Patients who no-show to primary care appointments interrupt clinicians' efforts to provide continuity of care. Prior literature reveals no-shows among diabetic patients are common. The purpose of ...this study is to assess whether no-shows to primary care appointments are associated with increased risk of future emergency department (ED) visits or hospital admissions among diabetics.
A prospective cohort study was conducted using data from 8,787 adult diabetic patients attending outpatient clinics associated with a medical center in Indiana. The outcomes examined were hospital admissions or ED visits in the 6 months (182 days) following the patient's last scheduled primary care appointment. The Andersen-Gill extension of the Cox proportional hazard model was used to assess risk separately for hospital admissions and ED visits. Adjustment was made for variables associated with no-show status and acute care utilization such as gender, age, race, insurance and co-morbid status. The interaction between utilization of the acute care service in the six months prior to the appointment and no-show was computed for each model.
The six-month rate of hospital admissions following the last scheduled primary care appointment was 0.22 (s.d. = 0.83) for no-shows and 0.14 (s.d. = 0.63) for those who attended (p < 0.0001). No-show was associated with greater risk for hospitalization only among diabetics with a hospital admission in the prior six months. Among diabetic patients with a prior hospital admission, those who no-showed were at 60% greater risk for subsequent hospital admission (HR = 1.60, CI = 1.17-2.18) than those who attended their appointment. The six-month rate of ED visits following the last scheduled primary care appointment was 0.56 (s.d. = 1.48) for no-shows and 0.38 (s.d. = 1.05) for those who attended (p < 0.0001); after adjustment for covariates, no-show status was not significantly related to subsequent ED utilization.
No-show to a primary care appointment is associated with increased risk for hospital admission among diabetics recently hospitalized.
Manufacturing systems are often subject to resource failure. In past work, we developed ldquorobustrdquo supervisory controllers for the single-unit resource allocation model. These guarantee that if ...any subset of resources fail, parts in the system requiring failed resources do not block the production of parts not requiring failed resources. To establish supervisor correctness, we assumed that each part type required at most one unreliable resource in its route. We now relax this assumption using a central buffer and present supervisors that guarantee robust operation without assumptions on route structure.
Supervisory control for deadlock-free resource allocation has been an active area of manufacturing systems research. To date, most work assumes that allocated resources do not fail. Little research ...has addressed allocating resources that may fail. In our previous work (Lawley, M.,
2002
. Control of deadlock and blocking for production systems with unreliable resources. International Journal of Production Research, 40 (17), 4563-4582; Lawley, M. and Sulistyono, W.,
2002
. Robust supervisory control policies for manufacturing systems with unreliable resources. IEEE Transactions on Robotics and Automation, 18 (3), 346-359), we assumed a single unreliable resource and developed supervisory controllers to ensure robust deadlock-free operation in the event of resource failure. In this article, we assume that several unreliable resources may fail simultaneously. In this case, the controller must guarantee that a set of resource failures does not propagate through blocking to stall other portions of the system. That is, it must ensure that every part type not requiring any of the failed resources should continue to produce smoothly without disruption. To do this, the controller must constrain the system to states that serve as feasible initial states for (i) a reduced system when resource failures occur and (ii) an upgraded system when failed resources are repaired. We develop the properties that such a controller must possess and then develop supervisory controllers that satisfy these properties.
Supervisory control for deadlock-free resource allocation has been an active area of manufacturing systems research. To date, most work assumes that allocated resources do not fail. Little research ...has addressed allocating resources that may fail. In our previous work, we assumed a single unreliable resource and developed supervisory controllers to ensure robust deadlock-free operation in the event of resource failure. In this paper, we assume that several unreliable resources may fail simultaneously. In this case, a controller must guarantee that a set of resource failures does not propagate through blocking to stall other portions of the system. That is, the controller must ensure that every part type not requiring any of the failed resources should continue to produce smoothly without disruption. To do this, the controller must constrain the system to states that serve as feasible initial states for: 1) a reduced system when resource failures occur and 2) an upgraded system when failed resources are repaired. We develop the properties that such a controller must possess and then develop supervisory controllers that satisfy these properties. Note to Practitioners-For the past decade or so, researchers have begun to actively address the issue of ensuring smooth and continuous operation for flexibly automated manufacturing systems. This research effort has been motivated by the many failed attempts to implement flexible automation throughout the 1980s. During this time, much has been learned about modeling the control functions of a flexible, automated system. In fact, ladder logic control code can now be generated automatically from mathematical models, such as Petri nets, which compactly capture the required operating system logic. Because the code is based on a formal model with well-established properties, it is guaranteed to ensure proper operation without significant startup troubleshooting. One area that has not been investigated is controlling these systems when machines or tools "fail". The question is not how to fix what has failed, but rather how to control the system so that if something does fail, the system can continue producing items that do not require the failed elements. This is essential work since automated manufacturing systems consist of thousands of components, any of which are subject to failure. If failures in the system are not handled gracefully, it becomes difficult to keep the automated system running, in which case, system production does not meet expectations. In our previous work, we investigated ensuring smooth operation for systems with a single unreliable resource. We developed supervisory controllers to guarantee this requirement for these systems. In this paper, we extend the previous results to a more general class of systems where there are multiple unreliable resources. We establish a set of desired properties that the supervisory controller must possess in order to guarantee robust operation for these systems, and then develop a number of controllers that satisfy these properties.
This paper develops an integrated neural-network-based decision support system for predictive maintenance of rotational equipment. The integrated system is platform-independent and is aimed at ...minimizing expected cost per unit operational time. The proposed system consists of three components. The first component develops a vibration-based degradation database through condition monitoring of rolling element bearings. In the second component, an artificial neural network model is developed to estimate the life percentile and failure times of roller bearings. This is then used to construct a marginal distribution. The third component consists of the construction of a cost matrix and probabilistic replacement model that optimizes the expected cost per unit time. Furthermore, the integrated system consists of a heuristic managerial decision rule for different scenarios of predictive and corrective cost compositions. Finally, the proposed system can be applied in various industries and different kinds of equipment that possess well-defined degradation characteristics
Operations research in healthcare Xie, Xiaolan; Lawley, Mark A.
International journal of production research,
12/17/2015, 2015-12-17, Volume:
53, Issue:
24
Journal Article
Objectives:
Population health management is becoming increasingly important to organizations managing and providing primary care services given ongoing changes in health care delivery and payment ...systems. The objective of this study is to show how systems science methodologies could be incorporated into population health management to compare different interventions and improve health outcomes.
Methods:
The New York Academy of Medicine Cardiovascular Health Simulation model (an agent-based model) and data from the Behavioral Risk Factor Surveillance System were used to evaluate a lifestyle program that could be implemented in primary care practice settings. The program targeted Medicare-age adults and focused on improving diet and exercise and reducing weight.
Results:
The simulation results suggest that there would be significant reductions projected in the proportion of the Medicare-age population with diabetes after the implementation of the proposed lifestyle program for a relatively long term (3 and 5 years). Similar results were found for the subpopulations with high cholesterol, but the proposed intervention would not have a significant effect in the proportion of the population with hypertension over a time period of <5 years.
Conclusions:
Systems science methodologies can be useful to compare the health outcomes of different interventions. These tools can become an important component of population health management because they can help managers and other decision makers evaluate alternative programs in primary care settings.
Many outpatient clinics are experimenting with open access scheduling. Under open access, patients see their physicians within a day or two of making their appointment request, and long-term patient ...booking is very limited. The hope is that these short appointment lead times will improve patient access and reduce uncertainty in clinic operations by reducing patient no-shows. Practice shows that successful implementation can be strongly influenced by clinic characteristics, indicating that open access policies must be designed to account for local clinical conditions. The effects of four variables on clinic performance are examined: (1) the fraction of patients being served on open access, (2) the scheduling horizon for patients on longer-term appointment scheduling, (3) provider care groups, and (4) overbooking. Discrete event simulation, designed experimentation, and data drawn from an intercity clinic in central Indiana are used to study the effects of these variables on clinic throughput and patient continuity of care. Results show that, if correctly configured, open access can lead to significant improvements in clinic throughput with little sacrifice in continuity of care.
Abstract Depending on the patient's condition, up to 60% of inpatients are discharged to post–acute care facilities (PACFs). These patients may experience several days of nonmedical inpatient stay ...until the hospital finds a facility that fits their needs, contributing to overcrowding in upstream units. This article studies the feasibility of creating a “postdischarge unit” (PDU) for medically ready‐for‐discharge patients who experience transfer delays, to improve access to inpatient beds. We use a multistage stochastic program, solved with a dual dynamic programming algorithm, to address the PDU size and capacity question. The random variable is the number of bed requests from upstream units (e.g., emergency department). Our numerical analysis, using data from a large hospital, shows that a PDU can reduce costs and significantly reduce the number of patients waiting for transfer to PACFs that are occupying inpatient beds, as long as the percentage of these patients in the hospital is more than 4%. Compared to current practice in our partner hospital, a PDU could increase access to inpatient beds by up to 13% and result in 2%–21% cost savings. Results show that PDU capacity in hospitals with a larger number of patients waiting for transfer is more sensitive to variation in PDU renovation and operational costs. In addition to using fewer medical staff, a PDU can improve discharge transitions to lower levels of care and more efficiently utilize social workers and physical therapists assisting these patients.
We study the problem of appointment scheduling for outpatient clinics with stochastic patient re-entrance. It is motivated by the scheduling problem for Mohs Micrographic Surgery, which is a popular ...technique for the excision of skin cancers. Re-entrance occurs when a patient repeats upstream processes during the single-day outpatient appointment, usually after a medical test. In some surgical procedures, the number of re-entrances for each patient is unknown until medical test results become available, causing long patient waiting time and clinic overtime. To address these challenges, we develop a stochastic slot model, SMART, that captures the key characteristics of the appointment processes and stochastic re-entrance, utilizing a delay before possible re-entrance. We then design a sequential scheduling algorithm that balances patient waiting, clinic overtime and patient throughput while considering stochastic complications such as no-shows, processing time variability, and the number of re-entrances per patient. We establish several theoretical properties of the algorithm, including the optimality of the resulting schedules for the sequential scheduling setting. We apply SMART to appointment scheduling for Mohs clinics, where patients may have same-day re-entrance due to repetitive skin excisions. We compare SMART schedules to others that ignore re-entrance and to current scheduling practices via simulation studies under a more realistic setting. Numerical experiments show that SMART schedules dominate other schedules, and ignoring re-entrance can cost huge loss in efficiency. Furthermore, SMART schedules exhibit a special ‘stair-stepping’ pattern for the number of appointments, with most patients assigned to the early slots and many empty slots at the end.
•Develop a Slot Model for Appointment scheduling with stochastic Re-enTrance (SMART).•Theoretical guarantees of the algorithm for SMART are provided.•SMART can handle heterogeneous no-show types ethically.•Performance are evaluated on realistic grounds with parameters calibrated by data.•Ignoring stochastic re-entrance results in longer waiting time and clinic overtime.
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