Health Economics And Outcomes Research Research Articles

OP12 Exploring Preferences in Selecting Dissertation Topics Among Ph.D. Students in Health Economics and Outcomes Research (HEOR) in the United States

OP12 Exploring Preferences in Selecting Dissertation Topics Among Ph.D. Students in Health Economics and Outcomes Research (HEOR) in the United States

#SharingHEOR: Developing Modern Media for Communication and Dissemination of Health Economics and Outcomes Research.

Questions regarding the effectiveness and safety of health interventions and allocation of health care resources are frequently discussed in mainstream and social media. Additionally, government and foundation funders are increasingly mandating that results be disseminated to the lay public and patients may benefit from being able to digest scientific research regarding their health conditions. Therefore, it is important to widely disseminate and clearly communication health economics and outcomes research (HEOR) findings to a range of interested parties. Digital media features such as graphical or visual abstracts, infographics and videos are informative and add value to research articles by improving reader engagement with articles, potentially increasing their impact, and allowing results to be more widely disseminated. However, use of novel digital media for research dissemination has been relatively limited to date. In this article, we discuss the rationale for developing novel media to communicate and disseminate research findings and offer practical advice for doing so. We conclude by outlining a future agenda for research regarding HEOR communication and dissemination.

Introducing Open Science in Teaching Health Economic Modelling.

Open Science is gaining ground in all research fields, including health economics and outcomes research (HEOR). However, teaching Open Science is still in its infancy. This paper describes the design, implementation and evaluation of a teaching activity focusing on introducing Open Science during a Master's course during which participants have to develop a discrete event simulation. The teaching activity was organised as a series of lectures introducing different aspects of the Open Science philosophy and practices, such as good software coding practices, version control systems and reproducible research. The participants' increase in Open Science knowledge was elicited through a survey before and after the teaching innovation. After the teaching innovation, participants' knowledge of Open Science increased and they reported an improvement in Open Science-related skills, such as using a script-based statistical software, identifying and re-using open data, and collaborative script development. During the evaluation at the end of the course, the course participants mentioned that the Open Science-related content was interesting but would fit better within a course in which broader research-related content is taught. Based on this feedback, we will most likely narrow the scope of the Open-Science-related content in this course to Open Source Modelling which may better fit the scope of the course. This paper contains links to the teaching activities we developed and other resources which may be used to design teaching activities on Open Science. Herewith, we hope to inspire other teachers in including Open Science into their teaching.

Evaluating treatments in rare indications warrants a Bayesian approach.

Evaluating efficacy and real-world effectiveness for novel therapies targeting rare mutations or patient subpopulations with unmet needs is a growing challenge in health economics and outcomes research (HEOR). In these settings it may be difficult to recruit enough patients to run adequately powered randomized clinical trials, resulting in greater reliance on single-arm trials or basket trial designs. Additionally, evidence networks for performing network meta-analysis may be sparse or disconnected when comparing available treatments in narrower patient populations. These challenges create an increased need for use of appropriate methods for handling small sample sizes, structural modelling assumptions and more nuanced decision rules to arrive at "best-available evidence" on comparative and non-comparative efficacy/effectiveness. We advocate for greater use of Bayesian methods to address these challenges as they can facilitate efficient and transparent borrowing of information across varied data sources under flexible modelling assumptions, probabilistic sensitivity analysis to assess model assumptions, and more nuanced decision-making where limited power reduces the utility of classical frequentist hypothesis testing. We illustrate how Bayesian methods have been recently used to overcome several challenges of rare indications in HEOR, including approaches to borrowing information from external data sources, evaluation of efficacy in basket trials, and incorporating non-randomized studies into network meta-analysis. Lastly, we provide several recommendations for HEOR practitioners on appropriate use of Bayesian methods to address challenges in the rare disease setting.

Reimagining Patient Data Access for Researchers

ObjectivesWidespread use of electronic health records (EHRs) now makes it feasible to expand beyond health insurance claims data to include full EHR data for health economics and outcomes research (HEOR) studies. We seek to develop ways to maximize researcher access to such data while strongly protecting patients’ privacy rights. MethodsWe analyzed alternative organizational structures and intellectual property rights assignments as they now exist and compared these with structures and intellectual property rights assignments that would maximize access to data for HEOR studies and minimize transactions costs. We analyzed data protection requirements and financial incentives at 3 levels: patient decision making, patients’ data aggregators, and final aggregation across patients’ data. ResultsCreating new HEOR data systems requires new organizations and funding, while also protecting patients’ data privacy rights. The Cures Act enables a new market for trusted third parties (TTPs) to aggregate patients’ data. New secondary data aggregators must combine individuals’ aggregated EHRs into usable HEOR databases. Maximal patient participation requires complete health insurance coverage of costs that healthcare providers charge for transmitting patients’ data to TTPs. The new secondary system to aggregate data from many TTPs into usable HEOR optimally has external funding. ConclusionsImportant steps remain uncompleted to achieve maximally available HEOR data while protecting patients’ privacy rights. HEOR information is a public good, so private incentives to support creation and operation of this new system remain incomplete. Public and private support can expand this system to optimally improve people’s health.

EPH153 A Natural Language Processing Solution for Health Economics and Outcomes Research Systematic Literature Review

Systematic literature review (SLR), an important tool in health economics and outcomes research (HEOR), is routinely conducted to understand the research landscape and synthesize evidence related to disease burden and treatment options. Conducting an SLR is time-consuming and labor-intensive. In this work, we report on a Natural Language Processing (NLP) solution that automates and accelerates tasks in the SLR process.

HTA69 Towards a Minimum Information Set Needed to Define Patient-Facing Digital Health Technology for Outcomes Research

Umbrella terms for Digital Health Technology (DHT) (digital health, eHealth, mHealth, telehealth/telemedicine) are insufficiently defined for health economics and outcomes research (HEOR). Definitions of secondary terms for DHTs lack information about PICOTS (population, intervention, comparator, outcome, timing, and setting) and health system or technology frameworks. We aim to develop a minimum information set to define patient-facing DHTs for HEOR purposes.

Development and Implementation of a Healthcare Database Analysis Course for Graduate Students.

There emerged a need to develop and implement a new healthcare database analysis course for Health Economics and Outcomes Research (HEOR) graduate students, which would allow students to apply their biostatistics and study design skills to answer healthcare-related research questions using large datasets. This communication establishes the need for this course, describes how the course was conceptualized, provides an overview of the course content, course cohort, and course outcomes, and discusses lessons learned from this process. This course was developed to meet the need of HEOR graduates to perform real-world data studies. The course required students to conceptualize a study, apply their data analysis skills to analyze the data, and develop their scientific writing skills by preparing a conference abstract and research report that should be submitted for publication. Lessons learned include focusing more on developing advanced research methodologies and less time on preparing dissemination materials, which can instead be done in subsequent courses or for independent study credit.

Greece 2.0, Health Economics and Outcome Research and the Rise of Artificial Intelligence: Another Missed Opportunity or it's Time for Brilliance?

The EU National Recovery and Resilience Plan "Greece 2.0" includes, among other priorities, a framework to promote and reform the health system, with a focus on digitalization of health and the use of information technology applications. Greece 2.0 may offer a chance to address the current scarcity of high-quality, reliable data sources, which is limiting the spread and impact of health economics and outcomes research (HEOR). We also suspect that the use of artificial intelligence (AI) in HEOR will play an important role in Greece's health-care reform and that it will be critical for making real-world data-driven decisions, reducing policy uncertainty. Greece has a once-in-a-lifetime chance to start from scratch and potentially build data-centric AI systems that prioritise data quality over quantity and are built on scalable, flexible, and governable data collection. This commentary explains and critically considers the significance of developing and funding an innovative plan for using AI in HEOR as part of the Greece 2.0 framework. It also discusses ethical issues and the larger role of HEOR in health-care reform.

Machine Learning Methods in Health Economics and Outcomes Research—The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR.The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation—helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis.Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results.To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

P21 A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1 - Data from Wearable Devices

With the emerging use of machine learning (ML) techniques, there has been particular interest in utilizing data from wearable devices for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to data collected from wearables in HEOR.

MSR57 A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2 - Data from Non-Wearables

Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.

The Impact of Hypoglycemia on Productivity Loss and Utility in Patients With Type 2 Diabetes Treated With Insulin in Real-world Canadian Practice: Protocol for a Prospective Study.

BackgroundType 2 diabetes mellitus (T2DM) imposes a substantial burden owing to its increasing prevalence and life-threatening complications. In patients who do not achieve glycemic targets with oral antidiabetic drugs, the initiation of insulin is recommended. However, a serious concern regarding insulin is drug-induced hypoglycemia. Hypoglycemia is known to affect quality of life and the use of health care resources. However, health economics and outcomes research (HEOR) data for economic modelling are limited, particularly regarding utility values and productivity losses.ObjectiveThis real-world prospective study aims to assess the impact of hypoglycemia on productivity and utility in insulin-treated adults with T2DM from Ontario and Quebec, Canada.MethodsThis noninterventional, multicenter, 3-month prospective study will recruit patients from 4 medical clinics and 2 endocrinology or diabetes clinics. Patients will be identified using appointment lists and enrolled through consecutive sampling during routinely scheduled consultations. To be eligible, patients must be aged ≥18 years, diagnosed with T2DM, and treated with insulin. Utility and productivity will be measured using the EQ-5D-5L questionnaire and Institute for Medical Technology Assessment Productivity Cost Questionnaire, respectively. Questionnaires will be completed 4, 8, and 12 weeks after recruitment. Generalized estimating equation models will be used to investigate productivity losses and utility decrements associated with incident hypoglycemic events while controlling for individual patient characteristics. A total of 500 patients will be enrolled to ensure the precision of HEOR estimates.ResultsThis study is designed to fill a gap in the Canadian evidence on the impact of hypoglycemia on HEOR outcomes. More specifically, it will generate productivity and utility inputs for the economic modeling of T2DM.ConclusionsInsulin therapy is expensive, and hypoglycemia is a significant component of economic evaluation. Robust HEOR data may help health technology assessment agencies in future reimbursement decision-making.International Registered Report Identifier (IRRID)PRR1-10.2196/35461

Celebrating 10 years of the Journal of Comparative Effectiveness Research.

Celebrating 10 years of the Journal of Comparative Effectiveness Research.

POSB96 Economic Model for a Gene Therapy in an Ultra-Rare Disease: Expert Validation of Approach and Assumptions

A gene therapy has been developed to treat aromatic L-amino acid decarboxylase (AADC) deficiency, a rare genetic neurological disorder. An economic model to support Health Technology Assessments (HTAs) for this therapy requires the utilization of proxy data and various assumptions to overcome the challenges in paucity of data, such as natural history. This research aimed to validate the modelling assumptions and approach with health economics and outcomes research (HEOR) experts, following clinical validation.

PP290 Ongoing Swedish Initiatives To Improve The Potential For Real World Data Assessment Of Medical Devices

IntroductionIn 2020, the Swedish regions cemented a national managed-introduction project for medical devices, to ensure equal, cost-effective, and appropriate use. Health technology assessment (HTA) is an important component of the project. Swedish national health registers have world-class coverage and completeness but may lack information enabling adequate evaluation of medical devices. This study reviews the current situation and ongoing initiatives. Additionally, the potential for medical device health economics and outcomes research (HEOR) using Swedish registers is assessed.MethodsA review of Swedish national health registers was undertaken, focusing on available data, and contextualized for the purpose of HEOR for medical devices. Additionally, the review included an evaluation of the Swedish reimbursement authority's (The Dental and Pharmaceutical Benefits Agency, TLV) ongoing initiatives to improve the potential to follow-up the impact of the technologies they assess and develop value-based pricing schemes.ResultsFive registers were deemed the most relevant national health registers for device research. They include high-quality longitudinal data and are linkable on a per-patient basis. For devices, main limitations include limited data on specialized outpatient care, lags in updating certain registers, lack of laboratory data, and challenges in identifying the specific device used. Reports indicate that certain limitations are being addressed, including pilot-studies investigating the opportunity for automated reporting of data from regional systems, and app-collected patient-reported health data.ConclusionsSwedish registers provide comprehensive sources for HEOR studies, but limitations related to the assessment of medical device impact remain. As is common with register data reporting grouped diagnoses and interventions, specific devices are not directly identifiable in the national health registers. For some devices, this might be addressable through linkage with other data-sources. Swedish authorities are undertaking several initiatives that will likely improve the potential for HTA and follow-up of medical devices using national health register data.

The power of health economics and outcomes research (HEOR) in making decisions that matter

Deciding on approving and granting market access to new medical technologies such as pharmaceutical products, vaccines, or medical devices is a multifactorial research problem. Balancing out clinical performance, epidemiological implications, burden of disease, economic value, and patient preferences, among other factors, is in itself a challenging endeavor. However, this should be a mandatory requirement when making approval and market access decisions that might affect millions of people in a specific country setting. The aim of this reflection research article is twofold; first, it provides context on the important role that health economics and outcomes research (HEOR) plays in informing decision making for market access and reimbursement of new medical technologies. Second, it outlines the power of HEOR studies in guiding discussions when assessing the value of new medical technologies. Overall, this article aims at highlighting key HEOR considerations for healthcare professionals, students, and institutions interested in building analytical capabilities around this exciting and uninterruptedly growing field of knowledge.

PMU57 Need Assessment of Knowledge on Health Economics and Outcomes Research in Central Asia Countries

The present study examined level of health economics and outcomes research (HEOR) knowledge in Central Asia countries, assessing what expert in this field think and know about HEOR and how they used it in their work. Based on a review of available literature and professional opinion the web-based survey for needs assessment was developed to measure level of knowledge on HEOR and identify unmet needs on knowledge and gaps. Data analysis was conducted in Microsoft Excel using descriptive statistics to summarize the survey responders' demographic data, level of knowledge and preferred method for delivery of this knowledge. Survey data collected from 74 responders. The huge gap between science and action was identified in field interpretation of health economic evaluation and patient-reported outcomes, which was amounted 2.48 ± 1.52 and 2.56 ± 1.58 accordingly. The least gap on knowledge was amounted for systematic review and meta-analysis applications (mean = 0.98 ± 0.66). Also, self-perceived unmet needs on HEOR knowledge were smallest in field of understanding of types of health care costs. The 65 % of responders preferred to receive knowledge related to HEOR topics through academic continuing education and distance learning programs. Correlation analyses was performed to identify possible associations between HEOR topics self-assessed level and responders demographic data. The analysis of gaps on HEOR knowledge allowed current unmet needs on knowledge in perceptions among responders. This study provided clarification of the needs and topics of the educational programs for sustainable development of HEOR in Central Asia countries. The results of our study help, in a larger sense, to navigate society towards achieving the HEOR knowledge.

PRO73 Limitations of Matched Control Groups Using Stabilization Criteria: A Case Study in CAH

Defining appropriate matched controls is challenging in health economics and outcomes research (HEOR) studies. An HEOR study was conducted in patients with assumed classic congenital adrenal hyperplasia (CAH), a rare and potentially serious autosomal recessive disorder characterized by impaired cortisol synthesis and excess androgen production. Results from this study are presented to illustrate the challenges of selecting appropriate controls.

Algorithm to derive progression-based lines of therapy from a real-world non-small cell lung cancer (NSCLC) dataset.

e21172 Background: Analysis of Real World Data (RWD) from Electronic Health Records (EHR) for applications such as Health Economics and Outcomes Research (HEOR) or regulatory submissions requires identification of the lines of therapy (LoT) patients have received. LoTs are typically not captured in EHR and must be manually abstracted. As the use of RWD increases, there is a growing need to create algorithms that can work on RWD to extract LoT information in an automated manner with high accuracy. We present here the results of such an algorithm created on NSCLC RWD. Methods: 10950 advanced NSCLC patients from the ConcertAI Oncology RWD database who had received anti-neoplastic treatment after advanced diagnosis were used to build and validate this algorithm. These data were further enriched by expert nurse curators to fill in missing oral drug information and identify progression events. We developed a progression-based LoT (pLoT) model that identified LoT changes in sync with tumor progressions. If patients received multiple regimens before progression they were captured as nested regimens within the LoT. The algorithm uses complex rules to define combination of drugs as regimens (combination rule), identify resumption of regimens (gap rule) or dropping of drugs from regimens as new lines and to handle noisiness in RWD etc. Results: The LoT model accurately captures line changes triggered by progression events as well as any nested regimen changes due to adverse events etc. Patient level validation of LoT was carried out by clinical experts using an in-house tool and found to be consistent with literature & individual drug data. Cohort level analysis of top 3 combinations of therapies used in 1st & 2nd line treatment between 2015-2020 (8200 patients) are shown in Table. Sensitivity analysis on the combination rule showed that this parameter can be changed between 28-33 days without significantly impacting the LoT output (<1% impact). We use a 30 day combination rule as the default. Similarly, the gap rule parameter is quite robust and does not show significant variation between 45 – 90 days (<2% impact). We use 63 days. Conclusions: We have developed a robust algorithm to derive pLoT on RWD at scale assuming availability of curated progression data which can be used to support use cases such as HEOR, clinical development and regulatory submissions. pLoT is better suited for outcomes analysis compared to regimen based LoT since it distinguishes changes in treatment due to progression events from changes due to toxicity, drug availability, etc., and allows analysis on a more homogeneous patient population relating to their past clinical experience. [Table: see text]