ABSTRACT
OBJECTIVE: To develop and test a decision-support tool for prioritizing new competing Health Technologies (HTs) after their assessment using the mini-HTA approach. METHODS: A two layer value/risk tool was developed based on the mini-HTA. The first layer included 12 mini-HTA variables classified in two dimensions, namely value (safety, clinical benefit, patient impact, cost-effectiveness, quality of the evidence, innovativeness) and risk (staff, space and process of care impacts, incremental costs, net cost, investment effort). Weights given to these variables were obtained from a survey among decision-makers (at National/Regional level and hospital settings). A second layer included results from mini-HTA (scored as higher, equal or lower), which compares the performance of the new HT (in terms of the abovementioned 12 variables) with the available comparator. An algorithm combining the first (weights) and second (scores) layers was developed to obtain an overall score for each HT, which was then plotted in a value/risk matrix. The tool was tested using results from the mini-HTAs for three new HTs (Surgical Robot, Platelet Rich Plasma, Deep Brain Stimulation). RESULTS: No significant differences among decision-makers were observed as regards the weights given to the 12 variables, therefore, the median aggregate weights from decision-makers were introduced in the first layer. The dot plot resulting from the mini-HTA presented good power to visually discriminate between the assessed HTs. CONCLUSION: The decision-support tool developed here makes possible a robust and straightforward comparison of different competing HTs. This facilitates hospital decision-makers deliberations on the prioritization of competing investments under fixed budgets.
Subject(s)
Decision Making , Decision Support Techniques , Diffusion of Innovation , Hospitals , Software , Technology Assessment, Biomedical/methods , Algorithms , Health Care Surveys , Humans , Program Development , RiskABSTRACT
OBJECTIVES: To outline the methods used to build a discrete-event simulation model for use in decision-making in the context of waiting list management strategies for cataract surgery by comparing a waiting list prioritization system with the routinely used first-in, first-out (FIFO) discipline. METHODS: The setting was the Spanish health system. The model reproduced the process of cataract, from incidence of need of surgery (meeting indication criteria), through demand, inclusion on a waiting list, and surgery. "Nonexpressed Need" represented the population that, even with need, would not be included on a waiting list. Parameters were estimated from administrative data and research databases. The impact of introducing a prioritization system on the waiting list compared with the FIFO system was assessed. For all patients entering the waiting list, the main outcome variable was waiting time weighted by priority score. A sensitivity analysis with different scenarios of mean waiting time was used to compare the two alternatives. RESULTS: The prioritization system shortened waiting time (weighted by priority score) by 1.55 months (95% CI: 1.47 to 1.62) compared with the FIFO system. This difference was statistically significant for all scenarios (which were defined from a waiting time of 4 months to 24 months under the FIFO system). A tendency to greater time savings in scenarios with longer waiting times was observed. CONCLUSIONS: Discrete-event simulation is useful in decision-making when assessing health services. Introducing a waiting list prioritization system produced greater benefit than allocating surgery by waiting time only. Use of the simulation model would allow the impact of proposed policies to reduce waiting lists or assign resources more efficiently to be tested.
Subject(s)
Cataract Extraction , Computer Simulation , National Health Programs , Waiting Lists , Health Services Needs and Demand , Humans , SpainABSTRACT
BACKGROUND: In Spain, there are substantial variations in the utilization of health resources among regions. Because the need for surgery differs in patients with appropriate surgical indication, introducing a prioritization system might be beneficial. Our objective was to assess geographical variations in the impact of applying a prioritization system in patients on the waiting list for cataract surgery in different regions of Spain by using a discrete-event simulation model. METHODS: A discrete-event simulation model to evaluate demand and waiting time for cataract surgery was constructed. The model was reproduced and validated in five regions of Spain and was fed administrative data (population census, surgery rates, waiting list information) and data from research studies (incidence of cataract). The benefit of introducing a prioritization system was contrasted with the usual first-in, first-out (FIFO) discipline. The prioritization system included clinical, functional and social criteria. Priority scores ranged between 0 and 100, with greater values indicating higher priority. The measure of results was the waiting time weighted by the priority score of each patient who had passed through the waiting list. Benefit was calculated as the difference in time weighted by priority score between operating according to waiting time or to priority. RESULTS: The mean waiting time for patients undergoing surgery according to the FIFO discipline varied from 1.97 months (95% CI 1.85; 2.09) in the Basque Country to 10.02 months (95% CI 9.91; 10.12) in the Canary Islands. When the prioritization system was applied, the mean waiting time was reduced to a minimum of 0.73 months weighted by priority score (95% CI 0.68; 0.78) in the Basque Country and a maximum of 5.63 months (95% CI 5.57; 5.69) in the Canary Islands. The waiting time weighted by priority score saved by the prioritization system varied from 1.12 months (95% CI 1.07; 1.16) in Andalusia to 2.73 months (95% CI 2.67; 2.80) in Aragon. CONCLUSION: The prioritization system reduced the impact of the variations found among the regions studied, thus improving equity. Prioritization allocates the available resources within each region more efficiently and reduces the waiting time of patients with greater need. Prioritization was more beneficial than allocating surgery by waiting time alone.
Subject(s)
Cataract Extraction/standards , Catchment Area, Health/statistics & numerical data , Health Priorities/standards , Waiting Lists , Computer Simulation , Efficiency, Organizational , Health Services Needs and Demand/statistics & numerical data , Humans , Models, Organizational , National Health Programs/organization & administration , Ophthalmology/standards , Patient Selection , Quality Assurance, Health Care , SpainABSTRACT
OBJECTIVE: To describe the application of a probabilistic cost-effectiveness analysis to nasal continuous positive airway passage (nCPAP) treatment of obstructive sleep apnea syndrome (OSAS). MATERIAL AND METHODS: The probabilistic model was constructed from a discrete Markov model. This probabilistic approach is characterized by the introduction of variables as probability distributions. The model performed 2,000 Monte Carlo simulations, and incremental costs and effectiveness were calculated in each. The results were analyzed through the cost-effectiveness plane, the acceptability curve, the net benefit rule, and the expected value of perfect information (EVPI). RESULTS: The mean cost-effectiveness ratio for nCPAP treatment was 5,480 Euro/QALY (quality-adjusted life year). Using an acceptability threshold of 30,000 Euro/QALY, the probabilistic analysis showed that nCPAP was the optimal treatment in 98.5% of the simulations. The EVPI showed that the parameter causing greatest uncertainty in the final results was the quality of life gain through nCPAP treatment. CONCLUSIONS: The results of our probabilistic analysis are endorsed by previous deterministic studies confirming that nCPAP treatment of OSAS is the most cost-effective strategy. An additional advantage of probabilistic analysis is that it allows uncertainty to be quantified; in the present case the probability of making the wrong decision was below 5%. Furthermore, this study reveals that to reduce uncertainty, research should center on improving information on quality of life.
Subject(s)
Continuous Positive Airway Pressure/economics , Models, Economic , Sleep Apnea, Obstructive/economics , Sleep Apnea, Obstructive/therapy , Cost-Benefit Analysis , HumansABSTRACT
Objetivo: En este trabajo se presenta la aplicación del análisis coste-efectividad de tipo probabilístico al tratamiento con presión continua en la vía respiratoria por vía nasal (nasal continuous positive airway pressure, nCPAP) del síndrome de la apnea obstructiva del sueño (SAOS). Material y métodos: La base del estudio es un modelo de Markov probabilístico. Éste se caracteriza porque las variables se introducen en forma de distribuciones. El modelo se procesa mediante 2.000 simulaciones de Monte Carlo, cada una de las cuales calcula el coste y la efectividad incrementales. El resultado se analiza mediante el plano coste-efectividad, la curva de aceptabilidad, el beneficio neto y el valor esperado de la información perfecta. Resultados: La razón coste-efectividad del tratamiento con nCPAP media calculada es de 5.480 S/año de vida ajustado por calidad (AVAC). Utilizando como umbral de eficiencia la cifra de 30.000 S/AVAC, el análisis probabilístico muestra que en el 98,5% de las simulaciones el tratamiento con nCPAP es una práctica eficiente. El valor esperado de la información perfecta muestra que el parámetro que origina más incertidumbre en el resultado es la ganancia en calidad de vida producida por el tratamiento. Conclusiones: El análisis de tipo probabilístico ratifica el resultado de los estudios deterministas que caracterizan el tratamiento con nCPAP como una intervención eficiente. La ventaja añadida es que permite situar la incertidumbre en términos cuantitativos; en este caso la probabilidad de equivocarse es inferior al 5%. Además, el estudio muestra que para reducir esa incertidumbre la investigación debe centrarse en la mejora de la información referente a la calidad de vida
Objective: To describe the application of a probabilistic cost-effectiveness analysis to nasal continuous positive airway passage (nCPAP) treatment of obstructive sleep apnea syndrome (OSAS). Material and Methods: The probabilistic model was constructed from a discrete Markov model. This probabilistic approach is characterized by the introduction of variables as probability distributions. The model performed 2,000 Monte Carlo simulations, and incremental costs and effectiveness were calculated in each. The results were analyzed through the cost-effectiveness plane, the acceptability curve, the net benefit rule, and the expected value of perfect information (EVPI). Results: The mean cost-effectiveness ratio for nCPAP treatment was 5,480 S/QALY (quality-adjusted life year). Using an acceptability threshold of 30,000 S/QALY, the probabilistic analysis showed that nCPAP was the optimal treatment in 98.5% of the simulations. The EVPI showed that the parameter causing greatest uncertainty in the final results was the quality of life gain through nCPAP treatment. Conclusions: The results of our probabilistic analysis are endorsed by previous deterministic studies confirming that nCPAP treatment of OSAS is the most cost-effective strategy. An additional advantage of probabilistic analysis is that it allows uncertainty to be quantified; in the present case the probability of making the wrong decision was below 5%. Furthermore, this study reveals that to reduce uncertainty, research should center on improving information on quality of life
