used decision analysis based on data from the literature to evaluate cost-effectiveness from a modified societal perspective over patients' lifetimes, with outcomes expressed as incremental costs per quality-adjusted life-year (QALY) gained. Sensitivity tests were performed to evaluate the impacts of advancing age, CV thromboembolic event risk, different analytic horizons and alternate treatment strategies after UGI adverse events.</p> <p>Results</p> <p>Our main findings were: 1) the base model incremental cost-effectiveness ratio (ICER) for celecoxib versus nsNSAIDs was $31,097 per QALY; 2) the ICER per QALY was$19,309 for a model in which UGI ulcer and ulcer complication event risks increased with advancing age; 3) the ICER per QALY was $17,120 in sensitivity analyses combining serious CV thromboembolic event (myocardial infarction, stroke, CV death) risks with base model assumptions.</p> <p>Conclusion</p> <p>Our model suggests that chronic celecoxib is cost-effective versus nsNSAIDs in a population of 60-year-old OA patients with average risks of UGI events.</p

Parameters Affecting Length of Stay Among Neurosurgical Patients in an Intensive Care Unit

Aim: to determine the predictive factors on the length of stay of neurosurgical patients in the ICU setting. Methods: all patients admitted to the neurosurgical ICU between February 1 and July 31, 2011 were recruited. Patient demographics and clinical data for each variable were collected within 30 minutes of admission. The ICU length of stay was recorded and analyzed by linear regression model with statistical significance at p-value <0.05. Results: there were 276 patients admitted, of whom 89.1% were elective cases. The mean (95% CI) and median (min-max) of ICU length of stay were 2.36 (2.09-2.63) and 2 (1-25) days. The variables associated with ICU length of stay and their percent change (95% CI) were the Glasgow Coma Scale motor subscore (GCSm), 6.72% (-11.20 to -2.01) lower for every 1 point score change; blood pH, 1.16% (0.11 to 2.21) higher for every 0.01 unit change; and emergency admission type, 58.30% (29.16 to 94.0) higher as compared to elective admission. Conclusion: the GCSm, pH and emergency admission were found to be the main predictive variables of neurosurgical patient length of stay in the intensive care unit, however, the model should be further explored in a larger sample size and using subgroup analysis