No Magic Bullet: A Theory-Based Meta-Analysis of Markov Transition Probabilities in Studies of Service Systems for Persons With Mental Disabilities.

OBJECTIVE: A random-effects meta-analysis of studies that used Markov transition probabilities (TPs) to describe outcomes for mental health service systems of differing quality for persons with serious mental illness was implemented to improve the scientific understanding of systems performance, to use in planning simulations to project service system costs and outcomes over time, and to test a theory of how outcomes for systems varying in quality differ. METHODS: Nineteen systems described in 12 studies were coded as basic (B), maintenance (M), and recovery oriented (R) on the basis of descriptions of services provided. TPs for studies were aligned with a common functional-level framework, converted to a one-month time period, synthesized, and compared with theory-based expectations. Meta-regression was employed to explore associations between TPs and characteristics of service recipients and studies. RESULTS: R systems performed better than M and B systems. However, M systems did not perform better than B systems. All systems showed negative as well as positive TPs. For approximately one-third of synthesized TPs, substantial interstudy heterogeneity was noted. Associations were found between TPs and service recipient and study variables Conclusions: Conceptualizing systems as B, M, and R has potential for improving scientific understanding and systems planning. R systems appear more effective than B and M systems, although there is no "magic bullet" system for all service recipients. Interstudy heterogeneity indicates need for common approaches to reporting service recipient states, time periods for TPs, service recipient attributes, and service system characteristics. TPs found should be used in Markov simulations to project system effectiveness and costs of over time.

Scheduling rules to achieve lead-time targets in outpatient appointment systems.

This paper considers how to schedule appointments for outpatients, for a clinic that is subject to appointment lead-time targets for both new and returning patients. We develop heuristic rules, which are the exact and relaxed appointment scheduling rules, to schedule each new patient appointment (only) in light of uncertainty about future arrivals. The scheduling rules entail two decisions. First, the rules need to determine whether or not a patient's request can be accepted; then, if the request is not rejected, the rules prescribe how to assign the patient to an available slot. The intent of the scheduling rules is to maximize the utilization of the planned resource (i.e., the physician staff), or equivalently to maximize the number of patients that are admitted, while maintaining the service targets on the median, the 95th percentile, and the maximum appointment lead-times. We test the proposed scheduling rules with numerical experiments using real data from the chosen clinic of Tan Tock Seng hospital in Singapore. The results show the efficiency and the efficacy of the scheduling rules, in terms of the service-target satisfaction and the resource utilization. From the sensitivity analysis, we find that the performance of the proposed scheduling rules is fairly robust to the specification of the established lead-time targets.

A network flow approach for tactical resource planning in outpatient clinics.

This paper introduces a deterministic model to plan the physician requirements for outpatient clinics to achieve service targets for the appointment lead-times of patients. The Ministry of Health of Singapore has established targets for the median, 95th percentile, and 100th percentile of appointment lead-times for patients, since long appointment postponements are regarded as being unacceptable for health care services. The study is to match the capacity of the healthcare providers to the patient demand for a re-entry system, subject to restrictions on the appointment lead-times for patients. We propose a mixed-integer programming model for planning capacity with the minimization of the maximum required capacity as its objective. In the model we assume a finite planning horizon, deterministic arrivals, multiple types of patients, identical physicians, and dependent demand between types of patients. We solve this model with a Branch and Cut algorithm. We test the model with numerical experiments using real data from the chosen specialty at the outpatient clinic of the studied hospital. The results show the value of the proposed model via a systematic push-pull mechanism in scheduling patients' requests to minimize the objective. The clinic should use one of the appointment lead-time targets to determine the patients' appointment dates. Finally, from the sensitivity analyses we demonstrate that the objective is negatively correlated with first-visit patients' appointment lead-time targets, the discharge rates, and the re-visit patients' mean appointment lead-time; we find a positive correlation between the first-visit patients' mean appointment lead-time and the appointment lead-time targets.

Remanufacturing and energy savings.

Remanufactured products that can substitute for new products are generally claimed to save energy. These claims are made from studies that look mainly at the differences in materials production and manufacturing. However, when the use phase is included, the situation can change radically. In this Article, 25 case studies for eight different product categories were studied, including: (1) furniture, (2) clothing, (3) computers, (4) electric motors, (5) tires, (6) appliances, (7) engines, and (8) toner cartridges. For most of these products, the use phase energy dominates that for materials production and manufacturing combined. As a result, small changes in use phase efficiency can overwhelm the claimed savings from materials production and manufacturing. These use phase energy changes are primarily due to efficiency improvements in new products, and efficiency degradation in remanufactured products. For those products with no, or an unchanging, use phase energy requirement, remanufacturing can save energy. For the 25 cases, we found that 8 cases clearly saved energy, 6 did not, and 11 were too close to call. In some cases, we could examine how the energy savings potential of remanufacturing has changed over time. Specifically, during times of significant improvements in energy efficiency, remanufacturing would often not save energy. A general design trend seems to be to add power to a previously unpowered product, and then to improve on the energy efficiency of the product over time. These trends tend to undermine the energy savings potential of remanufacturing.