Inventory rationing for a system with heterogeneous customer classes

Many retailers find it useful to partition customers into multiple classes based on certain characteristics. We consider the case in which customers are primarily distinguished by whether they are willing to wait for backordered demand. A firm that faces demand from customers that are differentiated in this way may want to adopt an inventory management policy that takes advantage of this differentiation. We propose doing so by imposing a critical level (CL) policy: when inventory is at or below the critical level demand from those customers that are willing to wait is backordered, while demand from customers unwilling to wait will still be served as long as there is any inventory available. This policy reserves inventory for possible future demands from impatient customers by having other, patient, customers wait. We model a system that operates a continuous review replenishment policy, in which a base stock policy is used for replenishments. Demands as well as lead times are stochastic. We develop an exact and efficient procedure to determine the average infinite horizon performance of a given CL policy. Leveraging this procedure we develop an efficient algorithm to determine the optimal CL policy parameters. Then, in a numerical study we compare the cost of the optimal CL policy to the globally optimal state-dependent policy along with two alternative, more naïve, policies. The CL policy is slightly over 2 % from optimal, whereas the alternative policies are 7 and 27 % from optimal. We also study the sensitivity of our policy to the coefficient of variation of the lead time distribution, and find that the optimal CL policy is fairly insensitive, which is not the case for the globally optimal policy.     

Does increasing access-to-care delay accessing of care? Evidence from kidney transplantation

Policies increasing healthcare availability might decrease the cost of delaying accessing of care, leading to potential negative consequences if patients delay treatment. We analyze a policy designed to increase access to kidney transplantation through the use of time since dialysis inception to prioritize patients for transplant, which was piloted at 26 of the 271 kidney transplant centers in the United States in 2006 and 2007. We model the patient’s optimization problem comparing the benefits and costs of early waitlisting and predict that the policy change will lead to delayed waitlisting. To empirically test this prediction, we use difference-in-differences fixed effects panel regression techniques to analyze data on patients who began dialysis between 1/1/2000 and 12/31/2009. The results support the model’s prediction; patients on dialysis who waitlist for kidney transplantation increase pre-waitlist dialysis duration by 11.6 percent or approximately 76 days from a pre-policy mean of 652 days (SD = 654). With regard to waitlist outcomes, the policy is associated with a 4.5 percentage point decrease in the probability of receiving a deceased donor transplant, somewhat offset by a 3.0 percentage point increase in the probability of receiving a live donor transplant. On the extensive margin, patients on dialysis decrease their likelihood of ever waitlisting by 1.5 percentage points. We find an increase in pre-waitlist dialysis time and a decrease in the likelihood of waitlisting at all, especially among populations likely to have experienced increased access to transplantation through the policy change: patients self-identifying as Black or Hispanic rather than Non-Hispanic White, and patients without private insurance. These results suggest that some individuals may not benefit if their access to care increases, if the increase in access sufficiently decreases the penalty of delaying accessing of care.     

Focus: Health Equity: Meeting Dermatologic Needs in an Uninsured Population: Lessons Learned from a Referrals Cohort at a Student-Run Free Clinic

Unmet dermatologic needs of the uninsured patient population are important to identify and address, especially as the COVID-19 pandemic has introduced additional barriers of access to care. We describe the successful collaboration between a student-run free clinic and dermatology practice since 2012, highlighting excellent time to appointment intervals and resolution rates as well as the associated modest financial cost. We believe that the information provided in our report may serve as a proof of concept and facilitate the implementation of such collaborations throughout the United States.     

Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach

BackgroundUnkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of unkept outpatient clinic appointments across hospital trusts in the England. In addition, we aimed to examine the predictors of unkept outpatient clinic appointments across specialties at Imperial College Healthcare NHS Trust (ICHT). Our final aim was to train machine learning models to determine the effectiveness of a potential intervention in reducing unkept appointments.Methods and findingsUK Hospital Episode Statistics outpatient data from 2016 to 2018 were used for this study. Machine learning models were trained to determine predictors of unkept appointments and their relative importance. These models were gradient boosting machines. In 2017–2018 there were approximately 85 million outpatient appointments, with an unkept appointment rate of 5.7%. Within ICHT, there were almost 1 million appointments, with an unkept appointment rate of 11.2%. Hepatology had the highest rate of unkept appointments (17%), and medical oncology had the lowest (6%). The most important predictors of unkept appointments included the recency (25%) and frequency (13%) of previous unkept appointments and age at appointment (10%). A sensitivity of 0.287 was calculated overall for specialties with at least 10,000 appointments in 2016–2017 (after data cleaning). This suggests that 28.7% of patients who do miss their appointment would be successfully targeted if the top 10% least likely to attend received an intervention. As a result, an intervention targeting the top 10% of likely non-attenders, in the full population of patients, would be able to capture 28.7% of unkept appointments if successful. Study limitations include that some unkept appointments may have been missed from the analysis because recording of unkept appointments is not mandatory in England. Furthermore, results here are based on a single trust in England, hence may not be generalisable to other locations.ConclusionsUnkept appointments remain an ongoing concern for healthcare systems internationally. Using machine learning, we can identify those most likely to miss their appointment and implement more targeted interventions to reduce unkept appointment rates.

Sion Phillpott-Morgan and co-workers study occurrence and possible predictors of unkept outpatient appointments in the UK.     

Optimal buffer allocation in a multicell system

Motivated by a layout design problem in the electronics industry, we study in this article the allocation of buffer space among a set of cells. Each cell processes a given part family and has its own revenue-cost structure. The objective of the optimal allocation is to maximize the net profit function (total production profits minus total buffer allocation costs). According to the flow pattern of jobs, the cells are categorized into two types. A type 1 cell is modeled as a Jackson network; a type 2 cell is modeled as an ordered-entry system with heterogeneous servers. Both models have finite waiting room, due to the buffer capacity allocated to the cells. We show that under quite general conditions, the production rate of each cell of either type is an incresing and concave function of its buffer allocation. Exploiting this property, a marginal allocation scheme efficiently solves the optimal buffer allocation problem under increasing concave production profits and convex buffer space costs.     

Optimal allocation of building blocks between nutrient uptake systems in a microbe

 A bacterial cell must distribute its molecular building blocks among various types of nutrient uptake systems. If the microbe is to maximize its average growth rate, this allocation of building blocks must be adjusted to the environmental availabilities of the various nutrients. The adjustments can be found from growth balancing considerations. We give a full proof of optimality and uniqueness of the optimal allocation regime for a simple model of microbial growth and internal stores kinetics. This proof suggests likely candidates for optimal control regimes in the case of a more realistic model. These candidate regimes differ with respect to the information that the cells control system must have access to. We pay particular attention to one of the three candidates, a feedback regime based on a cellular control system that monitors only internal reserve densities. We show that allocation converges rapidly to balanced growth under this control regime. Received: 20 November 2000 / Revised version: 7 August 2001 / Published online: 21 February 2002     

Pitfalls in health communication: healthcare policy, institution, structure, and process

The state of health communication for a given population is a function of several tiers of structure and process: government policy, healthcare directives, healthcare structure and process, and the ethnosocial realities of a multicultural society. Common yet specific to these tiers of health communication is the interpersonal and intergroup use of language in all its forms. Language is the most common behavior exhibited by humankind. Its use at all tiers determines quality of healthcare and quality of life for healthcare consumers: patients and their families. Of note, at the consumer end, mounting evidence demonstrates that barriers to health communication contribute to poorer access to care, quality of care, and health outcomes. The lack of comprehensible and usable written and spoken language is a major barrier to health communication targeting primary and secondary disease prevention and is a major contributor to the misuse of healthcare, patient noncompliance, rising healthcare costs. In this paper, we cursorily examine the relationship among government policy, institutional directives, and healthcare structure and process and its influence on the public health, especially vulnerable populations. We conclude that limited health communication in the context of changing healthcare environments and diverse populations is an important underpinning of rising healthcare costs and sustained health disparities. More research is needed to improve communication about health at all tiers and to develop health communication interventions that are usable by all population groups.     

Use of computer terminals on wards to access emergency test results: a retrospective audit

ObjectiveTo assess delay in clinicians obtaining emergency biochemistry test results when the telephoning of results by laboratory staff is supplanted by installation of computer ward terminals.DesignRetrospective observational study.SettingAccident and emergency department and acute medical admissions ward of a teaching hospital.Sample3228 emergency requests for biochemistry tests sent from the accident and emergency department and 1836 from the medical admissions ward during August 1999 to January 2000 when there was no recorded telephone contact for results.ResultsThe results from 1443/3228 (45%) of urgent requests from accident and emergency and 529/1836 (29%) from the admissions ward were never accessed via the ward terminal. Results from 794/3228 (25%) of accident and emergency requests and 413/1836 (22%) of admissions ward requests were seen within 1 hour of becoming available while a further 491/3228 (15%) and 341/1836 (19%) respectively were accessed between 1 and 3 hours. In up to 43/1443 (3%) of the accident and emergency test results that were never looked at the findings might have led to an immediate change in patient management.ConclusionsWhen used as the sole substitute for telephoning results, the provision of terminal access to laboratory results on wards can hinder rather than promote the communication of emergency blood results to healthcare staff.

What is already known on this topic

Providing computer terminals on wards to access laboratory results is usually regarded as a service improvement for healthcare staffMany laboratories that transmit results to ward terminals dispense with telephoning emergency blood results

What this study adds

Many urgently requested results are not looked at if hospital staff need to access a computer terminal to obtain themIf ward terminals are used as a complete substitute for the telephone, results that would have led to an immediate change in patient management may pass unnoticed     

Estructura diagnóstica y funcional de una consulta de alta resolución de nódulo tiroideo

Appearance of a thyroid nodule has become a daily occurrence in clinical practice. Adequate thyroid nodule assessment requires several diagnostic tests and multiple medical appointments, which results in a substantial delay in diagnosis. Implementation of a high-resolution thyroid nodule clinic largely avoids these drawbacks by condensing in a single appointment all tests required for adequate evaluation of thyroid nodule. This paper reviews the diagnostic and functional structure of a high-resolution thyroid nodule clinic.     

The scanning behavior of juncos: A game-theoretical approach

Flocking birds frequently look up or “scan” while they are feeding on the ground. High scanning rates increase the probability that the birds in a flock will see an approaching predator in time to avoid predation; however, high scanning rates also decrease the feeding rates of the scanning individuals. Since the scanning rate that maximizes the survival probability of one individual depends on how frequently other birds in the same flock are scanning, the optimal scanning behavior must be modeled as a game. We develop a realistic model of scanning behavior and use it to find two game theoretical solutions—the “co-operative” or Pareto optimum and the “selfish” or Nash optimum. The observed scanning rates do not differ significantly from the co-operative optimal scanning rate. We argue that in a game where players meet again and again such apparent co-operation may be an evolutionarily stable strategy.     

A new branch and bound algorithm for loading problems in flexible manufacturing systems

Loading problems in flexible manufacturing systems involve assigning operations for selected part types and their associated tools to machines or machine groups. One of the objectives might be to maximize the expected production rate (throughput) of the system. Because of the difficulty in dealing with this objective directly, a commonly used surrogate objective is the closeness of the actual workload allocation to the continuous workload allocation that maximizes throughput. We test several measures of closeness and discuss correlations between these measures and throughput. Using the best measure, we show how to modify an existing branch and bound algorithm which was developed for the case of equal target workloads for all machine groups to accommodate unequal target workloads. We also develop a new branch and bound algorithm which can be used for both types of problems. The efficiency of the algorithm in finding optimal solutions is achieved through the application of better branching rules and improved dominance results. Computational results on randomly generated test problems indicate that the new algorithm performs well.     

Open access to orthopaedic appliances for general practitioners.

A clinic to which general practitioners can refer patients for some types of orthopaedic appliances was opened in North Clwyd in 1983. During 1985, 956 patients were referred by 82 general practitioners; 860 patients received an appliance, and the average waiting time was less than five weeks. Most referrals were for soft collars (44%), lumbar sacral supports (30%), and dorsilumbar supports (7%). Thirty eight patients failed to attend, 54 declined an appliance, and four referrals were considered to be inappropriate. A few patients were subsequently referred to consultant outpatient clinics, 22 for physiotherapy and 34 were referred simultaneously to the open access clinic. The referral rates for general practitioners with access to community hospitals were low. Such an arrangement merits wider consideration.     

Parentage analysis with few contributing breeders: validation and improvement

Validation of parental allocation using PAPA software (Duchesne P, Godbout MH, Bernatchez L. 2002. PAPA (package for the analysis of parental allocation): a computer program for simulated and real parental allocation. Mol Ecol Notes. 2:191-193.) was investigated under the assumption that only a small proportion of potential breeders contributed to the offspring sample. Inbreeding levels proved to have a large impact on allocation error rate. Consequently, simulations from artificial, unrelated parents may strongly underestimate allocation error, and so, whenever possible, simulations based on the actual parental genotypes should be run. An unexpected and interesting finding was that ambiguity (the highest likelihood is shared by several parental pairs) rates below 10% stood very close to exact allocation error rates (true proportions of wrong allocations). Hence, the ambiguity rate statistic may be viewed as a ready-made indicator of the resolution power of a specific parental allocation run and, if not exceeding 10%, used as an estimate of allocation error rate. It was found that the PAPA simulator, even with few contributing breeders, can be trusted to output reasonably accurate estimates of allocation error as long as those estimates do not exceed 15%. Indeed, most discrepancies between exact and estimated error then stood below 3%. Reproductive success variance had little impact on error estimate discrepancies within the same range. Finally, a (focal set) method was described to correct the estimated family sizes computed directly from parental allocations. Essentially, this method makes use of the detailed structure of the allocation probabilities associated with each parental pair with at least 1 allocated offspring. The allocation probabilities are expressed in matrix form, and the subsequent calculations are run based on standard matrix algebra. On average, this method provided better estimates of family sizes for each investigated combination of parameter values. As the size of offspring samples increased, the corrections improved until a plateau was finally reached. Typically, samples comprising 250, 500, and 1000 offspring would bring corrections in the order of 10-20%, 20-30%, and 30-40%, respectively.     

Impact of a new medical network system on the efficiency of treatment for eating disorders in Japan: a retrospective observational study

Background

In Japan, patients generally have free access to any hospital or clinic. This could lead to reduced efficiency in the treatment for eating disorders (EDs) because there are only a limited number of doctors who can treat ED patients. The objectives of this study were to examine the efficiency of a new trial system for consultation and appointments, a medical community network (MCN), in outpatient treatment for EDs. MCN schedules appointments for the first visit only by referral from another medical institution, not by patients themselves.

Methods

We analyzed the data of 342 outpatients (mean age = 28.9 ± 9.9 years; 328 female and 14 male) who visited the ED clinic at the University of Tokyo Hospital for the first time between January 2009 and July 2012 to investigate possible differences in treatment efficacy between the new (MCN+) system and the conventional (MCN-) system, which accepts reservations directly from patients.

Results

The no-show rate for MCN+ patients (0.8%) was significantly lower than that for the MCN- group (17.8%) (p < 0.001). MCN+ patients had a significantly shorter waiting period (8.4 days) for the first visit compared to MCN- patients (35.5 days, p < 0.001). In addition, the MCN+ group had a much higher rate of successive visits to the clinic (p < 0.05).

Conclusion

This new consultation system using a medical community network provided more efficient treatment for ED than did the appointment system in which the patients made their appointments by themselves.

     

External Validation and Calibration of IVFpredict: A National Prospective Cohort Study of 130,960 In Vitro Fertilisation Cycles

Background

Accurately predicting the probability of a live birth after in vitro fertilisation (IVF) is important for patients, healthcare providers and policy makers. Two prediction models (Templeton and IVFpredict) have been previously developed from UK data and are widely used internationally. The more recent of these, IVFpredict, was shown to have greater predictive power in the development dataset. The aim of this study was external validation of the two models and comparison of their predictive ability.

Methods and Findings

130,960 IVF cycles undertaken in the UK in 2008–2010 were used to validate and compare the Templeton and IVFpredict models. Discriminatory power was calculated using the area under the receiver-operator curve and calibration assessed using a calibration plot and Hosmer-Lemeshow statistic. The scaled modified Brier score, with measures of reliability and resolution, were calculated to assess overall accuracy. Both models were compared after updating for current live birth rates to ensure that the average observed and predicted live birth rates were equal. The discriminative power of both methods was comparable: the area under the receiver-operator curve was 0.628 (95% confidence interval (CI): 0.625–0.631) for IVFpredict and 0.616 (95% CI: 0.613–0.620) for the Templeton model. IVFpredict had markedly better calibration and higher diagnostic accuracy, with calibration plot intercept of 0.040 (95% CI: 0.017–0.063) and slope of 0.932 (95% CI: 0.839–1.025) compared with 0.080 (95% CI: 0.044–0.117) and 1.419 (95% CI: 1.149–1.690) for the Templeton model. Both models underestimated the live birth rate, but this was particularly marked in the Templeton model. Updating the models to reflect improvements in live birth rates since the models were developed enhanced their performance, but IVFpredict remained superior.

Conclusion

External validation in a large population cohort confirms IVFpredict has superior discrimination and calibration for informing patients, clinicians and healthcare policy makers of the probability of live birth following IVF.     

Optimal drug treatment regimens for HIV depend on adherence

Drug therapies aimed at suppressing the human immunodeficiency virus (HIV) are highly effective, often reducing the viral load to below the limits of detection for years. Adherence to such antiviral regimens, however, is typically far from ideal. We have previously developed a model that predicts optimal treatment regimens by weighing drug toxicity against CD4+ T-cell counts, including the probability that drug resistance will emerge. We use this model to investigate the influence of adherence on therapy benefit. For a drug with a given half-life, we compare the effects of varying the dose amount and dose interval for different rates of adherence, and compute the optimal dose regimen for adherence between 65% and 95%. Our results suggest that for optimal treatment benefit, drug regimens should be adjusted for poor adherence, usually by increasing the dose amount and leaving the dose interval fixed. We also find that the benefit of therapy can be surprisingly robust to poor adherence, as long as the dose interval and dose amount are chosen accordingly.     

A numerical study on the effect of the balance assumption in one-warehouse multi-retailer inventory systems

One-warehouse multi-retailer systems under periodic review have been studied extensively in the literature. The optimal policy has not been characterized yet. It would require solving a multi-dimensional dynamic program, which is hard due to the curse of dimensionality. In order to let the dynamic program decompose, researchers often make the so-called balance assumption. All available heuristics for periodic review distribution systems are based on some form of this assumption. For these heuristics, often further approximate steps are applied. We investigate the pure effect of the balance assumption in this paper. The balance assumption is the relaxation of a set of constraints in the original dynamic program and yields a lower bound model, which we solve exactly. This gives us a lower bound for the optimal cost of the original model. An upper bound for the true optimal cost is obtained by simulating the optimal policy for the relaxed problem with a slightly modified allocation rule. This modified policy is referred to as the LB heuristic policy. We use the relative gap between the upper and lower bound as a measure to assess the impact of the balance assumption. Based on extensive testing, we identify when the gap is small, and when not. For those instances with small gaps, both the lower bound is tight and the performance of the LB heuristic policy is close to the optimal. We also identify many practically relevant settings under which the balance assumption yields large gaps. For these instances, either the lower bound is poor or the LB heuristic policy is far from optimal, or both. In any case, it implies that more research is needed to develop better lower bounds and/or better heuristics for these instances.     

Optimizing Real-Time Vaccine Allocation in a Stochastic SIR Model

Real-time vaccination following an outbreak can effectively mitigate the damage caused by an infectious disease. However, in many cases, available resources are insufficient to vaccinate the entire at-risk population, logistics result in delayed vaccine deployment, and the interaction between members of different cities facilitates a wide spatial spread of infection. Limited vaccine, time delays, and interaction (or coupling) of cities lead to tradeoffs that impact the overall magnitude of the epidemic. These tradeoffs mandate investigation of optimal strategies that minimize the severity of the epidemic by prioritizing allocation of vaccine to specific subpopulations. We use an SIR model to describe the disease dynamics of an epidemic which breaks out in one city and spreads to another. We solve a master equation to determine the resulting probability distribution of the final epidemic size. We then identify tradeoffs between vaccine, time delay, and coupling, and we determine the optimal vaccination protocols resulting from these tradeoffs.     

Development of predictive models to identify advanced-stage cancer patients in a US healthcare claims database

BackgroundAlthough healthcare databases are a valuable source for real-world oncology data, cancer stage is often lacking. We developed predictive models using claims data to identify metastatic/advanced-stage patients with ovarian cancer, urothelial carcinoma, gastric adenocarcinoma, Merkel cell carcinoma (MCC), and non-small cell lung cancer (NSCLC).MethodsPatients with ≥1 diagnosis of a cancer of interest were identified in the HealthCore Integrated Research Database (HIRD), a United States (US) healthcare database (2010–2016). Data were linked to three US state cancer registries and the HealthCore Integrated Research Environment Oncology database to identify cancer stage. Predictive models were constructed to estimate the probability of metastatic/advanced stage. Predictors available in the HIRD were identified and coefficients estimated by Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation to control overfitting. Classification error rates and receiver operating characteristic curves were used to select probability thresholds for classifying patients as cases of metastatic/advanced cancer.ResultsWe used 2723 ovarian cancer, 6522 urothelial carcinoma, 1441 gastric adenocarcinoma, 109 MCC, and 12,373 NSCLC cases of early and metastatic/advanced cancer to develop predictive models. All models had high discrimination (C > 0.85). At thresholds selected for each model, PPVs were all >0.75: ovarian cancer = 0.95 (95% confidence interval [95% CI]: 0.94–0.96), urothelial carcinoma = 0.78 (95% CI: 0.70–0.86), gastric adenocarcinoma = 0.86 (95% CI: 0.83–0.88), MCC = 0.77 (95% CI 0.68–0.89), and NSCLC = 0.91 (95% CI 0.90 – 0.92).ConclusionPredictive modeling was used to identify five types of metastatic/advanced cancer in a healthcare claims database with greater accuracy than previous methods.