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Objective: Effective management of hypertension requires not only medical intervention but also significant patient self-management. The challenge, however, lies in the diversity of patients' personal barriers to managing their condition. The objective of this research is to identify and categorize personalized barriers to hypertension self-management using the TASKS framework (Task, Affect, Skills, Knowledge, Stress). This study aims to enhance patient-centered strategies by aligning support with each patient's specific needs, recognizing the diversity in their unique circumstances, beliefs, emotional states, knowledge levels, and access to resources. This research is based on observations from a single study focused on eight patients, which may have been a part of a larger project. Results: The analysis of transcripts from eight patients and the Global Hypertension Practice Guidelines revealed 69 personalized barriers. These barriers were distributed as follows: emotional barriers (49%), knowledge barriers (24%), logical barriers (17%), and resource barriers (10%). The findings highlight the significant impact of emotional and knowledge-related challenges on hypertension self-management, including difficulties in home blood pressure monitoring and the use of monitoring tools. This study emphasizes the need for tailored interventions to address these prevalent barriers and improve hypertension management outcomes. [ABSTRACT FROM AUTHOR]

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. Highlights: This study aimed to identify a set of financial performance and quality indicators (PQIs) for evaluating home care in Canada. The researchers used a modified multi-phased Delphi research approach with endorsed consensus on three PQIs from the 34 reviewed. One PQI is operational, while the other two are systems-policy-focused. This study was motivated by the need to measure and improve the financial sustainability and efficiency of home care services in Canada. It contributes to the literature on PQIs for home care and provides a basis for future research and practice. This research is novel as it is the first to identify key cost-based PQIs through a national expert panel and modified Delphi methodology for use within a broader health system measurement framework, such as the Institute for Healthcare Improvement Quadruple Aim framework. The indicators identified in this study may provide an essential foundation for measuring operational and health system outcomes and require further engagement at local and regional levels, further development, and conceptual application within established health system performance frameworks. Evaluating the potential adoption and implementation of evidence-based PQIs is essential to measuring and improving home care system programming, including indicators reflective of the acceptability and potential usefulness of measures by population groups as an important future step. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we developed methods to extract them from inpatient electronic medical record (EMR) data using techniques compatible with current EMR systems., Methods: Four social determinants were targeted: patient language barriers, employment status, education, and whether the patient lives alone. Inpatients aged 18 and older with records in the Calgary-wide EMR system were studied. Algorithms were developed on the January 2019 hospital admissions (n=8,999) and validated on the January 2018 hospital admissions (n=8,839). SDOH documented as structured data were compared against those extracted from unstructured free-text notes., Results: More than twice as many patients had a note documenting a language barrier in EMR data than in structured data; 12 % of patients indicated by EMR notes to be living alone had a partner noted in their structured marital status. The Positive Predictive Value (PPV) of the elements extracted from notes was high, at 99 % (95 % CI 94.0 %-100.0 %) for language barriers, 98 % (95 % CI 92.6 %-99.9 %) for living alone, 96 % (95 % CI 89.8 %-98.8 %) for unemployment, and 88 % (95 % CI 80.0 %-93.1 %) for retirement., Conclusions: All SDOH elements were extracted with high PPV. SDOH documentation was largely missing in structured data and sometimes misleading., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Background: Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders., Objective: This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones., Methods: A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines., Results: A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms., Conclusions: Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general., (© Allison Grothman, William J Ma, Kendra G Tickner, Elliot A Martin, Danielle A Southern, Hude Quan. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Background: The International Classification of Diseases (ICD) is widely used by clinical coders worldwide for clinical coding morbidity data into administrative health databases. Accordingly, hospital data quality largely depends on the coders' skills acquired during ICD training, which varies greatly across countries., Objective: To characterise the current landscape of international ICD clinical coding training., Method: An online questionnaire was created to survey the 194 World Health Organization (WHO) member countries. Questions focused on the training provided to clinical coding professionals. The survey was distributed to potential participants who met specific criteria, and to organisations specialised in the topic, such as WHO Collaborating Centres, to be forwarded to their representatives. Responses were analysed using descriptive statistics., Results: Data from 47 respondents from 26 countries revealed disparities in all inquired topics. However, most participants reported clinical coders as the primary person assigning ICD codes. Although training was available in all countries, some did not mandate training qualifications, and those that did differed in type and duration of training, with college or university degree being most common. Clinical coding certificates most frequently entailed passing a certification exam. Most countries offered continuing training opportunities, and provided a range of support resources for clinical coders., Conclusion: Variability in clinical coder training could affect data collection worldwide, thus potentially hindering international comparability of health data., Implications: These findings could encourage countries to improve their resources and training programs available for clinical coders and will ultimately be valuable to the WHO for the standardisation of ICD training., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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 F 1 -score model (F 1 =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., (©Cheligeer Cheligeer, Guosong Wu, Seungwon Lee, Jie Pan, Danielle A Southern, Elliot A Martin, Natalie Sapiro, Cathy A Eastwood, Hude Quan, Yuan Xu. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 30.01.2024.)