Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why Is It Different?

Revolutionizing Acute Cardiac Care with Artificial Intelligence: Opportunities and Challenges

This manuscript reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The paper examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac CT, and MRI and discusses the regulatory landscape for AI in healthcare, categorizes AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalizability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

Barriers to Access to Cardiac Surgery: Canadian Situation and Global Context

Cardiovascular disease is the leading cause of morbidity and mortality worldwide. Cardiovascular care spans primary, secondary, and tertiary prevention and care, whereby tertiary care is particularly prone to disparities in care. Challenges in access to care especially affect low- and middle-income countries (LMICs), however, multiple barriers also exist and persist across high-income countries. Canada is lauded for its universal health coverage but is faced with health care system challenges and substantial geographic barriers. Canada possesses 203 active cardiac surgeons, or 5.02 per million population, ranging from 3.70 per million in Newfoundland and Labrador to 7.48 in Nova Scotia. As such, Canada possesses fewer cardiac surgeons per million population than the average among high-income countries (7.15 per million), albeit more than the global average (1.64 per million) and far higher than the low-income country average (0.04 per million). In Canada, adult cardiac surgeons are active across 32 cardiac centres, representing 0.79 cardiac centres per million population, which is just above the global average (0.73 per million). In addition to centre and workforce variations, barriers to care exist in the form of waiting times, sociodemographic characteristics, insufficient virtual care infrastructure and electronic health record interoperability, and health care governance fragmentation. Meanwhile, Canada has highly favourable surgical outcomes, well established postacute cardiac care infrastructure, considerable spending on health, robust health administrative data, and effective health technology assessment agencies, which provides a foundation for continued improvements in care. In this narrative review, we describe successes and challenges surrounding access to cardiac surgery in Canada and globally.

Developing a Program Costs Checklist of Digital Health Interventions: A Scoping Review and Empirical Case Study

INTRODUCTION

The rate of development and complexity of digital health interventions (DHIs) in recent years has to some extent outpaced the methodological development in economic evaluation and costing. Particularly, the choice of cost components included in intervention or program costs of DHIs have received scant attention. The aim of this study was to build a literature-informed checklist of program cost components of DHIs. The checklist was next tested by applying it to an empirical case, Mamma Mia, a DHI developed to prevent perinatal depression.

METHOD

A scoping review with a structured literature search identified peer-reviewed literature from 2010 to 2022 that offers guidance on program costs of DHIs. Relevant guidance was summarized and extracted elements were organized into categories of main cost components and their associated activities following the standard three-step approach, that is, activities, resource use and unit costs.

RESULTS

Of the 3448 records reviewed, 12 studies met the criteria for data extraction. The main cost categories identified were development, research, maintenance, implementation and health personnel involvement (HPI). Costs are largely considered to be context-specific, may decrease as the DHI matures and vary with number of users. The five categories and their associated activities constitute the checklist. This was applied to estimate program costs per user for Mamma Mia Self-Guided and Blended, the latter including additional guidance from public health nurses during standard maternal check-ups. Excluding research, the program cost per mother was more than double for Blended compared with Self-Guided (€140.5 versus €56.6, 2022 Euros) due to increased implementation and HPI costs. Including research increased the program costs to €190.8 and €106.9, respectively. One-way sensitivity analyses showed sensitivity to changes in number of users, lifespan of the app, salaries and license fee.

CONCLUSION

The checklist can help increase transparency of cost calculation and improve future comparison across studies.

Artificial Intelligence to Automate Health Economic Modelling: A Case Study to Evaluate the Potential Application of Large Language Models

BACKGROUND

Current generation large language models (LLMs) such as Generative Pre-Trained Transformer 4 (GPT-4) have achieved human-level performance on many tasks including the generation of computer code based on textual input. This study aimed to assess whether GPT-4 could be used to automatically programme two published health economic analyses.

METHODS

The two analyses were partitioned survival models evaluating interventions in non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC). We developed prompts which instructed GPT-4 to programme the NSCLC and RCC models in R, and which provided descriptions of each model's methods, assumptions and parameter values. The results of the generated scripts were compared to the published values from the original, human-programmed models. The models were replicated 15 times to capture variability in GPT-4's output.

RESULTS

GPT-4 fully replicated the NSCLC model with high accuracy: 100% (15/15) of the artificial intelligence (AI)-generated NSCLC models were error-free or contained a single minor error, and 93% (14/15) were completely error-free. GPT-4 closely replicated the RCC model, although human intervention was required to simplify an element of the model design (one of the model's fifteen input calculations) because it used too many sequential steps to be implemented in a single prompt. With this simplification, 87% (13/15) of the AI-generated RCC models were error-free or contained a single minor error, and 60% (9/15) were completely error-free. Error-free model scripts replicated the published incremental cost-effectiveness ratios to within 1%.

CONCLUSION

This study provides a promising indication that GPT-4 can have practical applications in the automation of health economic model construction. Potential benefits include accelerated model development timelines and reduced costs of development. Further research is necessary to explore the generalisability of LLM-based automation across a larger sample of models.

Global Access to Comprehensive Care for Paediatric and Congenital Heart Disease

Paediatric and congenital heart disease (PCHD) is common but remains forgotten on the global health agenda. Congenital heart disease is the most frequent major congenital anomaly, affecting approximately 1 in every 100 live births. In high-income countries, most children now live into adulthood, whereas in low- and middle-income countries, over 90% of patients do not get the care they need. Rheumatic heart disease is the most common acquired cardiovascular disease in children and adolescents. While almost completely eradicated in high-income countries, over 30-40 million people live with rheumatic heart disease in low- and middle-income countries. Challenges exist in the care for PCHD and, increasingly, adult congenital heart disease (ACHD) worldwide. In this review, we summarize the current status of PCHD and ACHD care through the health systems lens of workforce, infrastructure, financing, service delivery, information management and technology, and governance. We further highlight gaps in knowledge and opportunities moving forward to improve access to care for all those living with PCHD or ACHD worldwide.

International practices in health technology assessment and public financing of digital health technologies: recommendations for Hungary

Background

Evaluating and integrating digital health technologies is a critical component of a national healthcare ecosystem in the 2020s and is expected to even increase in significance.

Design

The paper gives an overview of international practices on public financing and health technology assessment of digital health technologies (DHTs) in five European Union (EU) countries and outlines recommendations for country-level action that relevant stakeholders can consider in order to support uptake of digital health solutions in Hungary. A scoping review was carried out to identify and gather country-specific classifications and international practices on the financing DHTs in five pioneering EU countries: Germany, France, Belgium, the United Kingdom and Finland.

Results

Several frameworks have been developed for DHTs, however there is no single, unified framework or method for classification, evaluation, and financing of digital health technologies in European context. European countries apply different taxonomy, use different assessment domains and regulations for the reimbursement of DHTs. The Working Group of the Hungarian Health Economic Society recommends eight specific points for stakeholders, importantly taking active role in shaping common clinical evidence standards and technical quality criteria across in order for common standards to be developed in the European Union single market.

Conclusion

Specificities of national healthcare contexts must be taken into account in decisions to allocate public funds to certain therapies rather than others.

Successes and challenges of artificial intelligence in cardiology

In the past decades there has been a substantial evolution in data management and data processing techniques. New data architectures made analysis of big data feasible, healthcare is orienting towards personalized medicine with digital health initiatives, and artificial intelligence (AI) is becoming of increasing importance. Despite being a trendy research topic, only very few applications reach the stage where they are implemented in clinical practice. This review provides an overview of current methodologies and identifies clinical and organizational challenges for AI in healthcare.

Implementation costs of hospital-based computerised decision support systems: a systematic review

BACKGROUND

The importance of accurately costing implementation strategies is increasingly recognised within the field of implementation science. However, there is a lack of methodological guidance for costing implementation, particularly within digital health settings. This study reports on a systematic review of costing analyses conducted alongside implementation of hospital-based computerised decision support systems.

METHODS

PubMed, Embase, Scopus and CINAHL databases were searched between January 2010 and August 2021. Two reviewers independently screened and selected original research studies that were conducted in a hospital setting, examined the implementation of a computerised decision support systems and reported implementation costs. The Expert Recommendations for Implementing Change Framework was used to identify and categorise implementation strategies into clusters. A previously published costing framework was applied to describe the methods used to measure and value implementation costs. The reporting quality of included studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards checklist.

RESULTS

Titles and abstracts of 1836 articles were screened, with nine articles eligible for inclusion in the review. Implementation costs were most frequently reported under the 'evaluative and iterative strategies' cluster, followed by 'provide interactive assistance'. Labour was the largest implementation-related cost in the included papers, irrespective of implementation strategy. Other reported costs included consumables, durable assets and physical space, which was mostly associated with stakeholder training. The methods used to cost implementation were often unclear. There was variation across studies in the overall quality of reporting.

CONCLUSIONS

A relatively small number of papers have described computerised decision support systems implementation costs, and the methods used to measure and value these costs were not well reported. Priorities for future research should include establishing consistent terminology and appropriate methods for estimating and reporting on implementation costs.

TRIAL REGISTRATION

The review protocol is registered with PROSPERO (ID: CRD42021272948).

Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment

Background

Artificial intelligence (AI) has attracted much attention because of its enormous potential in healthcare, but uptake has been slow. There are substantial barriers that challenge health technology assessment (HTA) professionals to use AI-generated evidence for decision-making from large real-world databases (e.g., based on claims data). As part of the European Commission-funded HTx H2020 (Next Generation Health Technology Assessment) project, we aimed to put forward recommendations to support healthcare decision-makers in integrating AI into the HTA processes. The barriers, addressed by the paper, are particularly focusing on Central and Eastern European (CEE) countries, where the implementation of HTA and access to health databases lag behind Western European countries.

Methods

We constructed a survey to rank the barriers to using AI for HTA purposes, completed by respondents from CEE jurisdictions with expertise in HTA. Using the results, two members of the HTx consortium from CEE developed recommendations on the most critical barriers. Then these recommendations were discussed in a workshop by a wider group of experts, including HTA and reimbursement decision-makers from both CEE countries and Western European countries, and summarized in a consensus report.

Results

Recommendations have been developed to address the top 15 barriers in areas of (1) human factor-related barriers, focusing on educating HTA doers and users, establishing collaborations and best practice sharing; (2) regulatory and policy-related barriers, proposing increasing awareness and political commitment and improving the management of sensitive information for AI use; (3) data-related barriers, suggesting enhancing standardization and collaboration with data networks, managing missing and unstructured data, using analytical and statistical approaches to address bias, using quality assessment tools and quality standards, improving reporting, and developing better conditions for the use of data; and (4) technological barriers, suggesting sustainable development of AI infrastructure.

Conclusion

In the field of HTA, the great potential of AI to support evidence generation and evaluation has not yet been sufficiently explored and realized. Raising awareness of the intended and unintended consequences of AI-based methods and encouraging political commitment from policymakers is necessary to upgrade the regulatory and infrastructural environment and knowledge base required to integrate AI into HTA-based decision-making processes better.