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To evaluate the impact of health information technology (HIT) for early detection of patient deterioration on patient mortality and length of stay (LOS) in acute care hospital settings.We searched MEDLINE and Epub Ahead of Print, In-ProcessOther Non-Indexed Citations and Daily, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus from 1990 to January 19, 2021.We included studies that enrolled patients hospitalized on the floor, in the ICU, or admitted through the emergency department. Eligible studies compared HIT for early detection of patient deterioration with usual care and reported at least one end point of interest: hospital or ICU LOS or mortality at any time point.Study data were abstracted by two independent reviewers using a standardized data extraction form.Random-effects meta-analysis was used to pool data. Among the 30 eligible studies, seven were randomized controlled trials (RCTs) and 23 were pre-post studies. Compared with usual care, HIT for early detection of patient deterioration was not associated with a reduction in hospital mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT interventions demonstrated a significant association with improved hospital mortality for the entire study cohort (odds ratio, 0.78 [95% CI, 0.70-0.87]) and reduced hospital LOS overall.HIT for early detection of patient deterioration in acute care settings was not significantly associated with improved mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT was associated with improved hospital mortality and LOS; however, these results should be interpreted with caution. The differences in patient outcomes between the findings of the RCTs and pre-post studies may be secondary to confounding caused by unmeasured improvements in practice and workflow over time.

Objective: Clinical protocols may decrease unnecessary variation in care and improve compliance with desirable therapies. We evaluated whether highly protocolized ICUs have superior patient outcomes compared with less highly protocolized ICUs. Design: Observational study in which participating ICUs completed a general assessment and enrolled new patients 1 day each week. Patients: A total of 6,179 critically ill patients. Setting: Fifty-nine ICUs in the United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study. Interventions: None. Measurements and Main Results: The primary exposure was the number of ICU protocols; the primary outcome was hospital mortality. A total of 5,809 participants were followed prospectively, and 5,454 patients in 57 ICUs had complete outcome data. The median number of protocols per ICU was 19 (interquartile range, 15–21.5). In single-variable analyses, there were no differences in ICU and hospital mortality, length of stay, use of mechanical ventilation, vasopressors, or continuous sedation among individuals in ICUs with a high versus low number of protocols. The lack of association was confirmed in adjusted multivariable analysis (p = 0.70). Protocol compliance with two ventilator management protocols was moderate and did not differ between ICUs with high versus low numbers of protocols for lung protective ventilation in acute respiratory distress syndrome (47% vs 52%; p = 0.28) and for spontaneous breathing trials (55% vs 51%; p = 0.27). Conclusions: Clinical protocols are highly prevalent in U.S. ICUs. The presence of a greater number of protocols was not associated with protocol compliance or patient mortality.

To evaluate effects of health information technology in the inpatient and ICU on mortality, length of stay, and cost. Methodical evaluation of the impact of health information technology on outcomes is essential for institutions to make informed decisions regarding implementation.EMBASE, Scopus, Medline, the Cochrane Review database, and Web of Science were searched from database inception through July 2013. Manual review of references of identified articles was also completed.Selection criteria included a health information technology intervention such as computerized physician order entry, clinical decision support systems, and surveillance systems, an inpatient setting, and endpoints of mortality, length of stay, or cost. Studies were screened by three reviewers. Of the 2,803 studies screened, 45 met selection criteria (1.6%).Data were abstracted on the year, design, intervention type, system used, comparator, sample sizes, and effect on outcomes. Studies were abstracted independently by three reviewers.There was a significant effect of surveillance systems on in-hospital mortality (odds ratio, 0.85; 95% CI, 0.76-0.94; I=59%). All other quantitative analyses of health information technology interventions effect on mortality and length of stay were not statistically significant. Cost was unable to be quantitatively evaluated. Qualitative synthesis of studies of each outcome demonstrated significant study heterogeneity and small clinical effects.Electronic interventions were not shown to have a substantial effect on mortality, length of stay, or cost. This may be due to the small number of studies that were able to be aggregately analyzed due to the heterogeneity of study populations, interventions, and endpoints. Better evidence is needed to identify the most meaningful ways to implement and use health information technology and before a statement of the effect of these systems on patient outcomes can be made.

To improve the safety of ventilator care and decrease the risk of ventilator-induced lung injury, we designed and tested an electronic algorithm that incorporates patient characteristics and ventilator settings, allowing near-real-time notification of bedside providers about potentially injurious ventilator settings.Electronic medical records of consecutive patients who received invasive ventilation were screened in three Mayo Clinic Rochester intensive care units. The computer system alerted bedside providers via the text paging notification about potentially injurious ventilator settings. Alert criteria included a Pao2/Fio2 ratio of300 mm Hg, free text search for the words "edema" or "bilateral + infiltrates" on the chest radiograph report, a tidal volume of8 mL/kg predicted body weight (based on patient gender and height), a plateau pressure of30 cm H2O, and a peak airway pressure of35 cm H2O. Respiratory therapists answered a brief online satisfaction survey. Ventilator-induced lung injury risk was compared before and after the introduction of ventilator-induced lung injury alert.The prevalence of acute lung injury was 42% (n = 490) among 1,159 patients receiving24 hrs of invasive ventilation. The system sent 111 alerts for 80 patients, with a positive predictive value of 59%. The exposure to potentially injurious ventilation decreased after the intervention from 40.6 ± 74.6 hrs to 26.9 ± 77.3 hrs (p = .004).Electronic medical record surveillance of mechanically ventilated patients accurately detects potentially injurious ventilator settings and is able to influence bedside practice at moderate costs. Its implementation is associated with decreased patient exposure to potentially injurious mechanical ventilation settings.

To identify whether delays in rapid response team activation contributed to worse patient outcomes (mortality and morbidity).Retrospective observational cohort study including all rapid response team activations in 2012.Tertiary academic medical center.All those 18 years old or older who had a rapid response team call activated. Vital sign data were abstracted from individual patient electronic medical records for the 24 hours before the rapid response team activation took place. Patients were considered to have a delayed rapid response team activation if more than 1 hour passed between the first appearance in the record of an abnormal vital sign meeting rapid response team criteria and the activation of an rapid response team.None.A total of 1,725 patients were included in the analysis. Data were compared between those who had a delayed rapid response team activation and those who did not. Fifty seven percent patients met the definition of delayed rapid response team activation. Patients in high-frequency physiologic monitored environments were more likely to experience delay than their floor counterparts. In the no-delay group, the most common reasons for rapid response team activation were tachycardia/bradycardia at 29% (217/748), respiratory distress/low SpO2 at 28% (213/748), and altered level of consciousness at 23% (170/748) compared with respiratory distress/low SpO2 at 43% (423/977), tachycardia/bradycardia at 33% (327/977), and hypotension at 27% (261/977) in the delayed group. The group with no delay had a higher proportion of rapid response team calls between 8:00 and 16:00, whereas those with delay had a higher proportion of calls between midnight and 08:00. The delayed group had higher hospital mortality (15% vs 8%; adjusted odds ratio, 1.6; p = 0.005); 30-day mortality (20% vs 13%; adjusted odds ratio, 1.4; p = 0.02); and hospital length of stay (7 vs 6 d; relative prolongation, 1.10; p = 0.02) compared with the no-delay group.Delays in rapid response team activation occur frequently and are independently associated with worse patient mortality and morbidity outcomes.

Information overload in electronic medical records can impede providers' ability to identify important clinical data and may contribute to medical error. An understanding of the information requirements of ICU providers will facilitate the development of information systems that prioritize the presentation of high-value data and reduce information overload. Our objective was to determine the clinical information needs of ICU physicians, compared to the data available within an electronic medical record.Prospective observational study and retrospective chart review.Three ICUs (surgical, medical, and mixed) at an academic referral center.Newly admitted ICU patients and physicians (residents, fellows, and attending staff).The clinical information used by physicians during the initial diagnosis and treatment of admitted patients was captured using a questionnaire. Clinical information concepts were ranked according to the frequency of reported use (primary outcome) and were compared to information availability in the electronic medical record (secondary outcome). Nine hundred twenty-five of 1,277 study questionnaires (408 patients) were completed. Fifty-one clinical information concepts were identified as being useful during ICU admission. A median (interquartile range) of 11 concepts (6-16) was used by physicians per patient admission encounter with four used greater than 50% of the time. Over 25% of the clinical data available in the electronic medical record was never used, and only 33% was used greater than 50% of the time by admitting physicians.Physicians use a limited number of clinical information concepts at the time of patient admission to the ICU. The electronic medical record contains an abundance of unused data. Better electronic data management strategies are needed, including the priority display of frequently used clinical concepts within the electronic medical record, to improve the efficiency of ICU care.

The care of critically ill patients generates large quantities of data. Increasingly, these data are presented to the provider within an electronic medical record. The manner in which data are organized and presented can impact on the ability of users to synthesis that data into meaningful information. The objective of this study was to test the hypothesis that novel user interfaces, which prioritize the display of high-value data to providers within system-based packages, reduce task load, and result in fewer errors of cognition compared with established user interfaces that do not.Randomized crossover study.Academic tertiary referral center.Attending, resident and fellow critical care physicians.Novel health care record user interface.Subjects randomly assigned to either a standard electronic medical record or a novel user interface, were asked to perform a structured task. The task required the subjects to use the assigned electronic environment to review the medical record of an intensive care unit patient said to be actively bleeding for data that formed the basis of answers to clinical questions posed in the form of a structured questionnaire. The primary outcome was task load, measured using the paper version of the NASA-task load index. Secondary outcome measures included time to task completion, number of errors of cognition measured by comparison of subject to post hoc gold standard questionnaire responses, and the quantity of information presented to subjects by each environment.Twenty subjects completed the task on eight patients, resulting in 160 patient-provider encounters (80 in each group). The standard electronic medical record contained a much larger data volume with a median (interquartile range) number of data points per patient of 1008 (895-1183) compared with 102 (77-112) contained within the novel user interface. The median (interquartile range) NASA-task load index values were 38.8 (32-45) and 58 (45-65) for the novel user interface compared with the standard electronic medical record (p.001). The median (interquartile range) times in seconds taken to complete the task for four consecutive patients were 93 (57-132), 60 (48-71), 68 (48-80), and 54 (42-64) for the novel user interface compared with 145 (109-201), 125 (113-162), 129 (100-145), and 112 (92-123) for the standard interface (p.0001), respectively. The median (interquartile range) number of errors per provider was 0.5 (0-1) and two (0.25-3) for the novel user interface and standard electronic medical record interface, respectively (p = .007).A novel user interface was designed based on the information needs of intensive care unit providers with a specific goal of development being the reduction of task load and errors of cognition associated with filtering, extracting, and using medical data contained within a comprehensive electronic medical record. The results of this simulated clinical experiment suggest that the configuration of the intensive care unit user interface contributes significantly to the task load, time to task completion, and number of errors of cognition associated with the identification, and subsequent use, of relevant patient data. Task-specific user interfaces, developed from an understanding of provider information requirements, offer advantages over interfaces currently available within a standard electronic medical record.

Introduction: Point-of-care decision support is best presented in the form of checklists or algorithms containing brief prioritized information. While this approach has been widespread in operating room settings, it is lacking in other acute care environments. Assessing information needs at the poin