Patients CYP DNA tested and treated according to the personalized prescribing system had a significant decrease in hospitalizations and emergency department visits, resulting in potential cost savings. Providers had a high satisfaction rate with the clinical utility of the system and followed recommendations when appropriate.

Our results suggest that FMT may be a cost-saving intervention in managing RCDI. Implementation of FMT for RCDI may help decrease the economic burden to the healthcare system.

Clostridium difficile infection (CDI) is costly. Current guidelines recommend metronidazole as first-line therapy and vancomycin as an alternative. Recurrence is common. Faecal microbiota transplantation (FMT) is an effective therapy for recurrent CDI (RCDI). This study explores the cost-effectiveness of FMT, vancomycin and metronidazole for initial CDI. We constructed a decision-analytic computer simulation using inputs from published literature to compare FMT with a 10-14-day course of oral metronidazole or vancomycin for initial CDI. Parameters included cure rates (baseline value (range)) for metronidazole (80% (65-85%)), vancomycin (90% (88-92%)) and FMT(91% (83-100%)). Direct costs of metronidazole, vancomycin and FMT, adjusted to 2011 dollars, were $57 ($43-72), $1347 ($1195-1499) and $1086 ($815-1358), respectively. Our effectiveness measure was quality-adjusted life years (QALYs). One-way and probabilistic sensitivity analyses were conducted from the third-party payer perspective. Analysis using baseline values showed that FMT($1669, 0.242 QALYs) dominated (i.e. was less costly and more effective) vancomycin ($1890, 0.241 QALYs). FMT was more costly and more effective than metronidazole ($1167, 0.238 QALYs), yielding an incremental cost-effectiveness ratio (ICER) of $124 964/QALY. One-way sensitivity analyses showed that metronidazole dominated both strategies if its probability of cure were >90%; FMT dominated if it cost <$584. In a probabilistic sensitivity analysis at a willingness-to-pay threshold of $100 000/QALY, metronidazole was favoured in 55% of model iterations; FMT was favoured in 38%. Metronidazole, as the first-line treatment for CDIs, is less costly. FMT and vancomycin are more effective. However, FMT is less likely to be economically favourable, and vancomycin is unlikely to be favourable as first-line therapy when compared with FMT.

Outcomes research studies use clinical and administrative data generated in the course of patient care or from patient surveys to examine the effectiveness of treatments. Health care providers need to understand the limitations and strengths of the real-world data sources used in outcomes studies to meaningfully use the results. This paper describes five types of databases commonly used in the United States for outcomes research studies, discusses their strengths and limitations, and provides examples of each within the context of pain treatment. The databases specifically discussed are generated from (1) electronic medical records, which are created from patient-provider interactions; (2) administrative claims, which are generated from providers' and patients' transactions with payers; (3) integrated health systems, which are generated by systems that provide both clinical care and insurance benefits and typically represent a combination of electronic medical record and claims data; (4) national surveys, which provide patient-reported responses about their health and behaviors; and (5) patient registries, which are developed to track patients with a given disease or exposure over time for specified purposes, such as population management, safety monitoring, or research.

Introduction Patients with germline TP53 pathogenic variants (Li–Fraumeni syndrome [LFS]) are at extremely high lifetime risk of developing cancer. Recent data suggest that tumor surveillance for patients with LFS may improve survival through early cancer detection. The objective of this study was to assess the cost‐effectiveness of a cancer surveillance strategy for patients with LFS compared with those whose tumors present clinically. Methods A Markov decision analytic model was developed from a third‐party payer perspective to estimate cost‐effectiveness of routine cancer surveillance over a patient's lifetime. The model consisted of four possible health states: no cancer, cancer, post‐cancer survivorship, and death. Model outcomes were costs (2015 United States Dollars [USD]), effectiveness (life years [LY] gained), and incremental cost‐effectiveness ratio (ICER; change in cost/LY gained). One‐way sensitivity analyses and probabilistic sensitivity analyses examined parameter uncertainty. Results The model showed a mean cost of $46 496 and $117 102 and yielded 23 and 27 LY for the nonsurveillance and surveillance strategies, respectively. The ICER for early cancer surveillance versus no surveillance was $17 125 per additional LY gained. At the commonly accepted willingness to pay threshold of $100 000/life‐year gained, surveillance had a 98% probability of being the most cost‐effective strategy for early cancer detection in this high‐risk population. Conclusions Presymptomatic cancer surveillance is cost‐effective for patients with germline pathogenic variants in TP53. Lack of insurance coverage or reimbursement in this population may have significant consequences and leads to undetected cancers presenting in later stages of disease with worse clinical outcomes.

Objectives Pharmacist-led diabetes collaborative drug therapy management (CDTM) has been shown to improve outcomes. Whether such programs are effective specifically in Medicaid patients, who face barriers to access and self-management, has not been well characterized. This pilot study explores glycemic control, utilization and costs associated with pharmacist-led CDTM in a small population of Medicaid patients with type 2 diabetes mellitus (T2DM). Methods A pre-post, historical cohort study was conducted of patients with T2DM and Medicaid coverage who received pharmacist-led CDTM in community-based primary clinics between 2008-2012. Outcomes included change in HbA1c, healthcare costs and utilization. Results This study included 79 Medicaid patients with T2DM who received pharmacist-led CDTM. A subset of 46 patients with Medicaid coverage through an affiliated Medicaid Plan, Healthy U, was identified for additional analysis. At 6-months follow-up, HbA1c was a mean (SD) of 2.0% (2.0) lower than the baseline of 10.3% (1.7). Primary care clinic encounters increased by a mean (median) of 3.4 (2) visits. Per patient health system charges increased by a mean (median) of $4,392 ($620) and the amount paid by Medicaid in the Healthy U subset was $822 ($68) higher in the follow-up period. Conclusion A pharmacist-led diabetes CDTM intervention was associated with improved glycemic control in Medicaid patients, which corresponded with a higher number of primary care visits and observed costs. These findings are consistent with studies not limited to Medicaid, suggesting that CDTM can be effective in type 2 diabetes patients with Medicaid coverage.

Pharmacometric models are powerful tools that can be used for a variety of purposes in clinical pharmacology, drug development and dose individualization. Lewis Sheiner famously described the process of learning versus confirming in the setting of drug development [1] and as applied to pharmacometrics. An example of learning is the quantitative description of the human coagulation network [2] which was then paired with prospective clinical data [3] to confirm the predictive performance of this model. Additionally, models can be used to help design clinical trials via simulation [4] and even provide information for the labelling of drug products [5]. The clinically relevant goal is to determine if and how doses can be individualized.There are limitations to the use of pharmacometric approaches, and this is the most relevant for models that are built for the purpose of learning. Sometimes, these models are limited by the data driving model development (e.g. population size and variability; i.e. healthy volunteer studies). In this case, the ability to extrapolate the model needs to be verified in a well-designed clinical trial in the appropriate population or via external model validation. Even validated 1,2 (confirming) models have limitations. An example of these limitations includes whether all the necessary information to execute the model (i.e. covariates) are available. If they are not, the model cannot be used clinically.The motivation for this editorial was a recently published paper in this journal that described an external model validation [7] for a previously published model [8]. This model was used to investigate the influence of organ failure, as well as inflammation (using C-reactive protein as a biomarker) on midazolam clearance in critically ill children [8]. The work reported in the paper sought to evaluate the predictability of the original model in children and extrapolate the predictability in two new populations, namely, adults and preterm neonates [7]. However, the original model did not accurately predict the clearance or concentrations of midazolam in preterm neonates (median prediction error; MPE > 60%, Table 1), and the authors correctly concluded that this model was not applicable to this population.At face value, the MPEs (based on population level predictions) for clearance values were <30% in all groups aged from term neonates through to adults (Table 1). It would have been useful to include variability parameters around the estimates of the MPEs (e.g. confidence interval or an estimate of precision). The model underpredicted concentrations of midazolam in adults by around 60% (Table 1). A dose prediction based on this model would result in a higher, potentially toxic, dose. The authors correctly suggested that the underprediction occurred due to the original model not accurately estimating the volume of distribution in adults.The limitation of the paper is that the analysis is done using population-level predictions. Ideally, the model could be used with individual data (drug concentr...

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