The ethical and economic impact of defaults.
ABSTRACT Medical care offered to the critically ill often occurs by default, unfolding automatically unless concerted effort is made to do otherwise. In their scope, defaults include traditional approaches to treatment and decision making, as well as policies deliberately set to promote specific health outcomes. Defaults are ethically sound to the extent that they foster patient well-being and autonomy. Unfortunately in practice, some defaults lead to ineffective, unwanted, and expensive care. This article reviews the ethical and economic impact of defaults, paying special attention to their influence on the practice of cardiopulmonary resuscitation and admission to the intensive care unit.
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ABSTRACT: We sought to test whether variations across regions in end-of-life (EOL) treatment intensity are associated with regional differences in patient preferences for EOL care. Dual-language (English/Spanish) survey conducted March to October 2005, either by mail or computer-assisted telephone questionnaire, among a probability sample of 3480 Medicare part A and/or B eligible beneficiaries in the 20% denominator file, age 65 or older on July 1, 2003. Data collected included demographics, health status, and general preferences for medical care in the event the respondent had a serious illness and less than 1 year to live. EOL concerns and preferences were regressed on hospital referral region EOL spending, a validated measure of treatment intensity. A total of 2515 Medicare beneficiaries completed the survey (65% response rate). In analyses adjusted for age, sex, race/ethnicity, education, financial strain, and health status, there were no differences by spending in concern about getting too little treatment (39.6% in lowest spending quintile, Q1; 41.2% in highest, Q5; P value for trend, 0.637) or too much treatment (44.2% Q1, 45.1% Q5; P = 0.797) at the end of life, preference for spending their last days in a hospital (8.4% Q1, 8.5% Q5; P = 0.965), for potentially life-prolonging drugs that made them feel worse all the time (14.4% Q1, 16.5% Q5; P = 0.326), for palliative drugs, even if they might be life-shortening (77.7% Q1, 73.4% Q5; P = 0.138), for mechanical ventilation if it would extend their life by 1 month (21% Q1, 21.4% Q5; P = 0.870) or by 1 week (12.1% Q1, 11.7%; P = 0.875). Medicare beneficiaries generally prefer treatment focused on palliation rather than life-extension. Differences in preferences are unlikely to explain regional variations in EOL spending.Medical Care 06/2007; 45(5):386-93. · 2.94 Impact Factor
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ABSTRACT: Concern regarding wide variations in spending and intensive care unit use for patients at the end of life hinges on the assumption that such treatment offers little or no survival benefit. To explore the relationship between hospital "end-of-life" (EOL) treatment intensity and postadmission survival. Retrospective cohort analysis of Pennsylvania Health Care Cost Containment Council discharge data April 2001 to March 2005 linked to vital statistics data through September 2005 using hospital-level correlation, admission-level marginal structural logistic regression, and pooled logistic regression to approximate a Cox survival model. A total of 1,021,909 patients > or =65 years old, incurring 2,216,815 admissions in 169 Pennsylvania acute care hospitals. EOL treatment intensity (a summed index of standardized intensive care unit and life-sustaining treatment use among patients with a high predicted probability of dying [PPD] at admission) and 30- and 180-day postadmission mortality. There was a nonlinear negative relationship between hospital EOL treatment intensity and 30-day mortality among all admissions, although patients with higher PPD derived the greatest benefit. Compared with admission at an average intensity hospital, admission to a hospital 1 standard deviation below versus 1 standard deviation above average intensity resulted in an adjusted odds ratio of mortality for admissions at low PPD of 1.06 (1.04-1.08) versus 0.97 (0.96-0.99); average PPD: 1.06 (1.04-1.09) versus 0.97 (0.96-0.99); and high PPD: 1.09 (1.07-1.11) versus 0.97 (0.95-0.99), respectively. By 180 days, the benefits to intensity attenuated (low PPD: 1.03 [1.01-1.04] vs. 1.00 [0.98-1.01]; average PPD: 1.03 [1.02-1.05] vs. 1.00 [0.98-1.01]; and high PPD: 1.06 [1.04-1.09] vs. 1.00 [0.98-1.02]), respectively. Admission to higher EOL treatment intensity hospitals is associated with small gains in postadmission survival. The marginal returns to intensity diminish for admission to hospitals above average EOL treatment intensity and wane with time.Medical care 02/2010; 48(2):125-32. · 2.94 Impact Factor
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ABSTRACT: Little is known about prognostic factors that determine outcomes after in-hospital cardiopulmonary resuscitation. We studied prospectively 294 consecutive patients who were resuscitated in a university teaching hospital. Forty-one patients (14 per cent) were discharged from the hospital; three quarters of them were still alive six months later. A multivariate analysis revealed that pneumonia, hypotension, renal failure, cancer, and a homebound life style before hospitalization were significantly associated with in-hospital mortality (P less than 0.05). None of the 58 patients with pneumonia and none of the 179 in whom resuscitation took longer than 30 minutes survived to be discharged. On the other hand, fully 42 per cent of the patients who survived for 24 hours after resuscitation left the hospital. At the time of discharge from the hospital and again six months later, 93 per cent of the survivors were mentally intact. Although depression was generally present at the time of discharge, it tended to resolve subsequently. However, all patients reported some decrease in functional capacity, often attributed to fear. This persisted at six months after discharge. Age alone did not appear to influence the prognosis for survival after cardiopulmonary resuscitation or the adjustment to chronic illness after discharge from the hospital.New England Journal of Medicine 10/1983; 309(10):569-76. · 54.42 Impact Factor