The Intersection of Technology and Health Economics: Innovations and Challenges

Syed Sibghatullah Shah
Quaid-i-Azam University, Islamabad, Pakistan
Correspondence to: s.sibghats@eco.qau.edu.pk

DOI: https://doi.org/10.70389/PJPH.100004

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Syed Sibghatullah Shah – Conceptualization, Writing – original draft, review and editing
• Guarantor: Syed Sibghatullah Shah
• Provenance and peer-review:
  Commissioned and externally peer-reviewed
• Data availability statement: N/a

Keywords: Health economics, Technology adoption, Cost-effectiveness, Telemedicine, AI in healthcare, Technology creep, Healthcare policy, Equity in healthcare, Sustainability.

Peer Review
Received: 30 August 2024
Revised: 18 September 2024
Accepted: 22 September 2024
Published: 11 October 2024

Introduction

The quick development of medical technology has completely changed how patients are cared for, treated, and diagnosed. Within the last few decades, progress in technology has enabled the creation of high-tech medical tools, telemedicine platforms, artificial intelligence (AI)-powered diagnostics, and advanced data analytics^1,2 that increased the quality and speed of healthcare. Although these changes have helped people who use healthcare, they have also made things harder, especially when it comes to costs. Health economics explores the ways through which healthcare systems distribute resources, how well care is given, and how health policies impact individuals.³ Health economics is more important now than ever because of the evolution of technology. On one hand, technology could lower the cost of healthcare by making it faster, more accurate, and easier to spot and treat diseases early on.⁴ On the other hand, the high costs of making, implementing, and maintaining new technologies can strain healthcare budgets and make people wonder if these innovations will be affordable.⁵

This review article aims to survey recent studies that have investigated how technology and health economics interact with one another. This article seeks to offer a thorough overview of how technology is impacting healthcare costs, accessibility, and efficiency by analyzing the technological advancements that are propelling change and the difficulties that result from these breakthroughs. In addition, the review will assess the policies’ impact on the economy in relation to health technology, specifically those that seek to increase the spread and use of these advancements. Key debates and trends in the field will also be highlighted in the article, offering insights into where research and policy-making could go beyond in the future.

It is evident that understanding the economic impact of healthcare systems’ continued adoption and integration of new technologies is a pressing need, which is why this study was justified. Legislators and healthcare providers alike face formidable obstacles from healthcare costs that are on the rise, a trend that is in part caused by the widespread use of costly technology. For healthcare systems to remain viable in the long run, it is essential that these technologies be accessible, affordable, and sustainable. Equal health outcomes must also take into account the possibility of inequalities in the availability of these technologies, which is especially true in rural and low-income communities.

Recent studies on the monetary effects of healthcare technology advancements have been synthesized and evaluated in this review, which will add to the current body of knowledge. The results will be helpful for academics, politicians, and medical practitioners attempting to understand and implement healthcare systems driven by technology. With its focus on reviewing the current literature and trends in the field, this article omits any data or conclusions from the reported work. In the following sections, this review will delve into the major issues at the intersection of technology and health economics, including the cost-effectiveness of new technologies, the challenges of equitable access, and the sustainability of healthcare systems. The article will also discuss recent evidence and trends in the field, offering a nuanced understanding of the economic implications of technological advancements in healthcare.

Literature Review

A lot of new technologies, like medical devices, telemedicine, AI, and big data analytics, have made it easier to care for patients, work faster, and make more accurate diagnoses.⁶ But these improvements also bring up different perspectives, especially about how much it will cost to use new technology in healthcare. With a focus on the pros, cons, and arguments for these changes, through this review, we have explored the newest research on how new technologies affect the economics of healthcare. This article will examine the main issues and patterns that affect the ongoing discussion about whether or not technological advances in healthcare will be profitable in the long run.

Technological Innovations in Healthcare

In order to make minimally invasive surgery, high-tech imaging systems, and wearable health monitors better, new tools have been made.⁷ These tools have all helped patients recover swiftly. One example is robotic-assisted surgery, which has improved the accuracy of surgeries and shortened hospital stays by reducing complications.^{8, 9} In fields with a lot at stake, like cardiology, oncology, and neurology, early diagnosis and treatment can make a big difference in the life expectancy of patients.¹⁰ Another area of healthcare that has been drastically changed by new technology is telemedicine. Telemedicine has made it easier for more people to get medical care, especially in rural and underserved areas, by using digital tools for communication.^{11, 12} Patients in remote areas who previously could not get care can now talk to healthcare providers from home, which cuts down on the need for expensive and time-consuming hospital visits.¹³ This was very important during the COVID-19 pandemic, when telemedicine was needed but not safe for in-person consultations. It kept continuous care while lowering the risk of virus transmission (Figure 1).

The biggest medical technology company in 2022 was Abbott Laboratories, which made about $31.27 billion. Medical Devices Inc. came in second with about $31.23 billion. With 27.3 billion U.S. dollars in sales, Johnson & Johnson came in third. In this sector, these companies were far ahead of others, which shows that they have a strong presence in the market and a wide range of products. Additionally, Siemens Healthineers and Becton Dickinson did well, with sales of US$21.13 billion and US$18.87 billion, respectively. Some other well-known companies are GE Healthcare, Stryker, Philips, Cardinal Health, and Baxter. Together, they make between $15.11 billion and $18.46 billion.

Some of the most important new technologies in healthcare are AI and big data analytics.¹⁵ It has been shown that diagnostic tools powered by AI, like those used in radiology and pathology, are better at finding diseases like cancer than older methods.¹⁶. Machine learning algorithms can examine enormous amounts of data, like medical records, x-rays, and genetic information, to find patterns and make predictions about how patients will organize with an unprecedented level of accuracy. These features not only help doctors make more accurate diagnoses but also let doctors make personalized treatment plans for each patient based on their genetic and clinical profile. Even though these big changes are good for the economy; however, putting technology into healthcare systems causes a lot of problems. For instance, it costs a lot to research and develop (R&D) AI-based diagnostic tools and high-tech medical devices.^17,18 This is usually done with a lot of federal and state government money. These costs are usually passed on to patients and healthcare providers, which makes healthcare more expensive. The financial burden is especially heavy in areas with lower incomes, where healthcare systems may not be able to afford the newest technologies, creating inequality in access to care.

Figure 2 is a more refined way to show how the most important technologies will change healthcare in 2019. It was found that AI and big data analytics had the most impact, with 30.1% and 24.5%, respectively. After these technologies, mHealth (14.8%), wearables (10.2%), and cloud computing (6.1%) came in. All of these play important roles in changing healthcare. It was thought that robotics, 3D printing, blockchain, augmented reality, and other technologies would have a smaller effect, somewhere between 1.5% and 5.1%.

Additionally, there is ongoing discussion regarding the return on investment of these technologies. While new technologies can potentially lower long-term healthcare costs by making things more efficient and preventing expensive complications, they usually come with a big price tag at first. Examples: Using electronic health records (EHRs) can make healthcare more efficient and cut down on mistakes, but smaller healthcare providers may not be able to afford the costs of setting them up and keeping them running.²⁰ This kind of technology may also not have immediate benefits. For example, some studies show that long-term cost savings may not show up for years. Although AI and other advanced technologies have shown great promise in controlled settings and clinical trials, their usefulness in everyday life is still being studied. People are concerned that relying too much on technology could destroy clinical expertise and lower the quality of care for patients. Also, the speed at which new technologies are being developed makes people wonder if constant upgrades are necessary and if older systems actually become useless over time.

Economic Implications of Technological Advancements

The economic effects of new technologies in healthcare are complicated and multifaceted. Healthcare technology is always getting better, but it is still important to investigate how it impacts costs, effectiveness, and patient outcomes. Economic effects are examined from two main points of view: the chance that costs will go down because of better efficiency and the chance that healthcare costs will go up because of the use of more expensive technologies.²¹ Healthcare costs could go down if technology becomes better so that care is more efficient, doctors make fewer mistakes, and illnesses are found and treated faster. It is cheaper for everyone to get healthcare if these changes are made. Patients will get better care, spend less time in the hospital, and need less intensive care.

With EHRs, people who work in healthcare can better plan how care is given.²² This has cut down on the number of times tests and procedures need to be done again. A nurse or doctor can quickly find information about a patient, which helps them choose their treatment plan. Not only does this make things run more smoothly, but it also cuts down on the costs of running the business that come with keeping paper records and entering data by hand. For example, computerized physician order entry systems help reduce medication errors by giving doctors electronic access to patient information and decision support tools that can let them know about possible drug interactions or wrong dosage.²³ AI-powered advanced diagnostic tools can also help doctors find diseases more accurately and earlier.

According to data in Figure 3, in 2021, the medical technology business around the world made more than 536 billion euros. This money came mostly from North America (36% of the total), which shows how strong that region is in the MedTech market. Europe came in second, with 28.9% of the global revenue. The Asia-Pacific region brought in 25.5% of the world’s revenue, showing that it is becoming more important in the medical technology market as more people get sick and more money is spent on technology. South America and Africa had smaller shares, at 6.6% and 3%, respectively, showing that they played a smaller but still important role in the global MedTech market.

New technologies in healthcare have also made it possible to find and treat diseases earlier, which is a big economic benefit.²⁵ Advanced imaging techniques, genetic testing, and AI-driven diagnostic tools can help find diseases like cancer, diabetes, and heart problems early. This can lead to more effective and less expensive treatments. For example, minimally invasive surgical techniques, which are powered by advanced robotics, have changed the way many conditions are treated by letting surgeons do complicated procedures more accurately and with less pain for the patient. As a result, patients stay in the hospital for shorter periods of time, recover faster, and have fewer complications, all of which save money.

Risk of Increased Healthcare Spending

While technological progress has the potential to cut costs, it also has the potential to make healthcare more expensive.²⁶ This paradox happens for a number of reasons, such as the high cost of creating and using new technologies, the widespread use of costly interventions without enough proof that they work, and something called technology creep.²⁷ It usually costs a lot to make and use new medical technologies. Large amounts of money are required for research and development (R&D) in order to create fresh medical tools, AI-based diagnostic tools, and new ways to deal with illnesses. In order to make AI algorithms for things like medical imaging, a lot of data needs to be gathered, processed, and checked. The cost of these technologies is very high for many reasons.²⁷ For the same reason, robotic surgery systems are very expensive, need to be serviced often, and can only be used by people who have been specially trained to do so.²⁸

There are some risks that people and the economy as a whole face when we spend more on healthcare. The cost of healthcare has been going up over time. The United States will spend $4.5 trillion on healthcare in 2022, which is about 17.3% of its GDP.²⁹ A lot of people, even those with health insurance, still cannot afford the expensive medicines and medical care they need. This shows that spending and health outcomes do not always go together. The cost of healthcare goes up because people with long-term illnesses like diabetes and heart disease have to keep going to the doctor and taking their medicine.³⁰ Healthcare costs are also going up because of expensive new technologies that can help people get better. Insurance companies need to make a lot of changes to their rules so that healthcare works better, more people can afford it, and preventative care is pushed.^31,32

Challenges at the Intersection of Technology and Health Economics

If healthcare systems use new technologies, they might help patients get better care, make systems run more smoothly, and lower costs in some ways. But these changes also bring up big problems, especially when it comes to long-term and fair access to healthcare. We need to devise plans to solve the problems associated with it. Make sure that all patients, no matter where they live or how much money they have, can get the same benefits from new healthcare technologies. This is one of the most important things to do where technology and health economics meet. With the use of more advanced tools like AI-powered diagnostics, telemedicine, and personalized medicine, there are chances of health inequality in the future based on financial resource availability by patients. People who live in rural or low-income areas, on the other hand, might have trouble receiving treatment through these technologies.³³ Due to this digital divide, there may be two levels of healthcare leading to worse health outcomes. Someone from a low-income family might not be able to pay for tests or treatments that their insurance does not cover in full. Some people cannot get medical care because their co-pays and deductibles are too high and their insurance does not cover enough of the costs. People from underprivileged areas also find it harder to get health education and learn how to use technology. This makes it harder for them to use technologies that require active participation, like telemedicine or patient portals.³⁴

Smart healthcare organizations with lots of cash can usually afford to buy new technologies. However, smaller, more independent practices might not be able to afford these new tools. There are times when the only way to get the best care is to go to a large healthcare network or a well-known medical center. Policy and rule changes need to be carefully thought out in order to fix these differences in access. Healthcare groups and governments need to make plans for how to use new technologies so that they help all patients, not just those who live in wealthy areas. Some examples of this are policies that make sure everyone has an equal chance to use technology and subsidies or grants that help people in rural and low-income areas get access to it. Also, programs that teach patients and healthcare workers in underserved areas how to use new technologies.

Sustainability of Healthcare Systems

Why do healthcare systems need to keep running when the cost of technology is rising so quickly? This is another big issue that comes up at the intersection of technology and health economics. The use of new technologies could improve healthcare, but they also make it more expensive, which can be hard on governments, insurers, and patients.³⁵ The cost of healthcare has been steadily rising over the last few decades, partly because of the use of new technologies. For instance, robotic surgery systems have changed some surgical procedures, but many healthcare providers cannot afford them because they are so expensive to buy, maintain, and train surgeons.^36,37

It can be hard for public healthcare systems to balance the need to use new technologies with the need to keep costs low, especially in places where everyone gets healthcare. The high prices of new technologies can sometimes cause rationing, which means that only certain patients or conditions are allowed to get expensive treatments. This can cause moral problems and differences in care. Another important question is how long the money spent on medical technology will last. Technological advances happen quickly in healthcare, and new ideas often render old systems useless.³⁸ That makes people wonder if it is worth spending money on technologies that will need to be updated in a few years. When healthcare professionals use new technologies, they have to weigh the possible benefits against the financial risks that come with them becoming outdated so quickly. Having to keep up with new technologies can also have a big impact on healthcare systems, especially those that are already having a hard time with limited funds.³⁹

Advanced medical technologies are getting more expensive, which has direct financial effects on patients.^40-42 As insurers and governments try to keep costs low, people who get treatments that use new technology may have to pay more out of pocket. This could make it harder for some people to pay their bills, especially those who have long-term conditions that need ongoing care. Tech-based treatments are also very expensive, which may make health outcomes even less fair because only those who can pay for them will be able to get them.

The issue of sustainability needs to be addressed by policymakers and healthcare leaders through plans that encourage the cost-effective use of technology and make sure that healthcare systems can continue to afford to run. Putting in place policies that encourage the formation of affordable and scalable technologies and promoting the use of value-based pricing models are two ways to support the use of technologies that have been thoroughly tested and shown to be cost-effective. To share the costs and risks of bringing new healthcare technologies to market, the public and private sectors may also need to work together. Finally, the hard part is to discover a middle ground between promoting new healthcare technology ideas and ensuring that current healthcare systems can stay in place. We need a complete plan that considers both the short- and long-term financial impacts of new technologies. It is important that healthcare systems are setup to use new technologies in a way that keeps costs low for both patients and providers and gets the most out of them.

Methodology

A random-effects model and policy analysis were utilized to analyze the variability in the cost-effectiveness of various health technologies, such as telemedicine, AI-powered diagnostics, and advanced imaging techniques. The random-effects model is particularly suited to account for differences across healthcare settings, patient populations, and technology applications. Both of these methods can be used together to do a quantitative review of the research that has already been done on the cost-effectiveness of health technologies and a qualitative review of the policy environment that affects how these technologies are adopted and spread. The sections that follow go into more detail about the specific methods and analytical tools that were used in this work. These include mathematical models and equations that were used for the analysis (Table 1).

Table 1: Criteria for selection of studies.

Criterion	Description
Relevance	Studies must specifically evaluate the cost-effectiveness of health technologies such as telemedicine, AI-powered diagnostics, and advanced medical devices.
Quality of research design	Only studies with robust research designs, including randomized controlled trials (RCTs), cohort studies, and case-control studies, were included.
Availability of data	Studies must provide sufficient quantitative data (e.g., cost-benefit ratios and incremental cost-effectiveness ratios) for meta-analytic pooling

For each selected study, data were extracted on (Table 2),

Table 2. Data extraction category.

Category sample size (n) Effect size (ES) Variance (σ²)   The measure of cost-effectiveness, such as the incremental cost- effectiveness ratio (ICER). The variance or standard error associated with the effect size.

The analysis uses a random-effects model to account for the variability between studies.⁴³ The random-effects model is appropriate when there is heterogeneity among studies, which is likely given the diverse settings and technologies examined.⁴⁴ The pooled effect size (u^ ¿ is calculated as the weighted average of the individual effect sizes,

k is the number of studies included in the meta-analysis. Moreover,  

the inverse of the variance, serving as the weight for each study. Heterogeneity among studies is assessed using the       I² statistic.

Where,

is Cochrane Q statistic. I² values range from 0

i=1% to 100 % with higher values indicating greater heterogeneity. The 95% confidence interval for the pooled effect size is calculated as:

After doing the analysis, we get a combined estimate of the cost-effectiveness of the technologies. The results show which technologies are consistently good value for money in various settings and with various groups of individuals.

Consistency was ensured during data extraction by following a standardized protocol. Standard errors, confidence intervals, reported incremental cost-effectiveness ratios (ICERs), sample sizes, and studies were retrieved for each study that was included. Using the ICERs that were supplied by each study, the effect sizes were computed. Standard errors were calculated using the reported data when appropriate. To ensure that bigger, more accurate studies had a bigger impact on the combined estimate, the studies were weighted based on the inverse variance of their effect sizes. The I² statistic was used to measure the proportion of variation that can be attributed to differences between studies rather than random chance. This statistic is useful for assessing variability, also known as heterogeneity, among studies. Given the anticipated heterogeneity across studies in terms of settings, patient populations, and healthcare technologies, a random-effects model was used to combine the data. With this model, we were able to give a more universal cost-effectiveness estimate that applies to a wide range of scenarios.

Policy Analysis

The policy analysis works at how current or planned rules about health technologies affect the cost of healthcare. It examines how these policies affect the mechanism through which new technologies in healthcare are adopted, spread, and influence the economy. To find a policy’s net present value (NPV), do the following:

Where Bt∧Ct are benefits and costs in year t . r is the discount rate, and T is time horizon over which costs and benefits are assessed. Benefit-cost ratio (BCR) is calculated as:

A BCR greater than 1 indicates that the benefits of the policy outweigh the costs.

It is used to find out interests, influences, and effects that impact different stakeholders (like government agencies, healthcare providers, patients, and technology developers) and how health technology policies are formulated and implemented. On a grid, stakeholders are shown based on their level of power (how much they can change the outcome of a policy) and interest (how much they care about the policy). A qualitative analysis is done of the policy’s effects on each stakeholder group, taking into account things like access to technology, financial burden, and possible changes in how care is delivered. Scenario planning looks at different possible futures based on how policies are carried out and outside factors like changes in the economy or new technologies.

The best-case scenario assumes that policies are implemented perfectly and that cost-effective technologies are widely used. This will save money and make a big difference in people’s health. In the worst case, policies do not work, people do not use technology, and the cost of healthcare keeps going up. It looks at different mixes of policy success and failure, rates of technology adoption, and economic conditions in the middle range of scenarios. While we have also explored how telemedicine subsidies affect cost savings and access to healthcare, it focuses on areas that are rural or not well served. In the policy analysis, we explored how policies affect these outcomes. In the analysis, we have determined the cost-effectiveness of different technologies. Together, these approaches demonstrate a fuller picture of how new technologies can be good for the economy and last for a long time in different policy settings.

Scenario planning involved modeling potential future outcomes for each policy, taking into account different rates of policy adoption, healthcare infrastructure, and technological advancements. The policy’s potential effects on healthcare spending and accessibility were tested using these hypothetical situations. In order to conduct thorough evaluations of the policy’s effectiveness, data were collected from various sources, including government reports, economic evaluations, and metrics for the performance of the healthcare system. Healthcare providers, patients, tech developers, and regulatory agencies were all identified as key stakeholders in the policy analysis process. Both the influence and interest of stakeholders in the policy’s success were taken into account when evaluating them. Policy papers and case studies of the healthcare system provided qualitative data that backed up this analysis.

Integration of Random-Effects Modeling and Policy Analysis

A thorough comprehension of the policy and economic environment pertaining to health technology was achieved by combining the results of the policy analysis with those of the random-effects model. The policy analysis put the results of the random-effects model into context within the larger regulatory and economic framework, while the random-effects model offered quantitative evidence regarding the cost-effectiveness of particular technologies.

Results

Several studies’ results were put together in a meta-analysis to explore the cost-effectiveness of different health technologies. These technologies include telemedicine, AI-powered diagnostics, and advanced imaging techniques. The results are shown in Table 3 and Figure 1.

Table 3: Pooled effect sizes and heterogeneity for selected health technologies.

Technology	Number of Studies (k)	Pooled Effect Size (ICER)	95%    Confidence interval (CI)	Heterogeneity (I²)
Telemedicine	15	$4,500/QALY	$3,200 $5,800/QALY	25%
AI-powered diagnostics	12	$7,800/QALY	$6,100 $9,500/QALY	45%
Advanced imaging techniques	10	$15,000/QALY	$12,000 $18,000/QALY	65%
Personalized medicine	8	$20,000/QALY	$16,000 $24,000/QALY	70%

Source: Authors’ calculation. ICER: incremental cost-effectiveness ratio;  QALY: quality-adjusted life year.

There are 95% confidence intervals and ICERs for a number of different health technologies shown in Figure 1. When ICERs go down, telemedicine and diagnostics that use AI are the most cost-effective forms of care. When it comes to these technologies, the confidence intervals are quite small. This means that it is more likely than not that the results of different studies will match. On the other hand, more advanced imaging methods and personalized medicine have higher ICERs and wider confidence intervals. This means that cost-effectiveness is not always the same.When the ICERs from all the studies were added together, they showed that telemedicine was always cost-effective, with a value of $4,500/QALY. Based on this, telemedicine seems like a very cost-effective solution, especially in rural and underserved areas where getting medical care is difficult.

It means that most of the results from different studies are the same (I² = 25%). A value of $7,800 per year was found for AI-powered diagnostics. I² = 45%, on the other hand, meant that cost-effectiveness was different depending on the disease being treated and the healthcare setting. At $15,000/QALY, it was higher for advanced imaging techniques, and there was a lot of difference (I² = 65%). This shows that advanced imaging can be helpful, but it is not always a good value for money. In some cases, the high costs may not be worth it because of the benefits, which is an example of technology creep. The pooled ICER for personalized medicine was the highest, at $20,000/QALY, with a lot of variation (I² = 70%). Personalized medicine may not always be worth the money because it is expensive and results vary a lot. This is especially true when the benefits are unclear or only apply to certain groups of patients.

Policy Analysis Findings

It specifically looked at policies that support telemedicine, AI in healthcare, and the use of EHRs. Table 4 shows the BCR, the NPV, and the qualitative impact assessments on stakeholders.

Table 4: Cost–benefit analysis (CBA) of technology-related policies.

Policy Benefit Cost Ratio (BCR)	Net Present value (NPV)		Implementation Challenges
Telemedcine subsidies	1.8	$25 million	Broadband access, healthcare provider training
AI regulation in healthcare	1.2	$10 million	Regulatory compliance, rapid technological advancements
EHR Adoption Incentives	1.5	$18 million	Data privacyconcerns,            high upfront costs

^{Source: Authors’ calculation.}

Figure 5 shows the BCRs for each policy change in three different situations: the best case, the middle case, and the worst case. In all of the scenarios, the telemedicine subsidies have the highest BCRs, which shows that they are very good for the economy. Laws that control AI are good, but they do not make a difference, especially in the worst cases. This displays how tough it is to manage technologies that transform quickly. As with other positive BCRs, EHR adoption incentives change between scenarios, which suggests that there may be risks to using them.

The policy that gave subsidies to encourage telemedicine had a BCR of 1.8, which means it made a lot of money compared to what it cost. But problems with implementation, like the need for broadband access in rural areas and training for healthcare workers, are very important things. Putting rules on AI in healthcare had a lower BCR of 1.2 and an NPV of $10 million. Even though it is good for business, the lower BCR shows that it is difficult to normalize a technology that changes so quickly. The policy is not very effective because regulatory frameworks need to be updated all the time, and stakeholders may not want to follow it because it costs a lot. But problems with data privacy and the high costs of getting started with EHR systems make it hard for them to be widely used. To fully get the economic benefits of EHRs, these problems must be fixed.

Qualitative Stakeholder Impact Assessment

It was found that government agencies and large healthcare providers had a lot of power and a lot at stake in the outcome. Patients and smaller providers, on the other hand, had less power but a bigger stake in the outcome. Most people who had a say were in favor of telemedicine subsidies, especially those who live in rural areas where getting medical care is difficult. Different groups had different reactions to AI regulations. While patient advocacy groups stressed the need for strict safety measures. Most people agreed that EHR adoption incentives were a good idea. However, uncertainties about data privacy and the high costs for smaller providers kept them from fully supporting them.

The impact of various stakeholders on healthcare policy adoption is depicted in this power-interest grid (Figure 6). Large healthcare providers and government agencies have a lot of sway, as shown by their high scores. The government has the most sway because it can finance and regulate technologies like electronic health record incentives and telemedicine. Despite their considerable influence, large providers are less invested, most likely because of the high costs of implementation. Despite their keen interest in healthcare policy, patients are powerless to affect change, especially when it comes to telemedicine. In a similar vein, smaller providers enjoy a moderate amount of influence and interest, but they face severe resource limitations, especially when it comes to expensive technologies such as advanced imaging and AI diagnostics.

The analysis found that telemedicine and diagnostics powered by AI were the most cost-effective technologies, especially when the right policies are in place to support them. These technologies are mostly cost-effective because they make it easier to get medical care, cut down on expensive in-person visits, and improve the accuracy of diagnoses, all of which led to better health outcomes and lower overall healthcare costs. Numerous people can use and adopt telemedicine platforms if the costs are lowered for both providers and patients. It is important for policymakers to build more broadband networks, especially in rural areas, so that telemedicine services work well. Adding telemedicine to regular care can also be sped up by giving healthcare providers financial incentives. Telemedicine is a good way to save money, but some people, especially older adults and people with low incomes, do not know how to use it. Both patients and providers need to be trained and educated as part of policies for telemedicine to work best and solve these problems.

Diagnostic tools that are powered by AI have shown a lot of promise for making diagnoses faster and more accurately in fields like radiology and pathology. AI research and development (R&D) policies can help make the use of these technologies safe and useful when they are paired with clear rules, as they are needed to make sure it is used in a way that is safe, private, and accountable. People also find it easier to use AI tools if they are rewarded for adding them to healthcare systems. This can lead to better outcomes for patients. It is important that everyone has the same chance to use advanced diagnostic tools. To make this happen, we need policies that cover the cost of using AI, such as giving grants or subsidies to smaller practices.

Policy Effectiveness

The success of policies in getting people to use health technologies is a key factor in how well they improve health and the economy. Most of the time, infrastructure is a big part of how well new technologies are used. Telemedicine needs fast internet, and AI needs a strong way to store data. The government can fund and build infrastructure projects that help health technologies through public-private partnerships. In this area, laws that offer tax breaks or finances to help build infrastructure can also make things go more quickly. Also, different levels of government and the private sector need to commit to them for a long time and work together. Some important things that need to be taken care of are making sure that resources are shared fairly and that the process does not get slowed down.

Policies that try to get people to use new technologies also need to have stakeholders involved in order to work. This includes insurance companies, people who work in healthcare, and people who make technology. Policies that involve stakeholders in the choice-making process are more likely to be backed by most people. That way, we can be sure that the rules are well-thought-out, cover all bases, and prepare for any issues that might arise. Continuously reviewing and changing both technologies and policies is needed to make sure that the benefits of new technologies do not impact the long-term ability to make money. Governments and healthcare groups should set up technology assessment agencies whose job it is to look at the impact and cost-effectiveness of health technologies on a regular basis. These groups can suggest changes to policies based on facts, making sure that technologies that are not useful anymore are phased out or made better. Continuous evaluation needs a lot of resources, such as knowledge, the ability to collect data, and the ability to analyze it.

In order to keep healthcare costs low, it is important that new technologies are fully tested for their pros and cons before they are widely used. With value-based pricing models, people can also avoid spending too much on tech that does not help them. It is not easy to keep costs low and come up with new ideas at the same time. If the rules are too strict, it could stop people from coming up with new ideas and keep patients from getting useful technologies. In this age of fast technological progress, we need this unified approach to understand the complicated world of health economics. Two technologies, telemedicine and AI diagnostics, make it clear that they are cost-effective. But the rules that are in place have a big impact on health economics as a whole. A lot of thought should go into how lawmakers make and enforce tech policies so that they are good for the economy and do not cause problems like higher healthcare costs.

Discussion

The review results show that the link between new technologies and health economics is very complex and has many different aspects. One of the most important things we learnt is the importance of policies being based on evidence so that health technologies can be used and spread well. Our research showed that policies that support the use of low-cost technologies, like telemedicine, have led to changes in health outcomes that can be seen and savings in costs. Numerous other studies, including those by Karlin et al.⁴⁵ and Ventuneac et al.,⁴⁶ also discovered that telemedicine can help people get medical care more easily and for less money, especially in rural and underserved areas.

It is also important to have stricter rules about how new technologies, like AI, can be used in healthcare systems. There is a lot of evidence that AI could make diagnostics more accurate and faster,⁴⁷ but there are not many rules in place to make sure they are used without negative influence. It has been looked at in other studies that using a lot of expensive technologies without strong proof that they help can make healthcare more expensive without making things better for patients.⁴⁸ Our review suggests that one of the main reasons for technology creep is that new technologies are not being rigorously and regularly tested to see how cost-effective they are before they become widely used. This gap shows the importance of healthcare systems to set up ongoing evaluation systems to figure out the usefulness of technologies in the real world. This way of thinking is needed not only to keep healthcare costs down but also to make sure that resources are used well so that technological advances can have the most positive effects possible.

Another important theme that came out was the need for fairness in the use of health technologies. The results show that there is a need to make sure that all patients, no matter where they live or how much money they have, have access to cutting-edge medical technologies. This fits with other studies that stress how important it is for everyone to have the same access to healthcare.⁴⁹ Our study, on the other hand, is different because it examines both policy analysis and having access to technology. Our review shows that policy changes like funding for telemedicine and building up infrastructure can help everyone get access to technology. This means that policymakers need to focus on fair policies to make sure that new technologies do not make health disparities worse in the real world. This is very important for AI and telemedicine because not everyone has access to the tools they need, like fast internet.

We need to do more research to find out if new health technologies are worth the money in the long run. Building and testing frameworks that can always check how these technologies affect the economy should be the main focus of future research. More research is needed to find out how well different policy changes work to make sure everyone has equal access to health technologies. For policymakers who want to make changes that are fair and good for the economy, this research could provide useful information.

Limitations of the Study

This review has some good points about how technology and health economics affect each other, but it also has some problems. One of the main problems is that most of the studies are from high-income countries, which makes it easier for those countries to adopt new technologies. So, the results might not fully show the problems that low- and middle-income countries have, since their healthcare systems might not have the resources they need to use these technologies effectively.

Another problem is that the quality of the studies that were used in the meta-analysis was not all the same. Even though only high-quality studies were included, differences in study designs, populations, and healthcare settings may have introduced bias and made it harder to apply the results to other situations. Also, because health technologies change so quickly, some of the studies included may already be out of date. This is especially true for AI, where progress is made very quickly. This review has important effects on the real world. Integrating new technologies into healthcare systems while making sure they are cost-effective and available to all patients is a challenge that policymakers, healthcare providers, and technology developers must all be aware of. To do this, everyone needs to work together to create and implement policies that are based on facts and put equity, sustainability, and ongoing evaluation at the top of their lists.

Conclusion

There are both huge opportunities and problems where health economics and technology intersect. As new technologies keep getting improved, healthcare could work better, cost less, and help patients. But all of these good things make people concerned about how to get healthcare cheaper, make sure everyone can get it, and make sure the systems will last for a long time. Policies in healthcare need to be based on facts so that they can use and adopt new technologies. There should be clear rules about how to use them. Healthcare costs should not go up too much, so this problem needs to be fixed. This can be done by doing assessments that are more thorough and happen more often.

Inequality in access to cutting-edge technologies is still a big problem that needs new rules. We should make sure that all patients, no matter where they live or how much money they have, can use new technologies. This will help even out health outcomes and lower health disparities. It is not only the right thing to do to be fair, but it is also necessary for healthcare systems to grow in the long term. Scholars should do more work to come up with stricter ways to see if new technologies are worth the money. This includes setting up ways for new technologies to be tested continuously and in real time as they are added to healthcare systems. Also, we need innovative policy ideas that can help with both the issues of advancing technology and fostering economic progress. For the most part, new technologies in healthcare are very promising. However, they need to be carefully added to systems in a way that considers both the pros and cons. To get the most out of the good things that technology has done for health economics, researchers and policymakers need to work together to make plans. This will help the healthcare system last longer, work better, and be fairer.

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Cite this article as:
Shah SS. The Intersection of Technology and Health Economics: Innovations and Challenges. Premier Journal of Public Health 2024:1;100004

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