Predictors of patient entry into alcohol treatment after initial diagnosis.

To improve the quality of care for alcohol-related disorders, key transitions in the continuum of care, including treatment entry, must be fully understood. The purpose of this study was to investigate identifiable predictors of patient entry into a substance-use treatment program following the initial diagnosis of an alcohol-related disorder on a medical or surgical inpatient unit. An administrative computerized database was used to identify the sample for this study. Inpatient and outpatient records were obtained from the Little Rock VAMC/DHCP. Predictors of patient entry into treatment within six months of the initial diagnosis of an alcohol related disorder included age younger than than 60 (odds ratio [OR] = 4.6), not married (OR = 1.7), primary diagnosis of an alcohol-related disorder (OR = 7.7), diagnosis of a comorbid drug (OR = 4.3) or psychiatric disorder (OR = 3.6), diagnosis by a medical as opposed to a surgical specialty (OR = 6.0), and African American (OR = 1.7).

The feasibility of using automated data to assess guideline-concordant care for schizophrenia.

This study examines the feasibility of using automated computer data versus written medical record data to identify patients receiving guideline concordant treatment for schizophrenia. Central elements of care derived from published practice guidelines for schizophrenia were examined for a convenience sample of 28 patients who received acute inpatient treatment. The results showed that automated data were superior to medical record data for identifying some elements of guideline-concordant treatment. Not only were the elements of care examined in this study clinically significant and within the current capabilities of the existing computer information system, but they are also likely related to patient outcomes. Implications for clinical care, future research, and health care quality improvement efforts are discussed.

Identifying patterns of disruptive behavior in long-term care residents.

OBJECTIVES: To determine the frequency, timing, and pattern of 45 operationalized disruptive behaviors (DB) in older people in long-term care units. DESIGN: Nursing staff collected prospective descriptive data over 21 consecutive shifts for each patient to document prevalence, frequency, and co-occurrences of DBs. SETTING: All of the eight long-term care units and one acute/admission unit of a large Veterans Administration Medical Center (VAMC). Each 40-bed unit had patients with varying levels of cognitive impairment and skilled nursing needs. PARTICIPANTS: The sample consisted of 240 hospitalized VA patients with a mean age of 72.8 (SD = 8.6) years and mean length of stay of 4.02 (SD = 8.6) years. Residents had dementia, a psychiatric diagnosis, or mixed dementia and psychiatric diagnoses. MEASUREMENTS: The Disruptive Behavior Scale (DBS), an instrument designed for collecting patient-level data on 45 separate DBs. RESULTS: In a 24-hour period, the average frequency was 3.6 DBs per subject. We found that 41.2% of DB occurred during the day shift, 39.2% during the evening shift, and 19.6% during the night shift. In 32% of observed occurrences, only one DB occurred within the hour. In the remaining 68% of observations, two or more DBs occurred within the same hour. We found two behaviors, Does Not Follow Directions and Excessive Motor Activity, to occur with multiple behaviors in multiple categories. Several characteristic patterns were noted; e.g., physically aggressive behaviors rarely co-occurred with verbal DBs. Physically nonaggressive behaviors seemed to occur most frequently with other physically nonaggressive behaviors and, to a lesser extent, with verbal DBs. CONCLUSIONS: These findings lend support to the existence of patterns of DBs in long-term care patients, a useful step toward targeting interventions early in the behavioral sequence.

Geographic market areas for psychiatric and medical outpatient treatment.

The research objective was to measure the variation in the size of a facility's market areas across different diagnostic categories. Specifically, the market area radii for outpatient psychiatric services are compared to the radii for outpatient medical services. Data were collected from the outpatient clinics of the Little Rock Veterans Administration Medical Center. Visits were categorized into 100 diagnostic groups. The market radius for each diagnostic group was defined as the 75th quartile of the distribution of distances traveled. All psychiatric diagnostic groups had significantly (p < 0.05) smaller market area radii than the overall sample radius. The average market area radius across psychiatric illnesses was 62.2 miles, which was significantly (p < 0.05) smaller than the average radius across medical illnesses (90.6 miles). Results suggest that rural patients with mental illness may not receive adequate care and that specialized outreach programs may need to be developed to better serve this population.