Assessing agreement between population-level administrative pharmaceutical databases and patient-reported medication dispensation in cardiac rehabilitation patients.

BACKGROUND: Pharmacoepidemiology has emerged as a crucial field in evaluating the use and effects of medications in large populations to ensure their safe and effective use. This study aimed to assess the agreement of cardiac medication use between a provincial medication database, the Pharmaceutical Information Network (PIN), and reconciled medication data from confirmation through patient interviews for patients referred to cardiac rehabilitation. METHODS: The study included data from patients referred to the TotalCardiology Rehabilitation CR program, and medication data was available in both TotalCardiology Rehabilitation charts and PIN. The accuracy of medication data obtained from patient interviews was compared to that obtained from PIN with proportions and kappa statistics to evaluate the reliability of PIN data in assessing medication use. RESULTS: Patient-reported usage was higher for statins (41.6 %) vs. 38.4 %), ACE/ARB, beta-blockers (75.7 %) vs. 73.7 %), DOAC (3.5 %) vs. 2.6 %), and ADP-receptor antagonists (71.0 %) vs. 68.1 %) than if PIN was used. Patient-reported usage data was lower for Ezetimibe (4.7 vs. 4.8 %), Aldosterone antagonists (5.4 %) vs. 5.5 %), digoxin (0.9 %) vs. 1.0 %), calcium channel blockers (19.2 vs. 19.9 %) and warfarin (7.2 %) vs. 8.1 %). The results indicated that the differences between the two sources were very small, with an average agreement of 95.3 % and a kappa of 0.70. CONCLUSION: The study's results, which show a high level of agreement between PIN and patient self-reporting, affirm the reliability of PIN data as a source for obtaining an accurate assessment of medication use. This finding is crucial in the context of pharmacoepidemiology research, where the accuracy of data is paramount. Further research to explore the complementary use of both data sources will be valuable.

Identifying cost-based quality and performance indicators for home care: a modified delphi method study.

BACKGROUND: This study, part of a multi-study program, aimed to identify a core set of cost-based quality and performance indicators using a modified Delphi research approach. Conceptually, this core set of cost-based indicators is intended for use within a broader health system performance framework for evaluating home care programming in Canada. METHODS: This study used findings from a recently published scoping review identifying 34 cost-focused home care program PQIs. A purposive and snowball technique was employed to recruit a national panel of system-level operational and content experts in home care. We collected data through progressive surveys and engagement sessions. In the first round of surveying, the panel scored each indicator on Importance, Actionable, and Interpretable criteria. The panel set the second round of ranking the remaining indicators' consensus criteria. The panel ranked by importance their top five indicators from operational and system perspectives. Indicators selected by over 50% of the panel were accepted as consensus. RESULTS: We identified 13 panellists. 12 completed the first round which identified that 30 met the predetermined inclusion criteria. Eight completed the ranking exercise, with one of the eight completing one of two components. The second round resulted in three PQIs meeting the consensus criteria: one operational and two systems-policy-focused. The PQIs: "Average cost per day per home care client," "Home care service cost (mean) per home care client 1y, 3y and 7y per health authority and provincially and nationally", and "Home care funding as a percent of overall health care expenditures." CONCLUSIONS: The findings from this study offer a crucial foundation for assessing operational and health system outcomes. Notably, this research pioneers identifying key cost-based PQIs through a national expert panel and modified Delphi methodology. This study contributes to the literature on PQIs for home care and provides a basis for future research and practice. These selected PQIs should be applied to future research to test their applicability and validity within home care programming and outcomes. Researchers should apply these selected PQIs in future studies to evaluate their applicability and validity within home care programming and outcomes.

Validated administrative data based ICD-10 algorithms for chronic conditions: A systematic review.

OBJECTIVE: This systematic review aimed to identify ICD-10 based validated algorithms for chronic conditions using health administrative data. METHODS: A comprehensive systematic literature search using Ovid MEDLINE, Embase, PsycINFO, Web of Science and CINAHL was performed to identify studies, published between 1983 and May 2023, on validated algorithms for chronic conditions using administrative health data. Two reviewers independently screened titles and abstracts and reviewed full text of selected studies to complete data extraction. A third reviewer resolved conflicts arising at the screening or study selection stages. The primary outcome was validated studies of ICD-10 based algorithms with both sensitivity and PPV of ≥70 %. Studies with either sensitivity or PPV <70 % were included as secondary outcomes. RESULTS: Overall, the search identified 1686 studies of which 54 met the inclusion criteria. Combining a previously published literature search, a total of 61 studies were included for data extraction. The study identified 40 chronic conditions with high validity and 22 conditions with moderate validity. The validated algorithms were based on administrative data from different countries including Canada, USA, Australia, Japan, France, South Korea, and Taiwan. The algorithms identified included several types of cancers, cardiovascular conditions, kidney diseases, gastrointestinal disorders, and peripheral vascular diseases, amongst others. CONCLUSION: With ICD-10 prominently used across the world, this up-to-date systematic review can prove to be a helpful resource for research and surveillance initiatives using administrative health data for identifying chronic conditions.

Development of Data Quality Indicators for Improving Hospital International Classification of Diseases-Coded Health Data Quality Globally.

BACKGROUND: Hospital inpatient data, coded using the International Classification of Diseases (ICD), is widely used to monitor diseases, allocate resources and funding, and evaluate patient outcomes. As such, hospital data quality should be measured before use; however, currently, there is no standard and international approach to assess ICD-coded data quality. OBJECTIVE: To develop a standardized method for assessing hospital ICD-coded data quality that could be applied across countries: Data quality indicators (DQIs). RESEARCH DESIGN: To identify a set of candidate DQIs, we performed an environmental scan, reviewing gray and academic literature on data quality frameworks and existing methods to assess data quality. Indicators from the literature were then appraised and selected through a 3-round Delphi process. The first round involved face-to-face group and individual meetings for idea generation, while the second and third rounds were conducted remotely to collect online ratings. Final DQIs were selected based on the panelists' quantitative and qualitative feedback. SUBJECTS: Participants included international experts with expertise in administrative health data, data quality, and ICD coding. RESULTS: The resulting 24 DQIs encompass 5 dimensions of data quality: relevance, accuracy and reliability; comparability and coherence; timeliness; and Accessibility and clarity. These will help stakeholders (eg, World Health Organization) to assess hospital data quality using the same standard across countries and highlight areas in need of improvement. CONCLUSIONS: This novel area of research will facilitate international comparisons of ICD-coded data quality and be valuable to future studies and initiatives aimed at improving hospital administrative data quality.

Achieving high inter-rater reliability in establishing data labels: a retrospective chart review study.

BACKGROUND: In medical research, the effectiveness of machine learning algorithms depends heavily on the accuracy of labeled data. This study aimed to assess inter-rater reliability (IRR) in a retrospective electronic medical chart review to create high quality labeled data on comorbidities and adverse events (AEs). METHODS: Six registered nurses with diverse clinical backgrounds reviewed patient charts, extracted data on 20 predefined comorbidities and 18 AEs. All reviewers underwent four iterative rounds of training aimed to enhance accuracy and foster consensus. Periodic monitoring was conducted at the beginning, middle, and end of the testing phase to ensure data quality. Weighted Kappa coefficients were calculated with their associated 95% confidence intervals (CIs). RESULTS: Seventy patient charts were reviewed. The overall agreement, measured by Conger's Kappa, was 0.80 (95% CI: 0.78-0.82). IRR scores remained consistently high (ranging from 0.70 to 0.87) throughout each phase. CONCLUSION: Our study suggests the detailed manual for chart review and structured training regimen resulted in a consistently high level of agreement among our reviewers during the chart review process. This establishes a robust foundation for generating high-quality labeled data, thereby enhancing the potential for developing accurate machine learning algorithms.

Myocardial Infarction Within 30 Days of Discharge From an Emergency Department: A Descriptive Study of Albertan Women.

Background: Cardiovascular diseases (CVDs) are the leading cause of premature death for Canadian women, which may be due partly to a lack of awareness of the presentation of acute coronary events in emergency departments (EDs). To address an identified gap in women's cardiovascular care, we sought to describe the clinical and comorbid factors of women who, following discharge from an ED, suffered a myocardial infarction (MI). Methods: Descriptive analyses were completed on a cohort of women who presented to an ED in Alberta, Canada, between January 1, 2010 and December 31, 2020, were discharged, and within 30 days of their index ED visit, were admitted to the hospital with an MI. The cohort was explored for clinical and comorbid data, ED visits pre-MI, type of MI, and presenting complaint/ primary diagnosis for the index ED visit. Results: 1380 women were included in this analysis with a mean age of 67 (standard deviation ±13) years. The frequencies of hypertension, diabetes, and dyslipidemia among the youngest women, aged 18-45 years, were 47.5%, 31.3%, and 48.8%, respectively. Women across all ages demonstrated a high prevalence of traditional CVD risk factors, and 22% of women presented to an ED 2 or more times within the 30 days pre-MI. Conclusions: Regardless of their age, the women in this cohort had notable CVD risk factors. Future research is required to better understand the phenomenon of women presenting multiple times to an ED pre-MI. Research is needed on life-stage-specific factors of women presenting to EDs pre-MI, to help reduce MI incidence.

Contexte: Les maladies cardiovasculaires représentent la principale cause de décès prématuré chez les Canadiennes, ce qui peut être en partie attribuable à un manque de connaissance des manifestations des événements coronariens aigus dans les services d'urgence. Pour combler une lacune observée dans les soins cardiovasculaires chez les femmes, nous avons tenté de décrire les facteurs cliniques et les facteurs de comorbidité chez les femmes qui, après avoir reçu leur congé du service d'urgence, ont subi un infarctus du myocarde (IM). Méthodologie: Des analyses descriptives ont été menées sur une cohorte de femmes qui se sont présentées dans un service d'urgence en Alberta, au Canada, entre le 1er janvier 2010 et le 31 décembre 2020, qui ont reçu leur congé et qui, dans les 30 jours suivant leur visite de référence aux urgences, ont été admises à l'hôpital pour un IM. L'analyse de la cohorte portait sur les données cliniques et les données de comorbidité, les consultations au service d'urgence avant l'IM, le type d'IM et la raison/le diagnostic primaire lors de la consultation de référence. Résultats: Cette analyse a porté sur 1380 femmes dont l'âge moyen était de 67 (écart-type ± 13) ans. Les fréquences d'hypertension, de diabète et de dyslipidémie chez les femmes les plus jeunes, âgées de 18 à 45 ans, étaient respectivement de 47,5 %, de 31,3 % et de 48,8 %. Les femmes de tous les âges présentaient une prévalence élevée de facteurs de risque classiques de maladies cardiovasculaires, et 22 % des femmes s'étaient présentées à un service d'urgence au moins 2 fois au cours des 30 jours ayant précédé l'IM. Conclusions: Indépendamment de l'âge, les femmes de cette cohorte présentaient des facteurs de risque de maladies cardiovasculaires notables. D'autres recherches s'imposent afin de mieux comprendre le phénomène des femmes qui se présentent plusieurs fois dans un service d'urgence dans la période précédant un IM. Une recherche doit être menée sur les facteurs spécifiques aux stades de la vie des femmes qui se présentent aux urgences avant un IM afin d'aider à réduire l'incidence des infarctus du myocarde.

A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.

BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study.

BACKGROUND: Inpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls. OBJECTIVE: This study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model. METHODS: A cohort of 4323 adult patients admitted to 3 acute care hospitals in Calgary, Alberta, Canada from 2016 to 2021 were randomly sampled. Trained reviewers determined falls from patient charts, which were linked to electronic medical records and administrative data. The BERT-based language model was pretrained on clinical notes, and a fall detection algorithm was developed based on a neural network binary classification architecture. RESULTS: To address various use scenarios, we developed 3 different Alberta hospital notes-specific BERT models: a high sensitivity model (sensitivity 97.7, IQR 87.7-99.9), a high positive predictive value model (positive predictive value 85.7, IQR 57.2-98.2), and the high F1-score model (F1=64.4). Our proposed method outperformed 3 classical ML algorithms and an International Classification of Diseases code-based algorithm for fall detection, showing its potential for improved performance in diverse clinical settings. CONCLUSIONS: The developed algorithm provides an automated and accurate method for inpatient fall detection using multidisciplinary progress record notes and a pretrained BERT language model. This method could be implemented in clinical practice to improve patient safety and reduce the occurrence of falls in hospitals.

Automated extraction of weight, height, and obesity in electronic medical records are highly valid.

Objective: Coding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under-reported and thus unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule-based algorithm for automating the detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMRs). Methods: In this cross-sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs. A rule-based algorithm was created which looks for patients' height and weight values recorded in EMRs. Clinical notes were split into sentences and searched for height and weight, and BMI was computed. Results: The study cohort consisted of 1904 patients with 50.8% females and 43.3% > 64 years of age. The final model to identify obesity within EMRs resulted in a sensitivity of 92.9%, specificity of 98.4%, positive predictive value of 96.7%, negative predictive value of 96.6%, and F1 score of 94.8%. Conclusions: This study developed a highly valid rule-based EMR algorithm that detects height and weight. This could allow large-scale analyses using obesity that were previously not possible.

Impact of myocardial perfusion and coronary calcium on medical management for coronary artery disease.

AIMS: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) remains one of the most widely used imaging modalities for the diagnosis and prognostication of coronary artery disease (CAD). Despite the extensive prognostic information provided by MPI, little is known about how this influences the prescription of medical therapy for CAD. We evaluated the relationship between MPI with computed tomography (CT) attenuation correction and prescription of acetylsalicylic acid (ASA) and statins. METHODS AND RESULTS: We performed a retrospective analysis of consecutive patients who underwent SPECT MPI at a single centre between 2015 and 2021. Myocardial perfusion abnormalities and coronary calcium burden were assessed, with attenuation correction imaging 77.8% of patients. Medication prescriptions before and within 180 days after the test were compared. Associations between abnormal perfusion and calcium burden with ASA and statin prescription were assessed using multivariable logistic regression. In total, 9908 patients were included, with a mean age 66.8 ± 11.7 years and 5337 (53.9%) males. The prescription of statins increased more in patients with abnormal perfusion (increase of 19.2 vs. 12.0%, P < 0.001). Similarly, the presence of extensive CAC led to a greater increase in statin prescription compared with no calcium (increase 12.1 vs. 7.8%, P < 0.001). In multivariable analyses, ischaemia and coronary artery calcium were independently associated with ASA and statin prescription. CONCLUSION: Abnormal MPI testing was associated with significant changes in medical therapy. Both calcium burden and perfusion abnormalities were associated with increased prescriptions of medical therapy for CAD.

Exploring the reliability of inpatient EMR algorithms for diabetes identification.

INTRODUCTION: Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes phenotyping algorithms. MATERIALS AND METHODS: A chart review on 3040 individuals was completed, and 583 had diabetes. We linked EMR data on these individuals to the International Classification of Disease (ICD) administrative databases. The following EMR-data-based diabetes algorithms were developed: (1) laboratory data, (2) medication data, (3) laboratory and medications data, (4) diabetes concept keywords and (5) diabetes free-text algorithm. Combined algorithms used or statements between the above algorithms. Algorithm performances were measured using chart review as a gold standard. We determined the best-performing algorithm as the one that showed the high performance of sensitivity (SN), and positive predictive value (PPV). RESULTS: The algorithms tested generally performed well: ICD-coded data, SN 0.84, specificity (SP) 0.98, PPV 0.93 and negative predictive value (NPV) 0.96; medication and laboratory algorithm, SN 0.90, SP 0.95, PPV 0.80 and NPV 0.97; all document types algorithm, SN 0.95, SP 0.98, PPV 0.94 and NPV 0.99. DISCUSSION: Free-text data-based diabetes algorithm can yield comparable or superior performance to a commonly used ICD-coded algorithm and could supplement existing methods. These types of inpatient EMR-based algorithms for case identification may become a key method for timely resource planning and care delivery.

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.

Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study.

BACKGROUND: Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection. METHODS: This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision-recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated. RESULTS: There were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835-0.978), PR AUC of 0.637 (95% CI 0.528-0.746), and F1 score of 0.79 (0.67-0.90). CONCLUSIONS: Our findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.

The incidence rates of surgical site infections following total hip and knee arthroplasty were 0.5 and 0.52 per 100 surgical procedures. The incidence of SSIs varied significantly between care facilities (ranging from 0.53 to 1.71 per 100 procedures). The optimal machine learning model achieved a ROC AUC of 0.906 (95% CI 0.835­0.978), PR AUC of 0.637 (95% CI 0.528­0.746), and F1 score of 0.79 (0.67­0.90).

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.

Optimal medical therapy improves outcomes in patients with diabetes mellitus and acute myocardial infarction.

AIMS: We aimed to explored the association between the use of optimal medical therapy (OMT) in patients with myocardial infarction (AMI) and diabetes mellitus (DM) and clinical outcomes. METHODS: Bleeding complications in a Multicenter registry of patients discharged with diagnosis of Acute Coronary Syndrome (BleeMACS) is an international registry that enrolled participants with acute coronary syndrome followed up for at least 1 year across 15 centers from 2003 to 2014. Baseline characteristics and endpoints were analyzed. RESULTS: Among 3095 (23.2%) patients with AMI and DM, 1898 (61.3%) received OMT at hospital discharge. OMT was associated with significantly reduced mortality (4.3% vs. 10.8%, p < 0.001), re-AMI (4.4% vs. 8.1%, p < 0.001), and composite endpoint of death/re-AMI (8.0% vs. 17.6%, p < 0.001). No difference was observed among regions. Propensity score matching confirmed that OMT significantly associated with lower mortality. After adjusting for confounding variables, OMT, drug-eluting stents, and complete revascularization were independent protective factors of 1-year mortality, whereas left ventricular ejection fraction and age were risk factors. CONCLUSIONS: Guideline-recommended OMT was prescribed at suboptimal frequencies with geographic variations in this worldwide cohort. OMT can improve long-term clinical outcomes in patients with DM and AMI. CLINICAL TRIAL REGISTRATION: NCT02466854 June 9, 2015.

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.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism.

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.

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.

Training and experience of coding with the World Health Organization's International Classification of Diseases, Eleventh Revision.

BACKGROUND: The new International Classification of Diseases, Eleventh Revision for Mortality and Morbidity Statistics (ICD-11) was developed and released by the World Health Organization (WHO) in June 2018. Because ICD-11 incorporates new codes and features, training materials for coding with ICD-11 are urgently needed prior to its implementation. OBJECTIVE: This study outlines the development of ICD-11 training materials, training processes and experiences of clinical coders while learning to code using ICD-11. METHOD: Six certified clinical coders were recruited to code inpatient charts using ICD-11. Training materials were developed with input from experts from the Canadian Institute for Health Information and the WHO, and the clinical coders were trained to use the new classification. Monthly team meetings were conducted to enable discussions on coding issues and to select the correct ICD-11 codes. The training experience was evaluated using qualitative interviews, a questionnaire and a coding quiz. RESULTS: total of 3011 charts were coded using ICD-11. In general, clinical coders provided positive feedback regarding the training program. The average score for the coding quiz (multiple choice, True/False) was 84%, suggesting that the training program was effective. Feedback from the coders enabled the ICD-11 code content, electronic tooling and terminologies to be updated. CONCLUSION: This study provides a detailed account of the processes involved with training clinical coders to use ICD-11. Important findings from the interviews were reported at the annual WHO conferences, and these findings helped improve the ICD-11 browser and reference guide.

Health data governance for research use in Alberta.

Alberta has rich clinical and health services data held under the custodianship of Alberta Health and Alberta Health Services (AHS), which is not only used for clinical and administrative purposes but also disease surveillance and epidemiological research. Alberta is the largest province in Canada with a single payer centralised health system, AHS, and a consolidated data and analytics team supporting researchers across the province. This paper describes Alberta's data custodians, data governance mechanisms, and streamlined processes followed for research data access. AHS has created a centralised data repository from multiple sources, including practitioner claims data, hospital discharge data, and medications dispensed, available for research use through the provincial Data and Research Services (DRS) team. The DRS team is integrated within AHS to support researchers across the province with their data extraction and linkage requests. Furthermore, streamlined processes have been established, including: 1) ethics approval from a research ethics board, 2) any necessary operational approvals from AHS, and 3) a tripartite legal agreement dictating terms and conditions for data use, disclosure, and retention. This allows researchers to gain timely access to data. To meet the evolving and ever-expanding big-data needs, the University of Calgary, in partnership with AHS, has built high-performance computing (HPC) infrastructure to facilitate storage and processing of large datasets. When releasing data to researchers, the analytics team ensures that Alberta's Health Information Act's guiding principles are followed. The principal investigator also ensures data retention and disposition are according to the plan specified in ethics and per the terms set out by funding agencies. Even though there are disparities and variations in the data protection laws across the different provinces in Canada, the streamlined processes for research data access in Alberta are highly efficient.