Deployment of a human-centred clinical decision support system for pulmonary embolism: evaluation of impact on quality of diagnostic decisions.

Pulmonary embolism (PE) is a serious condition that presents a diagnostic challenge for which diagnostic errors often happen. The literature suggests that a gap remains between PE diagnostic guidelines and adherence in healthcare practice. While system-level decision support tools exist, the clinical impact of a human-centred design (HCD) approach of PE diagnostic tool design is unknown. DESIGN: Before-after (with a preintervention period as non-concurrent control) design study. SETTING: Inpatient units at two tertiary care hospitals. PARTICIPANTS: General internal medicine physicians and their patients who underwent PE workups. INTERVENTION: After a 6-month preintervention period, a clinical decision support system (CDSS) for diagnosis of PE was deployed and evaluated over 6 months. A CDSS technical testing phase separated the two time periods. MEASUREMENTS: PE workups were identified in both the preintervention and CDSS intervention phases, and data were collected from medical charts. Physician reviewers assessed workup summaries (blinded to the study period) to determine adherence to evidence-based recommendations. Adherence to recommendations was quantified with a score ranging from 0 to 1.0 (the primary study outcome). Diagnostic tests ordered for PE workups were the secondary outcomes of interest. RESULTS: Overall adherence to diagnostic pathways was 0.63 in the CDSS intervention phase versus 0.60 in the preintervention phase (p=0.18), with fewer workups in the CDSS intervention phase having very low adherence scores. Further, adherence was significantly higher when PE workups included the Wells prediction rule (median adherence score=0.76 vs 0.59, p=0.002). This difference was even more pronounced when the analysis was limited to the CDSS intervention phase only (median adherence score=0.80 when Wells was used vs 0.60 when Wells was not used, p=0.001). For secondary outcomes, using both the D-dimer blood test (42.9% vs 55.7%, p=0.014) and CT pulmonary angiogram imaging (61.9% vs 75.4%, p=0.005) was lower during the CDSS intervention phase. CONCLUSION: A clinical decision support intervention with an HCD improves some aspects of the diagnostic decision, such as the selection of diagnostic tests and the use of the Wells probabilistic prediction rule for PE.

Interpreting and coding causal relationships for quality and safety using ICD-11.

Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts are causal. Based on the use of different types of codes and the development of a new mechanism for coding potential causal relationships, the ICD-11 provides an in-depth transformation of coding expectations as compared to ICD-10. An essential part of the causal relationship interpretation relies on the presence of "connecting terms," key elements in assessing the level of certainty regarding a potential relationship and how to proceed in coding a causal relationship using the new ICD-11 coding convention of postcoordination (i.e., clustering of codes). In addition, determining causation involves using documentation from healthcare providers, which is the foundation for coding health information. The coding guidelines and examples (taken from the quality and patient safety domain) presented in this article underline how new ICD-11 features and coding rules will enhance future health information systems and healthcare.

Effect of a Continuous Bedside Pressure Mapping System for Reducing Interface Pressures: A Randomized Clinical Trial.

Importance: Continuous bedside pressure mapping (CBPM) technology can assist in detecting skin areas with excessive interface pressure and inform efficient patient repositioning to prevent the development of pressure injuries (PI). Objective: To evaluate the efficacy of CBPM technology in reducing interface pressure and the incidence of PIs. Design, Setting, and Participants: This parallel, 2-group randomized clinical trial was performed at a tertiary acute care center. The study started to enroll participants in December 2014 and was completed in May 2018. Participants included adults partially or completely dependent for bed mobility. Statistical analysis was performed from September 2018 to December 2022. Intervention: Nursing staff using visual feedback from CBPM technology for 72 hours. Main Outcomes and Measures: Absolute number of sensing points with pressure readings greater than 40 mm Hg, mean interface pressure across all sensing points under a patient's body, proportion of participants who had pressure readings greater than 40 mm Hg, and pressure-related skin and soft tissue changes. Results: There were 678 patients recruited. After attrition, 260 allocated to the control group (151 [58.1%] male; mean [SD] age, 61.9 [18.5] years) and 247 in the intervention group (147 [59.5%] male; mean [SD] age, 63.6 [18.1] years) were included in analyses. The absolute number of sensing points with pressures greater than 40 mm Hg were 11 033 in the control group vs 9314 in the intervention group (P = .16). The mean (SD) interface pressure was 6.80 (1.63) mm Hg in the control group vs 6.62 (1.51) mm Hg in the intervention group (P = .18). The proportion of participants who had pressure readings greater than 40 mm Hg was 99.6% in both the control and intervention groups. Conclusions and Relevance: In this randomized clinical trial to evaluate the efficacy of CBPM technology in the reduction of interface pressure and the incidence of PIs in a tertiary acute care center, no statistically significant benefit was seen for any of the primary outcomes. These results suggest that longer duration of monitoring and adequately powered studies where CBPM feedback is integrated into a multifaceted intervention to prevent PI are needed. Trial Registration: ClinicalTrials.gov Identifier: NCT02325388.

ICD-11: A catalyst for advancing patient safety surveillance globally.

The World Health Organization's (WHO) international classification of disease version 11 (ICD-11) contains several features which enable improved classification of patient safety events. We have identified three suggestions to facilitate adoption of ICD-11 from the patient safety perspective. One, health system leaders at national, regional, and local levels should incorporate ICD-11 into all approaches to monitor patient safety. This will allow them to take advantage of the innovative patient safety classification methods embedded in ICD-11 to overcome several limitations related to existing patient safety surveillance methods. Two, application developers should incorporate ICD-11 into software solutions. This will accelerate adoption and utility of software-enabled clinical and administrative workflows relevant to patient safety management. This is enabled as a result of the ICD-11 application programming interface (or API) developed by the WHO. Third, health system leaders should adopt the ICD-11 using a continuous improvement framework. This will help leaders at national, regional and local levels to take advantage of specific existing initiatives which will be strengthened by ICD-11, including peer review comparisons, clinician engagement, and alignment of front-line safety efforts with post marketing surveillance of medical technologies. While the investment to adopt ICD-11 will be considerable, these will be offset by reducing the ongoing costs related to a lack of accurate routine information.

Exploring data reduction strategies in the analysis of continuous pressure imaging technology.

BACKGROUND: Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries. OBJECTIVE: To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data. METHODS: Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots. RESULTS: A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome. CONCLUSIONS: Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.

Computerised clinical decision support system for the diagnosis of pulmonary thromboembolism: a preclinical pilot study.

BACKGROUND: Recommendations for the diagnosis of pulmonary embolism are available for healthcare providers. Yet, real practice data show existing gaps in the translation of evidence-based recommendations. This is a study to assess the effect of a computerised decision support system (CDSS) with an enhanced design based on best practices in content and reasoning representation for the diagnosis of pulmonary embolism. DESIGN: Randomised preclinical pilot study of paper-based clinical scenarios in the diagnosis of pulmonary embolism. Participants were clinicians (n=30) from three levels of experience: medical students, residents and physicians. Participants were randomised to two interventions for the diagnosis of pulmonary embolism: a didactic lecture versus a decision tree via a CDSS. The primary outcome of diagnostic pathway concordance (derived as a ratio of the number of correct diagnostic decision steps divided by the ideal number of diagnostic decision steps in diagnostic algorithms) was measured at baseline (five clinical scenarios) and after either intervention for a total of 10 clinical scenarios. RESULTS: The mean of diagnostic pathway concordance improved in both study groups: baseline mean=0.73, post mean for the CDSS group=0.90 (p<0.001, 95% CI 0.10-0.24); baseline mean=0.71, post mean for didactic lecture group=0.85 (p<0.001, 95% CI 0.07-0.2). There was no statistically significant difference between the two study groups or between the three levels of participants. INTERPRETATION: A computerised decision support system designed for both content and reasoning visualisation can improve clinicians' diagnostic decision-making.

Healthcare provider and patient/family perceptions of continuous pressure imaging technology for prevention of pressure injuries: A secondary analysis of patients enrolled in a randomized control trial.

INTRODUCTION: Despite the availability of various pressure injury (PI) prevention strategies (e.g., risk identification, use of pressure re-distribution surfaces, frequent repositioning), they persist as a significant issue for healthcare systems worldwide. Continuous pressure imaging (CPI) is a novel technology that could be integrated within a comprehensive approach to the prevention of PIs. We studied the perceptions of healthcare providers and patients/families to identify facilitators and barriers to the use of this technology. METHODS: Hospitalized patients/family members from a randomized controlled trial assessing the efficacy of CPI in preventing PIs completed a survey after 72 hours (or upon discharge from hospital) of CPI monitoring. They were asked questions about prior and current experience with CPI technology. For healthcare providers, perceptions on the use of the device and its impact on care were explored through a survey distributed by email or hard copies. RESULTS: A total of 125 healthcare providers and 525 patients/family members completed the surveys. Of the healthcare providers, 95% either agreed/strongly agreed that the CPI technology was easy to use and 65% stated that the device improved how they provided pressure relief for patients. Identified issues with the device were cost, the fitting of the mattress cover, and the fixation of the patients/families on the device. Over a quarter of the patient/family respondents agreed/strongly agreed that the device influenced how pressure relief was provided. This response was statistically associated with whether the monitor was turned on (intervention arm; 52.7%) or off (control arm; 4.2%). DISCUSSION AND CONCLUSION: CPI technology was positively perceived by healthcare providers. Most patients/families felt it influenced care when the CPI monitor was turned on. Concerns raised around cost and the ease of use of these devices by healthcare providers may affect the decisions of healthcare system administrators to adopt and implement this technology.

Perceived discharge quality and associations with hospital readmissions and emergency department use: a prospective cohort study.

BACKGROUND: At hospital discharge, care is handed over from providers to patients. Discharge encounters must prepare patients to self-manage their health, but have been found to be suboptimal. Our study objectives were to describe and determine the correlates of perceived discharge quality and to explore the association between perceived discharge quality and postdischarge outcomes. METHODS: We conducted a prospective cohort study in medical inpatients admitted to a tertiary care hospital in Calgary, Canada. Perceived discharge quality was measured by the Care Transitions Measure (CTM). Linkage to administrative databases provided data for the composite outcome-90-day hospital readmission or emergency department visit. Logistic regression modelling was used to determine the association between global CTM scores, and the individual CTM components, and the composite outcome. RESULTS: A total of 316 patients were included in the analysis. The median CTM score was 80.0 (IQR 66.6-100.0). The distribution of CTM scores were significantly different based on comorbidity burden, with the median and maximum CTM scores being lower and the IQR being narrower, for those with six or more comorbidities compared with those with fewer comorbidities. CTM scores were not associated with the composite outcome, though a single CTM item-not understanding warning signs and symptoms-was (adjusted OR 3.46 (95% CI 1.02 to 11.73)). CONCLUSION: Perceived quality of discharge varies based on patient burden of comorbidities. While global perceived discharge quality was not associated with postdischarge outcomes, lack of patient understanding of warning symptoms was. Discharging healthcare teams should pay special attention to these priority patient groups and specific discharge process components.

Field testing a new ICD coding system: methods and early experiences with ICD-11 Beta Version 2018.

OBJECTIVE: A beta version (2018) of International Classification of Diseases, 11th Revision for MMS (ICD-11), needed testing. Field-testing involves real-world application of the new codes to examine usability. We describe creating a dataset and characterizing the usability of ICD-11 code set by coders. We compare ICD-11 against ICD-10-CA (Canadian modification) and a reference standard dataset of diagnoses. Real-world usability encompasses code selection and time to code a complete inpatient chart using ICD-11 compared with ICD-10-CA. METHODS AND RESULTS: A random sample of inpatient records previously coded using ICD-10-CA was selected from hospitals in Calgary, Alberta (N = 2896). Nurses examined these charts for conditions and healthcare-related harms. Clinical coders re-coded the same charts using ICD-11 codes. Inter-rater reliability (IRR) and coding time improved with ICD-11 coding experience (23.6 to 9.9 min average per chart). Code structure comparisons and challenges encountered are described. Overall, 86.3% of main condition codes matched. Coder comments regarding duplicate codes, missing codes, code finding issues enabled improvements to the ICD-11 Browser, Coding Tool, and Reference Guide. Training is essential for solid IRR with 17,000 diagnostic categories in the new ICD-11. As countries transition to ICD-11, our coding experiences and methods can inform users for implementation or field testing.

Developing EMR-based algorithms to Identify hospital adverse events for health system performance evaluation and improvement: Study protocol.

BACKGROUND: Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely implemented and contain detailed and comprehensive information regarding all aspects of patient care, offering a valuable complement to administrative data. Harnessing the rich clinical data in EMRs offers a unique opportunity to improve detection, identify possible risk factors of AE and enhance surveillance. However, the methodological tools for detection of AEs within EMR need to be developed and validated. The objectives of this study are to develop EMR-based AE algorithms from hospital EMR data and assess AE algorithm's validity in Canadian EMR data. METHODS: Patient EMR structured and text data from acute care hospitals in Calgary, Alberta, Canada will be linked with discharge abstract data (DAD) between 2010 and 2020 (n~1.5 million). AE algorithms development. First, a comprehensive list of AEs will be generated through a systematic literature review and expert recommendations. Second, these AEs will be mapped to EMR free texts using Natural Language Processing (NLP) technologies. Finally, an expert panel will assess the clinical relevance of the developed NLP algorithms. AE algorithms validation: We will test the newly developed AE algorithms on 10,000 randomly selected EMRs between 2010 to 2020 from Calgary, Alberta. Trained reviewers will review the selected 10,000 EMR charts to identify AEs that had occurred during hospitalization. Performance indicators (e.g., sensitivity, specificity, positive predictive value, negative predictive value, F1 score, etc.) of the developed AE algorithms will be assessed using chart review data as the reference standard. DISCUSSION: The results of this project can be widely implemented in EMR based healthcare system to accurately and timely detect in-hospital AEs.

Effect of Clinical Decision Support With Audit and Feedback on Prevention of Acute Kidney Injury in Patients Undergoing Coronary Angiography: A Randomized Clinical Trial.

Importance: Contrast-associated acute kidney injury (AKI) is a common complication of coronary angiography and percutaneous coronary intervention (PCI) that has been associated with high costs and adverse long-term outcomes. Objective: To determine whether a multifaceted intervention is effective for the prevention of AKI after coronary angiography or PCI. Design, Setting, and Participants: A stepped-wedge, cluster randomized clinical trial was conducted in Alberta, Canada, that included all invasive cardiologists at 3 cardiac catheterization laboratories who were randomized to various start dates for the intervention between January 2018 and September 2019. Eligible patients were aged 18 years or older who underwent nonemergency coronary angiography, PCI, or both; who were not undergoing dialysis; and who had a predicted AKI risk of greater than 5%. Thirty-four physicians performed 7820 procedures among 7106 patients who met the inclusion criteria. Participant follow-up ended in November 2020. Interventions: During the intervention period, cardiologists received educational outreach, computerized clinical decision support on contrast volume and hemodynamic-guided intravenous fluid targets, and audit and feedback. During the control (preintervention) period, cardiologists provided usual care and did not receive the intervention. Main Outcomes and Measures: The primary outcome was AKI. There were 12 secondary outcomes, including contrast volume, intravenous fluid administration, and major adverse cardiovascular and kidney events. The analyses were conducted using time-adjusted models. Results: Of the 34 participating cardiologists who were divided into 8 clusters by practice group and center, the intervention group included 31 who performed 4327 procedures among 4032 patients (mean age, 70.3 [SD, 10.7] years; 1384 were women [32.0%]) and the control group included 34 who performed 3493 procedures among 3251 patients (mean age, 70.2 [SD, 10.8] years; 1151 were women [33.0%]). The incidence of AKI was 7.2% (310 events after 4327 procedures) during the intervention period and 8.6% (299 events after 3493 procedures) during the control period (between-group difference, -2.3% [95% CI, -0.6% to -4.1%]; odds ratio [OR], 0.72 [95% CI, 0.56 to 0.93]; P = .01). Of 12 prespecified secondary outcomes, 8 showed no significant difference. The proportion of procedures in which excessive contrast volumes were used was reduced to 38.1% during the intervention period from 51.7% during the control period (between-group difference, -12.0% [95% CI, -14.4% to -9.4%]; OR, 0.77 [95% CI, 0.65 to 0.90]; P = .002). The proportion of procedures in eligible patients in whom insufficient intravenous fluid was given was reduced to 60.8% during the intervention period from 75.1% during the control period (between-group difference, -15.8% [95% CI, -19.7% to -12.0%]; OR, 0.68 [95% CI, 0.53 to 0.87]; P = .002). There were no significant between-group differences in major adverse cardiovascular events or major adverse kidney events. Conclusions and Relevance: Among cardiologists randomized to an intervention including clinical decision support with audit and feedback, patients undergoing coronary procedures during the intervention period were less likely to develop AKI compared with those treated during the control period, with a time-adjusted absolute risk reduction of 2.3%. Whether this intervention would show efficacy outside this study setting requires further investigation. Trial Registration: ClinicalTrials.gov Identifier: NCT03453996.

Coding mechanisms for diagnosis timing in the International Classification of Diseases, Version 11.

BACKGROUND: Diagnoses that arise after admission are of interest because they can represent complications of health care, acute conditions arising de novo, or acute decompensation of a chronic comorbidity occurring during the hospital stay. Three countries in the world have adopted diagnosis timing codes for a number of years. Their experience demonstrates the feasibility and utility of associating an International Classification of Diseases, Version 9 or International Classification of Diseases, Version 10 diagnostic code with information on diagnosis timing, either as part of a diagnostic field or as a separate field. However, diagnosis timing is not an integrated feature of these two classifications as it will be for International Classification of Diseases, Version 11. METHODS: We examine the different types of diagnosis timing that can be used to describe complex patients and present examples of how the new International Classification of Diseases, Version 11 codes may be used. RESULTS: Extension codes are one of the important new features of International Classification of Diseases, Version 11 and allow more specificity in diagnosis timing. CONCLUSION: Imbedded and standardized diagnosis timing information is possible within the International Classification of Diseases, Version 11 classification system.

Strategic Clinical Network Teams Improve Effectiveness, Team and Leadership Processes and Inputs: Theory-Based Longitudinal Survey.

Strategic Clinical Networks (SCNs) in Alberta include multidisciplinary teams that work toward health system innovation and improvement; however, what contributes to team effectiveness is unclear. This theory-informed longitudinal survey (n = 826) evaluated team effectiveness within SCNs and predictors of effectiveness. Satisfaction, inter-team relationships and seven predictors including team inputs and team and leadership processes improved over two years. Attitudinal outputs were predicted by the same factors over time, whereas performance outputs were predicted by different factors. This innovative study emphasizes that SCN teams and their effectiveness evolve over time and that team-based research can refine network evaluations.

A multi-step approach to developing a health system evaluation framework for community-based health care.

BACKGROUND: Community-based health care (CBHC) is a shift towards healthcare integration and community services closer to home. Variation in system approaches harkens the need for a conceptual framework to evaluate outcomes and impacts. We set out to develop a CBHC-specific evaluation framework in the context of a provincial ministry of health planning process in Canada. METHODS: A multi-step approach was used to develop the CBHC evaluation framework. Modified Delphi informed conceptualization and prioritization of indicators. Formative research identified evaluation framework elements (triple aim, global measures, and impact), health system levels (tiers), and potential CBHC indicators (n = 461). Two Delphi rounds were held. Round 1, panelists independently ranked indicators on CBHC relevance and health system tiering. Results were analyzed by coding agreement/disagreement frequency and central tendency measures. Round 2, a consensus meeting was used to discuss disagreement, identify Tier 1 indicators and concepts, and define indicators not relevant to CBHC (Tier 4). Post-Delphi, indicators and concepts were refined, Tier 1 concepts mapped to the evaluation framework, and indicator narratives developed. Three stakeholder consultations (scientific, government, and public/patient communities) were held for endorsement and recommendation. RESULTS: Round 1 Delphi results showed agreement for 300 and disagreement for 161 indicators. Round 2 consensus resulted in 103 top tier indicators (Tier 1 = 19, Tier 2 = 84), 358 bottom Tier 3 and 4 indicators, non-CBHC measure definitions, and eight Tier 1 indicator concepts-Mortality/Suicide; Quality of Life, and Patient Reported Outcome Measures; Global Patient Reported Experience Measures; Cost of Care, Access to Integrated Primary Care; Avoidable Emergency Department Use; Avoidable Hospitalization; and E-health Penetration. Post Delphi results refined Tier 3 (n = 289) and 4 (n = 69) indicators, and identified 18 Tier 2 and 3 concepts. When mapped to the evaluation framework, Tier 1 concepts showed full coverage across the elements. 'Indicator narratives' depicted systemness and integration for evaluating CBHC. Stakeholder consultations affirmed endorsement of the approach and evaluation framework; refined concepts; and provided key considerations to further operationalize and contextualize indicators, and evaluate CBHC as a health system approach. CONCLUSIONS: This research produced a novel evaluation framework to conceptualize and evaluate CBHC initiatives. The evaluation framework revealed the importance of a health system approach for evaluating CBHC.

The three-part model for coding causes and mechanisms of healthcare-related adverse events.

ICD-11 provides a promising new way to capture healthcare-related harm or injury. In this paper, we elaborate on the framework for describing healthcare-related events where there is a presumed causal link between an event and underlying healthcare-related factors. The three-part model for describing healthcare-related harm or injury in ICD-11 consists of (1) a healthcare-related activity that is the cause of injury or other harm (selected from Chapter 23 of ICD-11); (2) a mode or mechanism of injury or harm, related to the underlying cause (also from Chapter 23 of ICD-11); and (3) the harmful consequences of the event to the patient, selected from any of Chapters 1 through 22 of ICD-11 (most importantly, the injury or harm experienced by the patient). Concepts from these three elements are linked/clustered through postcoordination to reflect the three-part model in a single coded expression. ICD-11 contains many novel features, and the three-part model described here for healthcare-related adverse events is a notable example.

Development and validation of a social vulnerabilities survey for medical inpatients.

OBJECTIVES: Our objective was to validate a Social Vulnerabilities Survey that was developed to identify patient barriers in the following domains: (1) salience or priority of health; (2) social support; (3) transportation; and (4) finances. DESIGN: Cross-sectional psychometric study.Questions for one domain (health salience) were developed de novo while questions for the other domains were derived from national surveys and/or previously validated questionnaires. We tested construct (ie, convergent and discriminative) validity for these new questions through hypothesis testing of correlations between question responses and patient characteristics. Exploratory factor analysis was conducted to determine structural validity of the survey as a whole. SETTING: Patients admitted to the inpatient internal medicine service at a tertiary care hospital in Calgary, Canada. PARTICIPANTS: A total of 406 patients were included in the study. RESULTS: The mean age of respondents was 55.5 (SD 18.6) years, with the majority being men (55.4%). In feasibility testing of the first 107 patients, the Social Vulnerabilities Survey was felt to be acceptable, comprehensive and met face validity. Hypothesis testing of the health salience questions revealed that the majority of observed correlations were exactly as predicted. Exploratory factor analysis of the global survey revealed the presence of five factors (eigenvalue >1): social support, health salience, drug insurance, transportation barriers and drug costs. All but four questions loaded to these five factors. CONCLUSIONS: The Social Vulnerabilities Survey has face, construct and structural validity. It can be used to measure modifiable social vulnerabilities, such that their effects on health outcomes can be explored and understood.

Patient navigation programs in Alberta, Canada: an environmental scan.

BACKGROUND: Patient navigation is a complex intervention that has garnered substantial interest and investment across Canada. We conducted an environmental scan to understand the landscape of patient navigation programs within the health care system in Alberta, Canada. METHODS: We included patient navigation programs within Alberta Health Services (AHS) and Alberta's Primary Care Networks (PCNs). Key informants were asked in October 2016 to identify existing programs and their corresponding program contacts. These program contacts were invited to complete a telephone-based survey from October 2016 to July 2017, to provide program descriptions and eligibility criteria, and to identify gaps in navigation. Programs were included if they engaged patients on an individual basis, and either facilitated continuity of care or promoted patient and family empowerment. We tabulated results and calculated summary statistics for program characteristics. RESULTS: Ninety-five potentially eligible programs were identified by key informants. The response rate to the study survey was 73% (n = 69). After excluding programs not meeting inclusion criteria, we included a total of 58 programs in the study: 43 AHS programs and 15 PCN programs. Nearly all programs (93%, n = 54) delivered navigation via an individual acting as a navigator. A minority of programs also included nonnavigator components, such as Web-based resources (7%, n = 4) and process or structural changes to facilitate navigation (22%, n = 13). Certain patient subgroups were particularly well-served by patient navigation; these included patients with cancer, substance use disorders or mental health concerns, and pediatric patients. Gaps identified in navigation fell under 4 domains: awareness, resources, geographic distribution and integration. INTERPRETATION: Patient navigation programs are common and have extended beyond cancer care, from which the construct originated; however, gaps include a lack of awareness and inequitable access to the programs. These findings will be of interest to those developing and implementing patient navigation interventions in Alberta and other jurisdictions.

Mitigating imperfect data validity in administrative data PSIs: a method for estimating true adverse event rates.

QUESTION: Are there ways to mitigate the challenges associated with imperfect data validity in Patient Safety Indicator (PSI) report cards? FINDINGS: Applying a methodological framework on simulated PSI report card data, we compare the adjusted PSI rates of three hospitals with variable quality of data and coding. This framework combines (i) a measure of PSI rates using existing algorithms; (ii) a medical record review on a small random sample of charts to produce a measure of hospital-specific data validity and (iii) a simple Bayesian calculation to derive estimated true PSI rates. For example, the estimated true PSI rate, for a theoretical hospital with a moderately good quality of coding, could be three times as high as the measured rate (for example, 1.4% rather than 0.5%). For a theoretical hospital with relatively poor quality of coding, the difference could be 50-fold (for example, 5.0% rather than 0.1%). MEANING: Combining a medical chart review on a limited number of medical charts at the hospital level creates an approach to producing health system report cards with estimates of true hospital-level adverse event rates.

Prevalence of antimicrobial resistance genes and its association with restricted antimicrobial use in food-producing animals: a systematic review and meta-analysis.

BACKGROUND: There is ongoing debate regarding potential associations between restrictions of antimicrobial use and prevalence of antimicrobial resistance (AMR) in bacteria. OBJECTIVES: To summarize the effects of interventions reducing antimicrobial use in food-producing animals on the prevalence of AMR genes (ARGs) in bacteria from animals and humans. METHODS: We published a full systematic review of restrictions of antimicrobials in food-producing animals and their associations with AMR in bacteria. Herein, we focus on studies reporting on the association between restricted antimicrobial use and prevalence of ARGs. We used multilevel mixed-effects models and a semi-quantitative approach based on forest plots to summarize findings from studies. RESULTS: A positive effect of intervention [reduction in prevalence or number of ARGs in group(s) with restricted antimicrobial use] was reported from 29 studies for at least one ARG. We detected significant associations between a ban on avoparcin and diminished presence of the vanA gene in samples from animals and humans, whereas for the mecA gene, studies agreed on a positive effect of intervention in samples only from animals. Comparisons involving mcr-1, blaCTX-M, aadA2, vat(E), sul2, dfrA5, dfrA13, tet(E) and tet(P) indicated a reduced prevalence of genes in intervention groups. Conversely, no effects were detected for ß-lactamases other than blaCTX-M and the remaining tet genes. CONCLUSIONS: The available body of scientific evidence supported that restricted use of antimicrobials in food animals was associated with an either lower or equal presence of ARGs in bacteria, with effects dependent on ARG, host species and restricted drug.