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The benefits of data and analytics for health care systems and single providers is an increasingly investigated field in digital health literature. Electronic health records (EHR), for example, can improve quality of care. Emerging analytics tools based on artificial intelligence show the potential to assist physicians in day-to-day workflows. Yet, single health care providers also need information regarding the economic impact when deciding on potential adoption of these tools.
This paper examines the question of whether data and analytics provide economic advantages or disadvantages for health care providers. The goal is to provide a comprehensive overview including a variety of technologies beyond computer-based patient records. Ultimately, findings are also intended to determine whether economic barriers for adoption by providers could exist.
A systematic literature search of the PubMed and Google Scholar online databases was conducted, following the hermeneutic methodology that encourages iterative search and interpretation cycles. After applying inclusion and exclusion criteria to 165 initially identified studies, 50 were included for qualitative synthesis and topic-based clustering.
The review identified 5 major technology categories, namely EHRs (n=30), computerized clinical decision support (n=8), advanced analytics (n=5), business analytics (n=5), and telemedicine (n=2). Overall, 62% (31/50) of the reviewed studies indicated a positive economic impact for providers either via direct cost or revenue effects or via indirect efficiency or productivity improvements. When differentiating between categories, however, an ambiguous picture emerged for EHR, whereas analytics technologies like computerized clinical decision support and advanced analytics predominantly showed economic benefits.
The research question of whether data and analytics create economic benefits for health care providers cannot be answered uniformly. The results indicate ambiguous effects for EHRs, here representing data, and mainly positive effects for the significantly less studied analytics field. The mixed results regarding EHRs can create an economic barrier for adoption by providers. This barrier can translate into a bottleneck to positive economic effects of analytics technologies relying on EHR data. Ultimately, more research on economic effects of technologies other than EHRs is needed to generate a more reliable evidence base.
Data and analytics applications increasingly find their way into our health care systems. Some manifestations of data, like the electronic health record (EHR), have already been more established in many member countries of the Organisation of Economic Co-operation and Development. Analytics technologies such as computerized clinical decision support (CCDS) or advanced analytics (AA) based on big data and artificial intelligence (AI) still seem to be newcomers in this field. Hopes are high that data and analytics significantly improve quality, efficiency, and patient experience of health care delivery [
The common systematic review ideally represents a highly structured approach for searching, screening, including, and summarizing studies to answer a rather narrowly defined question [
Literature included in this review was identified via iterative structured keyword searches in the online databases PubMed and Google Scholar, as well as a complementary backward and manual search. The following keyword search on article titles was applied to both databases: (
The PubMed and Google Scholar searches generated 79 and 165 results, respectively (see
Literature search and study selection process.
Following in-depth review of the 50 final studies, 2 angles for categorization emerged. First, the studies were sorted according to the technology under research, resulting in 5 key categories, namely EHRs, CCDS, AA, business analytics (BA), and telemedicine. Second, studies were categorized based on the type of identified economic impact. This impact categorization consists of 2 combined components, namely mode (direct vs indirect) and direction (positive vs negative vs neutral vs mixed). Considering the impact mode, studies were categorized to have an
Overview of reviewed studies categorized by technology and economic impact.
At 60% (30/50) of identified articles, EHRs represented the most comprehensive body of literature by far. In terms of economic impact, overall, a rather ambiguous pattern could be observed, with 12 studies revealing a positive, 8 studies a negative, 4 studies a neutral, and 6 studies a mixed economic effect on providers. The majority of studies was US-based (20/50, 40%), with Asia (3/60, 5%), the rest of the world (2/60, 3%), and Europe (0/60) showing less research activity. The remaining 5 articles represented international literature reviews.
The 5 literature reviews included in the sample predominantly indicated mixed economic impacts of EHRs. All reviews included studies proving positive effects mostly via increased efficiency; however, for almost every review, another identified article indicated the opposite [
Cost-benefit analyses (CBA) were presented in 5 articles and also painted a slightly ambiguous picture, with significantly varying timelines for EHR installations to break even. The majority of studies indicated an average breakeven timeline between 3 and 8 years for EHR implementations in hospitals [
Besides the full CBAs, 6 studies examined the effects of EHR introductions on a variety of single-cost or revenue items. Encinosa and Bae [
Finally, a significant share of papers (14/30) examined indirect economic impacts of EHRs via changes in efficiency or productivity. Here, a rather negative image emerged, with only 4 studies revealing positive effects, and the remaining showing either negative (6/30) or neutral (4/30) effects. Due to significant heterogeneity, it was difficult to draw generalizable insights from this sample of literature. This was a takeaway the identified systematic reviews also revealed. 5 studies examined productivity changes after EHR introduction, where productivity was mostly defined as average patient volumes. Of these studies, 3 revealed no statistically significant changes, hence neutral economic impacts [
Studies examining the economic impact of CCDS on providers represented the second-largest share of identified articles, at 16% (8/50). A strong picture regarding the impact emerged, with all 8 studies revealing a positive economic impact on providers, predominantly of a direct nature. Again, the majority of articles was US-based (5/8, 63%), and others were located in the rest of the world (2/8, 30%). The remaining article represented an international literature review.
Bright et al [
In 3 articles, it was shown how CCDS systems could reduce the number of imaging studies, laboratory tests, or the amount of medicine utilized. Fleddermann et al [
Besides effects via reduced volumes in tests or studies, 3 other articles revealed cost savings via supporting decisions regarding care processes and workflows. Quadros et al [
The eighth article in the CCDS category by Elkin et al [
The recently increasingly prominent field of AA including AI, machine learning, and deep learning represented only 10% of the identified literature (5/50). A very homogenous picture was painted, with all 5 studies indicating indirect positive economic effects on providers; 3 articles were US-based, and others originated from Europe (1/5) and Asia (1/5).
The identified articles showed 2 main use cases of AA. First, 2 articles showed how AA could support decision making in the field of imaging. Lee [
The second use case represented the prediction of patients’ disease progression and the associated care processes. Wang et al [
Studies examining the economic impact of BA on providers represented 10% of identified articles (5/50). All 5 studies revealed a positive economic effect on providers, mostly of a direct nature. The majority of articles represented US-based (4/5) or Europe-based (1/5) case studies.
BA tools analyzing equipment utilization were examined in 2 articles. Stekel et al [
Examples of how BA applications improved billing by reducing revenue leakage or avoiding penalties were presented in 3 articles [
Studies examining the economic impact of telemedicine on providers represented the smallest share of identified articles (2/50). Both articles originated from Europe, with 1 study revealing an indirect positive effect and 1 a neutral effect.
Stoves et al [
At first indication, the presented results appear to generate an overall positive answer to the overarching research question of the economic impact data and analytics have on health care providers. Of the 50 reviewed articles, 31 indicated a positive impact either via direct cost or revenue effects or via efficiency or productivity improvements. Studies showed how EHRs can, for example, directly save storage and personnel costs associated with paper records or increase physician productivity by making information available when and where it is needed. Other studies proved that CCDS can save material and labor costs by avoiding redundant laboratory tests and imaging studies. A more nuanced look at the results, however, shows that it is very important to differentiate between the 5 identified technology categories. In line with other literature reviews, a mixed overall picture, at best, was revealed for the economic impact of EHRs, or “data,” on providers. From a provider’s perspective, 12 studies revealing a positive result and 18 revealing negative, neutral, or mixed results do not necessarily promote a quick decision on EHR investments, at least from an economic point of view. On the other hand, “analytics” applications like CCDS, AA, and BA seem to predominantly generate positive economic effects. Nevertheless, the small number of identified papers covering these technologies, yet again, points at the risk of discouraging rapid adoption by providers from an economic point of view. Ultimately, this review reveals a rather uncomfortable decision-making situation for providers with the economic impact of “data,” represented by EHRs, being exhaustively researched but revealing ambiguous results and “analytics” indicating positive results but being only sparsely investigated.
Considering the positive effects of EHRs on health care outcomes, the identified ambiguous results regarding the economic impact for providers also implies potentially missing out on the associated welfare gains across populations. Some nations’ policy makers already acknowledged this and incentivize EHR adoption. The United States Health Information Technology for Economic and Clinical Health Act from 2009 injected several billions of dollars into the system for subsidized EHR installations [
Another effect involving EHRs is also important to consider. Several of the included studies showed how EHR installations can act as a door-opener to other technologies that actually seem to predominantly have positive economic effects for the provider. Especially, 2 other technology categories identified in this review, namely CCDS and AA, strongly rely on data contained in EHRs. More precisely, 75% (6/8) of the CCDS tools and 60% (3/5) of the AA tools in the identified studies needed some sort of EHR input. Even if somewhat limited in quantity, the current identified research in these 2 fields revealed only positive economic effects for providers. Hence, EHR adoption can become a bottleneck to the positive economic effects of technologies further down the line like CCDS and AA. Following EHR adoption, providers are likely able to derive economic benefits from adjoined technologies identified in this review. More research dedicated to these economic effects of supplementing EHRs with adjoined technologies like CCDS and AA is needed to derive a more targeted evidence base.
Leaving policy implications aside, our work generates insights for providers as well. For providers considering an EHR installation, this review showed important factors for an economically feasible introduction. Eliminating all legacy costs like paper-based records and related dictation services, repurposing paper record space into clinical space, or installing new technology in a stepwise fashion (avoiding a big bang) are all important takeaways from this review. For hospitals or practices already using an EHR, adjacent technologies, like CCDS or AA, can provide economic benefits, potentially even resulting in a shorter breakeven time for the EHR installation. Additionally, the emerging opportunities to participate in value-based care plans utilizing EHR data or the utilization of business intelligence should not be fully neglected. Nevertheless, a number of other potential sources of economic value from data seems not to be currently covered by research. For example, no study was identified that covered the potential for direct monetization of anonymized patient data or the ability to drive patient volumes by marketing the application of advanced digital tools.
Ultimately, it is important to note that research on the economic impact of data and analytics on providers remains rather limited in geographies other than the United States and in technologies other than EHR. In general, this review did not identify geography as a predictor for the type of economic impact. However, with almost 65% (32/50) of included articles being US-based, more research in other geographies is needed to draw a definite conclusion whether geographies and related health care systems are significant drivers. From a technology perspective, the few studies covering technologies other than EHR revealed proof for economic advantages; however, no comprehensive cost-benefit analyses and few systematic reviews were identified for these technologies. In the field of AA, 80% (4/5) of identified studies only hypothesized economic benefits. In the near future, however, vendors of AA tools need to also provide high-quality proof for the economic advantages of their solutions.
This work is exposed to limitations that are mostly inherent to literature reviews in general. Only PubMed and Google Scholar online databases were searched; hence, relevant research captured exclusively by other databases could have been excluded. The sample of identified articles potentially lacks certain avenues of research not captured by the structured keyword search, thereby missing other technologies. The applied hermeneutic systematic search approach, however, worked against these limitations by explicitly allowing for iterative searches. Additionally, the systematic search was complemented by manual search techniques. On a different note, most identified studies are based in the United States; hence, conclusions might not be fully applicable to other geographies. Ultimately, it is important to note that the research subject “data” is almost exclusively represented by studies focusing on EHRs, thereby not touching on other potentially relevant sources and applications of data. Nevertheless, EHRs can be considered as a key data container in the context of health care. The research subject “analytics,” on the other hand, faces a very limited body of evidence, which strongly impacts the generalizability of this study’s findings. More research covering these other technologies is needed to generate a more holistic and reliable evidence base. Lastly, the intended broad spectrum of reviewed studies prevents a clear and uniform definition and quantification of “economic value.” Studies and respective results can, hence, not be compared on the same scale, also since the methodological quality of the original studies was not analyzed.
This review synthesized literature examining the economic value of data and analytics for health care providers. Five key technologies were identified, namely EHRs, CCDS, AA, BA, and telemedicine. Overall, 31 of the 50 reviewed articles indicated a positive economic impact, either via direct cost or revenue effects or via efficiency or productivity improvements. A more nuanced view showed that this is especially the case for less studied technologies like CCDS, AA (including AI and big data analysis), and BA. For the most extensively studied technology of EHRs, a more ambiguous view with varying economic impacts emerged. Since technologies like CCDS and AA strongly rely on EHR data, these ambiguous research findings have the potential to turn EHR adoption into a bottleneck for the adjoined technologies with mostly positive economic effects. This review also encourages discussions around how subsidization of EHRs, like that implemented in the United States and planned for Germany, could potentially unlock the proven economic potential of second-order adjoined technologies. It can be concluded that more research covering the economic effects of technologies other than EHRs would significantly improve the current evidence base and potentially drive adoption by health care providers.
The interlinked cycles of the hermeneutic process.
Detailed search queries used for the PubMed and Google Scholar database searches.
Overview of all published articles screened by title, abstract, and text (including reasons for exclusion).
Detailed syntheses of the final 50 studies included in this review.
advanced analytics
artificial intelligence
business analytics
cost-benefit analysis
complete blood count
computerized clinical decision support
clinical decision support
computerized physician/provider order entry
electronic health record
None declared.