Effects of Electronic Medical Record Display on Provider Ordering Behavior: Leveraging the EMR to Improve Quality and Costs

Assessing Usability and Ambulatory Clinical Staff Satisfaction with Two Electronic Health Records

BACKGROUND

A growing body of literature has linked usability limitations within electronic health records (EHRs) to adverse outcomes which may in turn affect EHR system transitions. NewYork-Presbyterian Hospital, Columbia University College of Physicians and Surgeons (CU), and Weill Cornell Medical College (WC) are a tripartite organization with large academic medical centers that initiated a phased transition of their EHRs to one system, EpicCare.

OBJECTIVES

This article characterizes usability perceptions stratified by provider roles by surveying WC ambulatory clinical staff already utilizing EpicCare and CU ambulatory clinical staff utilizing iterations of Allscripts before the implementation of EpicCare campus-wide.

METHODS

A customized 19-question electronic survey utilizing usability constructs based on the Health Information Technology Usability Evaluation Scale was anonymously administered prior to EHR transition. Responses were recorded with self-reported demographics.

RESULTS

A total of 1,666 CU and 1,065 WC staff with ambulatory self-identified work setting were chosen. Select demographic statistics between campus staff were generally similar with small differences in patterns of clinical and EHR experience. Results demonstrated significant differences in EHR usability perceptions among ambulatory staff based on role and EHR system. WC staff utilizing EpicCare accounted for more favorable usability metrics than CU across all constructs. Ordering providers (OPs) denoted less usability than non-OPs. The Perceived Usefulness and User Control constructs accounted for the largest differences in usability perceptions. The Cognitive Support and Situational Awareness construct was similarly low for both campuses. Prior EHR experience demonstrated limited associations.

CONCLUSION

Usability perceptions can be affected by role and EHR system. OPs consistently denoted less usability overall and were more affected by EHR system than non-OPs. While there was greater perceived usability for EpicCare to perform tasks related to care coordination, documentation, and error prevention, there were persistent shortcomings regarding tab navigation and cognitive burden reduction, which have implications on provider efficiency and wellness.

Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Economic impact of clinical decision support interventions based on electronic health records

Background

Unnecessary healthcare utilization, non-adherence to current clinical guidelines, or insufficient personalized care are perpetual challenges and remain potential major cost-drivers for healthcare systems around the world. Implementing decision support systems into clinical care is promised to improve quality of care and thereby yield substantial effects on reducing healthcare expenditure. In this article, we evaluate the economic impact of clinical decision support (CDS) interventions based on electronic health records (EHR).

Methods

We searched for studies published after 2014 using MEDLINE, CENTRAL, WEB OF SCIENCE, EBSCO, and TUFTS CEA registry databases that encompass an economic evaluation or consider cost outcome measures of EHR based CDS interventions. Thereupon, we identified best practice application areas and categorized the investigated interventions according to an existing taxonomy of front-end CDS tools.

Results and discussion

Twenty-seven studies are investigated in this review. Of those, twenty-two studies indicate a reduction of healthcare expenditure after implementing an EHR based CDS system, especially towards prevalent application areas, such as unnecessary laboratory testing, duplicate order entry, efficient transfusion practice, or reduction of antibiotic prescriptions. On the contrary, order facilitators and undiscovered malfunctions revealed to be threats and could lead to new cost drivers in healthcare. While high upfront and maintenance costs of CDS systems are a worldwide implementation barrier, most studies do not consider implementation cost. Finally, four included economic evaluation studies report mixed monetary outcome results and thus highlight the importance of further high-quality economic evaluations for these CDS systems.

Conclusion

Current research studies lack consideration of comparative cost-outcome metrics as well as detailed cost components in their analyses. Nonetheless, the positive economic impact of EHR based CDS interventions is highly promising, especially with regard to reducing waste in healthcare.

Behavioral Economics Interventions in Clinical Decision Support Systems

Background:  Clinical decision support (CDS) systems can improve safety and facilitate evidence-based practice. However, clinical decisions are often affected by the cognitive biases and heuristics of clinicians, which is increasing the interest in behavioral and cognitive science approaches in the medical field.

Objectives:  This review aimed to identify decision biases that lead clinicians to exhibit irrational behaviors or responses, and to show how behavioral economics can be applied to interventions in order to promote and reveal the contributions of CDS to improving health care quality.

Methods:  We performed a systematic review of studies published in 2016 and 2017 and applied a snowball citationsearch method to identify topical publications related to studies forming part of the BEARI (Application of Behavioral Economics to Improve the Treatment of Acute Respiratory Infections) multisite, cluster-randomized controlled trial performed in the United States.

Results:  We found that 10 behavioral economics concepts with nine cognitive biases were addressed and investigated for clinician decision-making, and that the following five concepts, which were actively explored, had an impact in CDS applications: social norms, framing effect, status-quo bias, heuristics, and overconfidence bias.

Conclusions:  Our review revealed that the use of behavioral economics techniques is increasing in areas such as antibiotics prescribing and preventive care, and that additional tests of the concepts and heuristics described would be useful in other areas of CDS. An improved understanding of the benefits and limitations of behavioral economics techniques is also still needed. Future studies should focus on successful design strategies and how to combine them with CDS functions for motivating clinicians.