John Rapoport

Social and Private Rates of Return from Industrial Innovations

I. Introduction, 221.—II. The sample of innovations, 222.—III. Estimation of social benefits: product innovations used by firms, 222.—IV. Parallel innovative efforts, time horizon, and rates of return, 226.—V. Product innovations used by households, 229.—VI. Process innovations, 231.—VII. Social and private rates of return, 233.—VIII. Factors associated with the gap between social and private rates of return, 235.—IX. Unemployment, repercussions on other markets, and future changes in technology, 238.—X. Conclusion, 239.

Association between Critical Care Physician Management and Patient Mortality in the Intensive Care Unit

Context Critical care physicians or physicians without specialized critical care training may manage patients in intensive care units. Contribution This study described 101832 patients in 123 intensive care units in the United States. Patients managed by critical care physicians were sicker, had more procedures, and had higher hospital mortality rates than those managed by other physicians. Analyses that adjusted for severity of illness and the tendency for sicker patients to be managed by critical care specialists still showed higher mortality among patients managed by the specialists. Caution Unrecognized confounders might diminish or invalidate the unexpected finding of higher mortality among patients managed by critical care specialists. The Editors The extent of involvement and supervision by critical care physicians varies somewhat in U.S. intensive care units (ICUs) (16). Some ICUs are organized as strictly closed services, in which critical care physicians, or intensivists, assume control and decision-making ability over all aspects of patient care, whereas in some hybrid ICUs, mandated consultation and management by critical care physicians is the primary administrative model. Most ICUs, however, are structured as completely open units, in which the admitting physicians retain full clinical and decisional responsibility and thus have the option to care for their patients with or without input from critical care physicians. Evidence from several settings suggests improved outcomes when critical care physicians assume substantial responsibility over the care and triage of ICU patients (1, 722). These studies, however, have methodological limitations and limited generalizability. Most are small, use historical controls or beforeafter study designs, and are limited to specific ICUs (for example, medical or surgical) in 1 or 2 centers. They have the usual risks for confounding by illness severity commonly seen in cross-sectional studies (7, 8, 1421) and retrospective analyses of administrative databases that were limited to certain diagnostic categories (12, 13). Recognizing the limitations of previously published studies and considerable variability in critical care management (CCM) in the United States, we examined data from 123 ICUs across the United States to assess the relationship between management by critical care physicians and hospital mortality rates of critically ill patients. These data were derived from a large national project that examined resource use in intensive care (2). At the beginning of our analysis, we hypothesized that CCM would be associated with improved outcomes in critically ill patients. Methods Patients Patients were identified through Project IMPACT (Cerner, Bel Air, Maryland), a national database of ICU patients. The Project IMPACT database is a large administrative database originally developed by the Society of Critical Care Medicine in 1996. Participation is voluntary. All data are collected at each institution by on-site data collectors who are certified in advance by Project IMPACT to assure standardization and uniformity in data definitions and database definitions and entry. The database for 2000 to 2004 included 142392 patients admitted to 123 ICUs in 100 U.S. hospitals. We excluded patients with missing data for variables of interest from our analysis, leaving 111907 patients. We included only the first ICU admission, reducing the number of patients to 106623, and then excluded patients who were managed only part time during their ICU stay, reducing the total observations to 101832. Variables Our primary outcome variable was hospital mortality. Our key exposure or risk factor was the same regardless of whether a patient was managed by a critical care physician during his or her ICU stay. This was ascertained in Project IMPACT by using the survey question, Was the patient managed by a critical care physician/team? Trained data entry personnel for Project IMPACT define CCM as treatment occurring when the physician is asked to take responsibility for the overall management of a patient in the critical care unit without having to first provide expertise about a single organ system. A physician should meet 1 or more of the following criteria to be considered a critical care physician: 1) be recognized by the institution as a critical care specialist within a specialty unit, even without a specialty board certification (such as burn or neurointensivist), and must treat the total patient and not a single organ system; 2) have passed critical care medicine board examinations or be qualified to take the examination; and 3) be trained in an accredited critical care fellowship. When a patient received CCM, it was documented, regardless of whether the treatment was for all or part of the ICU stay. Covariates included patient characteristics, such as demographic characteristics, diagnosis, and clinical condition at ICU admission. We also controlled for ICU and hospital characteristics. Severity of illness was measured by the Simplified Acute Physiology Score (SAPS) II. Through use of recently published work on SAPS (23), we added additional variables to SAPS II and modified coefficients in the logit model to derive a better fit. These included the patients age (<40 years, 40 to 59 years, 60 to 69 years, 70 to 79 years, and >79 years), sex, duration of hospital stay before ICU admission (<24 hours, 1 day, 2 days, 3 to 9 days, >9 days), patients location before ICU (transfer from outside emergency department, rehabilitation or skilled nursing facility, wards, or another hospital), clinical category (medical patient or other), and intoxication (yes or no). For this expanded SAPS II, the HosmerLemeshow goodness-of-fit P value was 0.38. (The Appendix provides more detail on the expanded SAPS II.) Supplement. Appendix Statistical Analysis We divided ICUs into 3 groups based on the percentage of patients receiving CCM for the entire stay: 95% of patients or more, 5% to 95% of patients, and 5% of patients or fewer. We excluded 4793 patients who received CCM for only part of the ICU stay from the analysis, leaving 2 patient management types: CCM for the entire stay and no CCM. For each of the 6 categories defined by the combination of patient management type and ICU group, we computed expected and actual mortality rates. Expected mortality was the mean SAPS II probability of mortality. Actual mortality was the percentage of patients who did not survive the hospital stay. We computed the standardized mortality ratio and its 95% CI, based on an exact Poisson distribution, as the ratio of actual to expected mortality. We developed a score to measure the propensity that a patient would be selected for CCM. We derived our score from a logistic regression model, with CCM as the dependent variable. The model was estimated on patients only from ICUs not mandating CCM. We screened all available patient characteristics known at the time of ICU admission and ICU characteristics for inclusion in the model. A propensity score was then estimated for each patient. Variables used to create the propensity score were age, Glasgow Coma Score, number of licensed hospital beds, insurance (commercial, Medicaid or Medicare, or self-pay), ventilation at ICU admission, tracheostomy at ICU admission, gastrointestinal bleeding, noninvasive ventilation at ICU admission, cerebrovascular event, chronic immunosuppression, chronic respiratory disease, acute renal failure, hospital location (rural, suburban, or urban), continuous sedation, and admission source (emergency department, another hospital, invasive procedures, or other non-ICU location). Figure 1 shows the proportion of patients managed by critical care physicians. Hospital mortality rates tend to increase from the first decile to the last decile of propensity and SAPS II. More details of the score and the sensitivity of results to changes in the propensity score are shown in the Appendix. Figure 1. Critical care management ( CCM ) and mortality. SAPS= Simplified Acute Physiology Score. We performed random-effects logistic regressions on the entire sample, using hospital death as the dependent variable. This method uses the within- and between-ICU variability inherent in the nesting of the patients into 123 ICUs. The crude model included only the risk factor CCM for the entire stay versus no CCM. Severity of illness (as measured by the expanded SAPS II score) and likelihood of selection for CCM (as measured by the propensity score) were then added to the model as control variables, along with all interactions of the control variables and risk factor. Where a statistically significant interaction term indicated that a control variable was an effect modifier, the regression was estimated within each quartile of the control variable. We repeated random-effects logistic regression analysis of mortality on several subsamples. The no-choice subsample included 2 groups of patients: those from ICUs in which 95% or more or 5% or fewer patients received CCM. In addition, the following subsamples were examined: patients not transferred from another hospital, patients with a respiratory diagnosis with ventilator support at ICU admission, patients with respiratory diagnosis without ventilator support at ICU admission, patients with ventilator support at ICU admission, patients with a diagnosis other than respiratory and no ventilator at ICU admission, patients with a circulatory diagnosis, patients with a diagnosis of infection, patients with at least 1 ICU procedure, and patients with no ICU procedures. The Appendix presents additional details of regression analyses. Role of the Funding Source Eli Lilly and the Department of Bioethics at the National Institutes of Health Clinical Center funded the study. The funding services had no role in the design, conduct, and analysis of the study and did not participate in the decision to submit the manuscript for publication.

A method for assessing the clinical performance and cost-effectiveness of intensive care units : a multicenter inception cohort study

ObjectivesTo present an approach for assessing intensive care unit (ICU) performance which takes into account both economic and clinical performance while adjusting for severity of illness. To present a graphic display which permits comparisons among a group of hospitals. DesignA multicenter, inception cohort study. SettingTwenty-five ICUs in U.S. hospitals that participated in the European and North American Study of Severity Systems for ICU Patients. PatientsConsecutive patients (n = 3,397) admitted to ICUs in participating hospitals between September 30, 1991 and December 27, 1991. Excluded were coronary care patients, burn patients, cardiac surgery patients and patients aged <18 yrs. Measurements and Main ResultsThe clinical performance index is the difference between observed hospital survival rate and survival rate predicted by the Mortality Probability Model measuring severity of illness at ICU admission. The economic performance (resource use) measure is a length of stay index, Weighted Hospital Days, which weights ICU days more heavily than non-ICU days. The economic performance index is the difference between actual mean resource use and the resource use predicted by a regression including severity of illness and percent of surgical patients. Both the clinical and economic performance indices are standardized to show how far a particular hospital is from the overall mean and are graphed together. Most of the 25 hospitals lie within 1 SD of the mean on both clinical and economic performance scales. The graph makes it easy to identify those hospitals that are outside this range. There is no evidence of a tradeoff between high clinical performance and high economic performance; i.e., it is possible to achieve both. ConclusionsCross-indexing of clinical and economic ICU performance is easy to calculate. It has potential as a research and evaluation tool used by physicians, hospital administrators, payers, and others. (Crit Care Med 1994; 22:1385–1391

Mortality probability models for patients in the intensive care unit for 48 or 72 hours: A prospective, multicenter study

ObjectiveTo develop models in the Mortality Probability Model (MPM II) system to estimate the probability of hospital mortality at 48 and 72 hrs in the intensive care unit (ICU), and to test whether the 24-hr Mortality Probability Model (MPM24), developed for use at 24 hrs in the ICU, can be used on a daily basis beyond 24 hrs. DesignA prospective, multicenter study to develop and validate models, using a cohort of consecutive admissions. SettingSix adult medical and surgical ICUs in Massachusetts and New York adjusted to reflect 137 ICUs in 12 countries. PatientsConsecutive admissions (n = 6,290) to the Massachusetts/New York ICUs were studied. Of these patients, 3,023 and 2,233 patients remained in the ICU and had complete data at 48 and 72 hrs, respectively. Patients <18 yrs of age, burn patients, coronary care patients, and cardiac surgical patients were excluded. Outcome MeasureVital status at the time of hospital discharge. ResultsThe models consist of five variables measured at the time of ICU admission and eight variables ascertained at 24-hr intervals. The 24-hr model demonstrated poor calibration and discrimination at 48 and 72 hrs. The newly developed 48− and 72-hr models—MPM48 and MPM72—contain the same 13 variables and coefficients as the MPM24. The models differ only in their constant terms, which increase in a manner that reflects the increasing probability of mortality with increasing length of stay in the ICU. These constant terms were adjusted by a factor determined from the relationship between the data from the six Massachusetts and New York ICUs and a more extensive data set, from which the ICU admission Mortality Probability Model (MPM0) and MPM24 were developed. This latter data set was assembled from ICUs in 12 countries. The MPM48 and MPM72 calibrated and discriminated well, based on goodness-of-fit tests and area under the receiver operating characteristic curve. ConclusionsModels developed for use among ICU patients at one time period are not transferable without modification to other time periods. The MPM48 and MPM72 calibrated well to their respective time periods, and they are intended for use at specific points in time. The increasing constant terms and associated increase in the probability of hospital mortality exemplify a common clinical adage that if a patients clinical profile stays the same, he or she is actually getting worse. (Crit Care Med 1994; 22:1351–1358)

Length of stay data as a guide to hospital economic performance for ICU patients.

Context. Length of stay data are increasingly used to monitor ICU economic performance. How such material is presented greatly affects its utility. Objective. To develop a weighted length of stay index and to estimate expected length of stay. To assess alternative ways to summarize weighted length of stay to evaluate ICU economic performance. Design. Retrospective database study. Subjects. Data for 751 ICU patients in 1998 at two hospitals used to develop weighted length of stay index. Data on 42,237 patients from 72 ICUs used as the basis of economic performance evaluation. Main Outcome Measures. Difference between actual and expected weighted length of stay, where expected weighted length of stay is based on patient clinical characteristics. Results. Length of stay statistically explains approximately 85 to 90% of interpatient variation in hospital costs. The first ICU day is approximately four times as expensive, and other ICU days approximately 2.5 times as expensive, as non-ICU hospital days. In a regression model for weighted length of stay, patient clinical characteristics explain 26% of variation. ICU economic performance can be measured by excess weighted length of stay of a “typical” patient or by occurrence of long excess weighted lengths of stay. Although different summary measures of performance are highly correlated, choice of measure affects relative ranking of some ICUs’ performance Conclusion. Providers of statistical data on ICU economic performance should adjust length of stay for patient characteristics and provide multiple summary measures of the statistical distribution, including measures that address both the typical patient and outliers.

Explaining variability of cost using a severity-of-illness measure for ICU patients.

Factors related to hospital resource use by intensive care unit (ICU) patients, including severity of illness at admission and intensity of therapy during the first 24 ICU hours were explored in this study. Analysis was based on 2,749 patients admitted to the general medical-surgical ICU at Baystate Medical Center, Springfield, Massachusetts, between February 1,1983 and January 10, 1985. Resource use was indexed by hospital length of stay (LOS) adjusted for differences between ICU and other hospital days. Severity of illness was measured by the Mortality Prediction Model (MPM0), a validated predictor of outcome but not previously used to analyze resource consumption. Intensity of therapy was measured using the Therapeutic Intervention Scoring System (TISS). The 10% of patients with longest ICU stays were significantly different from the other 90% with respect to previous ICU use, MPM probability, and TISS score. Variability in resource use was analyzed using four diagnosis-related groups (DRGs) accounting for large numbers of ICU patients. The relationship between severity of illness and resource use was nonlinear: as severity increased from low levels, resource use increased at a decreasing rate, reached a plateau, and eventually declined. Within each DRG, MPM0 explained a statistically significant percentage of the variability in resource use.

ICU and non-ICU cost per day

The purpose of this study was to compare the cost of a day spent in an intensive care unit and a day spent on a general nursing unit. A descriptive design was used, based on patient level data, to examine and compare unit costs per day for each of the ICU and non-ICU portions of a patient’s hospital stay. Records from 386 patients who were treated in a general medical/surgical ICU were analyzed. Records for patients who received both ICU and non-ICU care during their stay were retained. Patients were categorized according to whether they had received surgical care prior to admission to the ICU (surgical group) or had no surgical care (medical group). The groups were further divided, based on whether they were discharged from hospital (survivors), or died following transfers from the ICU (non-survivors). All four groups; surgical or medical, survivors and non-survivors, were analyzed separately. The ICU direct costs per day for survivors were between six and seven times those for non-ICU care. A one day substitution of general ward for ICU care would result in a cost reduction of

Timing of intensive care unit admission in relation to ICU outcome

1,200 per patient for survivors. The results suggest that the savings achieved by moving a patient from ICU to non-ICU care are considerable, particularly for less severe surviving patients. In making such decisions, however, clinicians must examine prospective benefits as well as costs. If the health outcomes are not influenced, the savings from substitution are considerable, and there is a strong economic argument for substitution.RésuméCe travail vise à comparer les coûts quotidiens de l’hospitalisation en unité de soins intensif (USI) à ceux de l’unité de soins réguliers. Un modèle basé sur l’observation des données spécifiques au patient est utilisé pour examiner et comparer les coûts quotidiens de chacune des périodes de séjour hospitalier dans l’USI et à l’extérieur de l’USI. Les dossiers de 386 patients traités dans une unité de soins intensifs médicochirurgicaux sont analysés. Seuls les dossiers de ceux qui ont reçu des soins à l’USI et hors de l’USI sont retenus. Deux catégories de patients sont établies; ceux qui ont reçu des soins chirurgicaux avant leur admission à l’USI (groupe chirurgical) et ceux qui n’ont pas reçu de soins chirurgicaux (groupe médical) avant leur admission à l’USI. Ces groupes ont été subdivisés: ceux qui ont reçu leur congé de l’hôpital (survivants) et ceux qui sont décédés après leur transfert de l’USI (décédés). Les quatre groupes: chirurgical, médical, survivant, décédé sont analysés indépendamment. Les coût directs par journée de séjour à l’USI par survivant se situent entre six et sept fois ceux du séjour hors USI. La substitution d’une journée d’USI pour une journée de séjour en unité régulière permet une économie de 1200

A comparison of intensive care unit utilization in Alberta and western Massachusetts

par survivant. Ces résultats permettent de croire que les économies réalisées en transférant un malade de l’USI vers une unité régulière sont importantes, particulièrement pour les survivants les moins malades. Cependant, en prenant de telles décisions, les cliniciens doivent tenir compte autant des bénéfices que des coûts. Si la substitution ne compromet pas le pronostic, les économies réalisées sont considérables et il devient impérieux d’adhérer à cette pratique.

Economies of scale in British intensive care units and combined intensive care/high dependency units.

This study assessed the relationship between admission time (from hospital admission to ICU admission) and mortality predicted by the Mortality Prediction Model (MPM), actual mortality, and resource use. All admissions, except elective surgery patients, to the general medical/surgical ICU of a tertiary care hospital during a 24-month period were studied (n = 1,889). Patients admitted to the ICU within 1 day of hospital admission had lower predicted and actual mortality, and used fewer resources than patients admitted later. Predicted mortality was higher than actual mortality for patients admitted to the ICU early and was lower than actual mortality for later ICU admissions. Transfers had higher predicted and actual mortality, and used more resources than nontransfer patients. Time from hospital admission to ICU admission can be a potentially useful variable in models of ICU outcome.