Expanding Kane's argument-based validity framework: What can validation practices in language assessment offer health professions education?

CONTEXT: One central consideration in health professions education (HPE) is to ensure we are making sound and justifiable decisions based on the assessment instruments we use on health professionals. To achieve this goal, HPE assessment researchers have drawn on Kane's argument-based framework to ascertain the validity of their assessment tools. However, the original four-inference model proposed by Kane - frequently used in HPE validation research - has its limitations in terms of what each inference entails and what claims and sources of backing are housed in each inference. The under-specification in the four-inference model has led to inconsistent practices in HPE validation research, posing challenges for (i) researchers who want to evaluate the validity of different HPE assessment tools and/or (ii) researchers who are new to test validation and need to establish a coherent understanding of argument-based validation. METHODS: To address these identified concerns, this article introduces the expanded seven-inference argument-based validation framework that is established practice in the field of language testing and assessment (LTA). We explicate (i) why LTA researchers experienced the need to further specify the original four Kanean inferences; (ii) how LTA validation research defines each of their seven inferences and (iii) what claims, assumptions and sources of backing are associated with each inference. Sampling six representative validation studies in HPE, we demonstrate why an expanded model and a shared disciplinary validation framework can facilitate the examination of the validity evidence in diverse HPE validation contexts. CONCLUSIONS: We invite HPE validation researchers to experiment with the seven-inference argument-based framework from LTA to evaluate its usefulness to HPE. We also call for greater interdisciplinary dialogue between HPE and LTA since both disciplines share many fundamental concerns about language use, communication skills, assessment practices and validity in assessment instruments.

Insights into insurance coverage for digital therapeutics: A qualitative study of US payer perspectives.

BACKGROUND: In the last decade there has been an increase in the development and marketing of digital therapeutic (DTx) products aiming to prevent, manage, or treat a medical disorder or disease. Health insurance coverage for these products is not well established, and payers are facing increasing pressure to include these products as a covered benefit. OBJECTIVE: To examine factors and characteristics that could drive health insurance coverage of DTx products from US payers' and coverage decision-makers' perspectives. METHODS: This was a qualitative noninterventional, cross-sectional study conducted from August 2022 to October 2022. Virtual focus group meetings with pharmacy benefit managers/directors or medical directors representing a range of health insurance organizations were held following a semistructured interview guide. Convenience and snowball sampling techniques were used to identify participants. Transcripts were coded and analyzed with Atlas.ti software to identify common themes and subthemes. RESULTS: Five focus group meetings and 1 individual interview were held from August to October 2022. Participants (n = 22) were mostly pharmacists (n = 18, 85%) with more than 15 years of experience (n = 18, 85%). Some participants indicated that DTx products for diabetes (n = 6, 29%), mental/behavioral health (n = 3, 14%), and substance abuse disorders (n = 3, 14%) were already covered by their organizations. The topics generating the most comments grouped by code were issues around the evidence for DTx (67 unique comments) and barriers for coverage (60 unique comments). Participants indicated they want to have evidence of effectiveness that is similar to traditional pharmaceutical products. Barriers for coverage included a need to revise benefit policies, exclusion of nonprescription products, and mechanisms for billing. DTx products with an indication for mental/behavioral health were viewed as most likely to be reimbursed. Coverage of DTx products may occur under either the pharmacy or medical benefit. CONCLUSIONS: Health care payers stated that evidence of effectiveness was a necessary condition for health insurance coverage of DTx products. Given these are relatively new in health care, payers had more questions than answers regarding how these products will be integrated into health benefits.

Real-world evidence for coverage determination of treatments for rare diseases.

Health technology assessment (HTA) decisions for pharmaceuticals are complex and evolving. New rare disease treatments are often approved more quickly through accelerated approval schemes, creating more uncertainties about clinical evidence and budget impact at the time of market entry. The use of real-world evidence (RWE), including early coverage with evidence development, has been suggested as a means to support HTA decisions for rare disease treatments. However, the collection and use of RWE poses substantial challenges. These challenges are compounded when considered in the context of treatments for rare diseases. In this paper, we describe the methodological challenges to developing and using prospective and retrospective RWE for HTA decisions, for rare diseases in particular. We focus attention on key elements of study design and analyses, including patient selection and recruitment, appropriate adjustment for confounding and other sources of bias, outcome selection, and data quality monitoring. We conclude by offering suggestions to help address some of the most vexing challenges. The role of RWE in coverage and pricing determination will grow. It is, therefore, necessary for researchers, manufacturers, HTA agencies, and payers to ensure that rigorous and appropriate scientific principles are followed when using RWE as part of decision-making.

Cost-effectiveness of HLA-B*58:01 testing to prevent Stevens-Johnson syndrome/toxic epidermal necrolysis in Vietnam.

Background: HLA-B*58:01 is strongly associated with allopurinol-induced Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) in Vietnam. This study assessed the cost-effectiveness of this testing to prevent SJS/TEN. Methods: A model was developed to compare three strategies: no screening, use allopurinol; HLA-B*58:01 screening; and no screening, use probenecid. A willingness-to-pay of three-times gross domestic product per capita was used. Results: Compared with 'no screening, use allopurinol', 'screening' increased quality-adjusted life-years by 0.0069 with the incremental cost of Vietnam dong (VND) 14,283,633 (US$617), yielding an incremental cost-effectiveness ratio of VND 2,070,459,122 (US$89,398) per quality-adjusted life-year. Therefore, 'screening' was unlikely to be cost-effective under the current willingness-to-pay. Testing's cost-effectiveness may change with targeted high-risk patients, reimbursed febuxostat or lower probenecid prices. Conclusion: The implementation of nationwide HLAB*58:01 testing before the use of allopurinol is not cost-effective, according to this analysis. This may be due to the lack of quality data on the effectiveness of testing and costing data in the Vietnamese population.

Linking assessment to real life practice - comparing work based assessments and objective structured clinical examinations using mystery shopping.

Objective Structured Clinical Examinations (OSCEs) and Work Based Assessments (WBAs) are the mainstays of assessing clinical competency in health professions' education. Underpinned by the extrapolation inference in Kane's Validity Framework, the purpose of this study is to determine whether OSCEs translate to real life performance by comparing students' OSCE performance to their performance in real-life (as a WBA) using the same clinical scenario, and to understand factors that affect students' performance. A sequential explanatory mixed methods approach where a grade comparison between students' performance in their OSCE and WBA was performed. Students were third year pharmacy undergraduates on placement at a community pharmacy in 2022. The WBA was conducted by a simulated patient, unbeknownst to students and indistinguishable from a genuine patient, visiting the pharmacy asking for health advice. The simulated patient was referred to as a 'mystery shopper' and the process to 'mystery shopping' in this manuscript. Community pharmacy is an ideal setting for real-time observation and mystery shopping as staff can be accessed without appointment. The students' provision of care and clinical knowledge was assessed by the mystery shopper using the same clinical checklist the student was assessed from in the OSCE. Students who had the WBA conducted were then invited to participate in semi-structured interviews to discuss their experiences in both settings. Overall, 92 mystery shopper (WBA) visits with students were conducted and 36 follow-up interviews were completed. The median WBA score was 41.7% [IQR 28.3] and significantly lower compared to the OSCE score 80.9% [IQR 19.0] in all participants (p < 0.001). Interviews revealed students knew they did not perform as well in the WBA compared to their OSCE, but reflected that they still need OSCEs to prepare them to manage real-life patients. Many students related their performance to how they perceived their role in OSCEs versus WBAs, and that OSCEs allowed them more autonomy to manage the patient as opposed to an unfamiliar workplace. As suggested by the activity theory, the performance of the student can be driven by their motivation which differed in the two contexts.

Assessment of Antimicrobial Stewardship through objective structured clinical examination in pharmacy education.

OBJECTIVES: To describe the implementation and assess whether an objective structured clinical examination (OSCE) is a viable assessment tool for testing Antimicrobial Stewardship (AMS) principles. METHODS: A three-station OSCE set in a hospital and community pharmacy was designed and mapped to the World Health Organisation's AMS intervention practical guide. This OSCE comprised 39 unique cases and was implemented across two campuses (Malaysia and Australia) at one institute. Stations were 8 min long and consisted of problem-solving and applying AMS principles to drug therapy management (Station 1), counselling on key antimicrobials (Station 2) or managing infectious diseases in primary care (Station 3). Primary outcome measure to assess viability was the proportion of students who were able to pass each case. KEY FINDINGS: Other than three cases with pass rates of 50, 52.8 and 66. 7%, all cases had pass rates of 75% or more. Students were most confident with referral to medical practitioner cases and switching from intravenous to oral or empirical to directed therapy. CONCLUSIONS: An AMS-based OSCE is a viable assessment tool in pharmacy education. Further research should explore whether similar assessments can help improve students' confidence at recognising opportunities for AMS intervention in the workplace.

Assessing the impact of single or short-term administration on a therapy's cost-effectiveness: a hypothetical disease-agnostic model.

AIMS: Assessing the value of single or short-term therapies (SSTs) within traditional cost-effectiveness analyses (CEAs) has been a topic of discussion as the number of SSTs increases, particularly regarding the effect of discounting on valuation. To quantify the impact of discounting in economic evaluations, a CEA of a hypothetical SST and equivalent chronic therapy was conducted using standard methods. MATERIALS AND METHODS: A lifetime Markov model was developed for a hypothetical chronic, progressive disease that could be treated with an SST, chronic therapy, or no novel treatment, termed standard of care (SoC). Incremental cost-effectiveness ratios (ICERs) with quality-adjusted life years (QALYs) comparing SST vs. SoC and an equivalent chronic therapy vs. SoC were assessed from a payer perspective. Both treatments had equal benefits and undiscounted lifetime costs; 3% discounting was applied to costs/benefits in the base case, and the impact of discounting was assessed. RESULTS: In the base case example, both the SST and equivalent chronic therapy vs. SoC had ICERs of $86,000/QALY without discounting. With 3% discounting, the ICER for the SST increased by 116% ($186,000/QALY) while the ICER for the chronic therapy increased by 10% ($95,000/QALY) despite equal clinical benefit. In scenario analyses, the ICER of the SST was consistently higher than the equivalent chronic therapy across a range of assumptions/inputs. Varying the cost/benefit discount rates had a greater impact on the SST. Differences in the ICERs between the therapies increased with increasing life expectancy/time horizon. LIMITATIONS: The simple model structure may not be reflective of acute or more complex diseases. Also, the scenario of perfect equivalency in efficacy and lifetime costs is hypothetical. CONCLUSIONS: This quantitative assessment showed the extent to which SST CEAs are highly sensitive to discounting, resulting in worse value assessments for SSTs than equivalent chronic therapies.

Recommendations for economic evaluations of cell and gene therapies: a systematic literature review with critical appraisal.

OBJECTIVE: No consensus exists on the ideal methodology to evaluate the economic impact and value of new, potentially curative gene therapies. We aimed to identify and describe published methodologic recommendations for the economic evaluation of gene therapies and assess whether these recommendations have been applied in published evaluations. METHODS: This study was conducted in three stages: a systematic literature review of methodologic recommendations for economic evaluation of gene therapies; an assessment of the appropriateness of recommendations; and a review to assess the degree to which the recommendations were applied in published evaluations. RESULTS: A total of 2,888 references were screened, 83 articles were reviewed to assess eligibility, and 20 papers were included. Fifty recommendations were identified, and 21 reached consensus thresholds. Most evaluations were based on naive treatment comparisons and did not apply consensus recommendations. Innovative payment mechanisms for gene therapies were rarely considered. The only widely applied recommendations related to modeling choices and methods. CONCLUSIONS: Methodological recommendations for economic evaluations of gene therapies are generally not being followed. Assessing the applicability and impact of the recommendations from this study may facilitate the implementation of consensus recommendations in future evaluations.

Cost-effectiveness analysis of five anti-obesity medications from a US payer's perspective.

BACKGROUND AND AIMS: To determine the cost-effectiveness of anti-obesity medications (AOM): tirzepatide, semaglutide, liraglutide, phentermine plus topiramate (PpT), and naltrexone plus bupropion (NpB). METHODS AND RESULTS: From a U.S. perspective we developed a Markov model to simulate weight change over a 40-year time horizon using results from clinical studies. According to the body mass index (BMI), cardiovascular diseases, diabetes and mortality risk were the health states considered in the model, being mutually exclusive. Costs of AOM, adverse events, cardiovascular events, and diabetes were included. We applied a 3% per-year discount rate and calculated the incremental cost-effectiveness ratios (ICERs) of cost per quality-adjusted life-year (QALY) gained. Probabilistic sensitivity analyses incorporated uncertainty in input parameters. A deterministic analysis was conducted to determine the robustness of the model. The model included a cohort of 78.2% females with a mean age of 45 years and BMI of 37.1 (SD 4.9) for females and 36.8 (SD 4.9) for males. NpB and PpT were the least costly medications and, all medications differed no more than 0.5 QALYs. Tirzepatide ICER was $355,616 per QALY. Liraglutide and semaglutide options were dominated by PpT. CONCLUSION: Compared to other AOM, PpT was lowest cost treatment with nearly identical QALYs with other agents.

A systematic review of real-world healthcare resource use and costs of Clostridioides difficile infections.

Objective: To conduct a systematic review of published real-world evidence describing the cost and healthcare resource use for Clostridiodes difficile infection (CDI) in the United States. Methods: A systematic literature review was conducted searching for terms for CDI and healthcare costs. Titles of articles and abstracts were reviewed to identify those that met study criteria. Studies were evaluated to examine overall design and comparison groups in terms of healthcare resource use and cost for CDI. Results: In total, 28 articles met the inclusion criteria. Moreover, 20 studies evaluated primary CDI or did not specify, and 8 studies1-8 evaluated both primary CDI and recurrent (rCDI). Data from Medicare were used in 6 studies. Nearly all studies used a comparison group, either controls without CDI (N = 20) or comparison between primary CDI and rCDI (N = 7). Two studies examined costs of rCDI by the number of recurrences. Overall, the burden of CDI is significant, with higher aggregate costs for patients with rCDI. Compared with non-CDI controls, hospital length of stay increased in patients with both primary and rCDI compared to patients without CDI. Patients with primary CDI cost healthcare systems $24,000 more than patients without CDI. Additionally, 2 studies that evaluated the impact of recurrence among those patients with an index case of CDI demonstrated significantly higher direct all-cause medical costs among those with rCDI compared to those without. Conclusion: CDI, and particularly rCDI, is a costly condition with hospitalizations being the main cost driver.

Screening and Assessment of Cancer-Related Fatigue: A Clinical Practice Guideline for Health Care Providers.

Cancer-related fatigue (CRF) is the most common side effect of cancer treatment. Regular surveillance is recommended, but few clinical practice guidelines transparently assess study bias, quality, and clinical utility in deriving recommendations of screening and assessment methods. The purpose of this clinical practice guideline (CPG) is to provide recommendations for the screening and assessment of CRF for health care professions treating individuals with cancer. Following best practices for development of a CPG using the Appraisal of Guidelines for Research and Evaluation (AGREE) Statement and Emergency Care Research Institute (ECRI) Guidelines Trust Scorecard, this CPG included a systematic search of the literature, quality assessment of included evidence, and stakeholder input from diverse health care fields to derive the final CPG. Ten screening and 15 assessment tools supported by 114 articles were reviewed. One screen (European Organisation for Research and Treatment of Cancer-Quality of Life Questionnaire-30 Core Questionnaire) and 3 assessments (Piper Fatigue Scale-Revised, Functional Assessment of Chronic Illness Therapy-Fatigue, and Patient Reported Outcome Measurement Information System [PROMIS] Fatigue-SF) received an A recommendation ("should be used in clinical practice"), and 1 screen and 5 assessments received a B recommendation ("may be used in clinical practice"). Health care providers have choice in determining appropriate screening and assessment tools to be used across the survivorship care continuum. The large number of tools available to screen for or assess CRF may result in a lack of comprehensive research evidence, leaving gaps in the body of evidence for measurement tools. More research into the responsiveness of these tools is needed in order to adopt their use as outcome measures. IMPACT: Health care providers should screen for and assess CRF using one of the tools recommended by this CPG.

Cost-effectiveness of esketamine nasal spray compared to intravenous ketamine for patients with treatment-resistant depression in the US utilizing clinical trial efficacy and real-world effectiveness estimates.

OBJECTIVE: The aim of this study was to estimate the cost-effectiveness of esketamine nasal spray relative to intravenous ketamine for patients with treatment-resistant depression (TRD) in the US. METHODS: We used a Markov model with a 1-month cycle length, and we estimated quality-adjusted life years (QALYs), costs (2020 USD), and incremental cost-effectiveness ratios (ICER) of esketamine relative to ketamine over a 3-year time horizon, from both the healthcare sector and patient perspectives. We ran the model using efficacy estimates from both clinical trial and real-world effectiveness (RWE) data. One-way and probabilistic sensitivity analyses (PSAs) were performed to evaluate the robustness of findings. RESULTS: Over a 3-year time horizon, the use of esketamine yielded 1.98 QALYs (RWE/clinical trial efficacy), and the use of ketamine yielded 2.03 QALYs (clinical trial efficacy) or 1.99 QALYs (RWE). Esketamine was dominated by ketamine using the healthcare perspective. ICERs were above $150,000/QALY threshold with the patient perspective. Under the healthcare perspective, PSA showed there are no scenarios where esketamine was cost-effective compared to ketamine. With the patient's perspective, the probability that esketamine was cost-effective compared to ketamine was 0.0055 (clinical trial efficacy) and 0.35 (RWE). LIMITATIONS: The data utilized for efficacy have limitations. The time horizon may fail to capture longer-term costs and benefits. CONCLUSIONS: In this decision analytic model evaluating esketamine versus ketamine for TRD, we found esketamine unlikely to be cost-effective under a healthcare sector perspective. Under a patient perspective, esketamine had similar effectiveness and was less costly than ketamine due to insurance coverage.

Cost-Effectiveness of KTE-X19 for Adults with Relapsed/Refractory B-Cell Acute Lymphoblastic Leukemia in the United States.

INTRODUCTION: Despite currently available treatments for adults with relapsed/refractory acute lymphoblastic leukemia (R/R ALL), survival outcomes remain poor, highlighting the need for new therapeutic strategies. This study estimates the cost-effectiveness of KTE-X19 to treat adults with R/R ALL from a US payer perspective. METHODS: The model had two components: a decision-tree, where pre-infusion costs for patients who ultimately did not receive KTE-X19 are accounted for, followed by a partitioned survival analysis, where all KTE-X19 infused patients would enter the three-state (pre-progression, progressed disease, death) model. Comparators included current standard of care treatments, i.e., blinatumomab (BLIN), inotuzumab ozogamicin (INO), and salvage chemotherapy (CHEMO). Both standard parametric and mixture cure models were used to model survival. Efficacy, safety, healthcare resource utilization, and health state utility inputs were derived from the ZUMA-3 trial (NCT02614066) and literature. Cost inputs were derived from literature or publicly available sources. Outcomes and costs were discounted 3% annually. Results of KTE-X19 versus comparators are reported as total and incremental life-years (LYs), quality-adjusted life-years (QALYs), costs, and resulting incremental cost-effectiveness ratio (ICER). Deterministic and probabilistic sensitivity analyses (PSA) and key scenario analyses were also performed. RESULTS: In the base case, incremental QALYs for KTE-X19 were 2.44, 3.26, and 4.61 versus BLIN, INO, and CHEMO, respectively. Incremental costs were $50,913, $251,532, and $432,027, respectively, resulting in ICERs of $20,843/QALY (versus BLIN), $77,271/QALY (versus INO), and $93,768/QALY (versus CHEMO). Deterministic sensitivity analysis results were most sensitive to subsequent allogeneic stem cell transplant rates and post-progression utilities. PSA found that KTE-X19 is 78.4%, 74.0%, and 75.4% likely to be cost-effective versus BLIN, INO, and CHEMO, respectively. Across most scenarios, at a willingness-to-pay (WTP) threshold of $150,000/QALY, KTE-X19 was cost-effective versus all treatments. CONCLUSIONS: Compared to current options for adults with R/R ALL, KTE-X19 is cost-effective, driven primarily by improved survival.

Several treatments for adults with relapsed/refractory B-cell precursor acute lymphoblastic leukemia (R/R B-ALL) have been approved in the past decade in the US, including blinatumomab (BLIN) and inotuzumab ozogamicin (INO). However, despite the high costs associated with these treatments, survival for patients remains poor. KTE-X19, an autologous anti-CD19 chimeric antigen receptor T-cell (CAR-T) therapy, approved by the Food and Drug Administration in October 2021, has potential to improve survival, but its economic value has not yet been determined. This model comprehensively evaluated the long-term clinical and economic value of KTE-X19 versus current treatments, including BLIN, INO, and salvage chemotherapy (CHEMO). Inputs were derived from key clinical trials, the literature, and other publicly available sources. The model used the perspective of a US third party payer over a patient lifetime. Compared to BLIN, INO and CHEMO, KTE-X19 resulted in improved quality of life as measured with incremental quality-adjusted life years (QALYs) of 2.44 (vs BLIN), 3.26 (vs INO), and 4.61 (vs CHEMO). Treatment with KTE-X19 had incremental costs of $50,913 (vs BLIN), $251,532 (vs INO), and $432,027 (vs CHEMO). KTE-X19 was found to provide good value for money based on incremental cost-effectiveness ratios of $20,843/QALY (vs BLIN), $77,271/QALY (vs INO), and $93,768/QALY (vs CHEMO). These values are well below the commonly accepted thresholds to determine economic value. Results were also found to be robust across sensitivity and scenario analyses.

Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study.

BACKGROUND: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state). METHODS: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use. FINDINGS: A total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose. INTERPRETATION: A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries. FUNDING: National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

Implementation of pharmacogenomic clinical decision support for health systems: a cost-utility analysis.

We constructed a cost-effectiveness model to assess the clinical and economic value of a CDS alert program that provides pharmacogenomic (PGx) testing results, compared to no alert program in acute coronary syndrome (ACS) and atrial fibrillation (AF), from a health system perspective. We defaulted that 20% of 500,000 health-system members between the ages of 55 and 65 received PGx testing for CYP2C19 (ACS-clopidogrel) and CYP2C9, CYP4F2 and VKORC1 (AF-warfarin) annually. Clinical events, costs, and quality-adjusted life years (QALYs) were calculated over 20 years with an annual discount rate of 3%. In total, 3169 alerts would be fired. The CDS alert program would help avoid 16 major clinical events and 6 deaths for ACS; and 2 clinical events and 0.9 deaths for AF. The incremental cost-effectiveness ratio was $39,477/QALY. A PGx-CDS alert program was cost-effective, under a willingness-to-pay threshold of $100,000/QALY gained, compared to no alert program.

Real-world persistence, adherence, health care resource utilization, and costs in people with type 2 diabetes switching from a first-generation basal insulin to a second-generation (insulin glargine 300 U/mL) vs an alternative first-generation basal insulin.

BACKGROUND: People with type 2 diabetes (T2D) who change their basal insulin (BI) may have variable persistence with therapy. Compared with first-generation (long-acting) BI analogs (insulin glargine 100U/mL [Gla-100]; insulin detemir [IDet]), second-generation (longer-acting) BI analogs (insulin glargine 300U/mL [Gla-300]; insulin degludec) have similar glycated hemoglobin (HbA1c) attainment and lowered hypoglycemia risk, which could impact treatment persistence. OBJECTIVE: To compare persistence, adherence, health care resource utilization (HRU), and costs for individuals switching from neutral protamine Hagedorn insulin or a first-generation BI analog with either the second-generation BI, Gla-300, or an alternative first-generation BI analog (Gla-100 or IDet). METHODS: We used Optum Clinformatics claims data from adults (aged ≥ 18 years) with T2D who had received BI (neutral protamine Hagedorn, Gla-100, IDet) in the 6-month baseline period, and switched to either Gla-300 or an alternative first-generation BI (Gla-100 or IDet; treatment switch = index date) between April 1, 2015, and August 31, 2019. Participants were followed for 12 months, until plan disenrollment, or until death, whichever occurred first. Cohorts were propensity score matched (PSM) on baseline characteristics. The primary outcome was the proportion who were persistent with therapy at 12 months. Secondary outcomes were adherence (proportion of days covered); change in HbA1c; and all-cause, diabetes-related, and hypoglycemia-related HRU and costs. RESULTS: PSM generated 3,077 participants/group (mean age: 68 years, 52% female). Cohorts were well balanced except for hospitalization, which was adjusted in models as a covariate. During the 12-month follow-up period, participants who received Gla-300 vs first-generation BI had greater persistence with (45.5% vs 42.1%; adjusted P = 0.0001), and adherence to (42.8% vs 38.2%; adjusted P = 0.0006), BI therapy and a statistically larger reduction in HbA1c at 12 months (-0.65% vs -0.45%; adjusted P = 0.0040). The proportion of participants achieving HbA1c less than 8% (47.2% vs 40.9%; P < 0.0001), but not less than 7% (21.2% vs 20.8%), was significantly higher for Gla-300 vs first-generation BI. All-cause (45.3 vs 65.9 per 100 patient-years [P100PY]) and diabetes-related (21.5 vs 29.1 P100PY), but not hypoglycemia-related, hospitalizations (1.0 vs 1.5 P100PY) were significantly (P < 0.0001) lower for Gla-300 vs first-generation BI. Similarly, all-cause (111.9 vs 148.8 P100PY), diabetes-related (54.8 vs 74.2 P100PY), and hypoglycemia-related (2.9 vs 5.7 P100PY) emergency department (ED) visits were significantly lower for Gla-300 (all P < 0.0001). Costs for all-cause hospitalizations and hypoglycemia-related ED visits were significantly lower for Gla-300 vs first-generation BI. Although pharmacy costs were significantly higher for Gla-300 vs first-generation BI, all-cause total health care costs were not significantly different: $41,255 vs $45,316 per person per year, respectively. CONCLUSIONS: In this claims-based analysis of people with T2D receiving BI, switching to Gla-300 was associated with significantly better persistence, adherence, and HbA1c reduction compared with switching to an alternative first-generation BI analog. All-cause HRU was significantly lower; despite significantly higher pharmacy costs, total health care costs were similar. DISCLOSURES: This study was funded by Sanofi US. Medical writing support was provided by Helen Jones, PhD, CMPP, of Evidence Scientific Solutions and funded by Sanofi US. Dr Wright is on the speakers' bureau and sits on the advisory boards for Abbot Diabetes, Bayer, Boehringer Ingelheim, Eli Lilly, and Sanofi; sits on the advisory board for Medtronic; and is a consultant for Abbot Diabetes, Bayer, Boehringer Ingelheim, and Eli Lilly. Dr Malone is on advisory boards for Novartis and Avalere and consults for Pear Therapeutics, Sarepta, and Strategic Therapeutics. Dr Trujillo sits on advisory boards for Novo Nordisk and Sanofi. Drs Gill, Zhou, and Preblick and Mr Li are employees and stockholders of Sanofi. Mr Huse is an employee of Evidera and a contractor for Sanofi. Dr Reid is a speaker and consultant for Novo Nordisk and Sanofi-Aventis and is a consultant for AstraZeneca and Intarcia.

Changes in predicted opioid overdose risk over time in a state Medicaid program: a group-based trajectory modeling analysis.

BACKGROUND AND AIMS: The time lag encountered when accessing health-care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one's opioid overdose risk changes over time. We aimed to identify longitudinal patterns of individual predicted overdose risks among Medicaid beneficiaries after initiation of opioid prescriptions. DESIGN, SETTING AND PARTICIPANTS: A retrospective cohort study in Pennsylvania, USA among Pennsylvania Medicaid beneficiaries aged 18-64 years who initiated opioid prescriptions between July 2017 and September 2018 (318 585 eligible beneficiaries (mean age = 39 ± 12 years, female = 65.7%, White = 62.2% and Black = 24.9%). MEASUREMENTS: We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency room or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores over time. FINDINGS: Among eligible beneficiaries, 0.61% had one or more occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time, including consistent low-risk (63%), consistent medium-risk (25%) and consistent high-risk (4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high- to medium-risk (3%) and another group that increased from medium- to high-risk over time (5%). CONCLUSIONS: More than 90% of Medicaid beneficiaries in Pennsylvania USA with one or more opioid prescriptions had consistent, predicted opioid overdose risks over 15 months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.

Five-year budget impact of a prescription digital therapeutic for patients with opioid use disorder.

BACKGROUND: Opioid use disorder (OUD) is associated with high healthcare resource utilization (HCRU) and costs. reSET-O is an FDA-cleared prescription digital therapeutic that delivers neurobehavioral therapy as an adjunct to treatment-as-usual (TAU; buprenorphine, face-to-face counseling, and contingency management). METHODS: A budget impact model was developed to evaluate reSET-O as an adjunct to TAU in OUD for a 1 million-member US mixed health plan over a 5-year time horizon. Model inputs included treatment costs and medical costs of hospitalizations, partial hospitalizations, intensive care unit stays, and emergency department visits. RESULTS: The base-case results and the alternative scenario analysis showed the addition of reSET-O was projected to result in consistently lower total yearly costs vs TAU and no treatment. The estimated total and per member per month (PMPM) budget impact over 5 years was -$763,026 and -$0.0116, respectively. When the upper range of cost estimates was used, the total and PMPM budget impacts over 5 years were -$2,481,563 and -$0.0378, respectively. Sensitivity analysis showed results were most sensitive to the proportion of patients untreated. CONCLUSION: The introduction of reSET-O in addition to TAU for OUD has the potential to reduce healthcare resource utilization and costs from 12 weeks up to 5 years.

Amortization of gene replacement therapies: A health policy analysis exploring a mechanism for mitigating budget impact of high-cost treatments.

With gene replacement therapies (GRTs) increasingly and rapidly reaching the healthcare marketplace, the vast potential for improving patient health is matched by the potential budgetary impact for healthcare payers. GRTs are highly valuable given their potential life-extending or even curative benefits and may provide significant cost-offsets compared with standard of care. Current healthcare systems are, however, struggling to fund such valuable but costly therapies. Some payers have already implemented specific financing models to account for the new treatment paradigms, but these do not address the budget impact in the year of acquisition or administration of these costly technologies. This health policy analysis aimed to assess the rationale and feasibility of amortization, within the context of financing healthcare technologies, and specifically GRTs. Amortization is an accounting concept applied to intangible assets that allows for spreading the cost an intangible asset over time, allowing for repayment to occur via interest and principal payments sufficient to repay the intangible asset in full by its maturity. Our systematic scoping review on the amortization of healthcare technologies found a very small literature base with even that being unclear and inconsistent in its understanding of the issues. Where amortization was proposed as a solution for funding costly, but highly valuable GRTs, the concept was not fully investigated in detail, nor was the feasibility of the approach fully challenged. However, by providing clear definitions of relevant concepts along with an example of amortization models applied to some example GRTs, we propose that amortization can offer a promising method for funding of extraordinarily high-value healthcare technologies, thereby increasing market and patient access for these technologies. Nonetheless, healthcare accounting principles and financing guidelines must evolve to apply amortization to the rapidly developing GRTs.

Self-Assessment of Therapeutic Decision-Making Skills in Pharmacy Students.

Objective. The ability of pharmacy students to self-regulate and reflect on knowledge and skills is important as proficient self-assessment skills guide learning strategies and prompt students to bridge their knowledge gaps. The objectives for this study were to determine how well third-year pharmacy students self-assess, explore the rationales behind their self-assessments and determine whether there is a correlation between self-assessment accuracy and academic performance.Methods. A quasi-experimental one-group pre-/post-test design was conducted with third-year pharmacy students. Examiner grades, student self-assessment grades, comparative reports, and end-of-semester grades were collected. Students were categorized into tertiles based on academic performance for data analysis. Paired t tests, Pearson r and percentage agreements were conducted to investigate self-assessment accuracy. Correlational statistical tests were implemented to examine the relationships between self-assessment accuracy and academic performance.Results. One hundred sixty-two third-year pharmacy students were included. On average, students demonstrated poor self-evaluation skills and underestimated themselves by 4.9%. Lower performing students were generally overconfident in evaluating their performance. There was no significant correlation between students' self-assessment accuracy and academic performance on the subsequent end-of-semester examination questions.Conclusion. Overall, students tended to underestimate their academic performance. Further research on self-assessment is needed to better understand how students think about their performance, which may help to improve education methods, such as inclusion of reflective practices after case-based activities.