Predictive analytics for cardio-thoracic surgery duration as a stepstone towards data-driven capacity management.

Effective capacity management of operation rooms is key to avoid surgery cancellations and prevent long waiting lists that negatively affect clinical and financial outcomes as well as patient and staff satisfaction. This requires optimal surgery scheduling, leveraging essential parameters like surgery duration, post-operative bed type and hospital length-of-stay. Common clinical practice is to use the surgeon's average procedure time of the last N patients as a planned surgery duration for the next patient. A discrepancy between the actual and planned surgery duration may lead to suboptimal surgery schedule. We used deidentified data from 2294 cardio-thoracic surgeries to first calculate the discrepancy of the current model and second to develop new predictive models based on linear regression, random forest, and extreme gradient boosting. The new ensamble models reduced the RMSE for elective and acute surgeries by 19% (0.99 vs 0.80, p = 0.002) and 52% (1.87 vs 0.89, p < 0.001), respectively. Also, the elective and acute surgeries "behind schedule" were reduced by 28% (60% vs. 32%, p < 0.001) and 9% (37% vs. 28%, p = 0.003), respectively. These improvements were fueled by the patient and surgery features added to the models. Surgery planners can benefit from these predictive models as a patient flow AI decision support tool to optimize OR utilization.

Contextualized Drug-Drug Interaction Management Improves Clinical Utility Compared With Basic Drug-Drug Interaction Management in Hospitalized Patients.

Drug-drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context-dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI-CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P < 0.01), with 4.0 PIs/1,000 MOs (P < 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.

Diagnostic performance of echocardiography to predict cardiac tamponade after cardiac surgery.

OBJECTIVES: Cardiac tamponade is a life-threatening complication after cardiac surgery. Echocardiography, both transthoracic (TTE) and transesophageal (TEE), may help to identify cardiac tamponade after surgery, but its diagnostic value remains unverified after cardiac surgery. METHODS: This retrospective single-centre cohort study used the electronic medical record and echocardiography database of the Catharina Hospital Eindhoven, a tertiary referral cardiothoracic centre, to identify patients who received echocardiography because they were clinically suspected of having cardiac tamponade within the 4 weeks after cardiac surgery. Overall diagnostic accuracy of both TTE and TEE was calculated (sensitivity, specificity, positive predictive value, negative predictive value, and receiver operation characteristics curves). Subgroup analyses were performed based on the timing of the echocardiography after primary surgery (<24, 24-72, >72 h). RESULTS: The query identified 427 echocardiographs, 373 TTEs and 54 TEEs, being performed in 414 patients (65% males, mean age 67 years). Of them, 116 patients underwent surgical re-exploration in which a cardiac tamponade was determined in 105 patients with a 30-day mortality of 8.6%. The area under the receiver operation characteristics curve for echocardiography in the 4 weeks after cardiac surgery was 0.78 [95% confidence interval (CI): 0.72-0.84, P < 0.001]. In the first 24 h after surgery was the positive predictive value of echocardiography 58.3% (95% CI: 28.6-83.5) with an area under the curve of 0.64 (95% CI: 0.49-0.80, P = 0.06). The diagnostic accuracy improved over time for both TTE and TEE. CONCLUSIONS: Diagnostic accuracy of echocardiography in the 4 weeks after cardiac surgery for cardiac tamponade is acceptable and improves over time. However, in the early postoperative phase (<24 h), the diagnostic accuracy of echocardiography is poor.

Intelligent checklists improve checklist compliance in the intensive care unit: a prospective before-and-after mixed-method study.

BACKGROUND: We examined whether a context and process-sensitive 'intelligent' checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds. METHODS: We conducted a single-centre prospective before-and-after mixed-method trial in a 35 bed medical and surgical ICU. Daily ICU ward rounds were observed during two periods of 8 weeks. We compared paper checklists (control) with a dynamic (digital) clinical checklist (DCC, intervention). The primary outcome was compliance with best clinical practice, measured as the percentages of checked items and unchecked critical items. Secondary outcomes included ICU stay and the usability of digital checklists. Data are presented as median (interquartile range). RESULTS: Clinical characteristics and severity of critical illness were similar during both control and intervention periods of study. A total of 36 clinicians visited 197 patients during 352 ward rounds using the paper checklist, compared with 211 patients during 366 ward rounds using the DCC. Per ICU round, a median of 100% of items (94.4-100.0) were completed by DCC, compared with 75.1% (66.7-86.4) by paper checklist (P=0.03). No critical items remained unchecked by the DCC, compared with 15.4% (8.3-27.3) by the paper checklist (P=0.01). The DCC was associated with reduced ICU stay (1 day [1-3]), compared with the paper checklist (2 days [1-4]; P=0.05). Usability of the DCC was judged by clinicians to require further improvement. CONCLUSIONS: A digital checklist improved compliance with best clinical practice, compared with a paper checklist, during ward rounds on a mixed ICU. CLINICAL TRIAL REGISTRATION: NCT03599856.