Improving Care for Individuals With Limited English Proficiency: Facilitators and Barriers to Providing Language Services in California Public Hospitals

Evaluates twelve public hospitals' efforts to improve language services for patients with limited English proficiency: policies and procedures, organizational commitment, strategies for change, training, effectiveness, and facilitators and barriers

Hospital implementation of health information technology and quality of care: are they related?

Recently, there has been considerable effort to promote the use of health information technology (HIT) in order to improve health care quality. However, relatively little is known about the extent to which HIT implementation is associated with hospital patient care quality. We undertook this study to determine the association of various HITs with: hospital quality improvement (QI) practices and strategies; adherence to process of care measures; risk-adjusted inpatient mortality; patient satisfaction; and assessment of patient care quality by hospital quality managers and front-line clinicians.This work was supported by a grant from the Commonwealth Fund. We are indebted to Anthony Shih and Anne-Marie Audet of the Fund for their advice, support, and constructive suggestions throughout the design and conduct of the study. We thank our colleagues - Raymond Kang, Peter Kralovec, Sally Holmes, Frances Margolin, and Deborah Bohr - for their valuable contributions to the development of the QAS, the CPS, and the database on which the analytic findings reported here were based. We also thank 3 M (TM) Health Information Systems' for use of its All Patient Refined Diagnosis Related Groups (APR-DRGs) software. We especially wish to thank Jennifer Drake for her contributions not only to survey development, but also to earlier analysis of survey findings relevant to this paper. (Commonwealth Fund)Published versio

High hospital admission rates and inappropriate care

This study tests whether the rate of inappropriate hospital admissions is high in areas with high medical admission rates. Seventy small geographic areas were formed by grouping Massachusetts ZIP codes by similarity of hospital use. Appropriateness of hospital admission was measured both by applying the Appropriateness Evaluation Protocol and by applying physicians\u27 judgment to the medical records of patients age sixty-five and older who were admitted for treatment of a medical condition in 1990-1992. No relationship between hospital admission rate and inappropriate admission rate was found, which calls into question the common assumption that areas with higher hospital use have more inappropriate use of hospital care

The Self-Adapting Focused Review System. Probability sampling of medical records to monitor utilization and quality of care

Medical record review is increasing in importance as the need to identify and monitor utilization and quality of care problems grow. To conserve resources, reviews are usually performed on a subset of cases. If judgment is used to identify subgroups for review, this raises the following questions: How should subgroups be determined, particularly since the locus of problems can change over time? What standard of comparison should be used in interpreting rates of problems found in subgroups? How can population problem rates be estimated from observed subgroup rates? How can the bias be avoided that arises because reviewers know that selected cases are suspected of having problems? How can changes in problem rates over time be interpreted when evaluating intervention programs? Simple random sampling, an alternative to subgroup review, overcomes the problems implied by these questions but is inefficient. The Self-Adapting Focused Review System (SAFRS), introduced and described here, provides an adaptive approach to record selection that is based upon model-weighted probability sampling. It retains the desirable inferential properties of random sampling while allowing reviews to be concentrated on cases currently thought most likely to be problematic. Model development and evaluation are illustrated using hospital data to predict inappropriate admissions

Factors associated with internal medicine physician job attitudes in the Veterans Health Administration

Abstract Background US healthcare organizations increasingly use physician satisfaction and attitudes as a key performance indicator. Further, many health care organizations also have an academically oriented mission. Physician involvement in research and teaching may lead to more positive workplace attitudes, with subsequent decreases in turnover and beneficial impact on patient care. This article aimed to understand the influence of time spent on academic activities and perceived quality of care in relation to job attitudes among internal medicine physicians in the Veterans Health Administration (VHA). Methods A cross-sectional survey was conducted with inpatient attending physicians from 36 Veterans Affairs Medical Centers. Participants were surveyed regarding demographics, practice settings, workplace staffing, perceived quality of care, and job attitudes. Job attitudes consisted of three measures: overall job satisfaction, intent to leave the organization, and burnout. Analysis used a two-level hierarchical model to account for the nesting of physicians within medical centers. The regression models included organizational-level characteristics: inpatient bed size, urban or rural location, hospital teaching affiliation, and performance-based compensation. Results A total of 373 physicians provided useable survey responses. The majority (72%) of respondents reported some level of teaching involvement. Almost half (46%) of the sample reported some level of research involvement. Degree of research involvement was a significant predictor of favorable ratings on physician job satisfaction and intent to leave. Teaching involvement did not have a significant impact on outcomes. Perceived quality of care was the strongest predictor of physician job satisfaction and intent to leave. Perceived levels of adequate physician staffing was a significant contributor to all three job attitude measures. Conclusions Expanding opportunities for physician involvement with research may lead to more positive work experiences, which could potentially reduce turnover and improve system performance

Using utilization review information to improve hospital efficiency

Hospitals are currently under great pressure to improve the efficiency of internal operations without sacrificing quality of care. In the rush to do this, they often overlook an extremely useful source of information that already exists--data routinely collected as part of the utilization review (UR) process. This article describes a system using UR data for management purposes that was developed in a large urban teaching hospital. The components described are: (1) data collected systematically by trained reviewers applying the Appropriateness Evaluation Protocol; (2) software for data collection using inexpensive, highly portable computers; and (3) formats for reporting UR findings to hospital administrators and physicians. Information derived from UR in the study hospital is discussed, as well as factors to be considered in adapting some or all of the system\u27s components in other hospitals

Small area variations in hospitalization rates: how much you see depends on how you look

This research investigates the degree that estimates of the magnitude of small area variations in hospitalization rates depend on both the estimation method and the number of years of data used. Hospital discharge abstracts for patients 65 and older from acute care hospitals in Massachusetts from 1982 to 1987 were analyzed. The SCV statistic, the approach used in many current small area variation studies, and empirical Bayes (EB), an approach that adjusts more fully for the effect of random variation, were compared. EB estimates based on 3 years of data were best able to predict future area-specific hospitalization rates. Compared to EB estimates using 3 years of data, the SCV statistic with 1 year of data overestimated the median amount of systematic variation by over 70% for the 68 conditions studied; with 3 years of data, the SCV overestimated the median by 55%. Regardless of method, the same conditions were identified as relatively more variable and the same geographic areas were found to have higher than expected hospitalization rates. The magnitude of differences in hospitalization rates depends on how the data are analyzed and how many years of data are used. Hospitalization rates across small geographic areas may vary substantially less than reported previously

Comparing the importance of disease rate versus practice style variations in explaining differences in small area hospitalization rates for two respiratory conditions

Many studies have reported large variations in age- and sex-adjusted rates of hospitalizations across small geographic areas. These variations have often been attributed to differences in medical practice style which are not reflected in differences in health care outcomes. There is, however, another potentially important source of variation that has not been examined much in the literature: geographic differences in the age-sex adjusted size of the pool of patients who present with the disease and are candidates for hospitalization. Previous studies of small area variations in hospitalization rates have only used data on hospitalizations. Thus, it has not been possible to distinguish the extent to which differences in hospitalization rates are due to (i). differences in the chance that patients diagnosed with a disease are admitted to a hospital, which we refer to as the \u27practice style effect,\u27 versus (ii). geographic differences in the total amount of diagnosed disease, which we refer to as the \u27disease effect.\u27 Elementary methods for estimating the relative strength of the two effects directly from the data can be misleading, since equal amounts of variability in each effect result in unequal impacts on hospitalization rates. In this paper we describe a model-based approach for estimating the relative importance of the practice style effect and the disease effect in explaining variations in hospitalization rates. The key to our approach is the use of data on both inpatient and outpatient visits. We use 1997 Medicare data for two respiratory medical conditions across 71 small areas in Massachusetts: chronic bronchitis and emphysema, and bacterial pneumonia. Based on a Poisson model for the process generating hospitalizations and outpatient visits, we use a Bayesian framework and Gibbs sampling to compute and compare the correlation between the number of people hospitalized and each of these two sources of variation. Our results show that for the two conditions, disease rate variation explains at least as much of the variation in hospitalization rates as does practice style variation