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Biomedical subjects

H Burstin

Publications and source records attributed to H Burstin.

11 recordsLinked to original sources
PubMed2001

Delay in seeking emergency care.

OBJECTIVE: To determine whether patient clinical and socioeconomic characteristics predict patient delay in coming to the emergency department (ED). METHODS: Adult ED patients at five urban teaching hospitals were surveyed regarding self-reported delay in coming to the ED. Delay was measured by self-perception as well as by the number of days ill and unable to work. Patient socioeconomic and clinical characteristics were obtained by survey questionnaire and chart review. Cross-sectional analysis within a prospective study of 4,094 consecutive patients was performed using a subset of 1,920 patients (84% eligible rate) to whom questionnaires were administered. RESULTS: Overall, 32% of the patients completing the survey reported delay in seeking ED care. Of these patients reporting delay, 71% thought their problem would go away or was not serious. Patients who were older, had higher acuity, or were frequent ED users reported less delay in coming to the ED, while patients without a regular physician or who were African American reported more delay. Perception of increased number of days ill prior to visiting the ED was reported by frequent ED users and those with worse baseline physical function, while patients who had higher acuity reported fewer days ill prior to coming to the ED. CONCLUSIONS: A patient's decision to delay coming to the ED often reflects a belief that his or her illness is either self-limited or not serious. The decision to delay correlates with patient characteristics and access to a regular physician. The correlates of delay in seeking ED care may depend on the delay measure used. Better understanding of patients at risk for delaying care may influence interventions to reduce delay.

Adult↗
PubMed2000

Using diagnoses to describe populations and predict costs.

The Diagnostic Cost Group Hierarchical Condition Category (DCG/HCC) payment models summarize the health care problems and predict the future health care costs of populations. These models use the diagnoses generated during patient encounters with the medical delivery system to infer which medical problems are present. Patient demographics and diagnostic profiles are, in turn, used to predict costs. We describe the logic, structure, coefficients and performance of DCG/HCC models, as developed and validated on three important data bases (privately insured, Medicaid, and Medicare) with more than 1 million people each.

Adolescent↗
PubMed2000

Principal inpatient diagnostic cost group model for Medicare risk adjustment.

The Balanced Budget Act (BBA) of 1997 required HCFA to implement health-status-based risk adjustment for Medicare capitation payments for managed care plans by January 1, 2000. In support of this mandate, HCFA has been collecting inpatient encounter data from health plans since 1997. These data include diagnoses and other information that can be used to identify chronic medical problems that contribute to higher costs, so that health plans can be paid more when they care for sicker patients. In this article, the authors describe the risk-adjustment model HCFA is implementing in the year 2000, known as the Principal Inpatient Diagnostic Cost Group (PIPDCG) model.

Adolescent↗
PubMed1998

Asking residents about adverse events in a computer dialogue: how accurate are they?

BACKGROUND: Although retrospective identification of adverse events is time-consuming, whether they are present and/or expected is often readily apparent to providers during the provision of care. METHODS: A computer program to flag admissions with possible adverse events was developed. Readmissions to the hospital within 31 days and admissions including more than one visit to the operating room (OR) were flagged. For surgical site infections, all admissions--including a visit to the OR--were flagged, but only a sample was evaluated in the reliability assessment. Residents in an urban, tertiary care hospital were questioned when inputting computerized discharge orders regarding adverse events among 391 cases sampled from 6,813 admissions for a two-month period. RESULTS: For the 228 readmissions (3.3% of all admissions) identified by the computer program, resident responses had a sensitivity of 57% and a specificity of 73% in detecting an unexpected readmission (nurse responses, 96% and 91%). For the 79 patients with a return to the OR, the residents' responses had a sensitivity of 86% and a specificity of 84% for detecting an unexpected return (versus 75% and 98% for the nurses' responses). For the 209 patients with an OR visit, the sensitivity and specificity for a surgical site infection were 85% and 98% for the residents and 54% and 99% for the nurses. DISCUSSION: Information systems can be used to screen for adverse events and to ask providers whether adverse events are unexpected, although the reliability of this approach is likely to vary by event type.

Boston↗
PubMed1998

Measuring and improving quality using information systems.

Information systems (IS) are increasingly important for measuring and improving quality. In this paper, we describe our integrated delivery system's plan for and experiences with measuring and improving quality using IS. Our approach is that for quality measurement to be practical, it must be integrated with the routine provision of care, and whenever possible should be done using IS. Thus, at one hospital, we now perform almost all quality measurement using IS. However, IS are not only useful for measuring care, but represent powerful tools for improving care using decision support. Specific areas in which IS has already been particularly effective include reducing the unnecessary use of laboratory testing, reporting important abnormalities to key providers rapidly, adverse drug event detection and prevention, initiatives to reduce the costs of drugs, and making critical pathways available to providers. The next wave of effort will be to promote widespread use of computerized guidelines, which is likely to prove more challenging. However, the advent of managed care in the U.S. has produced strong incentives to provide high quality care at low cost, and our perspective is that only with better IS than exist today will this be possible on a widespread basis. Such systems make feasible implementation of care improvement and cost reduction initiatives on a scale which could not previously be considered.

Decision Making, Computer-Assisted↗
PubMed1996

Diagnosis-based risk adjustment for Medicare capitation payments.

Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula.

Aged↗
PubMedDate not supplied

Using information systems to measure and improve quality.

Information systems (IS) are increasingly important for measuring and improving quality. In this paper, we describe our integrated delivery system's plan for and experiences with measuring and improving quality using IS. Our belief is that for quality measurement to be practical, it must be integrated with the routine provision of care and whenever possible should be done using IS. Thus, at one hospital, we now perform almost all quality measurement using IS. We are also building a clinical data warehouse, which will serve as a repository for quality information across the network. However, IS are not only useful for measuring care, but also represent powerful tools for improving care using decision support. Specific areas in which we have already seen significant benefit include reducing the unnecessary use of laboratory testing, reporting important abnormalities to key providers rapidly, prevention and detection of adverse drug events, initiatives to change prescribing patterns to reduce drug costs and making critical pathways available to providers. Our next major effort will be introduce computerized guidelines on a more widespread basis, which will be challenging. However, the advent of managed care in the US has produced strong incentives to provide high quality care at low cost and our perspective is that only with better IS than exist today will this be possible without compromising quality. Such systems make feasible implementation of quality measurement, care improvement and cost reduction initiatives on a scale which could not previously be considered.

Computer Communication Networks↗