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1.
Online J Issues Nurs ; 17(2): 7, 2012 Apr 03.
Artículo en Inglés | MEDLINE | ID: mdl-22686115

RESUMEN

Increasing numbers of patients are being treated for heart failure each year. One out of four of the heart failure patients who receives care in a hospital is readmitted to the hospital within 30 days of discharge. Effective discharge instruction is critical to prevent these patient readmissions. Co-production is a marketing concept whereby the customer is a partner in the delivery of a good or service. For example, a patient and nurse may partner to co-produce a patient-centered health regimen to improve patient outcomes. In this article we review the cost of treating heart failure patients and current strategies to decrease hospital readmissions for these patients along with the role of the nurse and the concept of co-producing health as related to heart failure patients. Next we describe our study assessing the degree to which discharge processes were co-produced on two hospital units having a preponderance of heart failure patients, and present our findings indicating minimal evidence of co-production. A discussion of our findings, along with clinical implications of these findings, recommendations for change, and suggestions for future research are offered. We conclude that standardized discharge plans lead to a mindset of 'one size fits all,' a mindset inconsistent with the recent call for patient-centered care. We offer co-production as a patient-centered strategy for customizing discharge teaching and improving health outcomes for heart failure patients.


Asunto(s)
Insuficiencia Cardíaca/enfermería , Comercialización de los Servicios de Salud/organización & administración , Alta del Paciente , Atención Dirigida al Paciente/organización & administración , Control de Costos , Insuficiencia Cardíaca/economía , Insuficiencia Cardíaca/terapia , Humanos , Comercialización de los Servicios de Salud/economía , Evaluación de Resultado en la Atención de Salud , Readmisión del Paciente , Satisfacción del Paciente , Atención Dirigida al Paciente/economía
2.
Harv Bus Rev ; 85(10): 139-44, 146, 166, 2007 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-17972501

RESUMEN

The customers who buy the most from you are probably not your best marketers. What's more, your best marketers may be worth far more to your company than your most enthusiastic consumers. Those are the conclusions of professors Kumar and Petersen at the University of Connecticut and professor Leone at Ohio State University, who analyzed thousands of customers in research focused on a telecommunications company and a financial services firm. In this article, the authors present a straightforward tool that can be used to calculate both customer lifetime value (CLV), the worth of your customers' purchases, and customer referral value (CRV), the value of their referrals. Knowing both enables you to segment your customers into four constituent parts: those that buy a lot but are poor marketers (which they term Affluents); those that don't buy much but are very strong salespeople for your firm (Advocates); those that do both well (Champions); and those that do neither well (Misers). In a series of one-year experiments, the authors demonstrated the effectiveness of this segmentation approach. Offering purchasing incentives to Advocates, referral incentives to Affluents, and both to Misers, they were able to move significant proportions of all three into the Champions category. Both companies reaped returns on their marketing investments greater than 12-fold--more than double the normal marketing ROI for their industries. The power of this tool is its ability to help marketers decide where to focus their efforts. Rather than waste funds encouraging big spenders to spend slightly more while overlooking the power of customer evangelists who don't buy enough to seem important, you can reap much higher rewards by nudging big spenders to make referrals and urging enthusiastic proponents of your wares to buy a bit more.


Asunto(s)
Competencia Económica/organización & administración , Derivación y Consulta/economía , Algoritmos , Competencia Económica/economía , Hospitales , Humanos , Estados Unidos
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