Opportunity and Opportunism in Artificial-Intelligence-Powered Data Extraction: A Value-Centered Approach

Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions. This charge is rapidly evolving due to forces such as artificial intelligence [AI], big data [opportunistic imaging, imaging prognostication], and advanced diagnostic technologies. A new "modernistic" paradigm is emerging whereby radiologists, in conjunction with computer algorithms, will be tasked with extracting as much information from imaging data as possible, often without a specific clinical question being posed and independent of any stated clinical need. In addition, AI algorithms are increasingly able to predict long-term outcomes using data from seemingly normal examinations, enabling AI-assisted prognostication. As these algorithms become a standard component of radiology practice, the sheer amount of information they demand will increase the need for streamlined workflows, communication, and data management techniques. In addition, the provision of such information raises reimbursement, liability, and access issues. Guidelines will be needed to ensure all patients have access to the benefits of this new technology and guarantee mined data do not inadvertently create harm. In this article, we discuss challenges and opportunities relevant to radiologists in this changing landscape, with an emphasis on ensuring that radiologists provide high-value care.

Health Economics in a World of Uneconomic Growth

Multiple, accelerating and interacting ecological crises are increasingly understood as constituting a major threat to human health and well-being. Unconstrained economic growth is strongly implicated in these growing crises, and it has been argued that this growth has now become "uneconomic growth", which is a situation where the size of the economy is still expanding, but this expansion is causing more harm than benefit. This article summarises the multiple pathways by which uneconomic growth can be expected to harm human health. It describes how health care systems-especially through overuse, low value and poor quality care-can themselves drive uneconomic growth. Health economists need to understand not only the consequences of environmental impacts on health care, but also the significance of uneconomic growth, and pay closer attention to the growing body of work by heterodox economists, especially in the fields of ecological and feminist economics. This will involve paying closer heed to the existence and consequences of diminishing marginal returns to health care consumption at high levels; the central importance of inequalities and injustice in health; and the need to remedy health economists' currently limited ability to deal effectively with low value care, overdiagnosis and overtreatment.

Climate change, health and sustainable healthcare: The role of health economics

Healthcare systems around the world are responding with increasing urgency to rapidly evolving ecological crises, most notably climate change. This Perspective considers how health economics and health economists can best contribute to protecting health and building sustainable healthcare systems in the face of these challenges.

Cost Effectiveness of Case Detection Strategies for the Early Detection of COPD

OBJECTIVES

The value of early detection and treatment of chronic obstructive pulmonary disease (COPD) is currently unknown. We assessed the cost effectiveness of primary care-based case detection strategies for COPD.

METHODS

A previously validated discrete event simulation model of the general population of COPD patients in Canada was used to assess the cost effectiveness of 16 case detection strategies. In these strategies, eligible patients (based on age, smoking history, or symptoms) received the COPD Diagnostic Questionnaire (CDQ) or screening spirometry, at 3- or 5-year intervals, during routine visits to a primary care physician. Newly diagnosed patients received treatment for smoking cessation and guideline-based inhaler pharmacotherapy. Analyses were conducted over a 20-year time horizon from the healthcare payer perspective. Costs are in 2019 Canadian dollars ($). Key treatment parameters were varied in one-way sensitivity analysis.

RESULTS

Compared to no case detection, all 16 case detection scenarios had an incremental cost-effectiveness ratio (ICER) below $50,000/QALY gained. In the most efficient scenario, all patients aged ≥ 40 years received the CDQ at 3-year intervals. This scenario was associated with an incremental cost of $287 and incremental effectiveness of 0.015 QALYs per eligible patient over the 20-year time horizon, resulting in an ICER of $19,632/QALY compared to no case detection. Results were most sensitive to the impact of treatment on the symptoms of newly diagnosed patients.

CONCLUSIONS

Primary care-based case detection programs for COPD are likely to be cost effective if there is adherence to best-practice recommendations for treatment, which can alleviate symptoms in newly diagnosed patients.

Internal barriers to efficiency: why disinvestments are so difficult. Identifying and addressing internal barriers to disinvestment of health technologies

Although efficiency is a core concept in health economics, its impact on health care practice still is modest. Despite an increased pressure on resource allocation, a widespread use of low-value care is identified. Nonetheless, disinvestments are rare. Why is this so? This is the key question of this paper: why are disinvestments not more prevalent and improving the efficiency of the health care system, given their sound foundation in health economics, their morally important rationale, the significant evidence for a long list of low-value care and available alternatives? Although several external barriers to disinvestments have been identified, this paper looks inside us for mental mechanisms that hamper rational assessment, implementation, use and disinvestment of health technologies. Critically identifying and assessing internal inclinations, such as cognitive biases, affective biases and imperatives, is the first step toward a more rational handling of health technologies. In order to provide accountable and efficient care we must engage in the quest against the figments of our minds; to disinvest in low-value care in order to provide high-value health care.

Can multi-criteria decision analysis (MCDA) be implemented into real-world drug decision-making processes? A Canadian provincial experience

OBJECTIVE

To describe the implementation of multi-criteria decision analysis (MCDA) into a Canadian public drug reimbursement decision-making process, identifying the aspects of the MCDA approach, and the context that promoted uptake.

METHODS

Narrative summary of case study describing the how, when, and why of implementing MCDA.

RESULTS

Faced with a fixed budget, a pipeline of expensive but potentially valuable drugs, and potential delays to drug decision making, the Ministry of Health (i.e., decision makers) and its independent expert advisory committee (IAB) sought alternative values-based decision processes. MCDA was considered highly compatible with current processes, but the ability as a stand-alone intervention to address issues of opportunity cost was unclear. The IAB nevertheless collaboratively voted to implement an externally developed MCDA with support from decision makers. After several months of engagement and piloting, implementation was rapid and leveraged strong pre-existing formal and informal communication networks. The IAB as a whole rates new submissions which serves as an input into the deliberative process.

CONCLUSIONS

MCDA can be a highly adaptable approach that can be implemented into a functioning drug reimbursement setting when facilitated by (i) a truly limited budget; (ii) a shared vision for change by end-users and decision makers; (iii) using pre-existing deliberative processes; and (iv) viewing the approach as a decision framework rather than the decision (when appropriate). Given the current limitations of MCDA, implementing an academically imperfect tool first and evaluating later reflects a practical solution to real-time fiscal constraints and impending delays to drug approvals that may be faced by decision makers.

Biases distorting priority setting

Modern health care faces an ever widening gap between technological possibilities and available resources. To handle this challenge we have constructed elaborate systems for health policy making and priority setting. Despite such systems many health care systems provide a wide range of documented low-value care while being unable to afford emerging high-value care. Accordingly, this article sets out asking why priority setting in health care has so poor outcomes while relevant systems are well developed and readily available. It starts to identify some rational and structural explanations for the discrepancy between theoretical efforts and practical outcomes in priority setting. However, even if these issues are addressed, practical priority setting may still not obtain its goals. This is because a wide range of irrational effects is hampering priority setting: biases. By using examples from the literature the article identifies and analyses a wide range of biases indicating how they can distort priority setting processes. Overuse, underuse, and overinvestment, as well as hampered disinvestment and undermined priority setting principles are but some of the identified implications. Moreover, while some biases are operating mainly on one level, many are active on the micro, meso and on the macro level. Identifying and analyzing biases affecting priority setting is the first, but crucial, step towards improving health policy making and priority setting in health care.

Transparency in Decision Modelling: What, Why, Who and How?

Transparency in decision modelling is an evolving concept. Recently, discussion has moved from reporting standards to open-source implementation of decision analytic models. However, in the debate about the supposed advantages and disadvantages of greater transparency, there is a lack of definition. The purpose of this article is not to present a case for or against transparency, but rather to provide a more nuanced understanding of what transparency means in the context of decision modelling and how it could be addressed. To this end, we review and summarise the discourse to date, drawing on our collective experience. We outline a taxonomy of the different manifestations of transparency, including reporting standards, reference models, collaboration, model registration, peer review and open-source modelling. Further, we map out the role and incentives for the various stakeholders, including industry, research organisations, publishers and decision makers. We outline the anticipated advantages and disadvantages of greater transparency with respect to each manifestation, as well as the perceived barriers and facilitators to greater transparency. These are considered with respect to the different stakeholders and with reference to issues including intellectual property, legality, standards, quality assurance, code integrity, health technology assessment processes, incentives, funding, software, access and deployment options, data protection and stakeholder engagement. For each manifestation of transparency, we discuss the 'what', 'why', 'who' and 'how'. Specifically, their meaning, why the community might (or might not) wish to embrace them, whose engagement as stakeholders is required and how relevant objectives might be realised. We identify current initiatives aimed to improve transparency to exemplify efforts in current practice and for the future.

Expanding disease and undermining the ethos of medicine

The expansion of the concept of disease poses problems for epidemiology. Certainly, new diseases are discovered and more people are treated earlier and better. However, the historically unprecedented expansion is criticised for going too far. Overdiagnosis, overtreatment, and medicalization are some of the challenges heatedly debated in medicine, media, and in health policy making. How are we to analyse and handle the vast expansion of disease? Where can we draw the line between warranted and unwarranted expansion? To address this issue, which has wide implications for epidemiology, we need to understand how disease is expanded. This article identifies six ways that our conception of disease is expanded: by increased knowledge (epistemic), making more phenomena count as disease (ontological), doing more (pragmatic), defining more (conceptual), and by encompassing the bad (ethic) and the ugly (aesthetic). Expanding the subject matter of medicine extends its realm and power, but also its responsibility. It makes medicine accountable for ever more of human potential dis-eases. At the same time it blurs the borders and undermines the demarcation of medicine. Six specific advices can guide our action clarifying the subject matter of medicine in general and epidemiology in particular. To avoid unlimited responsibility and to keep medicine on par with its end, we need to direct the expansion of disease to what effectively identifies or reduces human suffering. Otherwise we will deplete medicine and undermine the greatest asset in health care: trust.

Development and Validation of the Evaluation Platform in COPD (EPIC): A Population-Based Outcomes Model of COPD for Canada

BACKGROUND

We report the development, validation, and implementation of an open-source population-based outcomes model of chronic obstructive pulmonary disease (COPD) for Canada.

METHODS

Evaluation Platform in COPD (EPIC) is a discrete-event simulation model of Canadians 40 years of age or older. Three core features of EPIC are its open-population design (incorporating projections of future population growth, aging, and smoking trends), its incorporation of heterogeneity in lung function decline and burden of exacerbations, and its modeling of the natural history of COPD from inception. Multiple original data analyses, as well as values reported in the literature, were used to populate the model. Extensive face validity and internal and external validity evaluations were performed.

RESULTS

The model was internally validated on demographic projections, mortality rates, lung function trajectories, COPD exacerbations, costs and health state utility values, and stability of COPD prevalence over time within strata of risk factors. In external validation, it moderately overestimated the rate of overall exacerbations in 2 independent trials but generated consistent estimates of rate of severe exacerbations and mortality.

LIMITATIONS

In its current version, EPIC does not consider uncertainty in the evidence. Several components such as additional (e.g., environmental and occupational) risk factors, treatment, symptoms, and comorbidity will have to be added in future iterations. Predictive validity of EPIC needs to be examined prospectively against future empirical studies.

CONCLUSIONS

EPIC is the first multipurpose, open-source, outcome- and policy-focused model of COPD for Canada. Platforms of this type have the capacity to be iteratively updated to incorporate the latest evidence and to project the outcomes of many different scenarios within a consistent framework.

UNTANGLING, UNBUNDLING, AND MOVING FORWARD: FRAMING HEALTH TECHNOLOGY REASSESSMENT IN THE CHANGING CONCEPTUAL LANDSCAPE

OBJECTIVES

Health technology reassessment (HTR) is a policy process to manage health technologies throughout their lifecycle and ensure their ongoing optimal use. However, within an ever-evolving field, HTR is only one of many concepts associated with the optimization of health technologies. There is limited understanding of how other concepts and processes might differ and/or be interrelated. This study aims to describe the concepts underlying the various technology optimization processes and to reconcile their relationships within the HTR process.

METHODS

A synthesis of the literature on approaches to HTR was completed. An inductive synthesis approach was completed to catalogue common concepts and themes. Expert stakeholders were consulted to develop a schematic to diagrammatically depict the relationships among concepts and frame them within the HTR process.

RESULTS

A practical schematic was developed. Common concepts and themes were organized under six major domains that address the following discussion questions: (i) what is the value of the existing technology?; (ii) what is the current utilization gap?; (iii) what are the available tools and resources?; (iv) what are the levers for change?; (v) what is the desired outcome?; and (vi) who are the foundational actors?

CONCLUSIONS

Using these six questions to frame the issues faced by HTR will advance the common understanding of HTR, as well as improve implementation of HTR initiatives. These questions will clearly identify the process required to move forward within a complex healthcare system.

Costs and expected gain in lifetime health from intensive care versus general ward care of 30,712 individual patients: a distribution-weighted cost-effectiveness analysis

BACKGROUND

Clinicians, hospital managers, policy makers, and researchers are concerned about high costs, increased demand, and variation in priorities in the intensive care unit (ICU). The objectives of this modelling study are to describe the extra costs and expected health gains associated with admission to the ICU versus the general ward for 30,712 patients and the variation in cost-effectiveness estimates among subgroups and individuals, and to perform a distribution-weighted economic evaluation incorporating extra weighting to patients with high severity of disease.

METHODS

We used a decision-analytic model that estimates the incremental cost per quality-adjusted life year (QALY) gained (ICER) from ICU admission compared with general ward care using Norwegian registry data from 2008 to 2010. We assigned increasing weights to health gains for those with higher severity of disease, defined as less expected lifetime health if not admitted. The study has inherent uncertainty of findings because a randomized clinical trial comparing patients admitted or rejected to the ICU has never been performed. Uncertainty is explored in probabilistic sensitivity analysis.

RESULTS

The mean cost-effectiveness of ICU admission versus ward care was €11,600/QALY, with 1.6 QALYs gained and an incremental cost of €18,700 per patient. The probability (p) of cost-effectiveness was 95% at a threshold of €22,000/QALY. The mean ICER for medical admissions was €10,700/QALY (p = 97%), €12,300/QALY (p = 93%) for admissions after acute surgery, and €14,700/QALY (p = 84%) after planned surgery. For individualized ICERs, there was a 50% probability that ICU admission was cost-effective for 85% of the patients at a threshold of €64,000/QALY, leaving 15% of the admissions not cost-effective. In the distributional evaluation, 8% of all patients had distribution-weighted ICERs (higher weights to gains for more severe conditions) above €64,000/QALY. High-severity admissions gained the most, and were more cost-effective.

CONCLUSIONS

On average, ICU admission versus general ward care was cost-effective at a threshold of €22,000/QALY (p = 95%). According to the individualized cost-effectiveness information, one in six ICU admissions was not cost-effective at a threshold of €64,000/QALY. Almost half of these admissions that were not cost-effective can be regarded as acceptable when weighted by severity of disease in terms of expected lifetime health. Overall, existing ICU services represent reasonable resource use, but considerable uncertainty becomes evident when disaggregating into individualized results.

A Review of Empirical Analyses of Disinvestment Initiatives

BACKGROUND

Disinvesting in low-value health care services provides opportunities for investment in higher value care and thus an increase in health care efficiency.

OBJECTIVES

To identify international experience with disinvestment initiatives and to review empirical analyses of disinvestment initiatives.

METHODS

We performed a literature search using the PubMed database to identify international experience with disinvestment initiatives. We also reviewed empirical analyses of disinvestment initiatives.

RESULTS

We identified 26 unique disinvestment initiatives implemented across 11 countries. Nineteen addressed multiple intervention types, six addressed only drugs, and one addressed only devices. We reviewed 18 empirical analyses of disinvestment initiatives: 7 reported that the initiative was successful, 8 reported that the initiative was unsuccessful, and 3 reported that findings were mixed; that is, the study considered multiple services and reported a decrease in the use of some but not others. Thirty-seven low-value services were evaluated across the 18 empirical analyses, for 14 (38%) of which the disinvestment initiative led to a decline in use. Six of the seven studies that reported the disinvestment initiative to be successful included an attempt to promote the disinvestment initiative among participating clinicians.

CONCLUSIONS

The success of disinvestment initiatives has been mixed, with fewer than half the identified empirical studies reporting that use of the low-value service was reduced. Our findings suggest that promotion of the disinvestment initiative among clinicians is a key component to the success of the disinvestment initiative.