Adaptation of a Synergy Model-based Patient Acuity Tool for the Electronic Health Record

Nurse staffing decisions are often made without input from high-quality, reliable patient acuity measures, especially in medical-surgical settings. Staffing decisions not aligned with patient care needs can contribute to inadequate patient-to-nurse ratios and nurse burnout, potentially resulting in preventable patient harm and death. We conducted a proof-of-concept study to explore the feasibility of adapting an evidence-based patient acuity tool for use in the EHR. A retrospective cohort of pediatric medical-surgical inpatients was used to map electronic patient data variables. We developed an algorithm to calculate the score for one domain of the tool and validated it by comparing it with a score based on a manual chart review. Through multiple rounds of testing and refinement of the variables and algorithm, we achieved 100% concordance between scores generated by the algorithm and the manual chart review. Our proof-of-concept study demonstrates the feasibility and challenges of adapting an evidence-based patient acuity score for automation in the EHR. Further collaboration with data scientists is warranted to operationalize the tool in the EHR and achieve an automated acuity score that can improve staffing decisions, support nursing practice, and enhance team collaboration.