Table 1.
Summary of results.
| Year | Author | Location | Setting | Outcomes |
|---|---|---|---|---|
| Small Scale Centralized Operating Systems—Integration | ||||
| 2010 | Baumlin, K | Mount Sinai Medical Centre, New York, USA | Hospital (Emergency Department) | Post-intervention of an emergency department information system compared to pre-intervention: |
| ↓ Average ED length of stay from 6.69 to 4.75 h | ||||
| ↓ Doctor-to-disposition time from 3.64 to 1.74 h | ||||
| ↓ Triage to first-to-doctor time from 1.22 to 0.68 h | ||||
| ↓ X-ray turnaround time from 0.92 to 0.74 h | ||||
| ↓ CT scan turnaround time from 3.89 to 2.33 h | ||||
| ↓ Lab turnaround time from 2.03 to 1.44 h | ||||
| Small Scale Centralized Operating Systems—Communication and Coordination | ||||
| 2005 | Hemphill, R | Saint Francis Hospital, Oklahoma, USA | Hospital (Emergency Department) | Post-intervention of a bed management Access Centre compared to pre-intervention: |
| ↑ Expedition of direct admission of patients | ||||
| ↑ Transfer requests by 48% | ||||
| ↓ Denials due to “no capacity” by 54% | ||||
| 2015 | Morris, M | Carilion Clinic, Virginia, USA | Hospital (Emergency Department) | Post-intervention of a central transfer and communications center compared to pre-intervention: |
| ↑ Satisfaction, accountability, internal bed assignment times | ||||
| ↑ Internally supported ambulance discharge by 10% | ||||
| ↑ Patients moved internally to clean bed by 5% when assigned goal time of under 1 h | ||||
| 2013 | Tortorella, F | Anderson Cancer Centre, Texas, USA | Hospital | Post-intervention of a bed management system compared to pre-intervention: |
| ↑ Patient flow, patient experience, bed turnover time | ||||
| ↓ Time of room being notified as dirty, to cleaned and ready, from 63 to 49 min | ||||
| ↓ Bed turnover time from 111 to 49 min | ||||
| Small Scale Centralized Operating Systems—Early Warning and Prediction | ||||
| 2020 | Escobar, GJ | KPNC Hospital System, USA | Hospital (Non-ICU) | ↓ Mortality by 3 deaths avoided per 1000 eligible patients per year following intervention of an automated predictive model identifying high-risk patients |
| 2022 | Jerng, JS | National Taiwan University Hospital, Taiwan | Hospital (General Ward) | Decision support system group compared to conventional group: |
| ↓ Interval between admission and first rapid response activation (6.9 vs 9.8 days) | ||||
| ↓ Cardiopulmonary resuscitation (0.98% vs 1.35%) | ||||
| ↓ Length of hospitalization (23.3 vs 28.9 days) | ||||
| ↓ In-hospital deaths (15.0% vs 19.6%) | ||||
| 2017 | Kollef, MH | Barnes-Jewish Hospital, Missouri, USA | Hospital (Medicine Ward) | Post-intervention of a rapid response system compared to pre-intervention: |
| ↓ Hospital mortality | ||||
| ↓ Cardiopulmonary arrests per study year increment by 3.4 occurrences | ||||
| ↓ Median length of stay per study year increment by 0.08 days | ||||
| 2020 | Monteith, M | Hamilton Health Sciences, Ontario, Canada | Hospital (Acute Care Facilities) | ↓ Code blues called by 11% following intervention of an early warning system |
| 2021 | Na, SJ | Samsung Medical Centre, South Korea | Hospital (General Ward) | Post-intervention of an automated alert and activation system for medical emergency teams compared to pre-intervention: |
| ↓ Time from deterioration to emergency medical team activation from 60 to 34 min | ||||
| ↓ Unplanned ICU admission rates from 71.8% to 41.2% | ||||
| ↓ Hospital mortality from 38.5% to 27.2% | ||||
| 2011 | Sawyer, AM | Barnes-Jewish Hospital, Missouri, USA | Hospital (Medicine Ward) | Post-intervention of an automated sepsis screening and alert system compared to pre-intervention: |
| ↑ Number of patients receiving > 1 interventions by 15% | ||||
| ↑ Antibiotic escalation from 23.8% to 36.0% | ||||
| ↑ IV fluid administration from 23.8% to 38.2% | ||||
| ↑ Oxygen therapy from 8.3% to 20.2% | ||||
| ↑ Microbiologic cultures and radiographic imaging | ||||
| 2017 | Subbe, CP | Ysbyty Gwynedd Hospital, UK | Hospital (General Medicine Ward) | Post-intervention of an automated vital signs monitoring and notification system compared to pre-intervention: |
| ↑ Number of patients with DND order | ||||
| ↓ Cardiac arrests from 14 to 2 events | ||||
| ↓ Mortality from 173 to 147 patients | ||||
| 2015 | Umscheid, CA | University of Pennsylvania Health System, Pennsylvania, USA | Hospital (Non-Critical Care Services) | Post-intervention of an automated sepsis early warning and response system compared to pre-intervention: |
| ↓ Sepsis alert triggers for at-risk patients from 3.8% to 3.5% | ||||
| ↑ Early sepsis care, ICU transfer, system activations for general medicine units | ||||
| ↓ Trend in hospital mortality | ||||
| ↑ Trend in discharge from hospital | ||||
| 2021 | You, SH | Seoul National University Hospital | Hospital (Surgical Ward) | Post-intervention of an automated real-time alerting system compared to pre-intervention: |
| ↑ Medical emergency team alert activations from 14.4 to 26.3 per 1000 admissions | ||||
| ↓ In-hospital mortality from 15.1 to 12.9 per 1000 admissions | ||||
| 2014 | Young, RS | Northwestern Memorial Hospital, Illinois, USA | Hospital (Hematology-Oncology Units) | ↓ Number of codes per 100 unit discharges by 50% following intervention of a modified early warning score |
| Command Center | ||||
| 2017 | Chan, Carri | Johns Hopkins Hospital, Baltimore, USA | Hospital | Post-intervention of a command center compared to pre-intervention: |
| ↓ ED patients waiting for bed by 30% | ||||
| ↓ Time to retrieve data and identify patients for transfer by 1 h | ||||
| ↑ Occupancy from 85% to 92% | ||||
| ↓ Boarding of ED patients to medicine beds from 9.7 to 6.3 h | ||||
| 2022 | Collins, BE | Humber River Hospital, Ontario, Canada | Hospital | Harm score at HRH using a command center compared to all Ontario-based hospitals per 100 patients: |
| ↓ Harm score overall (2.2 vs 5.7) | ||||
| ↓ Harm score for medication conditions (1.0 vs 3.3) | ||||
| ↓ Harm score for infection conditions (0.5 vs 1.9) | ||||
| ↓ Harm score for patient accidents (0.1 vs 0.2) | ||||
| ↓ Harm score for associated procedures (0.8 vs 1.3) | ||||
| 2018 | Davenport, PB | Carilion Clinic, Virginia, USA | Hospital (Trauma and Emergency Care) | Post-intervention of a centralized operations center compared to pre-intervention: |
| ↑ Patient transfer volumes by 19% | ||||
| ↑ ED admission volume by 7% | ||||
| ↓ ICU patient length of stay by 0.3 days | ||||
| 2016 | Lovett, PB | Jefferson University Hospital, Pennsylvania, USA | Hospital | Post-intervention of a centralized Patient Flow Management Centre compared to pre-intervention: |
| ↑ Total admissions per month from 2677 to 2810 patients | ||||
| ↑ ED visits per month from 4850 to 5224 visits | ||||
| ↑ Completed patient transports per month 11,475 to 13,967 patients | ||||
| ↑ Mean patient transport time from 35 to 36 min | ||||
| ↓ Ambulance diversion per month from 86 to 7 h | ||||
| ↓ ED visits without medical team examination by 2.5% | ||||
| ↓ Median ED door to provider time from 74 to 41 min | ||||
| ↓ Mean EVS response time from 77 to 32 min | ||||
| ↓ Mean EVS turn time from 115 to 72 min | ||||
| ↓ Mean bed request to assign time from 153 to 105 min | ||||