A national system for monitoring intensive care unit demand and capacity: the Critical Health Resources Information System (CHRIS).
Ontology highlight
ABSTRACT:
SUBMITTER: Pilcher D
PROVIDER: S-EPMC8252483 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BACKGROUND:Strained intensive care unit (ICU) capacity represents a supply-demand mismatch in ICU care. Limited data have explored health care worker (HCW) perceptions of strain. METHODS:Cross-sectional survey of HCW across 16 Alberta ICUs. A web-based questionnaire captured data on demographics, strain definition, and sources, impact and strategies for management. RESULTS:658 HCW responded (33%; 95%CI, 32-36%), of which 452 were nurses (69%), 128 allied health (19%), 45 physicians (7%) and 33 administrators (5%). Participants (agreed/strongly agreed: 94%) reported that strain was best defined as "a time-varying imbalance between the supply of available beds, staff and/or resources and the demand to provide high-quality care for patients who may become or who are critically ill"; while some recommended defining "high-quality care", integrating "safety", and families in the definition. Participants reported significant contributors to strain were: "inability to discharge ICU patients due to lack of available ward beds" (97%); "increases in the volume" (89%); and "acuity and complexity of patients requiring ICU support" (88%). Strain was perceived to "increase stress levels in health care providers" (98%); and "burnout in health care providers" (96%). The highest ranked strategies were: "have more consistent and better goals-of-care conversations with patients/families outside of ICU" (95%); and "increase non-acute care beds" (92%). INTERPRETATION:Strain is perceived as common. HCW believe precipitants represent a mix of patient-related and operational factors. Strain is thought to have negative implications for quality of care, HCW well-being and workplace environment. Most indicated strategies "outside" of ICU settings were priorities for managing strain.
| S-EPMC6104911 | biostudies-literature
Project description:PURPOSE:To externally validate an intensive care unit (ICU) mortality prediction model that was created using the Ontario Critical Care Information System (CCIS), which includes the Multiple Organ Dysfunction Score (MODS). METHODS:We applied the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) recommendations to a prospective longitudinal cohort of patients discharged between 1 July 2015 and 31 December 31 2016 from 90 adult level-3 critical care units in Ontario. We used multivariable logistic regression with measures of discrimination, calibration-in-the-large, calibration slope, and flexible calibration plots to compare prediction model performance of the entire data set and for each ICU subtype. RESULTS:Among 121,201 CCIS records with ICU mortality of 11.3%, the C-statistic for the validation data set was 0.805. The C-statistic ranged from 0.775 to 0.846 among the ICU subtypes. After intercept recalibration to adjust the baseline risk, the mean predicted risk of death matched actual ICU mortality. The calibration slope was close to 1 with all CCIS data and ICU subtypes of cardiovascular and community hospitals with low ventilation rates. Calibration slopes significantly less than 1 were found for ICUs in teaching hospitals and community hospitals with high ventilation rates whereas coronary care units had a calibration slope significantly higher than 1. Calibration plots revealed over-prediction in high risk groups to a varying degree across all cohorts. CONCLUSIONS:A risk prediction model primarily based on the MODS shows reproducibility and transportability after intercept recalibration. Risk adjusting models that use existing and feasible data collection can support performance measurement at the individual ICU level.
| S-EPMC7223438 | biostudies-literature
Project description:BackgroundHealth workforce planning is important in ensuring that the recruitment, training and deployment of health workers are conducted in the most efficient way possible. However, in many developing countries, human resources for health data are limited, inconsistent, out-dated, or unavailable. Consequently, policy-makers are unable to use reliable data to make informed decisions about the health workforce. Computerized human resources information systems (HRIS) enable countries to collect, maintain, and analyze health workforce data.MethodsThe purpose of this article is twofold. First, we describe Uganda's transition from a paper filing system to an electronic HRIS capable of providing information about country-specific health workforce questions. We examine the ongoing five-step HRIS strengthening process used to implement an HRIS that tracks health worker data at the Uganda Nurses and Midwives Council (UNMC). Secondly, we describe how HRIS data can be used to address workforce planning questions via an initial analysis of the UNMC training, licensure and registration records from 1970 through May 2009.ResultsThe data indicate that, for the 25 482 nurses and midwives who entered training before 2006, 72% graduated, 66% obtained a council registration, and 28% obtained a license to practice. Of the 17 405 nurses and midwives who obtained a council registration as of May 2009, 96% are of Ugandan nationality and just 3% received their training outside of the country. Thirteen per cent obtained a registration for more than one type of training. Most (34%) trainings with a council registration are for the enrolled nurse training, followed by enrolled midwife (25%), registered (more advanced) nurse (21%), registered midwife (11%), and more specialized trainings (9%).ConclusionThe UNMC database is valuable in monitoring and reviewing information about nurses and midwives. However, information obtained from this system is also important in improving strategic planning for the greater health care system in Uganda. We hope that the use of a real-world example of HRIS strengthening provides guidance for the implementation of similar projects in other countries or contexts.
| S-EPMC3049180 | biostudies-literature
Project description:Strained intensive care unit (ICU) capacity represents a fundamental supply-demand mismatch in ICU resources. Strain is likely to be influenced by a range of factors; however, there has been no systematic evaluation of the spectrum of measures that may indicate strain on ICU capacity.We performed a systematic review to identify indicators of strained capacity. A comprehensive peer-reviewed search of MEDLINE, EMBASE, CINAHL, Cochrane Library, and Web of Science Core Collection was performed along with selected grey literature sources. We included studies published in English after 1990. We included studies that: (1) focused on ICU settings; (2) included description of a quality or performance measure; and (3) described strained capacity. Retrieved studies were screened, selected and extracted in duplicate. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Analysis was descriptive.Of 5297 studies identified in our search; 51 fulfilled eligibility. Most were cohort studies (n = 39; 76.5%), five (9.8%) were case-control, three (5.8%) were cross-sectional, two (3.9%) were modeling studies, one (2%) was a correlational study, and one (2%) was a quality improvement project. Most observational studies were high quality. Sixteen measures designed to indicate strain were identified 110 times, and classified as structure (n = 4, 25%), process (n = 7, 44%) and outcome (n = 5, 31%) indicators, respectively. The most commonly identified indicators of strain were ICU acuity (n = 21; 19.1% [process]), ICU readmission (n = 18; 16.4% [outcome]), after-hours discharge (n = 15; 13.6% [process]) and ICU census (n = 13; 11.8% [structure]). There was substantial heterogeneity in the operational definitions used to define strain indicators across studies.We identified and characterized 16 indicators of strained ICU capacity across the spectrum of healthcare quality domains. Future work should aim to evaluate their implementation into practice and assess their value for evaluating strategies to mitigate strain.This systematic review was registered at PROSPERO (March 27, 2015; CRD42015017931 ).
| S-EPMC5870068 | biostudies-other
Project description:PurposeAccess to critical care is a crucial component of healthcare systems. In low-income countries, the burden of critical illness is substantial, but the capacity to provide care for critically ill patients in intensive care units (ICUs) is unknown. Our aim was to systematically review the published literature to estimate the current ICU capacity in low-income countries.MethodsWe searched 11 databases and included studies of any design, published 2004-August 2014, with data on ICU capacity for pediatric and adult patients in 36 low-income countries (as defined by World Bank criteria; population 850 million). Neonatal, temporary, and military ICUs were excluded. We extracted data on ICU bed numbers, capacity for mechanical ventilation, and information about the hospital, including referral population size, public accessibility, and the source of funding. Analyses were descriptive.ResultsOf 1,759 citations, 43 studies from 15 low-income countries met inclusion criteria. They described 36 individual ICUs in 31 cities, of which 16 had population greater than 500,000, and 14 were capital cities. The median annual ICU admission rate was 401 (IQR 234-711; 24 ICUs with data) and median ICU size was 8 beds (IQR 5-10; 32 ICUs with data). The mean ratio of adult and pediatric ICU beds to hospital beds was 1.5% (SD 0.9%; 15 hospitals with data). Nepal and Uganda, the only countries with national ICU bed data, had 16.7 and 1.0 ICU beds per million population, respectively. National data from other countries were not available.ConclusionsLow-income countries lack ICU beds, and more than 50% of these countries lack any published data on ICU capacity. Most ICUs in low-income countries are located in large referral hospitals in cities. A central database of ICU resources is required to evaluate health system performance, both within and between countries, and may help to develop related health policy.
| S-EPMC4305307 | biostudies-literature
Project description:Background:Analgosedation is a cornerstone therapy for mechanically ventilated patients in intensive care units (ICU). To avoid inadequate sedation and its complications, monitoring of analgosedation is of great importance. The aim of this study was to investigate whether monitoring of analgosedative drug concentrations (midazolam and sufentanil) might be beneficial to optimize analgosedation and whether drug serum concentrations correlate with the results of subjective (Richmond Agitation-Sedation Scale [RASS]/Ramsay Sedation Scale) and objective (bispectral (BIS) index) monitoring procedures. Methods:Forty-nine intubated, ventilated, and analgosedated critically ill patients treated in ICU were clinically evaluated concerning the depth of sedation using RASS Score, Ramsay Score, and BIS index twice a day. Serum concentrations of midazolam and sufentanil were determined in blood samples drawn at the same time. Clinical and laboratory data were statistically analyzed for correlations using the Spearman's rank correlation coefficient rho (?). Results:Average age of the population was 57.8?±?16.0 years, 61% of the patients were males. Most frequent causes for ICU treatments were sepsis (22%), pneumonia (22%), or a combination of both (25%). Serum concentrations of midazolam correlated weakly with RASS (??=?-?0.467) and Ramsay Scores (??=?0.476). Serum concentrations of sufentanil correlated weakly with RASS (??=?-?0.312) and Ramsay Scores (??=?0.295). Correlations between BIS index and serum concentrations of midazolam (??=?-?0.252) and sufentanil (??=?-?0.166) were low. Conclusion:Correlations between drug serum concentrations and clinical or neurophysiological monitoring procedures were weak. This might be due to intersubject variability, polypharmacy with drug-drug interactions, and complex metabolism, which can be altered in critically ill patients. Therapeutic drug monitoring is not beneficial to determine depth of sedation in ICU patients.
| S-EPMC6137863 | biostudies-other
Project description:ObjectiveTo measure the association of intensive care unit (ICU) capacity strain with processes of care and outcomes of critical illness in a resource-limited setting.MethodsWe performed a retrospective cohort study of 5332 patients referred to the ICUs at 2 public hospitals in South Africa using the country's first published multicenter electronic critical care database. We assessed the association between multiple ICU capacity strain metrics (ICU occupancy, turnover, census acuity, and referral burden) at different exposure time points (ICU referral, admission, and/or discharge) with clinical and process of care outcomes. The association of ICU capacity strain at the time of ICU admission with ICU length of stay (LOS), the primary outcome, was analyzed with a multivariable Cox proportional hazard model. Secondary outcomes of ICU triage decision (with strain at ICU referral), ICU mortality (with strain at ICU admission), and ICU LOS (with strain at ICU discharge), were analyzed with linear and logistic multivariable regression.ResultsNo measure of ICU capacity strain at the time of ICU admission was associated with ICU LOS, the primary outcome. The ICU occupancy at the time of ICU admission was associated with increased odds of ICU mortality (odds ratio = 1.07, 95% confidence interval: 1.02-1.11; P = .004), a secondary outcome, such that a 10% increase in ICU occupancy would be associated with a 7% increase in the odds of ICU mortality.ConclusionsIn a resource-limited setting in South Africa, ICU capacity strain at the time of ICU admission was not associated with ICU LOS. In secondary analyses, higher ICU occupancy at the time of ICU admission, but not other measures of capacity strain, was associated with increased odds of ICU mortality.
| S-EPMC6548699 | biostudies-literature
Project description:INTRODUCTION:Patients admitted to a critical care medicine (CCM) environment, including an intensive care unit (ICU), are susceptible to harm and significant resource utilisation. Therefore, a strategy to optimise provider performance is required. Performance scorecards are used by institutions for the purposes of driving quality improvement. There is no widely accepted or standardised scorecard that has been used for overall CCM performance. We aim to improve quality of care, patient safety and patient/family experience in CCM practice through the utilisation of a standardised, repeatable and multidimensional performance scorecard, designed to provide a continuous review of ICU physician and nurse practice, as well as departmental metrics. METHODS AND ANALYSIS:This will be a mixed-methods, controlled before and after study to assess the impact of a CCM-specific quality scorecard. Scorecard metrics were developed through expert consensus and existing literature. The study will include 19 attending CCM physicians and approximately 300 CCM nurses. Patient data for scorecard compilation are collected daily from bedside flow sheets. Preintervention baseline data will be collected for 6 months for each participant. After this, each participant will receive their scorecard measures. Following a 3-month washout period, postintervention data will be collected for 6 months. The primary outcome will be change in performance metrics following the provision of scorecard feedback to subjects. A cost analysis will also be performed, with the purpose of comparing total ICU costs prior to implementation of the scorecard with total ICU costs following implementation of the scorecard. The qualitative portion will include interviews with participants following the intervention phase. Interviews will be analysed in order to identify recurrent themes and subthemes, for the purposes of driving scorecard improvement. ETHICS AND DISSEMINATION:This protocol has been approved by the local research ethics board. Publication of results is anticipated in 2019. If this intervention is found to improve patient- and unit-directed outcomes, with evidence of cost-effectiveness, it would support the utilisation of such a scorecard as a quality standard in CCM.
| S-EPMC5781100 | biostudies-literature
Project description:BACKGROUND:Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development. OBJECTIVE:This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups. METHODS:This survey study was performed with ICU staff from 4 ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire, by analyzing a preceding qualitative interview study with ICU staff, about the clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and chi-square tests to compare the distributions of item responses of the professional groups. RESULTS:In total, 86 of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated they felt confident using the patient monitoring equipment, but that high rates of false-positive alarms and the many sensor cables interrupted patient care. Regarding future improvements, respondents asked for wireless sensors, a reduction in the number of false-positive alarms, and hospital standard operating procedures for alarm management. Responses to the display devices proposed for remote patient monitoring were divided. Most respondents indicated it would be useful for earlier alerting or when they were responsible for multiple wards. AI for ICUs would be useful for early detection of complications and an increased risk of mortality; in addition, the AI could propose guidelines for therapy and diagnostics. Transparency, interoperability, usability, and staff training were essential to promote the use of AI. The majority wanted to learn more about new technologies for the ICU and required more time for learning. Physicians had fewer reservations than nurses about AI-based intelligent alarm management and using mobile phones for remote monitoring. CONCLUSIONS:This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine. TRIAL REGISTRATION:ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173.
| S-EPMC7307326 | biostudies-literature