We screened in total 1,309 publications, of which twenty-seven studies meet our inclusion criteria for this economic review.[5,13-38] The process of our literature search and the reasons for excluding several studies is provided within the PRISMA flow-diagram in Figure 1. An overview of the characteristics of the included studies is listed in Table 2.
Table 2: Characteristics of included studies (n=27)
Generally, twenty-two studies (81%) [5,13-16,18,20-25,28-31,32,34-38] out of the included twenty-seven studies report cost savings after implementing an EHR based CDS intervention. Four studies (15%) [17,26,27,33] report a rise in cost expenditure. The remaining study (4%)  did not detect significant differences in cost outcomes. In the majority of included studies the main cost outcome measures were related to laboratory test cost.[15-17,20,21,25,28,29,31,32,38]
Exploration of different front-end CDS intervention categories
According to the taxonomy by Wright et al., we identified ten (37%) studies [5,13,15,20,22,23,26,36-38] which explored EHR based CDS interventions based on point-of-care alerts or reminders (category 3). Three interventions (11%) [17,27,34] were order facilitators (category 2). Medication dosing support, relevant information display, and expert systems (categories 1, 4, and 5) were each reported only once from an economic perspective (11%).[18,19,24] In eight studies (30%), [14,16,25,28,30,31,33,35] interventions from two different categories were explored in combination. Moreover, we found three studies (11%) [21,29,32] in which the option to place a certain order or test, e.g., a laboratory test, was removed from the EHR CPOE system or the clinician’s laboratory ordering preference list. These restrictive frond-end CDS intervention types were not yet mentioned in the predefined categories by Wright et al.. Thus, we extend their taxonomy by a new category:
- Restriction of choice
The removal of an order option ultimately resulted in fewer laboratory tests and reduced healthcare expenditure in all studies.[14,28,31] Finally, we identified two different types of implemented hard-stops: an interruptive alert and a restrictive hard-stop. An interruptive alert requires a clicking response from the physician before being able to move forward. A restrictive hard-stop prevents the physician from ordering a test, e.g., by directing them to call the laboratory director. We grouped studies regarding the interruptive alert intervention to category 3 and the restrictive hard-stop intervention to category 7.
Economic impact for prevalent application areas
In Table 3, we summarized our findings and created an overview of application areas and cost outcome measures in relation to the applied CDS intervention types. The included studies show a high heterogeneity with regard to different types of reported cost outcomes and different intervention durations. Due to this complexity, it was not possible to conduct a subgroup analysis regarding the economic impact of each CDS-front end category. A detailed evidence synthesis of all included twenty-seven studies and a brief description of their intervention types, their application area, and the resulting economic impact are provided in an additional file [see Additional file 3].
Table 3: Application areas and cost outcome measures in relation to CDS intervention categories 1.-7.
Application areas for cost-savings: We identified four primary application areas based on their investigated prevalence that resulted in cost-savings after EHR based CDS implementation. Firstly, two studies report on reducing unnecessary Vitamin D routine testing, which led to a decrease in laboratory test cost of $300,000 and $1.4 mill. per year.
Secondly, two studies addressed the economic outcome of reducing waste in transfusion practice and red blood cell usage.[36,37] The acquisition product cost of red cell units was decreased with the help of EHR based CDS and resulted in cost savings of $4,821,000 after three years and about $62,715 within one year after implementation, respectively.
Thirdly, two cost-effectiveness analyses modeled the cost outcome of reducing antibiotic prescriptions for acute respiratory infection and acute bronchitis.[5,33] Gong et al. include a full accounting of costs into their Markov model, and explore that the implemented CDS intervention, called “suggested alternatives”, yielded more quality adjusted life years (QALYs) at a lower cost of $500,000 per 100,000 individuals over thirty years of implementation. Michaelidis et al. report only a small increase in costs compared to a printed decision support system, i.e., posters. However, the latter mainly results from a cost difference between the direct costs of poster printing and computer programming.
Lastly, five studies[20,29,31,32,38] report on the potential to reduce duplicate orders, e.g., duplicate laboratory tests, using hard-stops or order frequency rules. Order frequency rules prevent ordering the same test within a specified timeframe. Reducing duplicate laboratory tests resulted in savings of $3,395 after three months for a small patient size cohort, and up to $315,565 within twenty-four month for a large patient size cohort.
Application areas resulting in cost increase: We also identified risk areas, which possibly lead to a further increase in healthcare expenditure. One study found that specialized HIV laboratory testing cost increased by $14,000-$96,000 within six months after implementing a CPOE system with default settings. Another study reports that an unplanned change of a pre-selected default order for ‘complete blood count’ to ‘complete blood count with differential’ led to an average cost increase of $293.11 per day. Finally, the implementation of order sets as decision facilitators possibly entails adverse economic effects. One study found that only after uncoupling joint orders of Vitamin B12 and serum folate tests from predefined order sets, laboratory test cost decreased by about $26,719 per year. Similarly, another study removed the option to order daily routine tests from automated admission order sets and found savings of $26,416 after two months.
In Table 4, we present an overview of studies that conducted a cost-effectiveness-analysis (CEA) of EHR-based CDS interventions and include various cost data as well as economic outcome measures. One such economic outcome measure is the incremental cost-effectiveness ratio (ICER), which depicts the incremental change in costs divided by the incremental change in health outcome or effect.
Cost-effectiveness analyses aim to reveal the trade-offs in resource-allocation decisions. In this context, it is essential to investigate when and to what extend upfront and maintenance costs for an EHR based CDS system will be amortized by its benefits. The benefits can be measured in health outcomes, such as quality adjusted life years (QALYs), or the reduction of unnecessary healthcare utilization.
Table 4: Overview of cost data and cost outcome of model-based studies (n=4)
Generally, two studies report an increase in healthcare expenditure from a societal perspective[26,33] while the other two report cost savings from a societal perspective and the medical group’s perspective.[5,35] Notably, the measurement of effectiveness was single study-based estimates in all four studies.
Regarding the consideration of upfront implementation cost, Gong et al. include only base case consolidated cost data of $1.91 for a cohort of 100.000 individuals based on expert opinions. Sharifi et al. include intervention start-up cost for EHR modification of $2.7 mill. as well as other direct costs, such as professional care provider training. Michaelidis et al. report implementation and maintenance cost data, which is physician education per hour and medical record and CDS programming per patient of $18 in the base case. Lastly, Forrester et al. report CPOE CDS system cost as hardware, software, and maintenance costs starting from $373,000 in year one to $92,000 after five years, as well as personnel, $555,000 in year one, and indirect costs as 3% of the total cost. Interestingly, the indirect costs also include the HITECH Meaningful Use incentives in their model to simulate the financial incentives by the Centers for Medicare & Medicaid Services in the US.[35,42]
Studies lack consideration of all cost components
Despite revealing significant potentials for cost-savings, we could not assess the quality of the included studies because of missing cost information or non-consideration of all relevant cost components. According to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, most of the reported recommendations were not satisfied. All twenty-three non-model studies (85%) only calculate the economic outcome based on financial data reported before and after intervention implementation. For instance, this results from the computation of price per healthcare resource utilization multiplied by the quantity of used healthcare resources or services. Thus, even though it was not intended in those studies, it is necessary to mention that only four of the included studies adhere to sound economic evaluations as recommended by CHEERS.[5,35]
The challenge of heterogeneity for the CEA is also aggravated by considering different cost outcomes. Two studies neither report an incremental cost effectiveness ratio (ICER) for a predefined threshold directly, nor do they include comparative metrics.[33,35] Other standardized metrics, such as the return on investment or net present value, were also not examined in the included studies. Only one study reports on the net monetary benefit (NMB) of the intervention in relation to a predefined threshold.[5,44]
Additional studies worth mentioning
Notably, five more studies [45-49] meet most of our inclusion criteria but were excluded due to various, although little, deviations. Three studies [45-47] report cost-savings after a bundle of information technology was implemented simultaneously, but the economic benefit could not solely be attributed to the EHR based CDS intervention. The fourth publication is an NHS health technology assessment (HTA) report. In this HTA, an RCT was conducted in 79 general practices in the UK in which a multi-component intervention was installed using EHRs to reduce the number of antibiotic prescriptions for respiratory infections. The authors perform a basic cost-analysis focusing on the number of provider consultations as the cost of healthcare utilization. However, they found no difference in the cost outcome between the intervention and control period.
The last study worth mentioning compared retrospectively generated alerts by an advanced machine learning CDS system to alerts triggered through the home-grown EHR based CDS system. The authors calculated the healthcare cost of potentially prevented adverse drug events and medication errors and found that the advanced machine learning CDS system gave 68,2% more alerts resulting in cost savings of $60.67 per alert. After extrapolating these results to a local patient population of 747,985, they estimated savings of $1,294,457 over five years.