Gaines, Kim

Abstract Introduction Identification of hospitalized patients with suddenly unfavorable clinical course remains challenging. Models using objective data elements from the electronic health record may miss important sources of information available to nurses. Methods We recorded nurses’ perception of patient potential for deterioration in 2 medical and 2 surgical adult hospital units using a 5-point score at the start of the shift (the Worry Factor [WF]), and any time a change or an increase was noted by the nurse. Cases were evaluated by three reviewers. Intensive care unit (ICU) transfers were also tracked. Results 31 159 patient-shifts were recorded for 3185 unique patients during 3551 hospitalizations, with 169 total outcome events. Out of 492 potential deterioration events identified, 380 (77%) were confirmed by reviewers as true deterioration events. Likelihood ratios for ICU transfer were 17.8 (15.2–20.9) in the 24 hours following a WF > 2, and 40.4 (27.1–60.1) following a WF > 3. Accuracy rates were significantly higher in nurses with over a year of experience (68% vs 79%, P = 0.04). The area under the receiver operator characteristic curve (AUROC) was 0.92 for the prediction of ICU transfer within 24 hours. Discussion This is a higher accuracy than most published early warning scores. Conclusion Nurses’ pattern recognition and sense of worry can provide important information for the detection of acute physiological deterioration and should be included in the electronic medical record.

Background Do Not Resuscitate (DNR) orders are intended to safeguard patients' autonomy and prevent unwanted resuscitative care. However, DNR orders may be miscommunicated between health care providers, leading to errors honoring patient wishes during cardiac arrest events. This project focused on improving accuracy of DNR ordering processes for an academic, tertiary care hospital. Intervention We describe a performance improvement process and outcomes for implementation of an inpatient electronic ordering system that included an automated, decentralized printing process for resuscitation status armbands. Specific phases of this project involved: (a) identification of common factors contributing to errors honoring patients‘ resuscitation wishes, (b) design of an electronic ordering process, (c) design and integration of a new DNR armband and (d) evaluation of the impact of changes on communication accuracy. The primary outcome was percentage of patients with incorrect designation of resuscitation status on armbands compared to the active resuscitation order in the electronic medical record. Results After implementation of an electronic ordering process we identified that 37/196 (19%) patients had an armband that did not reflect their documented wishes versus 2/103 (2%) after integration of automated armband printing into the process (p<0.001). No armband discrepancies were found after the first two weeks of post-implementation audits. Conclusions Design and implementation of an electronic ordering and armband labeling process reduced discrepancies between patient wishes and the armband labeling of the patient's desired DNR status. It is anticipated that these improvements will reduce the risk of adverse outcomes, and better align clinical processes with patient wishes.