Influence of an Electronic Medical Record with a Clinical Decision Support System on Inpatient Falls, Nurses' Clinical Experiences, and Decision-Making Processes for Fall Prevention: A Mixed Methods Study

Abstract

Background: This thesis is informed by the researcher’s 25 years of operational management experience in New Zealand (NZ) private hospitals, including leading the design and implementation of an electronic medical record (EMR) with an integrated clinical decision support system (CDSS). International evidence shows that CDSS-enabled electronic health systems have advanced, offering evidence-based recommendations, workflow integration, and improvements in process measures and clinical performance, all with the potential to enhance fall-risk identification and outcomes. However, no NZ studies have explored the impact on inpatient fall management or nurses’ clinical decision-making in hospital settings.

Objective: Determine whether replacing a patient management system with an EMR and a CDSS for fall risk assessment affected the incidence of falls, and to explore nurses’ clinical experiences and decision-making in fall prevention.

Design: Employed a convergent mixed-methods approach consisting of three studies: a systematic review, a systems analysis, and a qualitative study, guided by the Donabedian Framework (Structure, Process, Outcomes).

Methods Systematic review: Systematically synthesised empirical evidence on the impact of digital health systems, including EMR and CDSS, on inpatient fall outcomes and nurses’ clinical decision-making that followed a deductive analysis.

Systems analysis: Examined whether replacing a patient management system with an EMR and CDSS for fall risk assessment influenced inpatient fall incidence at a private hospital using descriptive and inferential analyses of retrospective system data.

Qualitative study: Explored nurses’ experiences with the electronic fall risk assessment CDSS and its influence on fall-related clinical decision-making. A hybrid inductive/deductive approach was used, combining inductive coding with deductive organisation.

Integration of data: Using the mixed-methods synthesis, data from the systematic review, systems analysis, and qualitative study were integrated to develop a comprehensive understanding of how the EMR with CDSS may influence fall-prevention practices in private hospitals and to inform the overall discussion, conclusions, and recommendations.

Findings: Structural factors, particularly system design, usability, hardware, training, and staffing, influenced nurses’ interactions with the EMR and its embedded CDSS, thereby affecting workflow efficiency and data quality. At a process level, the CDSS improved coordination by strengthening communication, shared awareness of patient risk, and fall-related documentation, though use varied. In terms of outcomes, electronic systems enhanced the quality and visibility of fall reporting but did not reduce fall incidence. Nurses primarily relied on clinical judgement, highlighting an ongoing tension between CDSS design and clinical relevance and emphasising the need for nurses’ involvement in system design.

Conclusions: This thesis demonstrates that implementing an EMR with CDSS is a major organisational change that affects clinical practice, workflows, communication, and nurses’ experiences, indicating that technology alone does not improve patient safety. When systems misalign with workflows or lack clarity and usability, nurses develop workarounds that can compromise data quality, highlighting the need for co-design and ongoing training to support consistent documentation standards and confident use. While electronic systems offer benefits, fall prevention continues to rely on human judgement, vigilance, and engagement. Overall, the findings indicate that EMR with CDSS implementation influences fall prevention through complex interactions among system design, clinical processes, and human factors.