BACKGROUND: Clinical errors in intensive care units (ICUs) are consistently attributed to communication errors. Despite its importance for patient safety and quality in critical care settings, few studies consider interprofessional communication as more than the basic exchange of information. METHODS: We conducted a scoping review of interprofessional communication in ICUs to (1) characterize how communication is defined and measured and (2) identify contributions the field of health communication can make to team communication in ICUs. Through a series of queries in PubMed and Communication and Mass Media Complete databases, we identified and compared persistent gaps in how communication is framed and theorized in 28 publications from health services and 6 from social science outlets. We identified research priorities and suggested strategies for discussing communication more holistically in future health services research. RESULTS: 34 articles published from 1999 to 2021 were included. Six explicitly defined communication. Six were published in social science journals, but none were authored by a communication studies scholar. Half of the articles addressed communication as a transaction focused on information transfer, and the other half addressed communication as a process. CONCLUSIONS: Methodological implications are identified with the intent to encourage future interdisciplinary collaboration for studying communication in ICUs. We discuss the importance of (1) using language to describe communication that facilitates interdisciplinary engagement, (2) prioritizing communication as a process and using qualitative methods to provide insight, and (3) engaging health communication theories and experts to assist in developing more fruitful research questions and designs.

BACKGROUND: Successful implementation and utilization of Computerized Clinical Decision Support Systems (CDSS) in hospitals is complex and challenging. Implementation science, and in particular the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework, may offer a systematic approach for identifying and addressing these challenges. This review aimed to identify, categorize, and describe barriers and facilitators to CDSS implementation in hospital settings and map them to the NASSS framework. Exploring the applicability of the NASSS framework to CDSS implementation was a secondary aim. METHODS: Electronic database searches were conducted (21 July 2020; updated 5 April 2022) in Ovid MEDLINE, Embase, Scopus, PyscInfo, and CINAHL. Original research studies reporting on measured or perceived barriers and/or facilitators to implementation and adoption of CDSS in hospital settings, or attitudes of healthcare professionals towards CDSS were included. Articles with a primary focus on CDSS development were excluded. No language or date restrictions were applied. We used qualitative content analysis to identify determinants and organize them into higher-order themes, which were then reflexively mapped to the NASSS framework. RESULTS: Forty-four publications were included. These comprised a range of study designs, geographic locations, participants, technology types, CDSS functions, and clinical contexts of implementation. A total of 227 individual barriers and 130 individual facilitators were identified across the included studies. The most commonly reported influences on implementation were fit of CDSS with workflows (19 studies), the usefulness of the CDSS output in practice (17 studies), CDSS technical dependencies and design (16 studies), trust of users in the CDSS input data and evidence base (15 studies), and the contextual fit of the CDSS with the user's role or clinical setting (14 studies). Most determinants could be appropriately categorized into domains of the NASSS framework with barriers and facilitators in the "Technology," "Organization," and "Adopters" domains most frequently reported. No determinants were assigned to the "Embedding and Adaptation Over Time" domain. CONCLUSIONS: This review identified the most common determinants which could be targeted for modification to either remove barriers or facilitate the adoption and use of CDSS within hospitals. Greater adoption of implementation theory should be encouraged to support CDSS implementation.

BACKGROUND: Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients. OBJECTIVE: This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis. METHODS: A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included. RESULTS: The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development. CONCLUSIONS: There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods.

BACKGROUND: Over 50% of annual deaths in low-income and middle-income countries (LMICs) could be averted through access to high-quality emergency care. OBJECTIVES: We performed a scoping review of the literature that described at least one measure of emergency care access in LMICs in order to understand relevant barriers to emergency care systems. ELIGIBILITY CRITERIA: English language studies published between 1 January 1990 and 30 December 2020, with one or more discrete measure(s) of access to emergency health services in LMICs described. SOURCE OF EVIDENCE: PubMed, Embase, Web of Science, CINAHL and the grey literature. CHARTING METHODS: A structured data extraction tool was used to identify and classify the number of 'unique' measures, and the number of times each unique measure was studied in the literature ('total' measures). Measures of access were categorised by access type, defined by Thomas and Penchansky, with further categorisation according to the 'Three Delay' model of seeking, reaching and receiving care, and the WHO's Emergency Care Systems Framework (ECSF). RESULTS: A total of 3103 articles were screened. 75 met full study inclusion. Articles were uniformly descriptive (n=75, 100%). 137 discrete measures of access were reported. Unique measures of accommodation (n=42, 30.7%) and availability (n=40, 29.2%) were most common. Measures of seeking, reaching and receiving care were 22 (16.0%), 46 (33.6%) and 69 (50.4%), respectively. According to the ECSF slightly more measures focused on prehospital care-inclusive of care at the scene and through transport to a facility (n=76, 55.4%) as compared with facility-based care (n=57, 41.6%). CONCLUSIONS: Numerous measures of emergency care access are described in the literature, but many measures are overaddressed. Development of a core set of access measures with associated minimum standards are necessary to aid in ensuring universal access to high-quality emergency care in all settings.

BACKGROUND: In an effort to improve both quality of care and cost-effectiveness, various care-management programmes have been developed for high-need high-cost (HNHC) patients. Early identification of patients at risk of becoming HNHC (i.e. case finding) is crucial to a programme's success. We aim to systematically identify prediction models predicting future HNHC healthcare use in adults, to describe their predictive performance and to assess their applicability. METHODS: Ovid MEDLINE® All, EMBASE, CINAHL, Web of Science and Google Scholar were systematically searched from inception through January 31, 2021. Risk of bias and methodological quality assessment was performed through the Prediction model Risk Of Bias Assessment Tool (PROBAST). RESULTS: Of 5890 studies, 60 studies met inclusion criteria. Within these studies, 313 unique models were presented using a median development cohort size of 20,248 patients (IQR 5601-174,242). Predictors were derived from a combination of data sources, most often claims data (n = 37; 62%) and patient survey data (n = 29; 48%). Most studies (n = 36; 60%) estimated patients' risk to become part of some top percentage of the cost distribution (top-1-20%) within a mean time horizon of 16 months (range 12-60). Five studies (8%) predicted HNHC persistence over multiple years. Model validation was performed in 45 studies (76%). Model performance in terms of both calibration and discrimination was reported in 14 studies (23%). Overall risk of bias was rated as 'high' in 40 studies (67%), mostly due to a 'high' risk of bias in the subdomain 'Analysis' (n = 37; 62%). DISCUSSION: This is the first systematic review (PROSPERO CRD42020164734) of non-proprietary prognostic models predicting HNHC healthcare use. Meta-analysis was not possible due to heterogeneity. Most identified models estimated a patient's risk to incur high healthcare expenditure during the subsequent year. However, case-finding strategies for HNHC care-management programmes are best informed by a model predicting HNHC persistence. Therefore, future studies should not only focus on validating and extending existing models, but also concentrate on clinical usefulness.

OBJECTIVES: Machine learning (ML) has been increasingly used in clinical medicine including studies focused on Clostridioides difficile infection (CDI) to inform to clinical decision making. We aimed to summarize ML choices in studies that used ML to predict CDI or CDI outcomes. METHODS: We searched Ovid MEDLINE, Ovid EMBASE, Web of Science, medRxiv, bioRxiv and arXiv from inception to March 18, 2021. We included fully published studies that used ML where CDI constituted the study population, exposure or outcome. Two reviewers independently identified studies and abstracted outcomes. We summarized study characteristics and approaches to CDI definition and ML-specific modelling. RESULTS: Forty-three studies of prediction (n = 21), classification (n = 17) or inference (n = 5) were included. Approaches to defining CDI were labelling during a clinical study or chart review (n = 21), electronic phenotyping (n = 13) or not specified (n = 9). None of the studies using an electronic phenotype described phenotype validation. Almost all studies (n = 41, 95%) conducted supervised ML and the most common ML algorithms were penalized logistic regression (n = 20, 47%) and classification tree (n = 17, 40%). Approaches to feature selection and dimension reduction were heterogeneous. Metrics were evaluated in a held-out test set in 16 (37%) studies; only seven used a time-based split. In terms of reporting quality assessment, the most poorly reported items were data leakage prevention (n = 0, 0%), code availability (n = 8, 19%) and class imbalance management (n = 12, 43%). CONCLUSIONS: While many studies have used ML to investigate CDI or CDI outcomes, electronic phenotyping of CDI was uncommon and phenotype validation was not reported in any study. Methodological approaches were heterogeneous. Validating CDI electronic phenotypes, evaluating performances of CDI models during a silent trial and deploying a CDI classifier to guide clinical practice are important future goals.

Delirium is one of the common manifestations of acute brain dysfunction in critically ill patients. We aimed to evaluate the effect of family intervention on reducing the delirium incidence in patients hospitalized in the intensive care unit (ICU). We searched electronic databases for randomized clinical trials, cohort, and before-and-after studies up to September 2021 using the MeSH terms ("family" OR "family caregiver") AND ("delirium"). A total of 6 studies including 4199 patients were analyzed. Compared to the control group, the risk of delirium was 24% lower in the family intervention group (OR 0·76 [0·67-0·86], P = 0.20, I2 = 31%). Pooled data from two trials showed that family intervention was associated with fewer delirium days (SMD: -1.13, 95% CI: -1.91 to -0.34; P = 0.08; I2 = 67%;). However, there were no significant differences between the two groups in the length of ICU stay, mechanical ventilation duration, and mortality (ICU stay days: MD: -0.62 days; 95% CI: -1.49 to 0.24; P = 0.14; I2 = 72%; mechanical ventilation days: MD: -0.48 days; 95% CI: -2.10 to 1.13; P = 0.56; I2 = 0%; mortality: OR: 0.68, 95% CI: 0.22 to 2.09; P = 0.08; I2 = 67%). Current evidence supports the use of family intervention in reducing the delirium risk and delirium days in hospitalized ICU patients. However, its effects on reducing ICU stay length, ventilation duration, and mortality require further study. Future research should consider identifying the specific family intervention strategies and their duration.

Parental presence at the bedside (PPB) of critically ill children in the pediatric intensive care unit (PICU) is necessary for operationalizing family-centred care. Previous evidence syntheses emphasize parent-healthcare provider interactions at rounds and resuscitation; our focus is the parent–child dyad. Prior to embarking on further study, we performed a scoping review to determine the breadth and scope of the literature addressing PPB of critically ill children in the PICU. We searched five online databases (MEDLINE, EMBASE, CINAHL, Cochrane Library, and PSYCHINFO) and the grey literature to identify English and French reports from January 1960 to June 2020 addressing physical parental presence with children (birth to 18 years) in intensive care units, without limitation by methodology. Screening, reference selection, and data extraction were performed by two independent reviewers. Data were extracted into a researcher-designed tool. We identified 204 publications (81 quantitative, 68 qualitative, 22 mixed methods, and 9 descriptive case or practice change studies, and a further 24 non-study reports). PPB was directly assessed in 78 (38%) reports, and was the primary objective in 64 (31%). Amount or quality of presence was addressed by 114 reports, barriers and enablers by 152 sources, and impacts and outcomes by 134 sources. While only 6 reports were published in the first two decades of our search (1960–1980), 17 reports were published in 2019 alone. CONCLUSIONS: A relatively large body of literature exists addressing PPB of critically ill children. Separate systematic evidence syntheses to assess each element of PPB are warranted.

OBJECTIVE: To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis. MATERIALS AND METHODS: PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted. RESULTS: The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies. DISCUSSION: Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units. CONCLUSIONS: Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.

BACKGROUND: Emergency telehealth has been used to improve access of patients residing in rural and remote areas to specialist care in the hope of mitigating the significant health disparities that they experience. Patient disposition decisions in rural and remote emergency departments (EDs) can be complex and largely dependent on the expertise and experience available at local (receiving-end) hospitals. Although there has been some synthesis of evidence of the effectiveness of emergency telehealth in clinical practice in rural and remote EDs for nonacute presentations, there has been limited evaluation of the influence of contextual factors such as clinical area and acuity of presentation on these findings. OBJECTIVE: The aims of this systematic review are to examine the outcome measures used in studying the effectiveness of telehealth in rural and remote EDs and to analyze the clinical context in which these outcome measures were used and interpreted. METHODS: The search strategy used Medical Subject Headings and equivalent lists of subject descriptors to find articles covering 4 key domains: telehealth or telemedicine, EDs, effectiveness, and rural and remote. Studies were selected using the Population, Intervention, Comparator, Outcomes of Interest, and Study Design framework. This search strategy was applied to MEDLINE (Ovid), Cochrane Library, Scopus, CINAHL, ProQuest, and EconLit, as well as the Centre for Reviews and Dissemination databases (eg, National Health Service Economic Evaluation Database) for the search period from January 1, 1990, to May 23, 2020. Qualitative synthesis was performed on the outcome measures used in the included studies, in particular the clinical contexts within which they were interpreted. RESULTS: A total of 21 full-text articles were included for qualitative analysis. Telehealth use in rural and remote EDs demonstrated effectiveness in achieving improved or equivalent clinical effectiveness, appropriate care processes, and-depending on the context-improvement in speed of care, as well as favorable service use patterns. The definition of effectiveness varied across the clinical areas and contexts of the studies, and different measures have been used to affirm the safety and clinical effectiveness of telehealth in rural and remote EDs. The acuity of patient presentation emerged as a dominant consideration in the interpretation of interlinking time-sensitive clinical effectiveness and patient disposition measures such as transfer and discharge rates, local hospital admission, length of stay, and ED length of stay. These, together with clinical area and acuity of presentation, are the outcome determination criteria that emerged from this review. CONCLUSIONS: Emergency telehealth studies typically use multiple outcome measures to determine the effectiveness of the services. The outcome determination criteria that emerged from this analysis are useful when defining the favorable direction for each outcome measure of interest. The findings of this review have implications for emergency telehealth service design and policies.

BACKGROUND: Methods to visualise patient safety data can support effective monitoring of safety events and discovery of trends. While quality dashboards are common, use and impact of dashboards to visualise patient safety event data remains poorly understood. OBJECTIVES: To understand development, use and direct or indirect impacts of patient safety dashboards. METHODS: We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched PubMed, EMBASE and CINAHL for publications between 1 January 1950 and 30 August 2018 involving use of dashboards to display data related to safety targets defined by the Agency for Healthcare Research and Quality's Patient Safety Net. Two reviewers independently reviewed search results for inclusion in analysis and resolved disagreements by consensus. We collected data on development, use and impact via standardised data collection forms and analysed data using descriptive statistics. RESULTS: Literature search identified 4624 results which were narrowed to 33 publications after applying inclusion and exclusion criteria and consensus across reviewers. Publications included only time series and case study designs and were inpatient focused and emergency department focused. Information on direct impact of dashboards was limited, and only four studies included informatics or human factors principles in development or postimplementation evaluation. DISCUSSION: Use of patient-safety dashboards has grown over the past 15 years, but impact remains poorly understood. Dashboard design processes rarely use informatics or human factors principles to ensure that the available content and navigation assists task completion, communication or decision making. CONCLUSION: Design and usability evaluation of patient safety dashboards should incorporate informatics and human factors principles. Future assessments should also rigorously explore their potential to support patient safety monitoring including direct or indirect impact on patient safety.

Introduction: Effective information management in the emergency department (ED) can improve the control and management of ED processes. Dashboards, known as data management tools, efficiently provide information and contribute greatly to control and management of ED. This study aimed to identify performance indicators quality dashboard functionalities, and analyze the challenges associated with dashboard implementation in the ED. Methods: This systematic review began with a search in four databases (Web of Science, PubMed, Embase, and Scopus) from 2000 to May 30, 2020, when the final search for papers was conducted. The data were collected using a data extraction form and the contents of the extracted papers were analyzed through ED performance indicators, dashboard functionalities, and implementation challenges. Results: Performance indicators reported in the reviewed papers were classified as the quality of care, patient flow, timeliness, costs, and resources. The main dashboard functionalities noted in the papers included reporting, customization, alert creation, resource management, and real-time information display. The dashboard implementation challenges included data sources, data quality, integration with other systems, adaptability of dashboard functionalities to user needs, and selection of appropriate performance indicators. Conclusions: Quality dashboards facilitate processes, communication, and situation awareness in the ED; hence, they can improve care provision in this department. To enhance the effectiveness and efficiency of ED dashboards, officials should set performance indicators and consider the conformity of dashboard functionalities with user needs. They should also integrate dashboards with other relevant systems at the departmental and hospital levels.

CONTEXT: Alternative pain management interventions involving caregivers may be valuable adjuncts to conventional pain management interventions. OBJECTIVES: Use systematic review methodology to examine caregiver-facilitated pain management interventions in a hospital setting and whether they improve patient, caregiver, provider, or health system outcomes. METHODS: We searched MEDLINE, EMBASE, PsycINFO, CINAHL, and Scopus databases from inception to April 2020. Original research on caregiver-facilitated pain management interventions in hospitalized settings (i.e., any age) were included and categorized into three caregiver engagement strategies: inform (e.g., pain education), activate (e.g., prompt caregiver action), and collaborate (encourage caregiver's interaction with providers). RESULTS: Of 61 included studies, most investigated premature (n = 27 of 61; 44.3%) and full-term neonates (n = 19 of 61; 31.1%). Interventions were classified as activate (n = 46 of 61; 75.4%), inform-activate-collaborate (n = 6 of 61; 9.8%), inform-activate (n = 5 of 61; 8.2%), activate-collaborate (n = 3 of 61; 4.9%), or inform (n = 1 of 61; 1.6%) caregiver engagement strategies. Interventions that included an activate engagement strategy improved pain outcomes in adults (18-64 years) (e.g., self-reported pain, n = 4 of 5; 80%) and neonates (e.g., crying, n = 32 of 41; 73.0%) but not children or older adults (65 years and older). Caregiver outcomes (e.g., pain knowledge) were improved by inform-activate engagement strategies (n = 3 of 3). Interventions did not improve provider (e.g., satisfaction) or health system (e.g., hospital length of stay) outcomes. Most studies were of low (n = 36 of 61; 59.0%) risk of bias. CONCLUSION: Caregiver-facilitated pain management interventions using an activate engagement strategy may be effective in reducing pain of hospitalized neonates. Caregiver-facilitated pain management interventions improved pain outcomes in most adult studies; however, the number of studies of adults is small warranting caution pending further studies.

BACKGROUND: There has been a growing interest in using diagnosis-related groups (DRGs) payment to reimburse inpatient care worldwide. But its effects on healthcare and health outcomes are controversial, and the evidence from low- and middle- income countries (LMICs) is especially scarce. The objective of this study is to evaluate the effects of DRGs payment on healthcare and health outcomes in China. METHOD: A systematic review was conducted. We searched literature databases of PubMed, Cochrane Library, EMBASE, Web of Science, Chinese National Knowledge Infrastructure and SinoMed for empirical studies examining the effects of DRGs payment on healthcare in mainland China. We performed a narrative synthesis of outcomes regarding expenditure, efficiency, quality and equity of healthcare, and assessed the quality of evidence. RESULTS: Twenty-three publications representing thirteen DRGs payment studies were included, including six controlled before after studies, two interrupted time series studies and five uncontrolled before-after studies. All studies compared DRGs payment to fee-for-service, with or without an overall budget, in settings of tertiary (7), secondary (7) and primary care (1). The involved participants varied from specific groups to all inpatients. DRGs payment mildly reduced the length of stay. Impairment of equity of healthcare was consistently reported, especially for patients exempted from DRGs payment, including: patient selection, cost-shifting and inferior quality of healthcare. However, findings on total expenditure, out of pocket payment (OOP) and quality of healthcare were inconsistent. The quality of the evidence was generally low or very low due to the study design and potential risk of bias of included studies. CONCLUSION: DRGs payment may mildly improve the efficiency but impair the equity and quality of healthcare, especially for patients exempted from this payment scheme, and may cause up-coding of medical records. However, DRGs payment may or may not contain the total expenditure or OOP, depending on the components design of the payment. Policymakers should very carefully consider each component of DRGs payment design against policy goals. Well-designed randomised trials or comparative studies are warranted to consolidate the evidence of the effects of DRGs payment on healthcare and health outcomes in LMICs to inform policymaking.

PURPOSE: Patients and families may experience 'non-physical' harm from interactions with the healthcare system, including emotional, psychological, socio-behavioral or financial harm, some of which may be related to experiences of disrespect. We sought to use the current literature to develop a practical, improvement-oriented framework to recognize, describe and help prevent such events. DATA SOURCES: Searches were performed in PubMed, Embase, PsychINFO, CINAHL, Health Business Elite and ProQuest Dissertations & Theses: Global: Health & Medicine, from their inception through July 2017. STUDY SELECTION: Two authors reviewed titles, abstracts, full texts, references and cited-by lists to identify articles describing approaches to understanding patient/family experiences of disrespect. DATA EXTRACTION: Findings were evaluated using integrative review methodology. RESULTS OF DATA SYNTHESIS: Three-thousand eight hundred and eighty two abstracts were reviewed. Twenty three articles were identified. Components of experiences of disrespect included: (1) numerous care processes; (2) a wide range of healthcare professional and organizational behaviors; (3) contributing factors, including patient- and professional-related factors, the environment of work and care, leadership, policies, processes and culture; (4) important consequences of disrespect, including behavioral changes and health impacts on patients and families, negative effects on professionals' subsequent interactions, and patient attrition from organizations and (5) factors both intrinsic and extrinsic to patients that can modify the consequences of disrespect. CCONCLUSION: A generalizable framework for understanding disrespect experienced by patients/families in healthcare may help organizations better prevent non-physical harms. Future work should prospectively test and refine the framework we described so as to facilitate its integration into organizations' existing operational systems.

OBJECTIVES: To evaluate the effect of emergency department (ED) interventions on clinical, utilization, and care experience outcomes for older adults. DESIGN: A conceptual model informed, protocol-based systematic review. SETTING: Emergency Department (ED). PARTICIPANTS: Older adults 65 years of age and older. METHODS AND MEASUREMENT: Medline, Embase, CINAHL, and PsycINFO were searched for English-language studies published through December 2017. Studies evaluating the use of one or more eligible intervention strategies (discharge planning, case management, medication safety or management, and geriatric EDs including those that cited the 2014 Geriatric ED Guidelines) with adults 65 years of age and older were included. Studies were classified by the number of intervention strategies used (ie, single strategy or multi-strategy) and key intervention components present (ie, assessment, referral plus follow-up, and contact both before and after ED discharge ["bridge"]). The effect of ED interventions on clinical (functional status, quality of life [QOL]), patient experience, and utilization (hospitalization, ED return visit) outcomes was evaluated. RESULTS: A total of 2000 citations were identified; 17 articles describing 15 unique studies (9 randomized and 6 nonrandomized) met eligibility criteria and were included in analyses. ED interventions showed a mixed pattern of effects. Overall, there was a small positive effect of ED interventions on functional status but no effects on QOL, patient experience, hospitalization at or after the initial ED index visit, or ED return visit. CONCLUSION: Studies using two or more intervention strategies may be associated with the greatest effects on clinical and utilization outcomes. More comprehensive interventions, defined as those with all three key intervention components present, may be associated with some positive outcomes.

The excessive use of resources, inefficiency and poor service quality in public hospitals has led developing countries to create reforms in public hospital governance, including autonomization. Hospital autonomy refers to the delegation of administrative rights to the hospital management team. The purpose of the present research is to review different aspects of hospital autonomy reforms in developing countries, such as incentives, preparations, obstacles and facilitators to change prior to implementation, impacts on achieving Universal Health Coverage (UHC) goals, challenges, outcomes and implications for implementation. A systematic review of the evidence from developing countries was performed. The results showed that these countries have undertaken autonomy reforms in order to improve the efficiency, quality and accountability of their hospitals. Also, studies emphasized the role of the World Bank in facilitation and guidance, and identified bureaucratic culture and political instability as barriers to change for the implementation of hospital autonomy reform. Preparations were limited to two key areas, i.e. ensuring access to healthcare services and ensuring the implementation of these reforms. The main challenges were lack of infrastructure, poor planning and policymaking, poor programme control, limited decision rights, inappropriate incentives and weaknesses in the accountability system. The results indicated that these reforms had no discernible effect on quality, efficiency and other management indicators, while leading to an increase in hospital costs and out-of-pocket payments. Also, implementing these reforms affected the progress toward achieving UHC. Overall, the results showed that there are two factors in failed implementation of these hospital reforms in developing countries: (1) lack of a systematic and holistic view, and (2) incomplete or poor implementation of different aspects of these reforms.

BACKGROUND: Implementation of evidence-based bundles in intensive care units is integral to improving quality of care and patient outcomes. However, it increases the burden of data collection and analysis required for performance monitoring and feedback of an inter-disciplinary care team. Health information technology including electronic health records and data analytic tools could automate this process and provide real-time feedback to the team. AIM: This integrative literature review aimed to examine the extent to which technology-enabled performance monitoring and feedback contributed to improving quality of care and patient outcomes when implementing evidence-based bundles. METHODS: A literature search of scientific databases was conducted using PubMed, Embase, Scopus, CINHAL and Ovid Medline. RESULTS: Of nine studies included in this review, all reported improved compliance of the team with evidence-based bundles, ranging from 3% to 60% post implementation of technology-enabled performance monitoring and feedback. Significant reductions (p<.05) in hospital acquired infections were also reported in five studies. CONCLUSIONS: Overall, the addition of documentation fields to electronic health records was essential in providing real-time feedback to teams and improving their compliance with evidence-based bundles. Further research is needed to assess the effectiveness of technology-enabled performance monitoring and feedback in improving patient outcomes on a larger scale, especially in resource-limited settings such as community hospitals.

BACKGROUND: Emergency department crowding is a major global healthcare issue. There is much debate as to the causes of the phenomenon, leading to difficulties in developing successful, targeted solutions. AIM: The aim of this systematic review was to critically analyse and summarise the findings of peer-reviewed research studies investigating the causes and consequences of, and solutions to, emergency department crowding. METHOD: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A structured search of four databases (Medline, CINAHL, EMBASE and Web of Science) was undertaken to identify peer-reviewed research publications aimed at investigating the causes or consequences of, or solutions to, emergency department crowding, published between January 2000 and June 2018. Two reviewers used validated critical appraisal tools to independently assess the quality of the studies. The study protocol was registered with the International prospective register of systematic reviews (PROSPERO 2017: CRD42017073439). RESULTS: From 4,131 identified studies and 162 full text reviews, 102 studies met the inclusion criteria. The majority were retrospective cohort studies, with the greatest proportion (51%) trialling or modelling potential solutions to emergency department crowding. Fourteen studies examined causes and 40 investigated consequences. Two studies looked at both causes and consequences, and two investigated causes and solutions. CONCLUSIONS: The negative consequences of ED crowding are well established, including poorer patient outcomes and the inability of staff to adhere to guideline-recommended treatment. This review identified a mismatch between causes and solutions. The majority of identified causes related to the number and type of people attending ED and timely discharge from ED, while reported solutions focused on efficient patient flow within the ED. Solutions aimed at the introduction of whole-of-system initiatives to meet timed patient disposition targets, as well as extended hours of primary care, demonstrated promising outcomes. While the review identified increased presentations by the elderly with complex and chronic conditions as an emerging and widespread driver of crowding, more research is required to isolate the precise local factors leading to ED crowding, with system-wide solutions tailored to address identified causes.

OBJECTIVE: To evaluate the effectiveness of rapid response teams using early identification of clinical deterioration in reducing the occurrence of in-hospital mortality and cardiorespiratory arrest. DATA SOURCES: The MEDLINE, LILACS, Cochrane Library, Center for Reviews and Dissemination databases were searched. STUDY SELECTION: We included studies that evaluated the effectiveness of rapid response teams in adult hospital units, published in English, Portuguese, or Spanish, from 2000 to 2016; systematic reviews, clinical trials, cohort studies, and prepost ecological studies were eligible for inclusion. The quality of studies was independently assessed by two researchers using the Newcastle-Ottawa, modified Jadad, and Assessment of Multiple Systematic Reviews scales. DATA EXTRACTIONS: The results were synthesized and tabulated. When risk measures were reported by the authors of the included studies, we estimated effectiveness as 1-RR or 1-OR. In pre-post studies, we estimated effectiveness as the percent decrease in rates following the intervention. RESULTS: Overall, 278 studies were identified, 256 of which were excluded after abstract evaluation, and two of which were excluded after full text evaluation. In the meta-analysis of the studies reporting mortality data, we calculated a risk ratio of 0.85 (95%CI 0.76 - 0.94); and for studies reporting cardiac arrest data the estimated risk ratio was 0.65 (95%CI 0.49 - 0.87). Evidence was assessed as low quality due to the high heterogeneity and risk of bias in primary studies. CONCLUSION: We conclude that rapid response teams may reduce in-hospital mortality and cardiac arrests, although the quality of evidence for both outcomes is low.