[show abstract][hide abstract] ABSTRACT: Despite limited evidence regarding their utility, infrared thermal detection systems (ITDS) are increasingly being used for mass fever detection. We compared temperature measurements for 3 ITDS (FLIR ThermoVision A20M [FLIR Systems Inc., Boston, MA, USA], OptoTherm Thermoscreen [OptoTherm Thermal Imaging Systems and Infrared Cameras Inc., Sewickley, PA, USA], and Wahl Fever Alert Imager HSI2000S [Wahl Instruments Inc., Asheville, NC, USA]) with oral temperatures (≥ 100 °F = confirmed fever) and self-reported fever. Of 2,873 patients enrolled, 476 (16.6%) reported a fever, and 64 (2.2%) had a confirmed fever. Self-reported fever had a sensitivity of 75.0%, specificity 84.7%, and positive predictive value 10.1%. At optimal cutoff values for detecting fever, temperature measurements by OptoTherm and FLIR had greater sensitivity (91.0% and 90.0%, respectively) and specificity (86.0% and 80.0%, respectively) than did self-reports. Correlations between ITDS and oral temperatures were similar for OptoTherm (ρ = 0.43) and FLIR (ρ = 0.42) but significantly lower for Wahl (ρ = 0.14; p < 0.001). When compared with oral temperatures, 2 systems (OptoTherm and FLIR) were reasonably accurate for detecting fever and predicted fever better than self-reports.
[show abstract][hide abstract] ABSTRACT: Hospitals will increasingly bear the costs for healthcare-acquired conditions such as infection. Our goals were to estimate the costs attributable to healthcare-acquired infection (HAI) and conduct a sensitivity analysis comparing analytic methods.
A random sample of high-risk adults hospitalized in the year 2000 was selected. Measurements included total and variable medical costs, length of stay (LOS), HAI site, APACHE III score, antimicrobial resistance, and mortality. Medical costs were measured from the hospital perspective. Analytic methods included ordinary least squares linear regression and median quantile regression, Winsorizing, propensity score case matching, attributable LOS multiplied by mean daily cost, semi-log transformation, and generalized linear modeling. Three-state proportional hazards modeling was also used for LOS estimation. Attributable mortality was estimated using logistic regression.
Among 1253 patients, 159 (12.7%) developed HAI. Using different methods, attributable total costs ranged between $9310 to $21,013, variable costs were $1581 to $6824, LOS was 5.9 to 9.6 days, and attributable mortality was 6.1%. The semi-log transformation regression indicated that HAI doubles hospital cost. The totals for 159 patients were $1.48 to $3.34 million in medical cost and $5.27 million for premature death. Excess LOS totaled 844 to 1373 hospital days.
Costs for HAI were considerable from hospital and societal perspectives. This suggests that HAI prevention expenditures would be balanced by savings in medical costs, lives saved and available hospital days that could be used by overcrowded hospitals to enhance available services. Our results obtained by applying different economic methods to a single detailed dataset may inform future cost analyses.
Medical care 10/2010; 48(11):1026-35. · 3.24 Impact Factor
[show abstract][hide abstract] ABSTRACT: Organisms resistant to antimicrobials continue to emerge and spread. This study was performed to measure the medical and societal cost attributable to antimicrobial-resistant infection (ARI).
A sample of high-risk hospitalized adult patients was selected. Measurements included ARI, total cost, duration of stay, comorbidities, acute pathophysiology, Acute Physiology and Chronic Health Evaluation III score, intensive care unit stay, surgery, health care-acquired infection, and mortality. Hospital services used and outcomes were abstracted from electronic and written medical records. Medical costs were measured from the hospital perspective. A sensitivity analysis including 3 study designs was conducted. Regression was used to adjust for potential confounding in the random sample and in the sample expanded with additional patients with ARI. Propensity scores were used to select matched control subjects for each patient with ARI for a comparison of mean cost for patients with and without ARI.
In a sample of 1391 patients, 188 (13.5%) had ARI. The medical costs attributable to ARI ranged from $18,588 to $29,069 per patient in the sensitivity analysis. Excess duration of hospital stay was 6.4-12.7 days, and attributable mortality was 6.5%. The societal costs were $10.7-$15.0 million. Using the lowest estimates from the sensitivity analysis resulted in a total cost of $13.35 million in 2008 dollars in this patient cohort.
The attributable medical and societal costs of ARI are considerable. Data from this analysis could form the basis for a more comprehensive evaluation of the cost of resistance and the potential economic benefits of prevention programs.
[show abstract][hide abstract] ABSTRACT: Health care costs for HIV infection are often reported from the economic perspective of third party payors and little data exist to show how total costs are distributed across specific health service categories. We used a retrospective cohort design to measure total medical costs for 1 year in a randomly selected sample of 280 patients treated for HIV infection at an urban health care facility. Inpatient and outpatient costs were measured from the economic perspective of the health care provider. Hospital costs included ward, ancillary, and procedure costs. Ambulatory included medications, primary and specialty care, case management, ancillary, and behavioral comorbidity treatment costs. The mean total was $20,114 per patient, of which $6,322 was for inpatient and $13,842 was for ambulatory services. Specific ambulatory costs were: medications, $9,257; primary, specialty and ancillary services, $3,470; and behavioral comorbidity treatment, $1,111. The mean annual outpatient ancillary cost was $841. Over 30% of the total service cost was for building and administrative overhead and approximately 25% of both hospital and clinic costs were for ancillary services. Independent predictors of high cost were CD4 counts, Medicaid eligibility, and behavorial comorbidities. Our outpatient costs were higher, with less variation than previously reported. Increasingly, there has been a shift of HIV care from hospital to ambulatory settings. We postulate that reimbursement rates have not captured the recent flourishing of ambulatory care. If reimbursement is not commensurate with outpatient advances, providers may be paradoxically underreimbursed for improving care.
AIDS PATIENT CARE and STDs 01/2007; 20(12):876-86. · 3.09 Impact Factor
[show abstract][hide abstract] ABSTRACT: Hospital-associated infection is well recognized as a patient safety concern requiring preventive interventions. However, hospitals are closely monitoring expenditures and need accurate estimates of potential cost savings from such prevention programs. We used a retrospective cohort design and economic modeling to determine the excess cost from the hospital perspective for hospital-associated infection in a random sample of adult medical patients. Study patients were classified as being not infected (n=139), having suspected infection (n=8), or having confirmed infection (n=17). Severity of illness and intensive unit care use were both independently associated with increased cost. After controlling for these confounding effects, we found an excess cost of $6767 for suspected infection and $15,275 for confirmed hospital-acquired infection. The economic model explained 56% of the total variability in cost among patients. Hospitals can use these data when evaluating potential cost savings from effective infection-control measures.
[show abstract][hide abstract] ABSTRACT: Emergency department (ED) physicians often are uncertain about where in the hospital to triage patients with suspected acute cardiac ischemia. Many patients are triaged unnecessarily to intensive or intermediate cardiac care units.
To determine whether use of a clinical decision rule improves physicians' hospital triage decisions for patients with suspected acute cardiac ischemia.
Prospective before-after impact analysis conducted at a large, urban, US public hospital.
Consecutive patients admitted from the ED with suspected acute cardiac ischemia during 2 periods: preintervention group (n = 207 patients enrolled in March 1997) and intervention group (n = 1008 patients enrolled in August-November 1999).
An adaptation of a previously validated clinical decision rule was adopted as the standard of care in the ED after a 3-month period of pilot testing and training. The rule predicts major cardiac complications within 72 hours after evaluation in the ED and stratifies patients' risk of major complications into 4 groups--high, moderate, low, and very low--according to electrocardiographic findings and presence or absence of 3 clinical predictors in the ED.
Safety of physicians' triage decisions, defined as the proportion of patients with major cardiac complications who were admitted to inpatient cardiac care beds (coronary care unit or inpatient telemetry unit); efficiency of decisions, defined as the proportion of patients without major complications who were triaged to an ED observation unit or an unmonitored ward.
By intention-to-treat analysis, efficiency was higher in the intervention group (36%) than the preintervention group (21%) (difference, 15%; 95% confidence interval [CI], 8%-21%; P<.001). Safety was not significantly different (94% in the intervention group vs 89%; difference, 5%; 95% CI, -11% to 39%; P =.57). Subgroup analysis of intervention-group patients showed higher efficiency when physicians actually used the decision rule (38% vs 27%; difference, 11%; 95% CI, 3%-18%; P =.01). Improved efficiency was explained solely by different triage decisions for very low-risk patients. Most surveyed physicians (16/19 [84%]) believed that the decision rule improved patient care.
Use of the clinical decision rule had a favorable impact on physicians' hospital triage decisions. Efficiency improved without compromising safety.
JAMA The Journal of the American Medical Association 08/2002; 288(3):342-50. · 29.98 Impact Factor