Linked electronic health records (EHR) and Medicare claims data are excellent sources for amyotrophic lateral sclerosis (ALS) research. However, quality assessment of data in these sources is critical to produce accurate real-world evidence.

In this study we evaluated the consistency and concordance of ALS-linked EHR and Medicare data across the Greater Plain Collaborative (GPC) clinical research network. We used rule-based methods to assess the consistency of demographic, ALS diagnosis, riluzole prescription, and percutaneous endoscopic gastrostomy (PEG) procedure data in both data sources. We used Cohen’s Kappa statistics (k) to assess the concordance between the two data sources and multiple logistic regression models to identify potential driving factors of discordance.

Our findings revealed that demographic data was mostly consistent across most sites, with the highest discrepancies observed in race and ethnicity. For ALS-specific diagnosis, most sites demonstrate moderate (k = 0.41–0.60) to substantial (k = 0.61–0.80) concordance for ALS diagnosis, riluzole prescription, and PEG procedure. Sensitivity analysis showed that the data in both data sources were concordant across many sites (Sensitivity > 70%). However, results also revealed discordant data at some sites. Age distribution, sex, and the type of insurance used in the index encounter were key factors contributing to discordance.

These results provided insight into the level of ALS data concordance between EHR and Medicare, as well as illustrated quality issues requiring assessment before use in clinical research and in providing recommendations based solely on available secondary data.

The National Health Service in England has encouraged the development of specialised services for patients with high-intensity use of emergency departments: liaison psychiatry services in emergency departments and community services.

To evaluate the effect of different types of high-intensity use services on attendances at emergency departments, for those with 5 (HU5) or 10 (HU10) or more attendances in the previous 12 months, and the subset of this group with probable mental health problems, versus emergency departments without locality high-intensity use services.

A retrospective longitudinal study was conducted using nationally sourced data from 13 hospitals in England, spanning 1 April 2016 to 1 January 2020. An interrupted time-series analysis assessed the impact of three high-intensity use service models on weekly emergency department attendances: emergency department-based services with and without designated staffing, and community based services. Patients and clinicians contributed throughout the project.

A total of 2 938 510 emergency department attendances were analysed. Statistically significant associations were identified between the implementation of community-based services and reductions in weekly attendances by HU10 patients (rate ratio 0.83, 95% CI 0.71–0.97), and HU5 and HU10 patients with mental health-related attendances (HU5 rate ratio 0.82, 95% CI 0.75–0.91; HU10 rate ratio 0.70, 95% CI 0.59–0.84). Emergency department-based services with designated staff were associated with a reduction in mental health-related attendances among HU10 patients (rate ratio 0.88, 95% CI 0.80–0.98).

Services targeting patients with high-intensity use of emergency departments in England demonstrate promising potential in reducing emergency department attendances among this patient group. Community-based interventions show the most substantial impact, whereas hospital-based services often run by liaison psychiatry teams also contribute to reductions in those with high-intensity use with mental health issues. These findings support further development and expansion of tailored high-intensity use services across the country.

Concurrent psychiatric care in medical in-patients is associated with adverse hospital stay outcomes, but data from low- and middle-income countries is limited.

We aimed to investigate the association between psychiatric diagnosis–medication concordance and discharge disposition outcomes in acute medical in-patients.

This was a retrospective cross-sectional analysis of 16 868 adults presenting to the Department of Internal Medicine at Aga Khan University Hospital, Karachi, Pakistan (2018–2022) with specified acute care conditions. Patients were classified into four groups based on presence of a psychiatric diagnosis and/or psychiatric medication use: neither, medication without diagnosis, diagnosis without medication and both. Primary adverse outcomes were in-hospital mortality and leaving against medical advice. Multinomial logistic regression was used, adjusting for age, gender, Charlson Comorbidity Index score, medication burden and individual acute medical conditions.

A total of 2527 (15.0%) patients had a documented psychiatric diagnosis; however, 6279 (37.2%) received at least one psychiatric medication. Referencing patients with neither exposure, those receiving psychiatric medications without a documented diagnosis had the highest adjusted odds of mortality (adjusted odds ratio (aOR) 1.95, 95% CI 1.74–2.19, p < 0.001) and higher odds of leaving against medical advice (aOR 1.21, 95% CI 1.06–1.39, p = 0.006). Patients with a documented diagnosis but no psychiatric medications had higher odds of leaving against medical advice (aOR 1.28, 95% CI 1.04–1.57, p = 0.022) without a significant increase in mortality (aOR 1.21, 95% CI 0.98–1.49, p = 0.076). Patients with both a diagnosis and psychiatric medication usage showed no significant change in either outcome.

Diagnosis–treatment concordance may be associated with in-patient prognosis in acute care conditions.

Antidepressants are the most frequently prescribed medications in psychiatry. Medical records of thousands of individuals provide a valuable opportunity to explore prescribing patterns and identify factors that influence treatment outcomes.

To investigate antidepressant change patterns in depression and non-depression indications and assess clinical and genetic factors associated with outcomes of antidepressant treatment.

Using primary care records from the UK Biobank, we examined outcomes including number of antidepressant changes and discontinuation due to either side-effects or inadequate response. Genetic analyses including heritability estimation, genetic correlation and polygenic score association were performed.

A total of 82 633 individuals were prescribed at least 1 antidepressant. Of these, 28 332 individuals with at least 1 primary care depression diagnosis were classified as the depression group, and 24 543 individuals without evidence of depression were classified as the non-depression group. Citalopram and fluoxetine were the most prescribed antidepressants for depression, whereas amitriptyline dominated prescriptions for non-depression indications. Individuals with depression were more likely to stay on antidepressants longer than those without depression and to follow preferred antidepressants that changed over time. Antidepressant changes and discontinuation were associated with a range of psychiatric and somatic conditions, including recurrent depression (early discontinuation: odds ratio = 1.96; late discontinuation: odds ratio = 2.63) and anxiety (early discontinuation: odds ratio = 1.37; late discontinuation: odds ratio = 1.99) in the depression group, and pain-related conditions in the non-depression group. Genetic analyses identified two novel variants associated with early discontinuation of selective serotonin reuptake inhibitors. Notable genetic overlap was shown between these outcomes and multiple psychiatric and physical traits, including the number of antidepressant changes with anxiety and depression (rg = 0.81–0.83), and polygenic scores for depression and attention-deficit hyperactivity disorder showed significant predictive value with respect to treatment outcomes.

These findings characterise antidepressant change patterns in primary care records and highlight the potential value of integrating clinical and genetic data to better understand factors associated with treatment outcomes.

Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD), with diagnostic delays averaging 6–10 years leading to suboptimal treatment and poorer outcomes. Early identification of patients at risk for diagnostic conversion remains a critical clinical priority. We aimed to identify clinical predictors of BD conversion in a large population-based cohort and develop a clinically applicable prediction model.

We conducted a retrospective cohort study using electronic health records from the Catalan PADRIS-PRESTO database (2010–2019). Patients aged ≥18 years with incident MDD diagnosed in specialized mental health services (2015–2019) were followed until BD diagnosis, death, or study end. Time-varying Cox regression addressed proportional hazards violations, with competing risks analysis accounting for mortality.

Among 52,001 patients (median follow-up 4.0 years), 3,958 (7.61%) converted to BD. Psychotic features emerged as the strongest predictor (HR = 3.18, 95% CI: 2.92–3.45, E-value = 5.82), with consistent effects across follow-up. Anxiety disorders showed time-varying protection (baseline HR = 0.41, 95% CI: 0.35–0.48) that attenuated over time (interaction HR = 1.30 per period). The time-varying model achieved good discrimination (C-index = 0.745). Machine learning validation confirmed predictor importance rankings (Spearman r = 0.87).

Psychotic features during depressive episodes robustly predict BD conversion, warranting enhanced clinical monitoring. Our time-varying prediction model using routinely collected clinical data enables early risk stratification to guide diagnostic vigilance and treatment decisions.

Persons with substance use disorders (SUD) often suffer from additional comorbidities. Researchers have explored this overlap via phenome-wide association studies (PheWASs). However, PheWASs are largely cross-sectional, limiting our understanding of whether diagnoses predate the development of an SUD. We characterize whether polygenic scores (PGSs) are associated with time to comorbid diagnoses in electronic health records (EHR) after the first documented SUD diagnosis.

Using data from All of Us (N = 393,596), we explored: (1) whether social determinants of health (SDoHs) are associated with lifetime risk of SUD (N cases = 42,568) and (2) within a subset those with a diagnosed SUD and available genetic data SUD (N = 21,357), whether PGS for alcohol use disorders, cannabis use disorders, depression, externalizing, posttraumatic stress disorder, and schizophrenia were associated with subsequent diagnoses via a phenome-wide survival analysis.

Multiple SDoHs were associated with lifetime SUD diagnosis, with annual household income having the largest overall associations (e.g. <$10 K annually vs $100 K–$150 K annually: OR = 4.18; 95% CI = 3.92, 4.45). There were 86 phenome-wide significant PGS associations with subsequent diagnoses across various bodily systems. PGSs for alcohol use disorders, posttraumatic stress disorder, and schizophrenia were each associated with time to their respective diagnoses.

Social determinants, especially those related to income, have profound associations with lifetime SUD risk. Additionally, PGSs for psychiatric conditions are associated with multiple post-SUD diagnoses within those with a SUD, suggesting PGS may capture information beyond lifetime risk, including timing and severity of comorbidities related to SUD.

Vaccinations play a crucial role in public and personal health. Vaccines such as the Yellow Fever 17D vaccine are effective at preventing disease and provide lifelong immunity with few adverse events. Leveraging the increase in electronic medical records and the widespread use of machine learning algorithms in healthcare, this study aims to investigate and develop an algorithmic framework for analyzing clinical data on Yellow Fever 17D vaccinations in order to predict at-risk populations for adverse events following immunizations. This research incorporates parameters from the patient’s medical history and demographic information.

To build our analytic framework, we tested five machine learning algorithms: random forest, gradient boost, logistic regression, XGBoost, and Bernoulli naïve Bayes. We cleaned and managed the datasets through EHRchitect software. We assessed the performance of the algorithms using standard metrics, including precision, recall, F1-scores, accuracy, and the area under the receiver operating characteristic curve. Additionally, we implemented population sampling to mitigate potential biases common in clinical data and performed parameter ranking analysis to identify the most influential features in classifying patient reaction outcomes.

We found that logistic regression produced the highest performance, and through hyperparameter optimization, we developed a framework with precision, accuracy, and ROC scores of 87.7%, 99.0%, and 0.818, respectively.

In conclusion, this study examined five modeling algorithms to establish a framework for analyzing real-world data and to predict at-risk populations for adverse events following immunization. We incorporated a logistic regression algorithm to assess our clinical data to achieve a high-precision performance model for classifying patient predictions.

Increased mortality and reduced life expectancy are well documented among mental healthcare recipients. Whereas clinical research typically focuses on people with specific diagnoses, little is known about those who receive mental healthcare but have an unspecified or no diagnosis.

Using routinely collected mortality data, we aimed to explore how mortality and life expectancy differed between those with and without a specific mental health diagnosis.

Using the South London and Maudsley NHS Foundation Trust clinical records interactive search system, we assembled annual cohorts of people who had past or current mental health service receipt between 2015 and 2024. Mortality rates and life expectancy were ascertained for those with mental health diagnoses (ICD-10 F-codes), those with unspecified diagnoses (Z-codes) and those without any diagnosis. Age- and gender-standardised mortality ratios (SMRs) and life expectancy were calculated in relation to the local catchment comparator population.

Of the combined cohorts (n = 3 266 268) of people accessing mental health services, 57.7% had an F-code diagnosis, 13.0% a Z-code diagnosis and 29.3% no diagnosis. Annual SMRs (95% CI) for F-code diagnoses ranged from 2.25 (2.18–2.33) to 2.56 (2.46–2.65); for Z-code diagnoses from 1.88 (1.73–2.02) to 2.18 (2.00–2.36); and for no diagnosis from 1.59 (1.48–1.71) to 1.87 (1.72–2.01). Years of life lost were greatest for those with F-code diagnoses (females, 15.1 years; males, 16.7 years), followed by Z-codes (females, 11.8 years; males, 14.4 years) and no diagnosis (females, 9.4 years; males, 10.6 years). Raised SMRs were observed for both external- and natural-cause mortality for all groups.

People in contact with mental health services with unspecified or no mental health diagnosis have a substantially higher mortality and lower life expectancy compared with the general population. Further research is needed to characterise this group and study other outcomes, because they may fall outside care pathways.

Females are less likely than males to be diagnosed with attention–deficit hyperactivity disorder (ADHD). When diagnosed, females are older than males.

In this study, we examined the childhood antecedents of later ADHD diagnosis and its impact on adolescent/emerging adult outcomes, with a focus on females.

In this cohort study, we used data from a Welsh nation-wide electronic cohort of 13 593 individuals (n = 2680 (19.7%) females) diagnosed with ADHD and 578 793 individuals (n = 286 734 (49.5%) females) without ADHD. We compared females with later diagnoses (ages 12–25) to those with earlier, timely diagnoses (ages 5–11) and no diagnosis, in terms of childhood (ages 5–11) antecedents and adolescent/adult (ages 12–25) outcomes. We also tested for sex differences.

Although females with earlier ADHD diagnosis showed more health and educational difficulties in childhood than those with later diagnosed ADHD (odds ratios ranged from 0.18 to 0.92), there was clear evidence of these difficulties in females with later diagnosed ADHD, compared with females without ADHD (odds ratios: 1.07–9.02). In adolescence/early adulthood, females with later diagnosed ADHD used more healthcare services and had worse mental health, educational and socioeconomic outcomes than females diagnosed earlier (odds ratios: 1.39–4.96) and those without ADHD (odds ratios: 1.54–23.98). Many of these outcomes were exacerbated in females compared with males.

The results demonstrate that later ADHD diagnosis is associated with significant negative outcomes by adolescence and disproportionately disadvantages females. Despite later diagnosis, there was clear evidence of childhood mental health and educational difficulties when compared with females without ADHD. Therefore, timely childhood ADHD diagnosis may help to mitigate later risks, especially for females.

Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can have detrimental effects, prediction of the need for ECT could improve outcomes via more timely treatment initiation. Therefore, this study aimed to predict the need for ECT following admission to a psychiatric hospital.

This study was based on electronic health record (EHR) data from routine clinical practice. Adult patients admitted to a hospital within the Psychiatric Services of the Central Denmark Region between January 2013 and November 2021 were included in the study. The outcome was initiation of ECT >7 days (to not include patients admitted for planned ECT) and ≤67 days after admission. The data was randomly split into an 85% training set and a 15% test set. On the 7th day of the inpatient stay, machine learning models (extreme gradient boosting (XGBoost)) were trained to predict initiation of ECT and subsequently tested on the test set.

The cohort consisted of 41,610 patients with 164,961 admissions. In the held out test set, the trained model predicted ECT initiation with an area under the receiver operating characteristic curve of 0.94, 47% sensitivity, 98% specificity, positive predictive value (PPV) of 24% and negative predictive value (NPV) of 99%. The top predictors were the highest suicide assessment score and mean Brøset violence checklist score in the preceding three months.

EHR data from routine clinical practice may be used to predict need for ECT. This may lead to more timely treatment initiation.

The impact of guideline-directed medical therapy (GDMT) has not fully translated to decreases in the disproportionate rates of hospitalization and lengths of stay in African Americans with congestive heart failure (CHF). GDMT is optimized by registered nurses (RNs) and their use of clinical information. Yet, there are no instruments for measuring the influence of clinical information use and nursing care. The study assessed an instrument’s ability to measure the influence of RN performance of social, technical, and socio-technical care tasks on length of stay in the CHF hospitalizations of African Americans.

A sample of 200 RNs, who cared for 5060 African Americans with 14,123 heart failure hospitalizations, were surveyed. Descriptive statistics, Cronbach’s alpha, and a generalized linear regression assessed the instrument’s reliability and predictive validity.

The Cronbach’s alpha was 0.95 (95% CI: 0.94–0.96). The corrected item-total correlations for the 22 items ranged from 0.44 to 0.80. For an increase of one to four points per item in a RN’s performance, the estimated reductions in the patient’s length of stay were 3.34% (6.11,0.5), 6.58% (11.84,1), 9.70% (17.22,1.49), and 12.72% (22.28,1.99), respectively (P = 0.004).

Increases in a RN’s performance of social, technical, and socio-technical care tasks were significantly associated with clinically meaningful decreases in their patients’ length of stay. The instrument has strong potential for addressing the disproportionate impact of CHF by measuring and tailoring interventions to optimize nursing care and the use of clinical information in the provision and receipt of GDMT.

This study sought to obtain the views of doctors associated with the Royal College of Psychiatrists on the use of outcome measures in mental health services. An online survey was developed by the College’s working group on outcome measures and widely disseminated to psychiatrists through College channels.

In total, 339 completed responses were received. Respondents were mostly consultant psychiatrists; based in England; and working in the National Health Service with working-age adults. Almost half said they used outcome measures routinely, with almost half finding outcome measures clinically useful. Lack of time and inadequate information technology systems were identified as the top barriers to using outcome measures.

Based on our results, psychiatrists are generally keen to use outcome measures, but are often prevented from doing so effectively by pressures on services and lack of appropriate support. The Royal College of Psychiatrists and other relevant organisations could enhance the use of outcome measures in mental health services through improved guidance, providing additional resources and integration of measures into electronic patient records.

This study aimed to develop and evaluate a predictive model using electronic health record (EHR) data from a large south London mental health service, in order to identify patients 3 months following first referral who are at risk of subsequent high-intensity service use over the subsequent 12 months. Early identification of such patients may support proactive and personalised care planning, reducing the need for high-cost episodes of care. Predictive models were developed using information from 18 869 patients newly referred between 2007 and 2011. High-intensity use was defined as the top 10% of estimated mental healthcare expenditure. The model was developed using demographic, clinical and service use variables, and was validated on data from the periods 2012–2017 and 2018–2023.

A logistic regression model achieved an area under the receiver operating characteristic (AUROC) of 0.79 in development (sensitivity 0.82, specificity 0.54), with robust performance in validation sets (AUROC 0.81, 0.83, respectively). Key predictors included first 3 months service use, schizophrenia or eating disorder diagnoses and living alone. Natural language processing-derived features did not improve performance.

Routine EHR data performed well in predicting the risk of high-cost care, potentially enabling targeted interventions and more efficient resource allocation.

Identifying diagnoses from noncoded healthcare visit records presents logistical challenges when large number of records are screened. This study aimed to develop a screening process to identify otitis media (OM) diagnoses in free-text primary care visit records.

The free-text primary care records of 200 children aged 0 to 4 years were reviewed independently by three clinicians to determine whether OM was a diagnosis considered during each visit. Terms (abbreviations, words, and phrases) identifying visits where OM was considered or excluded were documented. These terms were used to design a software algorithm subsequently used to detect OM diagnosis within these primary care records. The diagnostic performance of the software algorithm was determined against the gold standard clinicians’ review and described using sensitivity, specificity, predictive values (PVs), and likelihood ratios (LRs) with 95% confidence intervals (CIs).

The 200 children had 10,034 primary care visits. Clinician review identified 917 (9%) visits where OM was considered, and 9117 (91%) visits where OM was excluded. The software algorithm identified 801/917 visits where OM was considered and 8705/9117 visits where OM was excluded. The algorithm sensitivity was 87% (95% CI 85–89), specificity 96% (95% CI 95–96), positive PV 66% (95% CI 63–69), negative PV 99% (95% CI 98–99), positive LR 19.33 (95% CI 17.54–21.31), and negative LR 0.13 (95% CI 0.11–0.16).

Software algorithms can assist in screening healthcare visit records. When combined with clinician review, they enable accurate identification of OM visits from non-coded records.

We describe the steps taken to assess and improve the research readiness of data within PCORnet®, specifically focusing on the results of the PCORnet data curation process between Cycle 7 (October 2019) and Cycle 16 (October 2024).

We describe the process for extending the PCORnet® CDM and for creating data checks.

We highlight growth in the number of records available across PCORnet between data curation Cycles 7 and 16 (e.g., diagnoses increasing from ∼3.7B to ∼6.9B and laboratory results from ∼7.7B to ∼15.1B among legacy DataMarts), present the current list of data checks and describe performance of the network. We highlight examples of data checks with relatively stable performance (e.g., future dates), those where performance has improved (e.g., RxNorm mapping), and others performance is more variable (e.g., persistence of records).

Studies are a crucial source of information on the design of new data checks. The attention of PCORnet partners is focused primarily on those metrics that are generally modifiable. A transparent data curation process is an essential component of PCORnet, allowing network partners to learn from one another, while also informing the decisions of study investigators on which sites to include in their projects. The quality issues that exist within PCORnet stem from the way that data are captured within healthcare generally. We have been able to make to make great strides on improving data quality and research readiness. Many of the techniques piloted within PCORnet will be broadly applicable to other efforts.