The blood utilization calculator, a target-based electronic decision support algorithm, increases the use of single-unit transfusions in a large academic medical center

Evaluation of an adaptive, rule-based dosing algorithm to maintain therapeutic anticoagulation during atrial fibrillation ablation

Background

Cerebral thromboembolism during atrial fibrillation (AF) ablation is an infrequent (0.17%) complication in part owing to strict adherence to intraprocedural anticoagulation. Failure to maintain therapeutic anticoagulation can lead to an increase in events, including silent cerebral ischemia.

Objective

To evaluate a computerized, clinical decision support system (CDSS) to dose intraprocedural anticoagulation and determine if it leads to improved intraprocedural anticoagulation outcomes during AF ablation.

Methods

The Digital Intern dosing algorithm is an adaptive, rule-based CDSS for heparin dosing. The initial dose is calculated from the patient's weight, baseline activated clotting time (ACT), and outpatient anticoagulant. Subsequent recommendations adapt based on individual patient ACT changes. Outcomes from 50 cases prior to algorithm introduction were compared to 139 cases using the algorithm.

Results

Procedures using the dosing algorithm reached goal ACT (over 300 seconds) faster (17.6 ± 11.1 minutes vs 33.3 ± 23.6 minutes pre-algorithm, P < .001). ACTs fell below goal while in the LA (odds ratio 0.20 [0.10-0.39], P < .001) and rose above 400 seconds less frequently (odds ratio 0.21 [0.07-0.59], P = .003). System Usability Scale scores were excellent (96 ± 5, n = 7, score >80.3 excellent). Preprocedure anticoagulant, weight, baseline ACT, age, sex, and renal function were potential predictors of heparin dose to achieve ACT >300 seconds and final infusion rate.

Conclusion

A heparin dosing CDSS based on rules and adaptation to individual patient response improved maintenance of therapeutic ACT during AF ablation and was rated highly by nurses for usability.

Pre-operative anemia and peri-operative transfusion are associated with poor oncologic outcomes in cancers of the esophagus: potential impact of patient blood management on cancer outcomes

BACKGROUND

Both Red Blood Cell (RBC) transfusion and anemia are thought to negatively impact cancer survival. These effects have been reported with mixed findings in cancer of the esophagus. The potential impact of the application of restrictive transfusion strategies on this patient population has not been defined.

MATERIALS AND METHODS

We conducted a retrospective study of esophagectomies and studied cases based on whether they were anemic or were transfused peri-operatively. Clinical characteristics and known clinicopathologic prognosticators were compared between these groups. Survival was compared by Cox proportional hazard modeling. Post-operative transfusions were assessed for compliance with restrictive transfusion thresholds.

RESULTS

Three-hundred ninety-nine esophagectomy cases were reviewed and after exclusions 348 cases were analyzed. The median length of follow-up was 33 months (range 1-152 months). Sixty-four percent of patients were anemic pre-operatively and 22% were transfused. Transfusion and anemia were closely related to each other. Microcytic anemia was uncommon but was evaluated and treated in only 50% of cases. Most anemic patients had normocytic RBC parameters. Transfusion but not anemia was associated with a protracted/prolonged post-operative stay. Transfusion and anemia were both associated with reduced survival however only anemia was associated with decreased survival in multi-variable modeling. Sixty-eight percent of patients were transfused post-operatively and 11% were compliant with the restrictive threshold of 7 g/dL.

CONCLUSIONS

Pre-operative anemia and transfusion are closely associated, however only anemia was found to compromise survival in our esophageal cancer cohort, supporting the need for more aggressive evaluation and treatment of anemia. Adherence to restrictive transfusion guidelines offers an opportunity to reduce transfusion rates which may also improve short-term outcomes.

Clinicians' Perceptions of an Artificial Intelligence-Based Blood Utilization Calculator: Qualitative Exploratory Study

BACKGROUND

According to the US Food and Drug Administration Center for Biologics Evaluation and Research, health care systems have been experiencing blood transfusion overuse. To minimize the overuse of blood product transfusions, a proprietary artificial intelligence (AI)-based blood utilization calculator (BUC) was developed and integrated into a US hospital's electronic health record. Despite the promising performance of the BUC, this technology remains underused in the clinical setting.

OBJECTIVE

This study aims to explore how clinicians perceived this AI-based decision support system and, consequently, understand the factors hindering BUC use.

METHODS

We interviewed 10 clinicians (BUC users) until the data saturation point was reached. The interviews were conducted over a web-based platform and were recorded. The audiovisual recordings were then anonymously transcribed verbatim. We used an inductive-deductive thematic analysis to analyze the transcripts, which involved applying predetermined themes to the data (deductive) and consecutively identifying new themes as they emerged in the data (inductive).

RESULTS

We identified the following two themes: (1) workload and usability and (2) clinical decision-making. Clinicians acknowledged the ease of use and usefulness of the BUC for the general inpatient population. The clinicians also found the BUC to be useful in making decisions related to blood transfusion. However, some clinicians found the technology to be confusing due to inconsistent automation across different blood work processes.

CONCLUSIONS

This study highlights that analytical efficacy alone does not ensure technology use or acceptance. The overall system's design, user perception, and users' knowledge of the technology are equally important and necessary (limitations, functionality, purpose, and scope). Therefore, the effective integration of AI-based decision support systems, such as the BUC, mandates multidisciplinary engagement, ensuring the adequate initial and recurrent training of AI users while maintaining high analytical efficacy and validity. As a final takeaway, the design of AI systems that are made to perform specific tasks must be self-explanatory, so that the users can easily understand how and when to use the technology. Using any technology on a population for whom it was not initially designed will hinder user perception and the technology's use.

Factors influencing clinicians' willingness to use an AI-based clinical decision support system

Background

Given the opportunities created by artificial intelligence (AI) based decision support systems in healthcare, the vital question is whether clinicians are willing to use this technology as an integral part of clinical workflow.

Purpose

This study leverages validated questions to formulate an online survey and consequently explore cognitive human factors influencing clinicians' intention to use an AI-based Blood Utilization Calculator (BUC), an AI system embedded in the electronic health record that delivers data-driven personalized recommendations for the number of packed red blood cells to transfuse for a given patient.

Method

A purposeful sampling strategy was used to exclusively include BUC users who are clinicians in a university hospital in Wisconsin. We recruited 119 BUC users who completed the entire survey. We leveraged structural equation modeling to capture the direct and indirect effects of “AI Perception” and “Expectancy” on clinicians' Intention to use the technology when mediated by “Perceived Risk”.

Results

The findings indicate a significant negative relationship concerning the direct impact of AI's perception on BUC Risk (ß = −0.23, p < 0.001). Similarly, Expectancy had a significant negative effect on Risk (ß = −0.49, p < 0.001). We also noted a significant negative impact of Risk on the Intent to use BUC (ß = −0.34, p < 0.001). Regarding the indirect effect of Expectancy on the Intent to Use BUC, the findings show a significant positive impact mediated by Risk (ß = 0.17, p = 0.004). The study noted a significant positive and indirect effect of AI Perception on the Intent to Use BUC when mediated by risk (ß = 0.08, p = 0.027). Overall, this study demonstrated the influences of expectancy, perceived risk, and perception of AI on clinicians' intent to use BUC (an AI system). AI developers need to emphasize the benefits of AI technology, ensure ease of use (effort expectancy), clarify the system's potential (performance expectancy), and minimize the risk perceptions by improving the overall design.

Conclusion

Identifying the factors that determine clinicians' intent to use AI-based decision support systems can help improve technology adoption and use in the healthcare domain. Enhanced and safe adoption of AI can uplift the overall care process and help standardize clinical decisions and procedures. An improved AI adoption in healthcare will help clinicians share their everyday clinical workload and make critical decisions.

Effect of risk, expectancy, and trust on clinicians' intent to use an artificial intelligence system -- Blood Utilization Calculator

A gap exists between the capabilities of artificial intelligence (AI) technologies in healthcare and the extent to which clinicians are willing to adopt these systems. Our study addressed this gap by leveraging 'expectancy-value theory' and 'modified extended unified theory of acceptance and use of technology' to understand why clinicians may be willing or unwilling to adopt AI systems. The study looked at the 'expectancy,' 'trust,' and 'perceptions' of clinicians related to their intention of using an AI-based decision support system known as the Blood Utilization Calculator (BUC). The study used purposive sampling to recruit BUC users and administered a validated online survey from a large hospital system in the Midwest in 2021. The findings captured the significant effect of 'perceived risk' (negatively) and 'expectancy' (positively) on clinicians' 'trust' in BUC. 'Trust' was also found to mediate the relationship of 'perceived risk' and 'expectancy' with the 'intent to use BUC.' The study's findings established pathways for future research and have implications on factors influencing BUC use.

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

OBJECTIVE

Considerable international investment in hospital electronic prescribing (ePrescribing) systems has been made, but despite this, it is proving difficult for most organizations to realize safety, quality, and efficiency gains in prescribing. The objective of this work was to develop policy-relevant insights into the optimization of hospital ePrescribing systems to maximize the benefits and minimize the risks of these expensive digital health infrastructures.

METHODS

We undertook a systematic scoping review of the literature by searching MEDLINE, Embase, and CINAHL databases. We searched for primary studies reporting on ePrescribing optimization strategies and independently screened and abstracted data until saturation was achieved. Findings were theoretically and thematically synthesized taking a medicine life-cycle perspective, incorporating consultative phases with domain experts.

RESULTS

We identified 23,609 potentially eligible studies from which 1367 satisfied our inclusion criteria. Thematic synthesis was conducted on a data set of 76 studies, of which 48 were based in the United States. Key approaches to optimization included the following: stakeholder engagement, system or process redesign, technological innovations, and education and training packages. Single-component interventions (n = 26) described technological optimization strategies focusing on a single, specific step in the prescribing process. Multicomponent interventions (n = 50) used a combination of optimization strategies, typically targeting multiple steps in the medicines management process.

DISCUSSION

We identified numerous optimization strategies for enhancing the performance of ePrescribing systems. Key considerations for ePrescribing optimization include meaningful stakeholder engagement to reconceptualize the service delivery model and implementing technological innovations with supporting training packages to simultaneously impact on different facets of the medicines management process.

Electronic clinical decision support to facilitate a change in clinical practice: Small details can make or break success

BACKGROUND

The use of electronic clinical decision support (CDS) is becoming common to change historically common clinical practices considered outdated by current guidelines. Preimplementation design of CDS tools is key to their success in changing clinical behaviors. Unfortunately, there are no established protocols for CDS tool development, and CDS failure can result from even small design flaws. This paper describes an example of a design oversight and how correction resulted in CDS success.

STUDY DESIGN AND METHODS

We performed a retrospective review of compliance with a CDS tool to encourage the use of prothrombin complex concentrate over plasma transfusion for the emergent reversal of warfarin. We identified a potential design flaw, made the necessary modifications, and repeated the compliance review.

RESULTS

After CDS, plasma orders declined by 150 units/mo; however, 48% of orders placed for non-warfarin coagulopathy were still for warfarin reversal. Hospital-wide, this noncompliance was 36% and was 80% in the emergency department. By simply relocating the qualifier "NOT on warfarin" from the end to the beginning of the order, noncompliance for warfarin reversal was reduced to 5% (P < .0001 by chi-square).

CONCLUSIONS

The successful use of electronic clinical decision support in the electronic medical record can depend on optimal design. Missing even small design elements such as the positioning of key terms within the tool can result in an ineffective CDS. Important design strategies to avoid poor performance are discussed as they relate to the CDS tool we describe.

Improvements in red blood cell transfusion utilization following implementation of a single-unit default for electronic ordering

BACKGROUND

The prevention of excessive allogeneic red blood cell (RBC) transfusion is an important component of patient blood management initiatives. In this investigation, changes in transfusion behaviors following modification of computerized physician order entry (CPOE) procedures for RBC transfusions to a single-unit default quantity were assessed.

STUDY DESIGN AND METHODS

This is an observational cohort study of adults for whom nonemergency allogeneic RBC transfusions were ordered in the 2 years before and 2 years after the date of modification of the CPOE system to a single-unit default (June 18, 2015). Changes in the frequency of single- versus multiunit RBC transfusion orders and other transfusion metrics were compared between preintervention and postintervention cohorts.

RESULTS

A total of 52,773 unique transfusion orders for 61,989 RBC units were included, of which 60,045 (96.9%) were transfused. Single-unit orders increased annually, from 10,404 (74.1%) in the first year to 11,645 (88.6%) in the last year, while multiunit orders decreased by more than half (p < 0.0001). The number of RBC units transfused decreased by 13.9% from 32,528 in the preintervention cohort to 27,497 in the post intervention cohort (p < 0.0001) with an estimated reduction in transfusion-related expenditures of nearly $4 million. The percentage of transfusions associated with a posttransfusion hemoglobin of10 g/dL or greater decreased by 34.5% (p < 0.0001).

CONCLUSION

Modification of the CPOE process such that nonemergency RBC transfusion orders were defaulted to a single unit was associated with decreased rates of multiunit RBC transfusion orders, lower transfusion volumes, and substantial cost savings.

Efficacy of topical tranexamic acid within a blood-saving programme for primary total hip arthroplasty: a pragmatic, open-label randomised study

BACKGROUND

Total hip arthroplasty entails considerable peri-operative blood loss, which may lead to acute post-operative anaemia and red blood cell transfusion. This study was aimed at assessing whether the addition of topical tranexamic acid to our ongoing blood-saving protocol for total hip arthroplasty was effective and safe.

MATERIALS AND METHODS

A pragmatic, prospective, open-label randomised study of patients scheduled for total hip arthroplasty at a single centre was conducted. Consecutive patients were randomly assigned to receive topical tranexamic acid (2 g) at the end of surgery (tranexamic group, n=125) or not (control group, n=129). A restrictive transfusion protocol was applied. Outcome measures were red blood cell loss at 24 hours after surgery, in-hospital transfusion rate, and incidence of thromboembolic complications.

RESULTS

Topical tranexamic acid was effective in reducing both red cell loss (mean difference: 138 mL [95% CI 87-189 mL]; p<0.001) in the 24h after surgery and in-hospital transfusion rates (12 vs 32.6%, for the tranexamic acid and control groups, respectively; p<0.001; relative risk=0.37 [95% CI 0.22-0.63]). However, relative red cell loss and transfusion rates were higher in females than in males, irrespectively of tranexamic acid use. The beneficial effect of tranexamic acid on transfusion was restricted to patients with pre-operative haemoglobin ≥13 g/dL (5.1 vs 24.8%; p<0.001). Topical tranexamic acid was well tolerated and no clinically apparent thromboembolic complications were witnessed.

DISCUSSION

The use of topical tranexamic acid after hip arthroplasty reduced red cell loss and transfusion rates; the efficacy of this strategy may be improved by reinforcing both pre-operative haemoglobin optimisation and adherence to the practice of transfusing single units of red cells.