Using the Electronic Health Record Data in Real Time and Predictive Analytics to Prevent Hospital-Acquired Postoperative/Surgical Site Infections

Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review

Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41666-023-00129-2.

Digitally enabled hemovigilance allows real time response to transfusion reactions

BACKGROUND

Transfusion carries a risk of transfusion reaction that is often underdiagnosed due to reliance on passive reporting. The study investigated the utility of digital methods to identify potential transfusion reactions, thus allowing real-time intervention for affected patients.

METHOD

The hemovigilance unit monitored 3856 patients receiving 43,515 transfusions under the hemovigilance program. Retrospective comparison data included 298,498 transfusions. Transfusion medicine physicians designed and validated algorithms in the electronic health record that analyze discrete data, such as vital sign changes, to assign a risk score during each transfusion. Dedicated hemovigilance nurses remotely monitor all patients and perform real-time chart reviews prioritized by risk score. When a reaction is suspected, a hemovigilance trained licensed clinician responds to manage the patient and ensure data collection. Board-certified transfusion medicine physicians reviewed data and classified transfusion reactions under various categories according to the Centers for Disease Control hemovigilance definitions.

RESULTS

Transfusion medicine physicians diagnosed 564 transfusion reactions (1.3% of transfusions)-a 524% increase compared to the previous passive reporting. The rapid response provider reached the bedside on average at 12.4 min demonstrating logistic feasibility. While febrile reactions were most diagnosed, recognition of transfusion-associated circulatory overload demonstrated the greatest relative increase. Auditing and education programs further enhanced transfusion reaction awareness.

DISCUSSION

The model of digitally-enabled expert real-time review of clinical data that prompts rapid response improved recognition of transfusion reactions. This approach could be applied to other patient deterioration events such as early identification of sepsis.

Development and Validation of a Semi-Automated Surveillance Algorithm for Cardiac Device Infections: Insights from the VA CART program

Procedure-related cardiac electronic implantable device (CIED) infections have high morbidity and mortality, highlighting the urgent need for infection prevention efforts to include electrophysiology procedures. We developed and validated a semi-automated algorithm based on structured electronic health records data to reliably identify CIED infections. A sample of CIED procedures entered into the Veterans' Health Administration Clinical Assessment Reporting and Tracking program from FY 2008-2015 was reviewed for the presence of CIED infection. This sample was then randomly divided into training (2/3) validation sets (1/3). The training set was used to develop a detection algorithm containing structured variables mapped from the clinical pathways of CIED infection. Performance of this algorithm was evaluated using the validation set. 2,107 unique CIED procedures from a cohort of 5,753 underwent manual review; 97 CIED infections (4.6%) were identified. Variables strongly associated with true infections included presence of a microbiology order, billing codes for surgical site infections and post-procedural antibiotic prescriptions. The combined algorithm to detect infection demonstrated high c-statistic (0.95; 95% confidence interval: 0.92-0.98), sensitivity (87.9%) and specificity (90.3%) in the validation data. Structured variables derived from clinical pathways can guide development of a semi-automated detection tool to surveil for CIED infection.