Diagnostic delay in pulmonary blastomycosis: a case series reflecting a referral center experience.

PURPOSE: The diagnosis of pulmonary blastomycosis is usually delayed because of its non-specific presentation. We aimed to assess the extent of diagnostic delay in hospitalized patients and detect the step in the diagnostic process that requires the most improvement. METHODS: Adult patients diagnosed with pulmonary blastomycosis during a hospital admission between January 2010 through November 2021 were eligible for inclusion. Patients who did not have pulmonary involvement and who were diagnosed before admission were excluded. Demographics and comorbid conditions, specifics of disease presentation, and interventions were evaluated. The timing of the diagnosis, antifungal treatment, and patient outcomes were noted. Descriptive analytical tests were performed. RESULTS: A total of 43 patients were diagnosed with pulmonary blastomycosis during their admissions. The median age was 47 years, with 13 (30%) females. Of all patients, 29 (67%) had isolated pulmonary infection, while 14 (33%) had disseminated disease, affecting mostly skin and musculoskeletal system. The median duration between the initial symptoms and health care encounters was 4 days, and the time to hospital admission was 9 days. The median duration from the initial symptoms to the diagnosis was 20 days. Forty patients (93%) were treated with empirical antibacterials before a definitive diagnosis was made. In addition, corticosteroid treatment was empirically administered to 15 patients (35%) before the diagnosis, with indications such as suspicion of inflammatory processes or symptom relief. In 38 patients (88%), the first performed fungal diagnostic test was positive. Nineteen patients (44%) required admission to the intensive care unit, and 11 patients (26%) died during their hospital stay. CONCLUSION: There was a delay in diagnosis of patients with pulmonary blastomycosis, largely attributable to the lack of consideration of the etiological agent. Novel approaches to assist providers in recognizing the illness earlier and trigger evaluation are needed.

The Impact of Health Information Technology for Early Detection of Patient Deterioration on Mortality and Length of Stay in the Hospital Acute Care Setting: Systematic Review and Meta-Analysis.

OBJECTIVE: 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. DATA SOURCES: We searched MEDLINE and Epub Ahead of Print, In-Process & Other 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. STUDY SELECTION: 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. DATA EXTRACTION: Study data were abstracted by two independent reviewers using a standardized data extraction form. DATA SYNTHESIS: 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. CONCLUSIONS: 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.

Implementation and evaluation of sepsis surveillance and decision support in medical ICU and emergency department.

OBJECTIVE: To improve the timely diagnosis and treatment of sepsis many institutions implemented automated sepsis alerts. Poor specificity, time delays, and a lack of actionable information lead to limited adoption by bedside clinicians and no change in practice or clinical outcomes. We aimed to compare sepsis care compliance before and after a multi-year implementation of a sepsis surveillance coupled with decision support in a tertiary care center. DESIGN: Single center before and after study. SETTING: Large academic Medical Intensive Care Unit (MICU) and Emergency Department (ED). POPULATION: Patients 18 years of age or older admitted to *** Hospital MICU and ED from 09/4/2011 to 05/01/2018 with severe sepsis or septic shock. INTERVENTIONS: Electronic medical record-based sepsis surveillance system augmented by clinical decision support and completion feedback. MEASUREMENTS AND MAIN RESULTS: There were 1950 patients admitted to the MICU with the diagnosis of severe sepsis or septic shock during the study period. The baseline characteristics were similar before (N = 854) and after (N = 1096) implementation of sepsis surveillance. The performance of the alert was modest with a sensitivity of 79.9%, specificity of 76.9%, positive predictive value (PPV) 27.9%, and negative predictive value (NPV) 97.2%. There were 3424 unique alerts and 1131 confirmed sepsis patients after the sniffer implementation. During the study period average care bundle compliance was higher; however after taking into account improvements in compliance leading up to the intervention, there was no association between intervention and improved care bundle compliance (Odds ratio: 1.16; 95% CI: 0.71 to 1.89; p-value 0.554). Similarly, the intervention was not associated with improvement in hospital mortality (Odds ratio: 1.55; 95% CI: 0.95 to 2.52; p-value: 0.078). CONCLUSIONS: A sepsis surveillance system incorporating decision support or completion feedback was not associated with improved sepsis care and patient outcomes.

Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach.

BACKGROUND: ICU operational conditions may contribute to cognitive overload and negatively impact on clinical decision making. We aimed to develop a quantitative model to investigate the association between the operational conditions and the quantity of medication orders as a measurable indicator of the multidisciplinary care team's cognitive capacity. METHODS: The temporal data of patients at one medical ICU (MICU) of Mayo Clinic in Rochester, MN between February 2016 to March 2018 was used. This dataset includes a total of 4822 unique patients admitted to the MICU and a total of 6240 MICU admissions. Guided by the Systems Engineering Initiative for Patient Safety model, quantifiable measures attainable from electronic medical records were identified and a conceptual framework of distributed cognition in ICU was developed. Univariate piecewise Poisson regression models were built to investigate the relationship between system-level workload indicators, including patient census and patient characteristics (severity of illness, new admission, and mortality risk) and the quantity of medication orders, as the output of the care team's decision making. RESULTS: Comparing the coefficients of different line segments obtained from the regression models using a generalized F-test, we identified that, when the ICU was more than 50% occupied (patient census > 18), the number of medication orders per patient per hour was significantly reduced (average = 0.74; standard deviation (SD) = 0.56 vs. average = 0.65; SD = 0.48; p < 0.001). The reduction was more pronounced (average = 0.81; SD = 0.59 vs. average = 0.63; SD = 0.47; p < 0.001), and the breakpoint shifted to a lower patient census (16 patients) when at a higher presence of severely-ill patients requiring invasive mechanical ventilation during their stay, which might be encountered in an ICU treating patients with COVID-19. CONCLUSIONS: Our model suggests that ICU operational factors, such as admission rates and patient severity of illness may impact the critical care team's cognitive function and result in changes in the production of medication orders. The results of this analysis heighten the importance of increasing situational awareness of the care team to detect and react to changing circumstances in the ICU that may contribute to cognitive overload.

A qualitative exploration of the discharge process and factors predisposing to readmissions to the intensive care unit.

BACKGROUND: Quantitative studies have demonstrated several factors predictive of readmissions to intensive care. Clinical decision tools, derived from these factors have failed to reduce readmission rates. The purpose of this study was to qualitatively explore the experiences and perceptions of physicians and nurses to gain more insight into intensive care readmissions. METHODS: Semi-structured interviews of intensive care unit (ICU) and general medicine care providers explored work routines, understanding and perceptions of the discharge process, and readmissions to intensive care. Participants included ten providers from the ICU setting, including nurses (n = 5), consultant intensivists (n = 2), critical care fellows (n = 3) and 9 providers from the general medical setting, nurses (n = 4), consulting physicians (n = 2) and senior resident physicians (n = 3). Principles of grounded theory were used to analyze the interview transcripts. RESULTS: Nine factors within four broad themes were identified: (1) patient factors - severity-of-illness and undefined goals of care; (2) process factors - communication, transitions of care; (3) provider factors - discharge decision-making, provider experience and comfort level; (4) organizational factors - resource constraints, institutional policies. CONCLUSIONS: Severe illness predisposes ICU patients to readmission, especially when goals of care were not adequately addressed. Communication, premature discharge, and other factors, mostly unrelated to the patient were also perceived by physicians and nurses to be associated with readmissions to intensive care. Quality improvement efforts that focus on modifying or improving aspects of non-patient factors may improve outcomes for patients at risk of ICU readmission.

Delayed Rapid Response Team Activation Is Associated With Increased Hospital Mortality, Morbidity, and Length of Stay in a Tertiary Care Institution.

OBJECTIVE: To identify whether delays in rapid response team activation contributed to worse patient outcomes (mortality and morbidity). DESIGN: Retrospective observational cohort study including all rapid response team activations in 2012. SETTING: Tertiary academic medical center. PATIENTS: 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. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: 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. CONCLUSIONS: Delays in rapid response team activation occur frequently and are independently associated with worse patient mortality and morbidity outcomes.

The Effect of an Electronic Checklist on Critical Care Provider Workload, Errors, and Performance.

PURPOSE: The strategy used to improve effective checklist use in intensive care unit (ICU) setting is essential for checklist success. This study aimed to test the hypothesis that an electronic checklist could reduce ICU provider workload, errors, and time to checklist completion, as compared to a paper checklist. METHODS: This was a simulation-based study conducted at an academic tertiary hospital. All participants completed checklists for 6 ICU patients: 3 using an electronic checklist and 3 using an identical paper checklist. In both scenarios, participants had full access to the existing electronic medical record system. The outcomes measured were workload (defined using the National Aeronautics and Space Association task load index [NASA-TLX]), the number of checklist errors, and time to checklist completion. Two independent clinician reviewers, blinded to participant results, served as the reference standard for checklist error calculation. RESULTS: Twenty-one ICU providers participated in this study. This resulted in the generation of 63 simulated electronic checklists and 63 simulated paper checklists. The median NASA-TLX score was 39 for the electronic checklist and 50 for the paper checklist (P = .005). The median number of checklist errors for the electronic checklist was 5, while the median number of checklist errors for the paper checklist was 8 (P = .003). The time to checklist completion was not significantly different between the 2 checklist formats (P = .76). CONCLUSION: The electronic checklist significantly reduced provider workload and errors without any measurable difference in the amount of time required for checklist completion. This demonstrates that electronic checklists are feasible and desirable in the ICU setting.

Testing modes of computerized sepsis alert notification delivery systems.

BACKGROUND: The number of electronic health record (EHR)-based notifications continues to rise. One common method to deliver urgent and emergent notifications (alerts) is paging. Despite of wide presence of smartphones, the use of these devices for secure alerting remains a relatively new phenomenon. METHODS: We compared three methods of alert delivery (pagers, EHR-based notifications, and smartphones) to determine the best method of urgent alerting in the intensive care unit (ICU) setting. ICU clinicians received randomized automated sepsis alerts: pager, EHR-based notification, or a personal smartphone/tablet device. Time to notification acknowledgement, fatigue measurement, and user preferences (structured survey) were studied. RESULTS: Twenty three clinicians participated over the course of 3 months. A total of 48 randomized sepsis alerts were generated for 46 unique patients. Although all alerts were acknowledged, the primary outcome was confounded by technical failure of alert delivery in the smartphone/tablet arm. Median time to acknowledgment of urgent alerts was shorter by pager (102 mins) than EHR (169 mins). Secondary outcomes of fatigue measurement and user preference did not demonstrate significant differences between these notification delivery study arms. CONCLUSIONS: Technical failure of secure smartphone/tablet alert delivery presents a barrier to testing the optimal method of urgent alert delivery in the ICU setting. Results from fatigue evaluation and user preferences for alert delivery methods were similar in all arms. Further investigation is thus necessary to understand human and technical barriers to implementation of commonplace modern technology in the hospital setting.

Information Needs Assessment for a Medicine Ward-Focused Rounding Dashboard.

To identify the routine information needs of inpatient clinicians on the general wards for the development of an electronic dashboard. Survey of internal medicine and subspecialty clinicians from March 2014-July 2014 at Saint Marys Hospital in Rochester, Minnesota. An information needs assessment was generated from all unique data elements extracted from all handoff and rounding tools used by clinicians in our ICUs and general wards. An electronic survey was distributed to 104 inpatient medical providers. 89 unique data elements were identified from currently utilized handoff and rounding instruments. All data elements were present in our multipurpose ICU-based dashboard. 42 of 104 (40 %) surveys were returned. Data elements important (50/89, 56 %) and unimportant (24/89, 27 %) for routine use were identified. No significant differences in data element ranking were observed between supervisory and nonsupervisory roles. The routine information needs of general ward clinicians are a subset of data elements used routinely by ICU clinicians. Our findings suggest an electronic dashboard could be adapted from the critical care setting to the general wards with minimal modification.

Impact of the Electronic Medical Record on Mortality, Length of Stay, and Cost in the Hospital and ICU: A Systematic Review and Metaanalysis.

OBJECTIVE: 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. DATA SOURCES: 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. STUDY SELECTION: 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 EXTRACTION: 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. DATA SYNTHESIS: 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. CONCLUSIONS: 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.

Protocols and Hospital Mortality in Critically Ill Patients: The United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study.

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.

Clinical data needs in the neonatal intensive care unit electronic medical record.

BACKGROUND: The amount of clinical information that providers encounter daily creates an environment for information overload and medical error. To create a more efficient EMR human-computer interface, we aimed to understand clinical information needs among NICU providers. METHODS: A web-based survey to evaluate 98 data items was created and distributed to NICU providers. Participants were asked to rate the importance of each data item in helping them make routine clinical decisions in the NICU. RESULTS: There were 23 responses (92% - response rate) with participants distributed among four clinical roles. The top 5 items with the highest mean score were daily weight, pH, pCO2, FiO2, and blood culture results. When compared by clinical role groupings, supervisory physicians gave individual data item ratings at the extremes of the scale when compared to providers more responsible for the daily clinical care of NICU patients. CONCLUSION: NICU providers demonstrate a need for large amounts of EMR data to help guide clinical decision making with differences found when comparing by clinical role. When creating an EMR interface in the NICU there may be a need to offer options for varying degrees of viewable data densities depending on clinical role.

Derivation and validation of a search algorithm to retrospectively identify mechanical ventilation initiation in the intensive care unit.

BACKGROUND: The development and validation of automated electronic medical record (EMR) search strategies are important for establishing the timing of mechanical ventilation initiation in the intensive care unit (ICU).Thus, we sought to develop and validate an automated EMR search algorithm (strategy) for time zero, the moment of mechanical ventilation initiation in the critically ill patient. METHODS: The EMR search algorithm was developed on the basis of several mechanical ventilation parameters, with the final parameter being positive end-expiratory pressure (PEEP), and was applied to a comprehensive institutional EMR database. The search algorithm was derived from a secondary retrospective analysis of a subset of 450 patients from a cohort of 2,684 patients admitted to a medical ICU and a surgical ICU from January 1, 2010, through December 31, 2011. It was then validated in an independent subset of 450 patients from the same period. The overall percent of agreement between our search algorithm and a comprehensive manual medical record review in the derivation and validation subsets, using peak inspiratory pressure (PIP) as the reference standard, was compared to assess timing of mechanical ventilation initiation. RESULTS: In the derivation subset, the automated electronic search strategy achieved an 87% (κ = 0.87) perfect agreement, with 94% agreement to within one minute. In validating this search algorithm, perfect agreement was found in 92% (κ = 0.92) of patients, with 99% agreement occurring within one minute. CONCLUSIONS: The use of an electronic search strategy resulted in highly accurate extraction of mechanical ventilation initiation in the ICU. The search algorithm of mechanical ventilation initiation is highly efficient and reliable and can facilitate both clinical research and patient care management in a timely manner.

Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives.

OBJECTIVES: Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters. MATERIALS AND METHODS: This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software. RESULTS: We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply-demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias. DISCUSSION: This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers. CONCLUSION: Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.

Impact of SARS-CoV-2 Vaccine Rollout on Hispanic and Non-Hispanic Admission and Mortality Trends: An Interrupted Time Series Analysis.

BACKGROUND: Challenges with SARS-CoV-2 vaccine prioritization, access, and hesitancy have influenced vaccination uptake. RESEARCH QUESTION: Was the impact of SARS-CoV-2 vaccine rollout on COVID-19 monthly admission and mortality trends different between Hispanic and non-Hispanic populations? STUDY DESIGN AND METHODS: We used interrupted time series analysis to conduct an ancillary study of the Viral Infection and Respiratory Illness Universal Study registry supplemented by electronic health record data from five participating Mayo Clinic sites in Florida, Arizona, Minnesota, and Wisconsin. We included hospitalized patients with COVID-19 admitted between April 2020 and December 2021. Our primary outcome was the impact of vaccine rollout on admission trends. Our secondary outcome was the impact of vaccine rollout on mortality trends. RESULTS: This interrupted time series analysis includes 6,442 patients. Vaccine rollout was associated with improved monthly hospital admission trends among both Hispanic and non-Hispanic patients. Among Hispanic patients, pre-vaccine rollout, monthly admissions increased by 12.9% (95% CI, 8.1%-17.9%). Immediately after vaccine rollout, patient admissions declined by -66.3% (95% CI, -75.6% to -53.9%). Post-vaccine rollout, monthly admissions increased by 3.7% (95% CI, 0.2%-7.3%). Among non-Hispanic patients, pre-vaccine rollout, monthly admissions increased by 35.8% (95% CI, 33.4%-38.1%). Immediately after vaccine rollout, patient admissions declined by -75.2% (95% CI, -77.6% to -72.7%). Post-vaccine rollout, monthly admissions increased by 5.6% (95% CI, 4.5%-6.7%). These pre-vaccine rollout admission trends were significantly different (P < .001). Post-vaccine rollout, the change in admission trend was significantly different (P < .001). The associated beneficial impact from vaccine rollout on monthly hospital admission trends among Hispanic patients was significantly lower. The trend in monthly mortality rate was fourfold greater (worse) among Hispanic patients (8.3%; 95% CI, 3.6%-13.4%) vs non-Hispanic patients (2.2%; 95% CI, 0.6%-3.8%), but this was not shown to be related to vaccine rollout. INTERPRETATION: SARS-CoV-2 vaccine rollout was associated with improved COVID-19 admission trends among non-Hispanic vs Hispanic patients. Vaccine rollout was not shown to influence mortality trends in either group, which were four times higher among Hispanic patients. Improved vaccine rollout may have reduced disparities in admission trends for Hispanic patients, but other factors influenced their mortality trends.

Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.

OBJECTIVE: This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs. MATERIALS AND METHODS: A diagnostic performance study was conducted using Chest X-Ray images from adult patients admitted to a medical intensive care unit between January 2003 and November 2014. X-ray images from 15,899 patients were assigned one of three prespecified categories: "ARDS", "Pneumonia", or "Normal". RESULTS: A two-step convolutional neural network (CNN) pipeline was developed and tested to distinguish between the three patterns with sensitivity ranging from 91.8% to 97.8% and specificity ranging from 96.6% to 98.8%. The CNN model was validated with a sensitivity of 96.3% and specificity of 96.6% using a previous dataset of patients with Acute Lung Injury (ALI)/ARDS. DISCUSSION: The results suggest that a deep learning model based on chest x-ray pattern recognition can be a useful tool in distinguishing patients with ARDS from patients with normal lungs, providing faster results than digital surveillance tools based on text reports. CONCLUSION: A CNN-based deep learning model showed clinically significant performance, providing potential for faster ARDS identification. Future research should prospectively evaluate these tools in a clinical setting.

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Data utilization for medical decision making at the time of patient admission to ICU.

OBJECTIVES: 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. DESIGN: Prospective observational study and retrospective chart review. SETTING: Three ICUs (surgical, medical, and mixed) at an academic referral center. SUBJECTS: Newly admitted ICU patients and physicians (residents, fellows, and attending staff). MEASUREMENTS AND MAIN RESULTS: 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. CONCLUSIONS: 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.

Connecting the dots: rule-based decision support systems in the modern EMR era.

The intensive care unit (ICU) environment is rich in both medical device and electronic medical record (EMR) data. The ICU patient population is particularly vulnerable to medical error or delayed medical intervention both of which are associated with excess morbidity, mortality and cost. The development and deployment of smart alarms, computerized decision support systems (DSS) and "sniffers" within ICU clinical information systems has the potential to improve the safety and outcomes of critically ill hospitalized patients. However, the current generations of alerts, run largely through bedside monitors, are far from ideal and rarely support the clinician in the early recognition of complex physiologic syndromes or deviations from expected care pathways. False alerts and alert fatigue remain prevalent. In the coming era of widespread EMR implementation novel medical informatics methods may be adaptable to the development of next generation, rule-based DSS.