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Biomedical subjects

A J Rotondi

Publications and source records attributed to A J Rotondi.

8 recordsLinked to original sources
PubMed1999

Assessing a team's problem solving ability: evaluation of the Team Problem Solving Assessment Tool (TPSAT).

By employing group consensus development methods, this research identified the variables that experts in team problem solving believe are the most important to a team's problem solving ability. These variables were used to develop a quantitative decision aid to allow health care managers and practitioners to estimate how effective a given problem solving group or team will be at solving a problem. This decision aid can be used to assess a team's problem solving potential before the time and effort is expended to convene the team. This report presents the design and initial evaluations of this decision aid.

Decision Making, Organizational↗
PubMed1999

Are readmissions to the intensive care unit a useful measure of hospital performance?

BACKGROUND: Although patients readmitted to intensive care units (ICUs) typically have poor outcomes, ICU readmission rates have not been studied as a measure of hospital performance. OBJECTIVES: To determine variation in ICU readmission rates across hospitals and associations of readmission rates with other ICU-based measures of hospital performance. RESEARCH DESIGN: Observational cohort study. SUBJECTS: One hundred three thousand nine hundred eighty four consecutive ICU patients who were admitted to twenty eight hospitals who were then transferred to a hospital ward in those 28 hospitals. MEASURES: Predicted risk of in-hospital death and ICU length of stay (LOS) were determined by a validated method based on age, ICU admission source, diagnosis, comorbidity, and physiologic abnormalities. Severity-adjusted mortality rates, LOS, and readmission rates were determined for each hospital. RESULTS: One or more ICU readmissions occurred in 5.8% patients who were initially classified as postoperative and in 6.4% patients who were initially classified as nonoperative. In-hospital mortality rate was 24.7% in patients who were readmitted as compared with 4.0% in other patients (P < 0.001). After adjusting for predicted risk of death, the odds of death remained 7.5 times higher (OR 7.5, 95% CI, 6.8-8.3). Readmitted patients also had longer (P < 0.001) ICU LOS (5.2 vs. 3.7 days) and hospital LOS (29.3 vs. 11.7 days). Severity-adjusted readmission rates varied across hospitals from 4.2% to 7.6%. Readmission rates were not correlated with severity-adjusted hospital mortality, ICU LOS, or hospital LOS. CONCLUSIONS: ICU patients who were subsequently readmitted have a higher risk of death and longer LOS after adjusting for severity of illness. However, readmission rates were not associated with severity-adjusted mortality or LOS. Those data indicate that ICU readmission may capture other aspects of hospital performance and may be complementary to these measures.

Female↗
PubMed1999

Community-wide assessment of intensive care outcomes using a physiologically based prognostic measure: implications for critical care delivery from Cleveland Health Quality Choice.

STUDY OBJECTIVES: To examine the applicability of a previously developed intensive care prognostic measure to a community-based sample of hospitals, and assess variations in severity-adjusted mortality across a major metropolitan region. DESIGN: Retrospective cohort study. SETTING: Twenty-eight hospitals with 38 ICUs participating in a community-wide initiative to measure performance supported by the business community, hospitals, and physicians. PATIENTS: Included in the study were 116,340 consecutive eligible patients admitted to medical, surgical, neurologic, and mixed medical/surgical ICUs between March 1, 1991, and March 31, 1995. MAIN OUTCOME MEASURES: The risk of hospital mortality was assessed using a previous risk prediction equation that was developed in a national sample, and a reestimated logistic regression model fit to the current sample. The standardized mortality ratio (SMR) (actual/predicted mortality) was used to describe hospital performance. RESULTS: Although discrimination of the previous national risk equation in the current sample was high (receiver operating characteristic [ROC] curve area = 0.90), the equation systematically overestimated the risk of death and was not as well calibrated (Hosmer-Lemeshow statistic, 2407.6, 8 df, p < 0.001). The locally derived equation had similar discrimination (ROC curve area = 0.91), but had improved calibration across all ranges of severity (Hosmer-Lemeshow statistic = 13.5, 8 df, p = 0.10). Hospital SMRs ranged from 0.85 to 1.21, and four hospitals had SMRs that were higher or lower (p < 0.01) than 1.0. Variation in SMRs tended to be greatest during the first year of data collection. SMRs also tended to decline over the 4 years (1.06, 1.02, 0.98, and 0.94 in years 1 to 4, respectively), as did mean hospital length of stay (13.0, 12.4, 11.6, and 11.1 days in years 1 to 4; p < 0.001). However, excluding the increasing (p < 0.001) number of patients discharged to skilled nursing facilities attenuated much of the decline in standardized mortality over time. CONCLUSIONS: A previously validated physiologically based prognostic measure successfully stratified patients in a large community-based sample by their risk of death. However, such methods may require recalibration when applied to new samples and to reflect changes in practice over time. Moreover, although significant variations in hospital standardized mortality were observed, changing hospital discharge practices suggest that in-hospital mortality may no longer be an adequate measure of ICU performance. Community-wide efforts with broad-based support from business, hospitals, and physicians can be sustained over time to assess outcomes associated with ICU care. Such efforts may provide important information about variations in patient outcomes and changes in practice patterns over time. Future efforts should assess the impact of such community-wide initiatives on health-care purchasing and institutional quality improvement programs.

APACHE↗
PubMed1998

Use of intensive care units for patients with low severity of illness.

OBJECTIVE: To determine variations among hospitals in use of intensive care units (ICUs) for patients with low severity of illness. DESIGN: Retrospective cohort study. SETTING: Twenty-eight hospitals with 44 ICUs in a large metropolitan region. PATIENTS: Consecutive eligible patients (N=104,487) admitted to medical, surgical, neurological, or mixed medical-surgical ICUs from March 1, 1991, to March 31, 1995. OUTCOME MEASURES: The predicted risk of in-hospital death for each patient was assessed using a validated method that is based on age, ICU admission source, diagnosis, severe comorbid conditions, and abnormalities in 17 physiologic variables. Admissions were classified as low severity if the patient's predicted risk of death was less than 1%. In a subset of 12,929 consecutive patients, use of 19 specific interventions typically delivered in ICUs was examined. RESULTS: Twenty thousand four hundred fifty-one admissions (19.6%) were categorized as low severity, including 23.6% of postoperative and 16.9% of nonoperative admissions. Alcohol and other drug overdoses accounted for 40.2% of nonoperative low-severity admissions; laminectomy and carotid endarterectomy accounted for 52.3% of postoperative low-severity admissions. Mortality among patients with low-severity illness was 0.3%, and only 28.6% received an ICU-specific intervention during the first ICU day. Although mean ICU length of stay was shorter (P<.001) in low-severity admissions (2.2 vs 4.7 days in nonoperative and 2.4 vs 4.2 days in postoperative admissions), low-severity admissions accounted for 11.1% of total ICU bed days. Rates of low-severity admissions varied (P<.001) across hospitals, ranging from 5% to 27% for nonoperative and 9% to 68% for postoperative admissions. CONCLUSIONS: A large proportion of patients admitted to the ICU have a low probability of death and do not receive ICU-specific interventions. Rates of low-severity admissions varied among hospitals. The development and implementation of protocols to target ICU care to patients most likely to benefit may decrease the number of low-severity ICU admissions and improve the cost-effectiveness of ICU care.

Adult↗
PubMed1997

Consensus conferences in critical care medicine. Methodologies and impact.

Consensus conferences for the purposes of producing practice guidelines are occurring with increasing frequency both nationally and internationally. The international collaboration of national sciences in these efforts could have a dramatic impact on international standards of care. Too little emphasis is given to conference evaluations in terms of validity of methods, quality of recommendations, and influence on clinical practice and patient outcome. This article provides an overview of consensus methods used to produce guidelines in critical care. It also discusses the strengths and weaknesses of these methods, and how these may influence consensus guidelines. Finally, a brief overview of theoretically sound methods that can serve as benchmarks to evaluate current methods, and the bases for the development of improved methods is provided.

Consensus Statements as Topic↗
PubMed1997

Benchmarking the perioperative process. I. Patient routing systems: a method for continual improvement of patient flow and resource utilization.

The article presents an overview of the design and application of a real-time patient routing system, based on barcode and local area network technology, that was designed to track the progress of patients during the perioperative process. We present data on all patients undergoing ambulatory surgery. Patients' progress during their surgical stay was recorded at 17 strategic events using this real-time patient tracking technology. These times were used to identify inefficiencies in the perioperative process by identifying bottlenecks and areas of high variation. We found that both raw and actual operating room (OR) utilization efficiency was less than 50%. Points of high variation in a patient's progress occurred during the time from admit to the hospital until the patient was ready for the OR; the time from when a patient was ready for the OR until they were called for; and the time a patient spends in the OR preoperative holding room. Causes for variation were identified and traced back to individual procedures, activities, and work processes. Multidisciplinary improvement teams were created to improve the pinpointed problem areas. The real-time patient routing system is a process that has proven to be highly valuable to all participants in the surgical process in bringing about rational, data driven efficiencies in perioperative services. This process has the potential to facilitate multidisciplinary cooperation in efforts to contain and reduce costs of perioperative services.

Appointments and Schedules↗
PubMed1996

The effect of managed care on ICU length of stay: implications for medicare.

OBJECTIVE: To determine whether insurance status (managed care vs traditional commercial and Medicare) influences resource consumption (as measured by length of stay [LOS]) in the intensive care unit (ICU). DESIGN: Retrospective analysis of the 1992 Massachusetts state hospital discharge database, using prospectively developed and validated risk-stratification models. SETTING: All nonfederal hospitals in Massachusetts. SUBJECTS: Of all adult hospitalizations where an ICU stay was incurred (n=104270), we selected those covered by 1 of 4 payer groups (n=88050): (1) commercial fee-for-service (patients aged <65 years); (2) commercial managed care (patients aged <65 years); (3) traditional Medicare (patients aged >/=65 years); and (4) Medicare-sponsored managed care (patients aged >/=65 years). MAIN OUTCOME MEASURE: Mean ICU LOS. ANALYSIS: The ICU LOS regression models were constructed using split-halves validation to adjust for differences in age, sex, severity of illness, diagnosis, discharge status, and payer. Separate models were constructed for those younger than 65 years and those aged 65 years or older. Robustness of the models was explored using goodness of fit and correlation. The effect of payer on hospital mortality was also explored using logistic regression. Observed minus predicted mean ICU LOS and mortality rates were correlated with managed care penetration at the hospital level. RESULTS: The ICU LOS models performed well (R2=0.84 and R2L [likelihood ratio statistic]=0.92 for the development set, and R2=0.83 and R2L=0.89 for the validation set). Significant covariables affecting LOS included age, severity of principal illness, comorbidity, reason for admission, and discharge status (P<.001 for each). Among the cohort younger than 65 years (n=27805), although unadjusted mean ICU LOS was shorter (2.9 vs 3.43 days; P<.05) for those covered by managed care organizations, payer status had no independent effect on ICU LOS (P=.48). Among those older than 65 years, there was neither a difference in unadjusted ICU LOS (3.94 vs 3.88 days; P>/=.05) nor an independent effect of payer on ICU LOS (P=.35). Unadjusted mortality was lower among managed care patients (3.9% vs 5.1% in patients aged <65 years [P<.05] and 8.7% vs 12.1% in patients aged > or = 65 years [P<.05]). Age, severity of principal diagnosis, comorbidity, and reason for admission significantly influenced mortality (P<.001). After controlling for these factors with the mortality model (R2L=0.92 and 0.89, C statistic [12 df]=8.45 and 17.58, and P=.75 and .13 [where a large P reflects good agreement] for the development and validation sets, respectively), payer continued to have a small but significant effect on mortality (odds ratios ranging from 1.67 at 0.1% probability of death to 1.11 at 30% probability of death.) Managed care penetration among the commercially insured varied across hospitals (n=82) from 0% to 68%. There was no correlation between managed care penetration and either ICU LOS (R2=0.04; P=.09) or mortality (R2=0.0; P=.88). CONCLUSIONS: Though patients covered under managed care consume fewer ICU resources, this appears to be primarily attributable to a difference in patient-related factors. Thus, as managed care case mix changes in the future to include sicker and older patients, the initial advantages of reduced resource consumption may diminish.

Adult↗