Lack of sexual orientation and gender identity (SOGI) data creates barriers for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people in health care. Barriers to SOGI data collection include physician misperception that patients do not want to answer these questions and discomfort asking SOGI questions. This study aimed to assess patient comfort towards SOGI questions across five quaternary care adult congenital heart disease (ACHD) centres.

A survey administered to ACHD patients (≥18 years) asked (1) two-step gender identity and birth sex, (2) acceptance of SOGI data, and (3) the importance for ACHD physicians to know SOGI data. Chi-square tests were used to analyse differences among demographic groups and logistic regression modelled agreement with statement of patient disclosure of SOGI improving patient–physician communication.

Among 322 ACHD patients, 82% identified as heterosexual and 16% identified as LGBTQ+, across the age ranges 18–29 years (39.4%), 30–49 years (47.8%), 50–64 years (8.7%), and > 65 years (4.0%). Respondents (90.4%) felt comfortable answering SOGI questions. Respondents with bachelor’s/higher education were more likely to “agree” that disclosure of SOGI improves patient–physician communication compared to those with less than bachelor’s education (OR = 2.45; 95% CI 1.41, 4.25; p = .0015).

These findings suggest that in this largely heterosexual population, SOGI data collection is unlikely to cause patient discomfort. Respondents with higher education were twice as likely to agree that SOGI disclosure improves patient–physician communication. The inclusion of SOGI data in future studies will provide larger samples of underrepresented minorities (e.g. LGBTQ+ population), thereby reducing healthcare disparities within the field of cardiovascular research.

Multimorbidity is common among general practice patients and increases a general practitioner’s (GP’s) workload. But the extent of multimorbidity may depend on its definition and whether a time delimiter is included in the definition or not.

The aims of the study were (1) to compare practice prevalence rates yielded by different models of multimorbidity, (2) to determine how a time delimiter influences the prevalence rates and (3) to assess the effects of multimorbidity on the number of direct and indirect patient contacts as an indicator of doctors’ workload.

This retrospective observational study used electronic medical records from 142 German general practices, covering 13 years from 1994 to 2007. The four models of multimorbidity ranged from a simple definition, requiring only two diseases, to an advanced definition requiring at least three chronic conditions. We also included a time delimiter for the definition of multimorbidity. Descriptive statistics, such as means and correlation coefficients, were applied.

The annual percentage of multimorbid primary care patients ranged between 84% (simple model) and 16% (advanced model) and between 74% and 13% if a time delimiter was included. Multimorbid patients had about twice as many contacts annually than the remainder. The number of contacts were different for each model, but the ratio remained similar. The number of contacts correlated moderately with patient age (r = 0.35). The correlation between age and multimorbidity increased from model to model up to 0.28 while the correlations between contacts and multimorbidity varied around 0.2 in all four models.

Multimorbidity seems to be less prevalent in primary care practices than usually estimated if advanced definitions of multimorbidity and a temporal delimiter are applied. Although multimorbidity increases in any model a doctor’s workload, it is especially the older person with multiple chronic diseases who is a challenge for the GP.

This study evaluated whether natural language processing (NLP) of psychotherapy note text provides additional accuracy over and above currently used suicide prediction models.

We used a cohort of Veterans Health Administration (VHA) users diagnosed with post-traumatic stress disorder (PTSD) between 2004–2013. Using a case-control design, cases (those that died by suicide during the year following diagnosis) were matched to controls (those that remained alive). After selecting conditional matches based on having shared mental health providers, we chose controls using a 5:1 nearest-neighbor propensity match based on the VHA's structured Electronic Medical Records (EMR)-based suicide prediction model. For cases, psychotherapist notes were collected from diagnosis until death. For controls, psychotherapist notes were collected from diagnosis until matched case's date of death. After ensuring similar numbers of notes, the final sample included 246 cases and 986 controls. Notes were analyzed using Sentiment Analysis and Cognition Engine, a Python-based NLP package. The output was evaluated using machine-learning algorithms. The area under the curve (AUC) was calculated to determine models' predictive accuracy.

NLP derived variables offered small but significant predictive improvement (AUC = 0.58) for patients that had longer treatment duration. A small sample size limited predictive accuracy.

Study identifies a novel method for measuring suicide risk over time and potentially categorizing patient subgroups with distinct risk sensitivities. Findings suggest leveraging NLP derived variables from psychotherapy notes offers an additional predictive value over and above the VHA's state-of-the-art structured EMR-based suicide prediction model. Replication with a larger non-PTSD specific sample is required.

Without a universal Emergency Medical Services (EMS) system in India, data on the epidemiology of patients who utilize EMS are limited. This retrospective chart review aimed to quantify and describe the burden of disease and patient demographics of patients who arrived by EMS to four Indian emergency departments (EDs) in order to inform a national EMS curriculum.

A retrospective chart review was performed on patients transported by EMS over a three-month period in 2014 to four private EDs in India. A total of 17,541 patient records were sampled from the four sites over the study period. Of these records, 1,723 arrived by EMS and so were included for further review.

A range of 1.4%-19.4% of ED patients utilized EMS to get to the ED. The majority of EMS patients were male (59%-64%) and adult or geriatric (93%-99%). The most common chief complaints and ED diagnoses were neurological, pulmonary, cardiovascular, gastrointestinal, trauma, and infectious disease.

Neurological, pulmonary, cardiovascular, gastrointestinal, trauma, and infectious disease are the most common problems found in patients transported by EMS in India. Adult and geriatric male patients are the most common EMS utilizers. Emergency Medical Services curricula should emphasize these knowledge areas and skills.

WijesekeraO, ReedA, ChastainPS, BiggsS, ClarkEG, KoleT, ChakrapaniAT, AshishN, RajhansP, BreaudAH, JacquetGA. Epidemiology of Emergency Medical Services (EMS) Utilization in Four Indian Emergency Departments. Prehosp Disaster Med. 2016;31(6):675–679.

The mental health burden on primary care is substantial and increasing. Anxiety is a major contributor. Stepped collaborative care (SCC) is implemented worldwide to improve patient outcomes, but long term real-world evaluations of SCC do not exist. Using routinely used electronic medical records from more than a decade, we investigated changes in anxiety prevalences, whether physicians made distinction between non-severe and severe anxiety, and whether these groups were referred and treated differently, both non-pharmacologically and pharmacologically.

Retrospective assessment of anxiety care parameters recorded by 54 general practitioners between 2003 and 2014, in the electronic medical records of a dynamic population of 49,841–69,413 primary care patients.

Substantial shifts in anxiety care parameters have occurred. The prevalence of anxiety symptoms doubled to 0.9% and of anxiety disorders almost tripled to 1.1%. Use of ICPC codes seemed comprehensive and use of instruments to support in anxiety level differentiation increased to 13% of anxiety symptom and 7% of anxiety disorder patients in 2014. Minimal interventions were used more frequently, especially for anxiety symptoms (OR 21 [95% CI 5.1–85]). The antidepressant prescription rates decreased significantly for anxiety symptoms (OR 0.5 [95% CI 0.4–0.8]) and anxiety disorders (OR 0.6 [95% CI 0.4–0.8]). More patients were referred to psychologists and psychiatrists.

We found shifts in anxiety care parameters that follow the principles of SCC. Future primary care research should comprehensively assess the use of the SCC range of therapeutic options, tailored to patients with all different anxiety severity levels.